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A case study of urban agriculture

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Title:
A case study of urban agriculture a life cycle assessment of vegetable production
Creator:
Fisher, Stephen ( author )
Place of Publication:
Denver, CO
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University of Colorado Denver
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English
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1 electronic file (266 pages). : ;

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Doctorate ( Doctor of Philosophy)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
Department of Civil Engineering, CU Denver
Degree Disciplines:
Civil engineering

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Subjects / Keywords:
Vegetable trade -- Government policy ( lcsh )
Urban agriculture ( lcsh )
Product life cycle -- Assessment ( lcsh )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Review:
The world's urban population surpassed the non-urban population for the first time in 2009. This marks what has been a steady global shift of providing more food to places it is not grown. Because food accounts for over 10 percent of the carbon footprint for the typical American city, this study adopts a social-ecological-infrastructural systems framework, a large component of which is recognizing urban activities and sectors belonging to infrastructure inside and outside the urban boundary. This is a key way to examine the embodied, life-cycle properties of the food we eat in cities. This study develops a product life cycle assessment (LCA) of a basket of vegetables (product) grown under two different formats. The first format is characterized by the large-scale, commercial growers that supply the typical supermarket. The second format is characterized by small-scale growers (less than 1 acre) that use higher land-use intensity and less mechanized practice. This second format is typically used by backyard gardeners, operators of neighborhood supported agriculture (NSA) and operators of some community supported agriculture (CSA) businesses. Published data is used for the large-scale format ; primary case-study data is used for the small-scale format. Results of scenarios of land use change and vegetable production for both distant farmland and urban settings found that shifts resulting from urban vegetable production are favorable in terms of greenhouse gas emissions, water use, and soil organic carbon. Surprisingly, urban vegetable is not categorically favorable for each metric; several key parameters are distinctly bottom-up. The results indicate that state and local policy could remove hurdles to urban agricultural production with these data supporting claims that benefits outweigh costs.
Thesis:
Thesis (Ph.D.)--University of Colorado Denver. Civil engineering
Bibliography:
Includes bibliographic references.
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System requirements: Adobe Reader.
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Department of Civil Engineering
Statement of Responsibility:
by Stephen Fisher.

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University of Colorado Denver
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|Auraria Library
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900880218 ( OCLC )
ocn900880218

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Full Text
A CASE STUDY OF URBAN AGRICULTURE:
A LIFE CYCLE ASSESSMENT OF VEGETABLE PRODUCTION
by
STEPHEN FISHER
B.S. University of California, Irvine, 1987
M.S. Stanford University, 1989
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Civil Engineering
2014


2014
STEPHEN FISHER
ALL RIGHTS RESERVED


This thesis for the Doctor of Philosophy degree by
Stephen Fisher
has been approved for the
Civil Engineering Program
by
Indrani Pal, Chair
Arunprakash Karunanithi, Advisor
John Brett
Gregory Cronin
Balaji Rajagopalan
Anuradha Ramaswami
15 May 2014
ii


Fisher, Stephen (Ph.Dv Civil Engineering)
A Case Study of Urban Agriculture: A Life Cycle Assessment of Vegetable Production
Thesis directed by Associate Professor Arunprakash Karunanithi
ABSTRACT
The world's urban population surpassed the non-urban population for the first time in 2009.
This marks what has been a steady global shift of providing more food to places it is not grown.
Because food accounts for over 10 percent of the carbon footprint for the typical American city,
this study adopts a social-ecological-infrastructural systems framework, a large component of
which is recognizing urban activities and sectors belonging to infrastructure inside and outside
the urban boundary. This is a key way to examine the embodied, life-cycle properties of the
food we eat in cities.
This study develops a product life cycle assessment (LCA) of a basket of vegetables (product)
grown under two different formats. The first format is characterized by the large-scale,
commercial growers that supply the typical supermarket. The second format is characterized by
small-scale growers (less than 1 acre) that use higher land-use intensity and less mechanized
practices. This second format is typically used by backyard gardeners, operators of
neighborhood supported agriculture (NSA) and operators of some community supported
agriculture (CSA) businesses. Published data is used for the large-scale format; primary, case-
study data is used for the small-scale format.
Results of scenarios of land use change and vegetable production for both distant farmland and
urban settings found that shifts resulting from urban vegetable production are favorable in
terms of greenhouse gas emissions, water use, and soil organic carbon. Surprisingly, urban
vegetable production is not categorically favorable for each metric; several key parameters can
shift the balance in favor or out of favor for either growing format, and these parameters are
distinctly bottom-up. The results indicate that state and local policy could remove hurdles to
urban agricultural production with these data supporting claims that benefits outweigh costs.
The form and content of this abstract are approved. I recommend its publication.
Approved: Arunprakash Karunanithi
iii


ACKNOWLEDGEMENTS
I would like to acknowledge the following individuals for guidance and inspiration and personal
communication in the development of this research proposal.
Will Allen
Growing Power, Inc.
Milwaukee, Wl
Molly Anderson, Ph.D.
Partridge Chair in Food and Sustainable
Agriculture Systems
College of the Atlantic
Bar Harbor, ME
John Brett, Ph.D.
Professor
Department of Anthropology
University of Colorado
Denver, CO
Beth Anne Fisher, PT, DPT, CSCS, CBP
Steve's wife
Stephen Cochenour
Common Roots CSA
Debbie Dalrymple
Farmyard CSA
Denver, CO
James Diekmann
Professor
Civil and Architectural Engineering
University of Colorado
Boulder, CO
Lisa Rogers
Feed Denver
Denver, CO
Lynn Johnson, Ph.D.
Professor
Department of Civil Engineering
University of Colorado
Denver, CO
Arunprakash Karunanithi, Ph.D.
Wheat Ridge, CO
Roberta Cook
Department of Agricultural and Resource
Eco omics
University of California
Davis, CA
Mike Comazzi
Vice President of Procurement
FreshPack Produce
Denver, CO
Greg Cronin, Ph.D.
Associate Professor
Associate Professor
Department of Civil Engineering
University of Colorado
Denver, CO
Corrie Knapp
IGERT Researcher
University of Colorado
Denver, CO
Sundari Kraft
Heirloom Gardens
Denver, CO
Debbi Main, Ph.D.
Department of Integrative Biology
University of Colorado
Denver, CO
Professor
Department of Health and Behavioral
Sciences
University of Colorado
Denver, CO
iv


Amirhossein Mehrkesh
Research assistant
University of Colorado
Denver, CO
Leslie Miller, Ph.D.
IGERT Researcher
University of Colorado
Denver, CO
Jon and Candice Orlando
Urbiculture Community Farms
Denver, CO
Anuradha Ramaswami, Ph.D.
Professor
Charles M. Denny, Jrv Chair in Science,
Technology, and Public Policy
School of Public Affairs
University of Minnesota
Minneapolis, MN
Quint Redmond
Agriburbia
Golden, CO
Luann Rudolph
IGERT program manager
Pam Sawyer
Student
University of Colorado
Colorado Springs, CO
Leigh Tesfatsion, Ph.D.
Professor
Department of Economics
University of Iowa
Ames, IA
Dawn Thilmany, Ph.D.
Professor
Department of Agricultural and Resource
Eco omics
Colorado State University
Fort Collins, CO
Jody Villeco
Whole Foods
Boulder, CO
National Science Foundation (NSF)
Integrative Graduate Education and
Research Traineeship (IGERT)
Award No. DGE-0654378
v


TABLE OF CONTENTS
Chapter
1 Introduction............................................................................1
1.1 The Agri-food System.............................................................1
1.2 Social Aspects...................................................................3
1.3 Urban Sustainability.............................................................4
1.4 Life Cycle Assessment............................................................3
1.5 LCAs of Food and Agriculture.....................................................4
2 Focus...................................................................................9
2.1 The Vegetable Basket.............................................................9
2.2 Different Growing Formats.......................................................11
2.3 Characteristics of Urban Farming................................................12
3 Academic Contribution..................................................................16
3.1 Methodology.....................................................................16
3.2 Primary Case Study Data.........................................................16
3.3 Interdisciplinary Impacts Assessment............................................17
3.4 Appropriate Scales for Hybrid LCA...............................................17
3.5 Land Use Change.................................................................17
3.6 Relevant Data for Decision-makers...............................................18
4 Objectives.............................................................................18
5 Methods................................................................................20
5.1 Goal Definition and Scoping.....................................................21
5.1.1 Functional Unit............................................................21
5.1.2 System Boundaries and Components...........................................22
5.1.3 Small-Scale Vegetable Production Format...................................24
5.1.4 Large-Scale Vegetable Production Format...................................32
vi


5.1.5 Seasonal Sources...........................................................34
5.1.6 Scenario Development for Land Use Change...................................37
5.1.7 Data Quality Objectives...................................................42
5.2 Inventory Analysis..............................................................45
5.2.1 Data Sources..............................................................45
5.2.1.1 Power.................................................................48
5.2.1.2 Water.................................................................50
5.2.1.3 Other Agricultural Inputs.............................................55
5.2.2 DNDC Carbon Model..........................................................57
5.2.2.1 Biogeochemical Processes..............................................57
5.2.2.2 Model Inputs..........................................................60
5.2.2.3 Climate Data..........................................................60
5.2.2.4 Special Procedure for Estimating Precipitation........................61
5.2.3 Data Quality..............................................................67
5.2.4 Sensitivity, Uncertainty, and Variability..................................69
5.2.5 Management of Limitation of Small Sample Size in the Case Study............70
5.2.6 Outputs and Emission Factors...............................................78
5.3 Impacts Assessment..............................................................80
5.4 Interpretation..................................................................84
5.4.1 Comparative LCA of Vegetables..............................................85
5.4.2 Water Footprint............................................................88
5.4.3 Land Use Change............................................................90
6 Conclusion..........................................................................94
6.1 Limitations.....................................................................95
6.2 Contributions...................................................................96
6.3 Further Research...............................................................100
References..............................................................................102
vii


Appendix
A. Field Data Collection Form (Example)............................................109
B. Geographic Sources for Fresh Vegetables.........................................110
C. Life Cycle Inventory by Functional Unit.........................................Ill
D. Input Parameters for DNDC........................................................112
E. Article: Nitrous Oxide Emissions.................................................113
F. Dominance Analysis...............................................................114
G. Emission Factor Worksheet........................................................115
H. DNDC Modeling Output.............................................................116
I. Impacts by Functional Unit.......................................................117
J. Impacts Graphs...................................................................118
viii


TABLE
LIST OF TABLES
1-1. Applications of LCA for evaluating agricultural production systems..............5
1-2. Impact categories and indicators................................................6
1-3. The potential environmental impacts of vegetables, sugar, and oil...............7
1- 4. Greenhouse gas intensity for various vegetables, production on farm.............8
2- 1. Vegetables that can be grown in Colorado.......................................10
2-2. Comparison of qualitative, presumed characteristics of vegetable production grown
under large-scale and small-scale formats.....................................15
5-1. Production Phases Small-scale Format.........................................28
5-2. Scenario Development for Land Use Change.......................................40
5-3. Data Types and Quality Objectives..............................................43
5-4. Resource Flows and LCI Categories..............................................48
5-5. Site-specific Water Use Parameters.............................................51
5-6. Water Meter Assignments........................................................55
5-7. Typical Enterprise Budget......................................................56
5-8. Dominance Analysis Results.....................................................72
5-9. Estimated Benchmarks for Dominant Nominally Independent Variables..............74
5-10. 95 Percent Confidence Intervals for Impacts from Dominant Nominally Independent
Variables.....................................................................77
5-11. Example Input-Output Table for the Growing Phase for the Field Tomato Large-
Scale Growing Format..........................................................79
5-12. Midpoint impact indicators, classification, and characterization factors.......82
5-13. Interpretation of Impacts Assessment...........................................85
5-14. Net Change in Impacts from Land Use Conversion.................................91
5-15. Net Impacts Including Land Use Change with Alternate Functional Unit...........93
ix


..3
..2
11
20
22
23
25
26
32
36
37
38
44
47
49
53
54
58
65
66
67
69
72
98
LIST OF FIGURES
Study Components.........................................................
Greenhouse gas emissions summary for Denver in 2005......................
Average U.S. per capita fresh vegetable consumption......................
Phases of the life cycle assessment......................................
Life Cycle Inventory Scope...............................................
Simplified process flow diagram..........................................
LCA System Boundary Small-scale Format.................................
A Farmyard CSA garden....................................................
LCA System Boundary Large-scale Format.................................
Denver Area Supermarket Share............................................
Supermarket sources and distances by vegetable...........................
Land Use Change and Conversion Scenarios.................................
Uncertainty and influence indicate what are "key" data...................
Data Sources The hybrid LCA relies on two broad categories of information.
Regions of Electricity for Grid Mix Identification.......................
Six Neptune T-10 water meters............................................
Small-scale water metering setup.........................................
Process Flow for the DNDC Model..........................................
Daily precipitation at Fort Benton, Montana..............................
Cumulative nitrous oxide flux for 1991 and 1996 with daily precipitation.
Cumulative nitrous oxide flux detail for 19 June to 8 July...............
Neptune T-10 water meter accuracy for 5/8-inch connections...............
Managing Small Sample Size in the Case Study.............................
Data Sources and Emissions Reporting.....................................
x


1 Introduction
Our agri-food system is the subject of increasing scrutiny from a global to a local scale. More
and more people are decrying farming policies and practices that have clear environmental and
human health impacts (Pimentel 1994). Issue-driven, quantitative indicators are needed by
policy makers to prioritize planning and spending of infrastructure in an informed manner. A
comparative life cycle assessment could provide quantitative information regarding the benefits
and impacts of small-scale growing (characteristic of urban areas) compared to large-scale
commercial growing. Such information is crucial to enable leadership in areas of environmental
stewardship, pollution, and energy intensity on an urban scale (Rochefort 1993, United Nations
Environment Programme [UNEP1 2009a, Vaughn 2003, Weiss 1989).
1.1 The Agri-food System
Urban gardening is farming at a small scale.14% of the U.S. land area is harvested in
"agriculture" everything from big industrial farms producing monocultures to just a few acres
producing a variety of organic vegetables, grains, and melons (U.S. Department of Agriculture
JjJSDAl_2010). Under this aggregate classification, 0.9% of all U.S. harvested acres is fresh
vegetables (U.S. Environmental Protection Ag_encv [EPA1_ 2010a). By comparison, 68% of all
harvested cropland is planted with one of the four commodity crops corn, cotton, soybean,
and wheat (USDA 2010). The USDA categorizes farms as agricultural operations with sales.
USDA statistics do not recognize farms of less than 1 acre in size and there is no good estimate
of the land area utilized for urban gardening (gardening may or may not generate sales). Yet
even with such a tiny amount of land devoted to vegetable production, fruit and vegetables
contribute about 8% of greenhouse gas emissions from all food, 4% of water embedded in food
products, and 15% of embodied energy (Institute of Grocery Distribution JIGD] 2007).
1


Fresh vegetable production at a small scale from urban gardening may possess qualities that
belie its seemingly small status in the agri-food system. For example, 40% of all produce
consumed in the U.S. in 1944 came from "Victory Gardens" (Pollan 2008). Denver had 41,500
Victory Gardens in 1943, valued at $578,125 ($7,122,319 in 2008 dollars). That is equivalent to
an average of about $172 per garden per year. These Victory Gardens ranged in size from a few
potted plants on a porch to large, multi-acre community gardens. Partly because Colorado has
only 354 farms most of those under 10 acres that produce vegetables, and that only 2% of
the average American's caloric intake is from fresh vegetables, only 0.2% of the food we eat in
the Denver metro area comes from Colorado (Masoner 2010). Apart from food production,
urban gardening has other interesting facets-food security, health, recreation, urban resiliency,
social justice, and employment, to name a few (Bellows 2004).
Under the urban gardening moniker exist community supported agriculture (CSA) and
neighborhood supported agriculture, both of which refer to a business model specifically, and
relative size of operation generally. CSA is a term generally describing a farm that delivers
produce to its shareholders within 100 miles. CSA farms can vary from less than one acre to
thousands, and be located within or outside urban areas. These farms, while sometimes large,
do not participate in the USDA farm commodity program and are free to plant any crop without
penalty (Raw Earth Living 2010).
Neighborhood supported agriculture (NSA) is a term describing small businesses that grow
vegetables on portions of residential or small commercial properties. Similar to CSAs,
shareholders pick up their produce at designated drop points. NSAs can differ from CSAs in size
and produce travel distance; NSAs are strictly neighborhood-scale. Finally, residents garden
either for food or hobby in their own yard or balcony, or their plot in a community garden which
2


may be collocated with a school or park. This study compares production of vegetables grown
under a large-scale format and a small-scale format, and these components are depicted on
Figure 1-1, the general scope of the research within the context of the agri-food system.
Figure 1-1: Study Components
This study does not examine other parts of the agri-food system, including processed food and
its packaging, further transport and distribution, cooking, preparation, consumption, waste, or
recycling.
1.2 Social Aspects
There is a large body of related research that explores the human side of food and agriculture,
both large- and small-scale. For example, in one article alone, Kloppenburg (2000) cited 22
3


peer-reviewed articles that discuss it. In that article, Kloppenburg used systems thinking and
convened a medium-scale, participatory survey to identify not components of the food system,
but the system attributes. This work is one of the most compelling because of the design of the
survey and working groups that is perhaps the best combination of ''ordinary people's" views
and those of "experts." As a final outcome of the work,14 attributes of food system
sustainability were identified and these are presented below (Kloppenburg 2000).
Relationship with the land
Knowledge & Communication
Proximate
Profit
Participation
Justice and ethics
Sustainably regulated
Sacred human expression
Healthful
Diversity
Culturally nourishing
Seasonality
Value-oriented economics
Relational
It is interesting to note that none of the attributes address environmental sustainability and
suggests that this is an area of either implicit embrace or possibly ignorance. This study
attempts to characterize labor hours and labor wages.
1.3 Urban Sustainability
In 2009, the world's urban population surpassed the non-urban population for the first time
(World Resources Institute [WRIJ 2010). This marks what has been a steady global shift of
providing more food to places it is not grown. In the U.SV for example, depending on the
vegetable variety and time of year, conventional produce travels, on average, from 500 to over
2,000 miles to terminal markets in the U.S. (Weber and Matthews 2008). The vast majority of
American farmland is devoted to the main commodity crops (i.ev corn, cotton, rice, soy, and
wheat) that require vast fields, large operations, and economies of scale. However, most is used
as animal feed, processed, or exported. Demand for fresh vegetables is met from a surprisingly
4


small amount of farmland. But constraints on traditional farmland are growing. These include
water scarcity, development pressure, degrading soils, nutrient runoff, and cost of inputs.
Urban vegetable production is a viable land use change to meet these challenges.
There is enough arable land area in the typical American city to support meeting all its fresh
vegetable demand many times over. In one study for Denver, Colorado, it was found that there
is over 10 times the land available to meet city demand for broccoli, carrot, spinach, tomato,
bell pepper, and potato (Brett,_et al. 2013). Also, in contrast to commercial farming, urban
vegetable production as food, health, and security, is advancing popularly on its own as an
advocacy platform, hobby, business, and occupation (Kloppenburg 2000). Some urban areas
around the globe provide up to 90 percent of fresh vegetable consumption through urban
gardens, others almost none (Cuba Ministry of Agriculture 2008).
But the link between the food we eat and urban sustainability exists equally whether the food
comes from a farm far away or one's own back yard. What commercial growers provide to
supermarkets is a significant cog in the food system and they rely on large contributions of
distributed infrastructure from farm to table. In fact, as shown on Figure 1-2, the food urbanites
eat ranks significantly with other notable urban sectors, such as transportation, housing, and
energy, in terms of fossil fuel consumption, emissions, water use, waste, and economic activity
(Hillman and Ramaswami 2010. Decker 2000).
1


Other Bldgs
Residential Bldgs 14%
Pickups/SUVs
Food 10%
34%
Fuel Processing
Cars
Air Travel
Commercial Trucks
Govt. Bldgs
Cement
Public Transit
170/-
[^4%
^3%
2%
1%
0%
5% 10% 15% 20% 25% 30% 35% 40%
Figure 1-2: Greenhouse gas emissions summary for Denver in 2005
The food sector includes an individual's total diet imported, local, processed,
and raw foods, (from Ramaswami^ et al. 2008).
Other notable characteristics of urban food are that it comprises 17 percent of fossil fuels use,
12.7 percent of the post-consumer waste stream, and 13 percent of consumer spending (Heller
and Keoleian 2000, Intergovernmental Panel on Climate Change [IPCC1 2008. Ramaswami^ et al.
2008). Even with the small fraction of all farmland devoted to vegetable production, fruit and
vegetables contribute about 8 percent of greenhouse gas emissions from all food, 4 percent of
water embedded in food products, and 15 percent of embodied energy (IGD 2007).
This paper adopts a social-ecological-infrastructural systems (SEIS) framework, a large
component of which is recognizing urban activities and sectors belonging to infrastructure inside
and outside the urban boundary (Ramaswami, et al. 2012). The framework establishes the
importance of including WRI Greenhouse Gas Protocol Scope 3 emissions and other indirect,
2


upstream supply-chain footprints when they are a large component of total emissions and
footprints of urban activities. This is a key way to examine the embodied, life-cycle properties of
the food we eat in cities.
In counting the impacts of the production, processing, and transport of food, one must extend
the boundary however far it exists, "...to address environmental sustainability both in terms of
resource use and global pollution impacts, activities and infrastructures within city boundaries
must be explicitly integrated with transboundary infrastructures that span hundreds of miles
and draw in vast quantities of natural resources, directly or indirectly, to meet city demand../'
(Ramaswami, et al. 2012). This is a main focus of greenhouse gas (GHG) accounting and
reporting for material flows in and out of the [usually jurisdictional] urban boundary. For
example, some infrastructure services utilize more fossil fuel outside city boundaries than within
(Ramaswami etal. 2012).
1.4 Life Cycle Assessment
The life cycle assessment (LCA) is a framework for identiTying impacts of a product's
manufacture, use, reuse, or disposal. The LCA is typically conducted on manufactured products,
but the concept has also been applied to operations research, supply chain analysis, human
resources, and a wide variety of capital projects. It has its roots in a multi-criteria study done for
the Coca Cola Company on their signature beverage. Later, the field of industrial ecology
adopted this approach and helped establish the International Standards Organisation (ISO)
Series 14000 standards on LCA.
The LCA framework and ISO methodology is used for this study because it is a widely-recognized
and accepted methodology for conducting inventories of resource use, output, and wastes, and
3


for reporting impacts to human health and environmental indicators. This study performs a
type of agricultural LCA, where considerations specific to an agricultural product come into play.
The LCA is discussed in more detail in Section 5.0
1.5 LCAs of Food and Agriculture
We have seen the food system modeled as an LCA to study material flows, and midpoint and
endpoint impacts (Heller and Keoleian 2000). There is a large body of literature that divides the
food system into many different components. The Commonwealth of Australia's (1994) and
Heller and Keoleian's (2000) reports are life cycle approaches that focus on inputs and
measurable outputs, some of which are sustainability indicators, such as health. Molly
Anderson's work on food systems integrity groups the food system into system functions and
spells out indicators and measurements in each (Anderson 2009). Heller and Keoleian (2003)
attempted to quantify the sustainability of the U.S. food system with over 100 environmental,
economic, and social indicators corresponding to life-cycle assessment and stakeholder groups.
A majority of research has been done in Scandinavia and the Low Countries. Miller, in her
master's thesis, revealed that little data is available for the U.S. (Miller 2010). Hayashi and Haas
both have spearheaded methodology papers for agricultural production systems. Hayashi
compiled a list of applications of LCA for agricultural production systems. Table 1-1 shows the
eleven studies, none of which has settings in the U.S. or comparable crops to the food basket of
this research proposal.
4


Table 1-1: Applications of LCA for evaluating agricultural production systems
Author(s) Issues Alternatives Functional units Cradle-to-gate Impact
Hanegraaf, Biewinga and van der Bijl (1998) Energy crop production in the Netherlands Route+Crop(GAP) 1 GJ and 1 ha Cradle-to-gate Midpoint
Cederberg and Mattsson (2000) Milk production in Germany milk) Conventional and organic farming 1000 kg ECM (energy corrected) Cradle-to-gate Midpoint
Haas, Wetterich and Kopke (2001) Grassland farming in Germany Intensive, extensive, and organic farming 1 ha and 11 milk Gate-to-gate Midpoint
Frick et ol. (2001) Arable crop rotations with clover-grass Integrated intensive, integrated extensive, and organic farming 1 ha and 1 kg dry matted Cradle-to-gate Midpoint
Brentrup et ol. (2001) Sugar beet production in Germany Sugar beet production with calcium ammonium nitrate (solid fertilizer), urea (solid fertilizer), and urea ammonium nitrate solution (liquid fertilizer) 11 of extractable sugar Cradle-to-gate Midpoint
Eide (2002) Industrial milk production in Norway Small, middle-sized, and large dairy 1000 L of drinking milk brought to the consumers Cradle-to-gave Midpoint
Gaillard and Nemecek (2002) Cereal and rape seed production in Switzerland Conventional, integrated intensive, integrated extensive, and organic production 1 ha and 1 kg Cradle-to-gate Midpoint
Bennet et ol. (2004) GM sugar beet production in the UK and Germany Conventional and GM- herbicide tolerant sugar beet 50000 kg fresh weight of sugar beet Cradle-to-gate Midpoint
Brentrup et ol. (2004) Winter wheat production in the UK (Nitrogen fertilizer rate) 11 of grain Cradle-to-gate Midpoint
Basset-Mens and van der Werf (2005) Pig production in France (red label), and organic agriculture Conventional GAP, a French quality label 1 ha and 1 kg of Pig Cradle-to-gate Midpoint
Anton, Montero and Munoz (2005) Greenhouse tomato production in Spain Soil cultivation, open, and closed hydroponic systems (+3 waste management scenarios) 1 kg of tomatoes Cradle-to-gate Midpoint
Source From Table 1(Hayashi 2006)
Haas, et al. (2000) present nuances of LCA methodology applied to agricultural production in
terms of scoping, impact categories, functional units, and system boundaries. The setting is
Scandinavia and Europe. They also present what they feel are relevant impact categories,
shown in Table 1-2.
5


Table 1-2: Impact categories and indicators
Impact category Environmental indicator
Resource consumption energy minerals Use of primary energy Use of P- & K-fertilizer
Global warming potential C2, CH4/ N2-emission
Soil function/strain grassland of other ecosystems (N-eutrophication, acidification) Accumulation of heavy metals NH3/ NOx/ S02/-emission
Water quality ground water (nitrate leaching) surface water (P-eutrophication) N-fertiiizing, N-farmgate-balance, potential of nitrate leaching, P-fertilizing, P- balance, % of drained area
Human and ecotoxicity Application of herbicides and antibiotics, potential of nitrate leaching, NH3/- emission
Biodiversity Grassland (number of species, date of first cut), hedges & field margins (density, diversity, state, care)
Landscape image (aesthetics) Grassland, hedges & field margins (see above), grazing animals (period, breed, alpine cattle keeping), layout of farmstead (regional type, buildings, garden)
Animal husbandry (appropriate animal welfare) Housing system & conditions, herd management (e.g. lightness, spacing, grazing season, care)
Source: Table 1(Haas 2000)
Mogensen's and Carlsson's papers represent a range of life cycle approaches from whole food
systems to individual raw foods (Mogensen 2008; Carlsson 1997). Mogensen has conducted an
LCA for a number of individual food items, including potato, onion, carrot, and tomato. Their
findings are excerpted in Table 1-3.
6


Table 1-3: The potential environmental impacts of vegetables, sugar, and oil
Impact per kg Sugar Oil Potatoes Carrots Onions Tomatoes
GWP 100 (kg C02eq) 0.96 3.6 0.22 0.12 0.38 3.5
Acidification potential (g S02 eq) 6.0 31 1.5 1.0 1.5 7.2
Nutrient enrichment (g Ns eq) -12.1 439 14.4 3.6 15.0 24.7
Photochemical smog (g ethane eq) 0.83 2.1 0.14 0.15 0.15 0.84
Land use (m2 year) 0.45 4.5 0.3 0.2 0.3 0.02
Functional unit is 1 kg food ex retail. Source: Table 5.10 (Mo^ensen 2008).
As shown, the impacts categories included global warming potential, acidification potential,
nutrient enrichment, photochemical smog, and land use. Data for the study was from the
Danish setting. The study quantified global warming potential for a number of raw and
processed foods, including potatoes, carrots, onions, and tomatoes. The conclusions of the
study were simply that the agricultural production phase of the life cycle of food is often the
phase with the most environmental impacts compared to the other phases of transport,
processing, 3d cosum6use.
Carlsson's work does include life cycle inventory information for production of carrot and
tomato, and regional transportation after the farm gate, but again, the setting is Europe.
Gossling et al. (2011) presented an article on reducing the carbon 'foodprint' of food consumed
in the tourism industry. A part of the study compiled information from a number of sources in
Europe on the C02-e footprint for the production of some vegetables. The values shown in
Table 1-4 could provide a check against values found for U.S. based production.
7


Table 1-4: Greenhouse gas intensity for various vegetables, production on farm
Vegetable kg C02-e/kg kg C02-e/1000 kcal Country Source
Potatoes 0.158 0.247 UK DEFRA,2007
0.160 0.250 Denmark LCA Food, 2003
0.261-0.274 0.442-0.464 Netherlands Koketalv 2001a,b
0.073-0.083 0.114-0.130 Sweden Cederberg et alv 2005a
0.100 0.156 Sweden Mattsson et alv 2001
Carrots 0.046 0.144 UK DEFRA,2007
0.122-0.234 0.381-0.730 Denmark Milj^styrelsen,
0.036 0.112 Sweden Cederberg et alv 2005
Onions 0.060 0.201 Sweden Cederberg et alv 2005
0.079 0.265 UK DEFRA,2007
0.382 1.28 Denmark Milj^styrelsen, 2006
Lettuce 0.602 5.46 UK DEFRA,2007
Tomatoes (greenhouse) 0.082 (unheated) 0.456 (unheated) Spain Anton, Montero, & Munoz, 2005
1.30 7.20 Sweden Moller Nielsen, 2007
5.90-28.50 33.00-158.00 UK Williams, Audsley, & Sandars, 20Ob
3.45-4.92 19.10-27.30 Denmark Milj^styrelsen, 2006a
Cucumber (greenhouse) 4.37 45.00 Denmark Milj^styrelsen, 2006
System boundary: farm production including all greenhouse gases based on lifecycle analysis.
a Lower value: conventional, higher value: organic production,
b Own calculation to include other GHGs.
c Organic production,
d Greenhouse, unheated,
e Higher values relate to cocktail tomatoes.
Source: Table 1(Gosslinq_2011).
8


2
Focus
This study uses existing research and published data but also makes direct measurements and
models agricultural emissions in a very site-specific manner. The focus is on the production side
of the agri-food system inputs required to produce a given vegetable. The processing,
packaging, and consumption phases are not included except for bulk transport packaging. A
comparative LCA performed on these two growing formats quantifies emissions and growing
efficiency in terms of inputs, such as water, fuel, and land area. The setting is the Denver
metropolitan area, recognizing that the typical vegetable purchased at the supermarket is, at
any given time, likely an imported item grown under a large-scale format.
2.1 The Vegetable Basket
What are the vegetables that can be grown in the Denver metropolitan area and how does that
compare with what we consume? This section describes the choice of vegetables on which to
perform the LCA. Table 2-1 shows that nearly all vegetables that are shown in USDA national
consumption tables can be grown in Colorado. A few common varieties that are not listed on
USDA consumption tables can also be grown in Colorado.
9


Table 2-1:
Vegetables that can be grown in Colorado
Vegetable variety Vegetable variety
Artichokes Kohlrabi
Arugula Leeks
Beets Lettuce, head
Broccoli Lettuce, leaf
Brussels Sprouts Mustard Greens
Cabbage Onions
Carrots Peas, snap
Cauliflower Peppers sweet bell
Chard Potatoes
Collard Greens Radishes
Corn, sweet Spinach
Cucumbers Squash
Eggplants Tomatoes
Green Beans Turnips
Kale
Notes:
Table refers to non-greenhouse grown
Not in USDA national consumption data tables
Sources:
http://www.ers.usda.gov/data/foodconsumption/
http://eatwhereulive.com/
http://www.grantfarms.com/home.php
http://farmvardcsa.com/
The consumption of specific fresh vegetables compared to total fresh vegetable consumption
was examined. This revealed that a small set, or "basket," of vegetables comprise a majority of
the total as shown on Figure 2-1.
10


r
Top 5 Vegetables by Calories

Lettuce, head^^^s. Tomatoes [ Carrotsx^^^H s
Onions
V
Figure 2-1: Average U.S. per capita fresh vegetable consumption.
Source: U.S. Department of Agriculture (USDA) Economic Research Service (ERS).
2009.
Because consumption quantities by weight and by calorie content are relatively similar, a basket
of four vegetables was chosen based on their contribution, by calorie, of the average American
diet of fresh vegetables. These are potato 53%; onion 12%; carrot 7%; and tomato 6%. These
comprise 78% of the fresh vegetable consumption of the average American. These are found
readily in any supermarket, and are all grown in the Denver metropolitan area by operators of
NSAs, CSAs, and individuals (USDA 2009). In the lettuce category, there is high variability due to
many popular types of lettuce (head and leaf), varieties (e.g. romaine, herbs, iceberg, etc.), and
market preference. None of the small-scale operations were growing iceberg, yet iceberg is a
prevalent crop for the large-scale growers. For these reasons, the lettuce category was dropped
from the study.
2.2 Different Growing Formats
The choice of two different growing formats stems from the desire to draw comparisons
between the ways we, as consumers, get our fresh vegetables. In general, there are a number
of ways. We can get our produce from grocery stores and supermarkets of all sizes, we can
11


grow them ourselves (typically only part of the year), we can mail- or internet-order, we can
drive to a farm, visit a farmers market, or pick up a share at a local CSA drop-off location.
Among all these ways to acquire our produce, there are two categories all the produce fall
under those of the scale of growing. Typically, these categories can be called ''large-scale" and
"small-scale." There is a third category which straddles the gap between large and small, but
the majority of our fresh vegetables come from a large-scale growing format. Many fewer still
come from a small-scale format, but more people engage in it.
What is meant by "format" and "scale?" Table 2-2 compiles a list of characteristics, largely by
way of comparison, that, taken together, identify what is a large-scale growing format and a
smaH-scale growing format.
2.3 Characteristics of Urban Farming
This section expands on the characteristics found in Table 2-2 with a focus on the grower as a
user/owner/operator of urban infrastructure. The growers can be categorized as CSA operators
and homeowners. While no attempt was made in the design of this study to fully characterize
behavior and motives of urban growers, this section provides this author's general observations
through his interactions in the urban gardening communities, associations, networks, and
events over more than 5 years.
Motives
The urban CSA growers appear to have no particular demographic except generally being
between the ages of 20 and 45. All urban growers (CSA and homeowners) appear to have
diverse backgrounds and vocations, from the sciences to liberal arts. But a common theme
12


among them is that our commercial food system is broken, if not harmful to the planet, and
they can make some kind of living (CSA operators) or hobby (homeowners) from urban
vegetable production while providing fresher, more nutritious, potentially less toxic, and more
delicious vegetables. There is a fairly universal belief that urban vegetable production can help
save the planet. But, for the CSA operators, profit is a concomitant motive.
Challenges
If profit is a motive, then the challenge is to have enough of it. It would be easy to place the
same financial demands of the typical urban resident on the CSA operator. These might include
such major expenses such as housing, automobile, utilities, and food. But one must recognize
that these expenses are not incurred by all individuals. In this sense, everyone has a slightly
different economic situation. If other sources of income are high enough, or certain expenses
are low enough, the tolerance for little to no profit is much greater. There are CSAs that fall in
this category. Some individuals may have the house paid off, or no automobile. Others may
secure grant funding, establishing the CSA as a non-profit entity. The author knows no CSA
operator that is "making a living" with the business in a conventional sense, yet CSAs still do
prevail and have a track record. A caveat is that Colorado was home to Grant Family Farms
(located in Weld County, well outside the urban boundary), one of the largest CSAs in the U.SV
growing on about 300 acres. At one point, Grant Family offered at least 4,500 shares, many to
Denver residents. It was clearly a for-profit business and had many employees; however, it
ultimately went bankrupt owing almost $10 million in debt and liabilities.
An additional challenge is that vegetable production is hard work, can involve many hours of
manual labor at inconvenient times, and is subject to the vagaries of hail storms, extreme heat,
13


pests, and, in some cases, uncertainty in land tenure. It is thought that these demands, placed
on relatively few owners and employees, explain why four of six initial participants in this study
dropped out. In order to replicate or add to this study, data gathering and reporting must be a
strong commitment on the part of the grower.
Practices
Urban growers seem to belong to a de facto fellowship defined by common knowledge shared
through such fora as the Denver Post gardening section, websites, local networking events,
extension service guidance, neighborly encounters, and hand-me-down knowledge. While there
are many ways to grow, there is a consistent ethic and this drives consistent growing practices.
Most have no interest in organic certification but are, in essence, an organic operation. Notions
about the desirability of compost, the aversion to pesticides and herbicides, and maximizing
yields seem to be fairly universal. One practice where the author noticed significant variation
was in tilling. Some appear to embrace no-till practices while others seem to be firm believers
in tilling extensively every year. Tilling, or course, provides a short term gain in workability, but
destroys humus and soil quality in the long term.
Irrigation is another practice with potentially significant variation. The homeowner is generally
aware of the extra water demand and water cost when growing vegetables. The homeowner is
in total control of the irrigation setup and application rate, and he directly experiences (for
better or for worse) the consequences. In contrast, CSA operators often require the
homeowner to provide water and the land for free, in exchange for a CSA share. This could
potentially lead to overwatering since the CSA operator is generally in control of the irrigation,
but does not necessarily experience the cost. Also, some plots may be over-irrigated because
14


the whole plot is connected to a single timer that is set to meet the demand of the most water-
thirsty plants. Individual homeowners may be more vigilant and not use automation.
Table 2-2: Comparison of qualitative, presumed characteristics of vegetable production
grown under large-scale and small-scale formats
Category Large-Scale Format1 Small-Scale Format1
Business status For-profit company Hobby; for-profit company; non-profit company
Investment capital High capital and operations and maintenance costs for infrastructure Low capital and operations and maintenance costs for infrastructure
Market Produce wholesalers; vertically-integrated retailers (supermarket conglomerates) Direct marketing; farmers markets; bartering; donations; sharing
Land area Greater than 1 acre (typically greater than 10 acres) Less than 1 acre (typically less than 1/4-acre)
Setting Rural; peri-urban; distant from consumer Peri-urban; urban; local to consumer
Fuel Higher fuel use from mechanized equipment Lower fuel use from little mechanization
Water Furrow (flood); spray; drip Spray; drip
Diversity Monoculture Intercropping, mixed plant varieties
Impacts to soil High or complete tillage Higher propensity for soil erosion Low, minimal, or no tillage Lower propensity for soil erosion
Pesticides and herbicides Heavy use and reliance of chemicals Low use and reliance of chemicals
Productivity Lower harvest per unit area Higher harvest per unit area
Fertilizer High inputs of synthetic N, P, K Little to no compost use Low inputs of N, P, K Higher compost use
Labor Low labor use per unit vegetable High labor use per unit vegetable
Soil Organic Carbon Lower Higher
Certified Organic status Yes and no (those that are not organic typically are not managed organically) Yes and no (those that are not organic typically are managed organically, but without certification)
Transport from farm gate Extensive packaging; long-distance shipping Minimal packaging
Notes:
1.Scale is primarily based on the expedient measure of land area under cultivation. However, the size of the
enterprise in terms of assets and sales be it a hobbyist in a residential garden or a corporation also has
relevance. The terms "large-scale'' and "small-scale^ are, therefore, used rather loosely and rely on a
compilation of characteristics found in this table. "The classification of farms by the value of their product
brings together enterprises which really have the same scale of production, regardless of acreage. Accordingly,
a highly intensive enterprise on a small tract of land falls into the same group as a relatively extensive
enterprise on a large tract; both are actually large-scale in terms of production and the employment of hired
labour" (Lenin 1917).
Sources: Partially from (Grace 2011)
15


3
Academic Contribution
This study offers a comprehensive, life cycle comparison of the two systems behind consumed
vegetables for a metropolitan area. While much literature compares large-scale growing
operations with regards to growing practices, crops, and inputs, none make a direct comparison
between large-scale and small-scale, as defined in this study. With regards to the vegetable for
sale through a CSA or NSA or local farmer's market (typically grown under the small-scale
format), no studies exist that couple the inputs to the production side of that vegetable with the
yield of its harvest in the Denver metropolitan area. Further contributions are described below.
3.1 Methodology
The methodology created to characterize urban and commercial vegetable production in a
comparative LCA is replicable for any urban area. This is because the study carefully selects data
sources that are commensurate with the components of interest. For example, another urban
area may have a different mix of supply chains for vegetables sold in grocery stores. Other
urban areas may have different climate, soils, and perhaps broadly different growing practices.
The methodology in this study captures these important differences.
3.2 Primary Case Study Data
Primary data, gathered in a case-study of small-scale vegetable growers, in a way that is
comparable to published data for large-scale vegetable growers is a significant contribution to
the literature.
16


3.3 Interdisciplinary Impacts Assessment
Most LCAs, including that found in this study are conducted with interest in environmental
impacts. These include resource use (for water, energy, fuel), greenhouse gas emissions,
ecotoxicity, and human health impacts. But this study adds additional impact metrics that are
less common in the literature such as soil organic carbon (SOC) (an indicator of soil health), land
use change, and employment. Soil organic carbon is highly related to current and antecedent
land use and reaches a steady-state at a 30-year horizon or less (Kim 2009; Morgan, et al. 2010).
Second, the labor indicator is used because there is a stark and measurable difference between
the labor inputs of large-scale and small-scale growing. This can be seen as an opportunity for
local employment, but a risk for corporate efficiency.
3.4 Appropriate Scales for Hybrid LCA
Finally, this study carefully chooses more appropriate, bottom-up data sources that match the
level of variability and scale of the components of the vegetable production system where many
other studies and greenhouse gas accounting reports use national or regional data. For
example, local case-study data and emissions modeling using local parameters are used instead
of one source touted as a national or regional average. In doing so, the accuracy is increased,
uncertainty is reduced and meaningful results can inform the transboundary conversation.
3.5 Land Use Change
First, the soil organic carbon indicator is used because the land use change that occurs when
large-scale agricultural farmland is converted back to natural grassland as urban gardening,
utilizing the small-scale format, increases. Under this assumption, the small-scale format
displaces the large-scale format in equal areas, weighted by crop productivity per land area. For
17


the small-scale format, this indicator assumes that a third of the land is converted from previous
use as turf, a third as bare soil, and a third as natural grassland (assumed to represent vacant,
weedy areas). These are the basis for a comparison of land use change.
3.6 Relevant Data for Decision-makers
The popularity of urban vegetable production on a small-scale is, in part, explained by the way
people feel about the food system, and is a response of sorts a response to the existing
dominance of large-scale production formats. Much of what we know about the supposed
benefits of small-scale production, then, is relative to large-scale production. Typically, the
belief is that small-scale is better than large-scale. However, a quantitative comparison of these
two formats using a life cycle approach that could corroborate these beliefs has not been done
on a scale that is relevant. Further, to have relevance with a number of municipal climate and
sustainability action plans, a life cycle assessment would have to be fairly specific to that locale.
This study provides relevant, local data to inform Denver metropolitan area leaders and
decision-makers.
18


4
Objectives
This study conducts an LCA to compare the environmental and social impact of key vegetables
grown in urban settings with that of commercial farming. The LCA will answer questions such as
"Which vegetable is more environmentally friendly?" or "Which growing format is more efficient
with resources?" These are fundamentally unquantified, unanswered questions that could
contribute to the policy dialogue with regards to incentives or disincentives to urban gardening.
The research plan has four major objectives. The first objective is to compile material and non-
material flows for the production of four vegetables under two different production formats,
including bulk transport to the point of purchase. This information does not exist for the small-
scale or urban gardening format and will be useful and revealing. For example, few have
measured their water use in a vegetable garden and related it to their yield. The data collected
during this research will provide a sense of growing efficiency with respect to inputs. The
second objective is to conduct an LCA of each format using collected and published data. This
will help us develop midpoint metrics that will quantify positive and negative impacts of urban
gardening to the environment, ecosystem, human health, and society. The third and fourth
objectives are to compare the environmental and social impacts of the two growing formats and
examine direct and indirect land use change, respectively.
19


5
Methods
The LCA was carried out using a modified version of the ISO Standards 14040 through 14043.
The method is carried out in four general phases: Goal and scope definition, Inventory analysis,
Impacts assessment, and Interpretation, as described below, on Figure 5-1, and in the following
sections.
Figure 5-1: Phases of the life cycle assessment
From (EPA 2006).
1. Goal Definition and Scoping Define and describe the product, process
or activity. Establish the context in which the assessment is to be made
and identify the boundaries and environmental effects to be reviewed
for the assessment.
2. Inventory Analysis Identify and quantify energy, water and materials
usage and environmental releases (e.gv air emissions, solid waste
disposal, waste water discharges).
3. Impact Assessment Assess the potential human and ecological effects
of energy, water, and material usage and the environmental releases
20


identified in the inventory analysis.
4. Interpretation Evaluate the results of the inventory analysis and
impact assessment to select the preferred product, process or service
with a clear understanding of the uncertainty and the assumptions used
to generate the results.
From (EPA 2006).
5.1 Goal Definition and Scoping
As described previously in terms of motivation and objectives, a comparative life cycle
assessment provides quantitative information regarding the benefits and impacts of small-scale
growing (characteristic of urban areas) compared to large-scale growing. Such information is
crucial to enable leadership in areas of environmental stewardship, pollution, energy intensity,
and land use on an urban scale. In addition to these goals, the LCA process:
compiles material and non-material flows for the production of a basket of four
vegetables under two different formats;
quantifies positive and negative impacts to the environment and society using midpoint
metrics; and
draws comparisons between the two formats using quantitative data.
5.1.1 Functional Unit
The functional unit for LCA is 1 pound of each type of vegetable, at the point of acquisition
consumer. Although the conversion factor for caloric value per pound of produce varies
substantially, from 82 to 349 kilocalories per pound [kcal/lb] (USDA 2009), the value of the LCA
is a comparison of a given unit of vegetable grown under the large scale format to the same unit
of vegetable grown under the small scale format. A number of other units are obtained by
simple conversion factors such as per metropolitan area, per household diet, per capita diet, or
calorie, for example.
21


5.1.2 System Boundaries and Components
The scope of the research is based primarily on geographic boundaries of population (including
many jurisdictional boundaries), but also those of process, time, and enterprise. Figure 5-2
depicts general product life cycle phases and the elements of which the research aims to
address. Phases often included in a product life cycle assessment that are not shown and not a
part of the scope include post-consumption (waste), roundput, and cradle-to-cradle pathways.
Scope of the research underlined
Figure 5-2: Life Cycle Inventory Scope
The scope of the research could be called "cradle-to-farm gate + transportation." This is
depicted on Figure 5-3, a simplified process flow diagram.
22


| | Included in study
| | Not included in study
Figure 5-3: Simplified process flow diagram
This is an emerging and developing aspect of life cycle impact assessment. No
formal methodologies exist. In the place of a methodology or model, a guidance
document will be used: "Guidelines for Social Life Cycle Assessment of Products -
Social and socio-economic LCA guidelines complementing environmental LCA and
Life Cycle Costing, contributing to the full assessment of goods and services within
the context of sustainable development" (UNEP 2009b).
Because the fate of vegetable after the point of sale is assumed to be identical, the consumption
and recycling phase were not included in the study. There is some literature that indicates that
a consumer of vegetables grown under the small-scale format, with such sources as their own
backyard, a CSA, a farmer's market, or even an organic section in a supermarket, is somewhat
less prone to food waste and tends to recycle more of what waste there is than the consumer of
conventional supermarket vegetables. But these are considered minor in the overall process
flow diagram.
23


Because little published data exists for the bulk packaging and processing from farm to truck, a
focused literature review was conducted to establish any justification for omitting this phase.
The post-harvest handling, processing, and packaging of fresh potato, carrot, onion, and tomato
vary. Potato, carrot, and onion can all be stored for months after harvest, while tomatoes less
so. In addition, tomatoes are much more fragile and require constant cool storage. Despite
these differences, available literature was used to estimate that this phase could be omitted
without much consequence. The basis for this is using a number of studies of impacts
associated with cool storage of the tomatoes after harvest and to the retail store. The most
comprehensive study found that this phase contributes a very small fraction of the total
environmental impact (EPA 2010b). Bulk packaging (paperboard, cardboard, plastics), however,
were estimated and included in the LCA.
5.1.3 Small-Scale Vegetable Production Format
A process and boundary flow diagram was created for the small-scale production format as
shown on Figure 5-4.
24


Preparation
Starts
Amending
Tilling
Sowing
Mulching
Wastes
Harvest
Picking
Grading
Packing
Wastes
Composting
Cleanup
Wastes
Amending
Composting
Tilling
Market
Transport
Retail packaging
Wastes
browing
Weeding
Thinning
Tilling
Amending
Wastes
Composting
| | Foreground system
| | Background system each with upstream embodied resource use
Figure 5-4: LCA System Boundary Small-scale Format
As discussed previously, a basket of four vegetables was chosen based on their contribution, by
calorie, of the average American diet of fresh vegetables. These are potato 53%; onion 12%;
carrot 7%; and tomato 6%. These comprise 78% of the fresh vegetable consumption of the
average American. These are found readily in any supermarket, and are all grown in the Denver
metropolitan area by operators of NSAs, CSAs, and individuals (USDA 2009).
For the vegetable grown under the small-scale format, local sources and primary data were
used. Initially, five growers were contacted and were willing to collect data that characterize
inputs and yield. These were Heirloom Gardens, LLC; Stephen Cochenour; Farmyard CSA; Dr.
John Brett; and Urbiculture Farms. Growers were given highly accurate water meters and a
series of forms to track inputs, wastes, and yields during the growing season. Of these, only
Potato
k Onion
^ Carrot
Tomato
System Boundary Small-scale Format
Direct l-uel (diesel) Direct l-uel (gasoline}
Water 1 1 Labor 1 vehicles Uirect lilectricity {grid} (none reported) tillers vehicles
Farm a ate /
25


Farmyard CSA and Dr. John Brett followed through with the whole season. One yard is shown
on Figure 5-5, below. Only data from these two growers were included in the study, and
resulted in a creating a significant limitation of the study.
The inventory for the small-scale format included the items required for five production phases.
These are:
Preparation;
Growing;
Harvest;
Cleanup; and
Farm Gate to Point of Sale
Figure 5-5: A Farmyard CSA garden
The single water tap, timer, and water meter can be seen on the figure, the typical
arrangement for all yards in the study.
26


Preparation
This phase includes working the plot to remove weeds, spread mulch or amendments, remove
debris from the previous season, and install irrigation equipment. This may involve motorized
equipment. This phase also involves "starts," or seedlings started in small trays. The trays are
kept indoors until there is enough heat and light for survival outdoors, in the ground or larger
pot. Some vegetables are started outdoors, directly from seed. The needs of the grower for
how much and what type of vegetable to be marketed by a target time frame dictates the use of
"starts."
Growing
This phase spans the time a seed or start is planted in the plot until the time of harvest.
Irrigation and weed control are two of the most important activities during this phase. Other
activities include thinning, amending soil, spreading mulch, and pest control.
Harvest
The harvest phase includes picking and packing for transport to retail sale. A certain amount of
waste occurs during this phase as vegetables not suitable for retail sale or consumption are
discarded. Typically, these are either returned to the soil upon picking, or are held onsite in a
compost heap. Boxes or bins are used to gather harvested produce and transport to a point of
sale or distribution, after the "farm gate," or plot.
Cleanup
The cleanup phase is ongoing from the start of the harvest of one vegetable to the final harvest
of the last vegetable. During this time, cover crops may be planted in preparation for the next
27


year. Otherwise, the small-scale growers typically keep all harvest residues and stubble in situ
for the next season.
Farm Gate to Point of Sale
The produce is then typically consolidated at the owner's property for distributing assortments
to bins or boxes for pickup or sale at a farmer's market. Transport and packaging is minimal.
After what is called the "farm gate," the produce travels to its next destination. For the small-
scale grower, this is typically achieved in non-commercial vehicles travelling short distances.
This data will be acquired directly for the small-scale growers. There is typically no loss of
vegetables. Those that are not sold are either given away or used to make compost.
These phases are listed in Table 5-1. The table also formed the basis for data collection through
the growing season. Appendix A presents a summary of collected data, compiled from the
6ti6 S6EISO.
Table 5-1: Production Phases Small-scale Format
Description Unit Other Unit(s)
Preparation Phase Land area area used for in-ground or potted growing (excluding starter trays) square feet acre
Labor labor for all people and activities related to growing all planting phases, vehicle trips, bookkeeping, etc. hours full time equivalent (FTE)
Vehicle trips all trips related to growing to/from gardens stores, distribution points, etc. miles number of trips; average miles per trip
Herbicide/Pesticide weed killer or pest control gallons application rate
Seed seed used for in-ground and starters lbs oz, packets, bags
Amendments compost, manure, peat, humus, etc. lbs bags; lbs per bag
Starter trays plastic, foam, cardboard trays each
28


Description Unit Other Unit(s)
Starter media rock wool, potting soil, etc. each lbs
Grow lights any lighting used in starters kWh number of lights; wattage of lights
Water for starts water used to grow starts gallons time at a given flow rate
time of
Mechanized Equipment- fossil fuel tillers, sod cutters, weed eaters, etc. gallons operation; consumption rate
time of
Mechanized Equipment- electricity mowers, tillers, etc. kWh operation; consumption rate
square yards;
Mulches plastic, wood, stone used in bed preparation square feet feet of set width; cubic feet; cubic yards
Drip Tape or hose tape, tube, emitters, etc. feet lbs; each
Other Irrigation Equip hoses, sprinklers, etc. each feet
Tarpaulin poly, canvas, etc. used to cover tools or areas prior to planting square feet acre
Wastes any waste to unclassified trash or dumpster such as hose, tape, bags, wood scraps, etc. lbs cubic feet

Growing Phase
labor for all people and activities related to
Labor growing all planting phases, vehicle trips, bookkeeping, etc. hours equivalent (FTE)
Vehicle trips all trips related to growing to/from gardens, stores, distribution points, etc. miles number of trips; average miles per trip
Herbicide/Pesticide weed killer or pest control gallons application rate
time of
Mechanized Equipment- fossil fuel tillers, sod cutters, weed eaters, heat, etc. gallons operation; consumption rate
time of
Mechanized Equipment- electricity mowers, tillers, heat, etc. kWh operation; consumption rate
square yards;
Mulches plastic, wood, stone used in bed preparation square feet feet of set width; cubic feet; cubic yards
29


Description Unit Other Unit(s)
Amendments compost, manure, peat, humus, etc. lbs cubic feet; cubic yards
Fertilizer N, P, K, etc. lbs cubic feet; cubic yards
Water water used for growing gallons time at a given flow rate
Wastes any waste to unclassified trash or dumpster such as hose, tape, bags, wood scraps, etc. lbs cubic feet
Planted area by type square feet devoted to a given crop square feet number of plants; area per plant
Harvest Phase
labor for all people and activities related to
Labor growing all planting phases, vehicle trips, bookkeeping, etc. hours equivalent (FTE)
Vehicle trips all trips related to growing to/from gardens, stores, distribution points, etc. miles number of trips; average miles per trip
time of
Mechanized Equipment- fossil fuel loaders, lifts, etc. gallons operation; consumption rate
time of
Mechanized Equipment- electricity lifts, mowers, etc. kWh operation; consumption rate
Production packaging boxes, plastic, etc. (exclude bins that are reused) square feet each; square yards; lbs
Water water use in preparing for transport or production packaging gallons time at a given flow rate
Harvest by type (weight, bushel, etc.) gross amount of produce picked lbs bushel; box; each; bag
any waste to unclassified trash or dumpster
Wastes such as hose, tape, bags, wood scraps, etc.; ALSO track produce unsuitable for sale or consumption. lbs cubic feet

Cleanup Phase
labor for all people and activities related to
Labor growing all planting phases, vehicle trips, bookkeeping, etc. hours equivalent (FTE)
Vehicle trips all trips related to growing to/from gardens, miles number of trips;
30


Description Unit Other Unit(s)
stores, distribution points, etc. average miles per trip
Composting any consumables used to create, process, or store compost. lbs each; feet; square feet; cubic feet
time of
Mechanized Equipment- fossil fuel tillers, mowers, blowers, etc. gallons operation; consumption rate
time of
Mechanized Equipment- electricity tillers, mowers, blowers, etc. kWh operation; consumption rate
Water water use in leaving site for next season gallons time at a given flow rate
Wastes any waste to unclassified trash or dumpster such as hose, tape, bags, wood scraps, etc. lbs cubic feet
labor for all people and activities related to
Labor growing all planting phases, vehicle trips, bookkeeping, etc. hours equivalent (FTE)
Transport trips for hauling to any distribution point or point of sale. miles number of trips; average miles per trip
time of
Mechanized Equipment- fossil fuel refrigeration, lifts gallons operation; consumption rate
time of
Mechanized Equipment- electricity refrigeration, storage facility utilities kWh operation; consumption rate
Packaging packaging required for final sale (only that in addition to Production Packaging) square feet each; square yards; square feet; lbs
each; square
Retail Sale any consumables used in sale lbs yards; square feet; lbs
Wastes any waste to unclassified trash or dumpster such as hose, tape, bags, wood scraps, etc. lbs cubic feet
31


5.1.4 Large-Scale Vegetable Production Format
| hertilizer (NHK) |

| Herbicides |

Potato
i Onion
^ Carrot
Tomato
Wastes
| | Foreground system
| | Background system each with upstream embodied resource use
Figure 5-6: LCA System Boundary Large-scale Format
Published enterprise budgets were adapted to match, as the data allowed, the phases identified
for the small-scale growing format. The inventory for the large-scale format includes the items
required for five production phases. These are:
Preparation;
Growing;
Harvest;
32
A process and boundary flow diagram was created for the large-scale production format as
shown on Figure 5-6.
System Boundary -arge-scale Format
][
Direct uel (diesel) Direct huel (gasoline)
generators Direct tlectricity (grid) generators
heavy farm mach. refrigeration tillers
lifts mechanized equip. lifts
vehicles vehicles
Farm aate


Cleanup; and
Farm Gate to Point of Sale
Preparation
This phase includes working the field to remove weeds, spread mulch or fertilizer, pesticides or
herbicides, remove debris from the previous season, and install irrigation equipment. This
typically involves heavy, diesel-powered farm machinery. Seed is sown through the use of a
mechanized seed drill.
Growing
This phase spans the time a seed is planted in the plot until the time of harvest. Irrigation and
weed control are two of the most important activities during this phase. Other activities may
include thinning, pesticide application, or herbicide application.
Harvest
The harvest phase includes manual or mechanized picking and packing for transport to retail
sale. A certain amount of waste occurs during this phase as vegetables not suitable for retail
sale or consumption are discarded. Typically, these are either returned to the soil upon picking,
or are held onsite in a compost heap. Boxes or bins are used to gather harvested produce and
transport to a point of sale or distribution, after the ''farm gate/' or plot.
Cleanuo
The cleanup phase may include tilling, burning, or mowing after the final harvest. During this
time, cover crops may be planted in preparation for the next year. Otherwise, growers typically
keep all harvest residues and stubble in situ for the next season.
33


Farm Gate to Point of Sale
The produce is then typically consolidated at the field for shipment to a distribution center or
directly to a customer. After what is called the "farm gate," the produce travels to its next
destination. Produce harvested from large-scale operations typically experience some minimal
packaging to transport it in bulk. From the farm gate, the produce can travel to a wholesale or
retail warehouse or directly to the retail point of sale. Determining how much produce arrives
from where is not a trivial procedure and is important for the study. It is required to compare
what are called "food miles" between locally grown produce (under the small-scale format) and
the [often] imported produce that stocks supermarkets.
5.1.5 Seasonal Sources
In the past, terminal market hubs in major metropolitan areas such as Chicago, Los Angeles,
Dallas, and Atlanta, served as intermediate wholesale clearinghouses before produce was then
transported to other wholesale markets and retailers (Pirog 2001). One wholesale market in
Denver was the Denargo Market, in what is now called the RiNo district. Over time, the terminal
markets ceased operations and the supply chain evolved to where each retailer, or group of
retailers in a single metropolitan area, source dedicated truck shipments directly from the farm.
The USDA's Agricultural Marketing Service (AMS) tracked the movement of produce between a
number of "terminal markets" within the U.S. Pirog carried out detailed food miles calculations
for produce whose final consumption location was Des Moines, Iowa (Pirog_2001). Many of his
factors for fuel and emissions are still valid and available today, updated. But the tracking of the
movement of produce within the country to that level of detail is no longer supported by the
34


AMS. So, replicating his work for a different point of consumption for a particular vegetable is
not possible using his sources.
At an even more aggregated level, Weber et al. conducted a food miles study in 2007 (Weber
and Matthews 2008). In this study, a different data set, currently maintained by the Leopold
Center (Leopold Center 2011) and the USDA's AMS, was used to calculate food miles and
environmental impacts of classes of food, from overall movements between states and from
international sources. The data at the Leopold Center could also be used per vegetable variety
to produce an aggregate estimate of food miles based on all sources (by state and country) of all
shipments to and within the U.S.
For greater accuracy, an investigation of the food miles of a given vegetable that retails in the
Denver metropolitan area is preferable to a national average. Such an investigation includes
determining if retailers that comprise the majority of the sale of vegetables grown under the
large-scale format are representative and similar in where and when they source potato, carrot,
onion, and tomato. The Figure 5-7 shows a market share report that reveals that relatively few
retail supermarket chains dominate a large sector in the Denver area. Just four retailers
represent about 84% of the market.
35


Figure 5-7: Denver Area Supermarket Share
As an indicator of practices for these top four retailers, a member of the wholesale produce
community was contacted (Comazzi 2012). From this interview, it was determined that
wholesale produce sourcing and movement is similar between the food service companies
supplying restaurants and institutions, and grocery stores. To meet fresh vegetable demand
year-round, retailers have an array of different sources across the seasons. A simple calculation
of weight and distance (commonly called ton.miles or ton.kilometers) can be accomplished
through any web-based map directions calculator and the payload capacity of refrigerated
tractor-trailers. These data can then be converted to fuel use and other midpoint impacts from
published data. Figure 5-8 presents the sources and distances for potato, carrot, onion, and
tomato in Denver metropolitan area supermarkets. Appendix B contains the complete seasonal
contribution by location, coupled with the typical harvest and storage practices.
36


Figure 5-8: Supermarket sources and distances by vegetable
5.1.6 Scenario Development for Land Use Change
It is assumed that land area representing the functional unit of a given vegetable can be put into
service or out of service based on the incidence of vegetable production elsewhere. A number
of researchers have characterized SOC and agricultural emissions for various long-term land
uses and as it relates to land use change. This study sets urban gardening as a type of land use
that displaces existing land uses. Conversion to urban garden involves displacement from one of
three land uses, namely:
1.Large-scale commercial farmland each new instance of urban gardening is assumed to
displace an equal amount of commercial farmland, adjusted for differences in land
productivity related to the functional unit.
37


2. Neglected and degraded urban areas in the existing urban setting, a new urban garden
can purpose these areas unused areas that typically have the poorest soil health and
lowest SOC levels.
3. Residential turf grass in the existing urban setting, the demand for space for an urban
garden may force conversion from turf grass.
These land uses and scenarios are shown on Figure 5-9. Although common land uses exist in the
urban setting, they are assumed to be similar in the degree of dense plant matter, cultivation,
and richly-maintained soil that urban vegetable gardening has.
Commercial Farming
9 locations
4 crops
Urban garden
displaces
demand and
returns to native
Grassland
(all locations)
$

LEGEND
1 Conversion Code
Not a part of study
Grasslands
converted to
garden during
urbanization *
.''
Urban Vegetable
harden
DGnv8
4 crops
V.'
Urban Turf
Cool-season Kentucky bluegrass
D6HV6
monoculture
Figure 5-9: Land Use Change and Conversion Scenarios
The array and variety of vegetable and production formats combined with land use scenarios
results in 19 unique vegetable-land use combinations.
38


These existing land uses are paired with the urban garden land use to form the following
scenarios in Table 5-2.
39


Table 5-2: Scenario Development for Land Use Change
Scenario No. Scenario Code Location Starting Land Use (years) Crop(s) Ending Land Use (years) Crop(s)
1 lal Terreton, ID Large-scale commercial farm Potato Native perennial grassland (30) Perennial grass
2 la2 Alamosa, CO
3 lbl Bakersfield, CA Carrot
4 lb2 Greeley, CO
5 lcl Mission, TX Onion
6 lc2 Deming, NM
7 lc3 Greeley, CO
8 lc4 Ontario, OR
9 ldl Culiacan, SIN Tomato
10 ld2 Bakersfield, CA
11 ld3 Punta Gorda, FL
12 2a Denver, CO Neglected urban land (30) n/a Urban garden Potato
13 2b Carrot
14 2c Onion
15 2d Tomato
16 3a Denver, CO Turf lawn (30) Cool season perennial grass Urban garden Potato
17 3b Carrot
18 3c Onion
19 3d Tomato
Notes: Scenario codes example: 1 c 3 conversion code crop location (crop-specific) Crop codes: a. Potato b. Carrot c. Onion d. Tomato Conversions are shown on Figure 5-5. Locations for large-scale commercial sources of vegetables sold in Denver are shown represented regionally and represent at least 75% of all sources at various times of the year. All locations for ending land uses are set to be the same as the starting land uses.
As shown in the table, a given land use is modeled for 30 years. For example, Scenario No.10
indicates that land used for tomato production in Bakersfield, California would return to native
grassland. Scenarios 15 and 19, for example, convert two common existing land uses (neglected
urban land and turf lawn, respectively) into urban tomato production. The 30-year time frame
was chosen because this amount of time is sufficient to achieve steady-state values for the
parameters of interest (soil organic carbon, CH4/ C02, N20 emissions), and is a time ame used
for environmental analysis.
40


Direct Land Use Change
Under this assumption, the amount of land required to grow 1 pound of tomato in Denver, for
example, would displace the amount of land required to grow 1 pound of tomato in Punta
Gorda, Florida. Because yields (pounds per square foot) are different for each vegetable and
production format,1 square foot of land in Denver would not necessarily displace 1 square foot
in Punta Gorda.
Indirect Land Use Change
Indirect land use change occurs when land use is changed in response to a land use elsewhere.
In our example above, Scenario No.10 could be considered as indirect land use change because
an urban-grown tomato would displace land used for tomato production in Bakersfield,
California and return it to native grassland. In this example, native grassland is the indirect land
use change. Other potential land uses, such as for growing biofuels or biomass, were not
considered because the regions in which vegetable production occurs have come to be such
regions because they are well suited for such production. This cannot be said for any particular
replacement crop such as biofuels or biomass. In practice, the reduced demand for, say,
tomatoes from Bakersfield, California, resulting from an increase in tomato production in
Denver either would not be felt in the market supply chain, or would be transferred to another
specialty crop (vegetable). Supply and demand market forces are beyond the scope of this
study. Also, even if significant urban fresh vegetable production would displace commercial
production, this could only be the case seasonally; for 6-8 months of the year, fresh vegetables
would still have to be obtained from the same areas in production.
41


Comparisons between commercial and urban production formats may be drawn by looking at
the respective steady-state values in Year 30. For example, a comparison between
commercially- and urban-grown tomatoes can be drawn by looking at the 30-year value for
Scenarios 9,10, and 11 and the 30-year value for Scenarios 15 or 19.
5.1.7 Data Quality Objectives
Data sources include direct measurement (primary data), peer-reviewed literature, industry
publications, government publications or databases, life-cycle databases, and engineering
judgment. The time frame for data collection is approximately April through November for the
smaH-scale growers, and year-round for the large-scale growers. Data is standardized to one
growing season or rotation of a given vegetable crop. Table 5-3 describes the data quality
objectives (DQO) for the study.
42


Table 5-3: Data Types and Quality Objectives
Category DQO
Measured material (gallons, each, lbs, hours) The target precision will be 10%
Site-specific data Primary: direct measurement Secondary: Allocation according to ISO 14041: 6.5.3 Allocation Procedure
Aggregated data Allocation according to ISO 14041: 6.5.3 Allocation Procedure
Estimated data Interview-based and subject to professional judgment. May be proved with subsequent data collection of the same item or with past collected data.
Data format Participants field notebooks and notes. Researcher transcription of live and phone conversations.
Data coverage A minimum of 80% of the material and energy inputs will be accounted for in the LCI.
Data origin Denver metropolitan area and states and countries from which growing occurs. All large-scale format data will be U.S.-based averages. No such averages will be taken from Europe.
Published data, when available, were used as data quality indicators. These included benchmark
values from other similar components of the small-scale format or the large-scale format.
Differences are noted but no attempt was made to reconcile them due to many independent
variables.
A sensitivity screening (or ''focusing" exercise per the European Commission J2010J) was not
conducted for this study. The results later show that some data and their contributions to
impacts are indeed insignificant. However at the outset, this was not known; every effort was
made to include all direct and indirect inputs, and Scope 1,2, and 3 resources and impacts.
Later, qualitative sensitivity was conducted on a few key variables, as those were discovered to
either have a large influence on the results, or if their data quality was suspect. The framework
established by the European Commission (European Commission 2010) was employed to
identify these key variables. This framework is shown on Figure 5-10. If used in conjunction
with an uncertainty analysis to indicate data quality, the interpretation of LCA results can be
43


greatly enhanced by being able to indicate the prevalence of high-quality/high sensitivity LCI
data. These data are most useful for drawing conclusions for decision makers.
Lack of
quality
Sensitivity / significance
Figure 5-10: Uncertainty and influence indicate what are "key7' data
Once key data are identified, other data may be dropped from the sensitivity
analysis because of their weak influence on impacts or reliance on data quality.
This is an iterative process. From Figure 26. (European Commission 2010).
Uncertainty and variability were not quantified and is traditionally problematic in LCAs that
include many inputs and processes. However, qualitative means were used to characterize
uncertainty and variability. These include:
careful planning and execution of the LCI process leading up to final results;
identifying outliers by using information from circumstances and published benchmarks;
formal and informal reviews of the LCI data development process;
focusing data quality efforts on values that significantly influence LCI results; and
ensuring that sample sizes for key data values are as large as possible, thus increasing the
reliability of the estimates.
From (EPA 1995).
High
priouty
(key data)
No priority
Low priority
(key data)
44


Sources of uncertainty are typically categorized as (1)random error in measurement and
sampling methods, (2) systematic error in measurement and sampling methods, (3) natural
variability, and (4) approximation in modeling (EPA 1995). For this study, natural (random)
variability and approximation in modeling are thought to be the most likely and relevant sources
of uncertainty.
5.2 Inventory Analysis
The LCI follows the product phases within the system boundary. In general, the inputs shown in
the background system are characterized by four direct resources and their functional units -
water (gallons), fuel (gallons), energy (kWh), and labor (hours). A number of inputs with little
differentiation (such as any manner or shape of plastics, paperboard, pesticides, etc.) are
converted to a generic mass quantity. Typically, the small-scale format requires primary data
gathering, while published farm enterprise budgets are used as representative for any given
large-scale grower.
Startup costs are an item worth addressing, since infrastructure requirements between the two
growing formats vary greatly and a number of fixed assets (e.gv tractors, pumps, etc.) have
longer-term depreciations and salvage dates. For this reason, the LCA only focuses on
consumables and activities for operations after startup. For the purposes of the study, a
consumable is something that is used up or replaced between 0 and 5 years, following generally
accepted return periods for fixed asset depreciation used in tax accounting.
5.2.1 Data Sources
Data sources range within two broad categories of information top-down, and bottom-up.
Top-down information is efficient to use and is published, replicable, and comparable. It is
45


appropriate for materials or resources that are not sensitive to location or scale, but there may
be a loss of precision and variability if applied to much smaller scales, locations, or components
of systems. Generally, manufactured items fall under this category. It is useful for LCA
background systems, indirect uses and emissions, and for projects where WRI Scope 2 or 3 are
reported.
Bottom-up information is more time consuming and unique, but has the ability to be more
accurate and representative of local and small scale characteristics of components within the
system boundary. The disadvantage of bottom up information is that it is not necessarily
comparable or replicable with other studies. This study uses a combination of information and
therefore is called a hybrid LCA. The specific sources are shown on Figure 5-11 and feed into the
hybrid LCA.
46


Top Down
U.S. LCI (NREL) Ecolnvent EIO-LCA
Background System Imlirect Use, Indirect Emissions, and 'or Scopes ^
Hybrid LCA
Fareground System Direct UseJ}irect Emissiomand/or Scope 1
Case Study
Gardens
Published
Enterprise
Budgets
DNDC Cooperative
iogeochemical Agricultural Denver
Soil/Plant Extension Water
Modelling Interviews
Bottom Up
DNDC Denitrification-Decomposition Model
EIO-LCA Economic Input/Output-Life Cycle
Assessment
LCA Life Cycle Assessment
NREL National Renewable Energy Laboratory
Scopes World Resources Institute
Greenhouse Gas Protocol that
categorizes emissions according to
Scopes 1,2,and 3.
U.S. LCI U.S. Life Cycle Inventory
Figure 5-11: Data Sources
The hybrid LCA relies on two broad categories of information.
The inventory is composed of all the direct and indirect inputs to the system boundary as
defined in Section 5.1.2 through 5.1.4. These are shown in Table 5-4 and fully expanded in
Appendix C. These inputs are described in more detail in the following sections.
47


Table 5-4: Resource Flows and LCI Categories
Category Specific Materials Region Unit
Fossil Fuel Diesel, gasoline (well to pump WTP) U.S. L
Electricity Electricity (Scope 1,2) eGrid region kWh
Soil Bagged potting soil organic U.S. kg
Fertilizer N, P, K, Zn, Mn, Mg, Cu, gypsum, sulfur, lime U.S. kg
Plastics (virgin resins) HDPE, LDPE, PP, PS, PET U.S. kg
Paper Cardboard U.S. kg
Chemicals Herbicide, pesticide, fungicide U.S. kg
Water Raw irrigation water Site-specific L
Water Potable irrigation water Denver, CO L
Transport Refrigerated tractor / 17-ton trailer Varies km
Transport Light pickup truck Denver, CO km
Web Hosting 20Mbsite,lyr. U.S. year
5.2.1.1 Power
Direct electrical power use (Scope 1 and 2) was used to estimate total energy demand (see
Section 5.3 Impact Assessment). In addition, Scope 3 electricity required the use of U.S. EPA's
Emissions & Generation Resource Integrated Database (eGRID) scheme (EPA 2012). The eGRID
conveniently breaks down the U.S. electrical grid into subregions of connectivity and similar grid
mix of fuel sources to generate electricity. The eGrid reports also estimate grid losses from
generating station to end-use (EPA 2012). For electrical power use in Culiacan, Mexico,
comparable data were compiled from several published sources as shown on Figure 5-12. Grid
mix fuel sources and subregions are important because each commercial grower belongs to a
distinct subregion, and these data are needed for emissions and upstream LCI.
48



Mexico Primary Fuel
(blue = diesel/oil)
5r:.
k
Mexico Grid Interconnection
Center for Energy Economics Bureau of Economic Geology, The University of Texas at Austin (UTA) and Instituto Tecnologico y de
Estudios Superiores de Monterrey. 2006. GuidetoElectricPowerinMexico.September.
U.S. Environmental Protection Agency (ERA). 2012. eGrid Subregions. Accessed online at:
http://www.epa.gQv/deanenerqy/dQcuments/eqridzips/eGRiD2012 eGRID subregions.ipq
Secretaria de Energia (SENER). 2012. Prospectiva del Sector Electrico 2012-2026.
Commission for Environmental Cooperation (CEC). 2011. North American Power Plant Air Emissions.
Figure 5-12: Regions of Electricity for Grid Mix Identification


5.2.1.2 Water
The agricultural extension office for each region growing vegetables sourced from Denver was
contacted to characterize the type of water, pumping depth (if any), type of irrigation, and
application rates. These data supplemented or superseded any applied water depths reported
in the enterprise budgets. In addition, fuel and electricity demands associated with pumping or
conveying irrigation water was noted. These data are summarized in Table 5-5.
Denver Water's energy intensity was calculated from total enterprise energy use (including
treatment pumping) per total potable water sales. Even though Denver Water generates a
significant amount of its own electricity, this was not accounted for since this would not be a
comparable energy intensity.
50


Table 5-5: Site-specific Water Use Parameters
Cooperative Extension Office / Source Crop Irrigation Type Applied Water (ft) Pumping Head (ft) Energy Intensity (kWh/AF) (or as noted) eGRID Region
Alamosa County 1899 E. Hwy 160 Monte Vista 81144 (719) 852-7381 Representing: Alamosa, CO David Holm Potato Pumped Center-pivot from shallow well 1.75 6.5 12.8 RMPA
Kern County 1031 S. Mount Vernon Ave Bakersfield, CA 93307 (661) 868-6200 Representing: Bakersfield, CA Joe Nunez Tomato 50% pumped drip from canal 2.5 10 20 CAMX
50% pumped drip from well 2.5 160 320 CAMX
Carrot 25% pumped drip from canal 2.5 10 20 CAMX
75% pumped drip from well 2.5 160 320 CAMX
Luna County 210B Poplar St. Deming, NM 88030 (575) 546-8806 Representing: Deming, NM Jack Blanford Onion Pumped drip from well 4 300 591 AZNM
Weld County 525 North 15th Ave Greeley 80631-2049 (970) 304-6535 Representing: Greeley, CO Thaddeus Gourd Carrot Gravity ditch furrow 2 - - -
Onion Gravity ditch furrow 3.5 - -- --
Hidalgo County 410 N 13th Ave Edinburg, TX 78541-3582 (956) 383-1026 Representing: Mission, TX Brad Callen Onion Gravity ditch furrow 2.8 - -- --
Malheur County 710 SW 5th Ave Ontario, OR 97914 (541) 881-1417 Representing: Ontario, OR Jim Clouser 208-741-7154 Onion 50% pumped drip from ditch 2.67 5 3.7 gal diesel/ac --
50% gravity ditch furrow 4 - -- --
51


Cooperative Extension Office / Source Crop Irrigation Type Applied Water (ft) Pumping Head (ft) Energy Intensity (kWh/AF) (or as noted) eGRID Region
Jefferson County Courthouse Rigby, ID 83442 (208) 745-6685 Representing: Terreton, ID Bill Bohl Potato Pumped Center-pivot from well 1.67 150 296 NWPP
Charlotte County 25550 Harbor View Rd #3 Port Charlotte, FL 33980 (941) 764-4340 Representing: Punta Gorda, FL Gene McAvoy Tomato 60% pumped drip from well 1.67 150 36 gal diesel/ac --
40% gravity ditch furrow 4 - -- --
Denver Water Representing: Denver, CO Personal communication with Alicia K. Andersen-Shyam, (303) 628-6653 and the 2012 Comprehensive Annual Financial Report. Tomato Potato Onion Carrot Potable tap Variable - 119 (365 per Mgal) RMPA
Industry Practice Representing: Culiacan, Sinaloa, Mexico Tomato Pumped drip from ditch 2.5 5 3.7 gal diesel/ac
Direct water use was measured in the small-scale operations using Neptune, residential utility
water meters. The meters are shown on Figure 5-13.
52


Figure 5-13: Six Neptune T-10 water meters
These were attached to the water supply to the gardens and case study participants noted their
water use in a manner similar to that depicted on Figure 5-14.
53


Neptune T-10 Meter
House Supply Leader Hose Irrigation to Plot Garden Plot
Figure 5-14: Small-scale water metering setup
Water meters were assigned to the individuals shown in Table 5-6. Only locations managed by
Dr. John Brett and Farmyard CSA provided whole-season data that could be used for the study.
Others were incomplete. The main ramification of the poor participation rate is that it creates a
very small sample size from which it is difficult to make statistical inferences. This is discussed
further in Section 5.2.5.
54


Table 5-6: Water Meter Assignments
Phone First Name Last Name Bus/other Name Meter No. Meter Address
(303) 949-xxxx Jon & Candice Orlando Urbiculture Community Farms 78221855 2xxx S Hazel Ct, Denver, CO 80219
(303) 250-xxxx Stephen Cochenour Stephen Cochenour 75389823 11xxxW38th Ave, Wheat Ridge, CO 80033
(303) 733-xxxx Debbie Dalrymple Farmyard CSA 46989710 lxxx S Fairfax St, Denver, CO 80222
(303) 556-xxxx John Brett John Brett 77669616 2xxx Bellaire St, Denver, CO 80207
(303) 956-xxxx Sundari Kraft Heirloom Gardens 74083435 3xxx Dover St. Wheat Ridge, CO 80033
(303) 733-xxxx Debbie Dalrymple Farmyard CSA 74083439 lxxx W Virginia Ave, Denver, CO 80223
5.2.1.3 Other Agricultural Inputs
For the large-scale format, enterprise budgets will be used as representing all large-scale
growers included in the study. A typical budget, for onion for example, is shown in Table 5-7.
55


Table 5-7: Typical Enterprise Budget
Item Quantity/ Acre Unit Price or Cost/Unit Value or Cost/Acre
GROSS RETURNS
Red Onion 650 box 8.00 5,200
OPERATING COSTS
Fertilizer:
15-15-15 1,500.00 lb 0.22 326
Irrigation:
DripTape 5 mil 13,755.00 foot 0.01 165
Water Pumped 40.00 acin 4.83 193
Seed:
Onion Transplants 73.00 thou 5.00 365
Custom:
Transplant Onion Labor 13,750.00 foot 0.02 220
Bagging Labor (harvest) 650.00 each 0.50 325
Herbicide:
Dacthal W-75 7.00 lb 18.85 132
Fungicide:
Ridomil Gold Bravo 2.00 lb 25.06 32
Carton:
Onion Bags (harvest) 650.00 each 0.50 325
Boxes 40 lb 650.00 each 1.10 715
Labor (machine) 19.51 hrs 12.42 242
Labor (non-machine) 51.20 hrs 9.32 477
Fuel Gasoline 10.41 gal 2.55 27
Fuel Diesel 47.54 gal 2.00 95
Lube 18
From Table 2 (UC Davis 200b). Costs and returns per acre to produce onion San Joaquin Valley
For the small-scale growers, bookkeeping forms were used to document actual inputs
throughout the season. These forms contain the information shown in Table 5-1. An example
of field forms was previously presented in Appendix A.
56


5.2.2 DNDC Carbon Model
The Denitrification-Decomposition (DNDC) model is a process-oriented computer simulation
model of carbon and nitrogen biogeochemistry in agroecosystems initially developed and
continually refined by Dr. Changsheng Li and others since the mid-1990s (University of New
Hampshire [UNH1 Institute for the Study of EarthA Oceans and Soace 2012). It was chosen,
among other carbon models, because it is suited for the parameters available and generates
parameters of interest for this research (Olander and Haugen-Kozyra 2011). It is also used
widely, is user-friendly, and has a track record in the research community.
5.2.2.1 Biogeochemical Processes
The process flow diagram for the DNDC model is shown on Figure 5-15. Selected
biogeochemical processes shown on Figure 5-15 that are of interest for this study are discussed
in more detail below.
Carbon Dioxide Processes
Plants can give off up to half of the carbon dioxide (C02) that they absorb through oxidation
(use) of sugars they manufactured from photosynthesis. In photosynthesis, C02 and water is
converted to other carbon-containing compounds and stored up in plant tissues. When parts of
the plant die (or the entire plant dies), most of this stored carbon is released again into the
atmosphere as C02 and other carbon-containing compounds through chemical processes. This
is carried out by soil bacteria, fungi, and insects. In the overall carbon cycle, only a very small
portion of the carbon in the plant incorporates itself into the soil. The process, also known as
humification, takes years to substantially build up the soil in terms of soil organic carbon (see
57


below). Extra C02 is emitted from the soil when tilling occurs and these emissions are unrelated
to cover cropping or crop residues (Ohio State University [OSU1 2013).
Figure 5-15: Process Flow for the DNDC Model
(reproduced from UNH 2009)
Soil Organic Carbon Processes
Soil organic matter is about 60% by weight carbon (OSU 2013). As described above, soil organic
matter is created by the cycling of carbon-containing compounds in plants (dead insects,
animals, and microorganisms in the same ecosystem also contribute). As organic matter
decomposes more fully, it starts to form humus, a material that itself provides a carbon and
energy source for soil microbes and plants. When in equilibrium with the soil/plant biome, soil
organic carbon is a fairly stable soil property. But when soils are tilled, much organic matter is
58


exposed to rapid oxidation and decomposition by being a food source of other bacteria and
microbes. This loss of humus is tantamount to soil erosion because, without humus, the soil
structure begins to be cohesionless like sand, and fails to support most plant and microbial
communities. Tilling and plowing are major practices to control weeds. For the commercial
farmer that wants to conserve soil organic carbon and humus, the consequence is often
increased herbicide use to reduce the need to till (OSU 2013).
Methane Processes
CH4 is emitted when soils are wet enough to enable anaerobic decomposition of organic matter.
This is similar to nitrous oxide, except that nitrous oxide production requires N nutrient. Insects
that feed on organic matter also produce methane, but direct emissions are often intercepted
and oxidized in the presence of soil microorganisms (Brevik 2012).
Nitrous Oxide Processes
N20 is produced when soil is saturated enough to create anaerobic conditions that allow soil
bacteria to convert N03~ to NO, N20, or N2. Typically, this occurs after achieving field capacity, or
the capacity of the soil to hold water before that water percolates beyond the reach of plant
roots. The presence of N03~ is a direct result of applied N, and, to a smaller degree, atmospheric
N2. In a general sense for the agricultural context, the presence of moisture and nutrients drive
the production of N20 (Brevik 2012). Nitrous oxide remains in the atmosphere for about 120
years before being transformed by chemical reactions or removed by a sink (EPA 2013).
59


5.2.2.2 Model Inputs
DNDC was selected, in part, because it has already characterized many parameters based on
published literature for the crops of interest in this study. Site- or crop-specific parameters
were overridden manually when needed. These data are tabulated for each of the 19 scenarios
in Appendix D.
Climate data required a high level of effort to prepare and was very site-specific. Precipitation,
in particular, required design of a statistical procedure to preserve wetness, dryness, and
randomness, while being representative of long-term averages. These are discussed in the
following sections.
5.2.2.3 Climate Data
Although DNDC demands daily climatological data, it allows for a number of levels of detail for
the data. In general, the fewer the climate data, the more assumptions the model must make
about daily atmospheric and soil conditions. Given the body of data readily available from web-
based government agencies and commercial weather sites, the level of detail selected in the
model contains a high number of preferred parameters. These are the following:
1. Daily maximum temperature (C)
2. Daily minimum temperature (C)
3. Daily precipitation (cm)
4. Daily average wind speed (m/s)
5. Relative humidity (%)
One other parameter can be used by DNDC, solar radiation (MJ/m2/d), but if omitted can be
estimated within the model, based on latitude. Solar radiation was not chosen as an input
because relatively few data sources record daily solar radiation.
60


Climate data was acquired from the National Oceanic and Atmospheric Administration (NOAA)
and WeatherUnderground (WUnderground), a website that compiles data on individual weather
stations as well as those operated or administered by local, state, or federal agencies. Although
long-term averages and normals are the standard choice for projecting climate parameters in
the future, a relatively recent period of record (generally 1995 to present) was used for the
DNDC model. There are several reasons for this choice of period of record. First, the period of
record could be consistent because it is available for all meteorological stations. Second, few
stations report longer-term daily normals for the required input parameters. Third, some
postulate that the period of record for long-term normals may be increasingly obsolete as we
experience climate change. Fourth, the gas emissions that DNDC calculates occur as a result of
minor, daily events of temperature, soil wetting, and soil drying. A shorter period of record is
advantageous to capture this phenomenon, explained in the following section.
5.2.2.4 Special Procedure for Estimating Precipitation
DNDC can use a separate climate input file for each year modeled. These inputs can be
historical or predictive. For this study, the historical period of record was used to represent
likely future conditions. The period of record was used to average, on a daily basis, each
climatological parameter. I did not attempt to agree with long-term, published normals with
this approach because the value of the study is a relative comparison of scenarios, not
necessarily the absolute quantities estimated by the model resulting from these scenarios.
The modeling period (30 years) would require either one climate parameters file used for all
years, or a separate file for any given year or group of years. For this study, a single climate
parameters file was used for each station, for each scenario. Selection of a single climate file for
all future years avoids the complicated exercise of making predictions either by stochastic
61


methods or climatological statistics, if available. The relatively short periods of record (ranging
from 5 to 15 years) allow for a reasonable sense of average conditions, while preserving daily
variations, especially in terms of precipitation events. Because the gas emissions that DNDC
calculates occur as a result of minor, daily events of temperature, soil wetting, and soil drying,
daily variation in the data set is essential. The single climate file contains the average of each
parameter for all days in the period of record. For example, the average precipitation for all
June 2nds is the average of each June 2nd for every year in the period of record. In other words,
n
TA
i=\
n
where P, is the parameter per year n.
The following paragraphs further explain why the daily precipitation is a key yet problematic
model input.
Soil nitrous oxide emissions display a high degree of sensitivity and variability to the day of
fertilizer application and daily rainfall distribution. At nearly all geographic and temporal scales,
this variability appears to be unrecognized when modeled emissions are reported for crop
production in product life cycle assessment studies, carbon accounting, and corporate social
responsibility reporting. Although many factors causing variability in nitrous oxide emissions are
recognized, we show that one important factor rainfall is not particularly suited for mere
arithmetic averaging, and reliable estimates at any scale should take into account the stochastic
and periodic nature of precipitation.
Variations in on-farm parameters such as agricultural practices, soil types, and precipitation
result in some degree of variation in dependent variables tied to them (Li 2000). For example, it
62


is known that nitrous oxide (N20) emissions are produced when sufficient nitrogen (N) and
moisture are present in soil (Li 2000; EPA 2010c; IPCC 2006). Nitrification is the aerobic
oxidation of ammonia to nitrate, and denitrification is the anaerobic reduction of nitrate to
nitrogen gas (N2). In this process, N20 is released. Inorganic N has a controlling influence on
these reactions while soil moisture and depth of N in the soil column dictate whether release of
N20 is a result of aerobic or anaerobic processes, both of which can occur at the same time
within a given soil profile (Kuenen and Robertson 1994). Denitrification is associated with water
in the soil pore space and nitrification is associated with air in the pore space (Davidson et al.
1986).
The combined effects of moisture, inorganic N, depth, temperature, and plant metabolism in
the root zone result in varying degrees of N20 flux. A study by Fisher focused on the coincidence
of N application and rainfall events that influence soil moisture and therefore N20 flux (Fisher
2013). Since N application is usually controlled and quantifiable, it is rainfall that introduces
uncertainty in N20 estimates.
As a means to illustrate one potentially significant source of uncertainty in N20 reporting, a
simple comparison was drawn using the Denitrification-Decomposition (DNDC) model to
estimate N20 emissions from dryland spring wheat production in two separate years near Fort
Benton, Montana (Fisher 2013). The only parameters that were allowed to change were
historical, daily rainfall events and the timing of a single, annual application of manure. It was
hoped that the comparison would expose the singular effect and potential source of variability
of coinciding rainfall and N application events. The model was set to run for 365 days, once for
1991 and once for 1996. These two years have almost identical precipitation (331 and 342
millimeters [mm] respectively) to the annual average of 341 mm for the period 1981 to 2010.
63


As hoped, the distribution of precipitation is unique and different for each year, as shown on
Figure 5-16. The results are shown on a daily basis on Figure 5-17. The large spike in N20
emissions seen on July 1 and 2 coincides with the application of manure on July 1(day 182 of
365). The peak daily N20 emissions for 1991 was 934 g per ha and for 1996, 229 g per ha, both
on July 1.Since all other parameters were identical, we focused our attention on rainfall
differences around the spike. As shown on Figure 5-18, the 1991 simulation had relatively
significant antecedent rainfall; on June 29, 7.1 mm of rainfall occurred and on June 30,18.3 mm
occurred. In 1996, the first antecedent rainfall event was 10.7 mm on June 26. The results
confirm that the strong relationship between the coincidence of N application and soil moisture
can result in the largest daily emissions of N2 of the year. Please see Appendix E for the
complete study.
64


3



J
Jll lL l_L l i I III j
1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361
Day of Year
Precip 1991
Precip 1996
Figure 5-16: Daily precipitation at Fort Benton, Montana
Years shown have nearly identical total annual rainfall.
65


3
3
N20-flux 1991
----1 N20-flux 1996
Predp 1991
Predp 1996
Day of Year
Figure 5-17: Cumulative nitrous oxide flux for 1991 and 1996 with daily precipitation.
66


Figure 5-18: Cumulative nitrous oxide flux detail for 19 June to 8 July.
5.2.3 Data Quality
Much of the data in the LCI for this study was not based on large sample sizes. There are only
five growing locations and other data is reported in summary from potentially a large sample
size, but this is unknown. Depending on the data source and LCI item, raw data may be in the
form of an aggregate, reported number or a range of measured values. For example, the
enterprise budgets for the large-scale format are reported as single data points and not even
ranges. Because of these data limitations, non-parametric statistical procedures are best suited
for uncertainty analysis and identifying "key" data, as described above. To improve data quality
primary case-study data, and first-hand industry data was used together with consistent
background LCA information from SimaPro (_PRe Consultants 2013). SimaPro is one of a handful
67


of internationally-recognized life cycle assessment tools and compiles data sets that have been
in use world-wide.
Overal the data quality is considered "good." Data quality was estimated against the following
data quality indicators (DQI):
Precision Data are either measured or modeled based on primary, case study information
or are derived from SimaPro databases. Precision is difficult to estimate due to the
differences in these sources, or the unavailability of the information.
Completeness As described in Section 5.1.2 through 5.1.4, every component except bulk,
post-harvest packaging and preservation were included.
Comparability Uniformity of methodology is high compared to other LCA studies. Some
consistency with other LCA studies may be lower due to the hybrid approach using primary,
case-study data.
Repeatability None of the data are proprietary and the method and data sources are easily
replicated.
Representativeness-Temporal representativeness is high. All LCI processes belong to
industries where technological innovation is relatively slow and the usefulness of its data lasts
a number of years. The DNDC model affords a site-specific evaluation of agricultural
production whose practices also do not change rapidly. The model was run for 30 years, and
each model run shows that asymptotic behavior dominates by 30 years of the same
agricultural practice(s).
It was convenient for later analysis to group certain data sources together addressing
comparability, repeatability, and representativeness. These three DQI were paired with the
data categories in Table 5-3. It was determined that aggregated data and estimated data relied
most on published literature and expert interviews. These were deemed average values with
little uncertainty. It was determined that measured material (gallons, each, lbs, hours) and site-
specific data were not necessarily to be trusted because of the poor participation rate of the
urban growers and therefore small sample size. For these reasons, parameters reported by the
urban growers failed the DQI tests for comparability, repeatability, and representativeness.
How this failure was managed in the impacts assessment is discussed in Section 5.2.5. All other
68


.1 .2 .3 .4 .5 .6 .7 .8.9 1 2 3 4 5 6 7 8 910 20 30 40 50 100 US pm
.023 -M5 .114 .23 .45 .68 1.14 1.82 2.27 4.54 6.81 11.36 22.7 mVh
Figure 5-19: Neptune T-10 water meter accuracy for 5/8-inch connections
Source (Neptune 2008).
5.2.4 Sensitivity, Uncertainty, and Variability
A sensitivity analysis allows key variables and assumptions to be changed to test their influence
on the results of the impact assessment. The preliminary choice of these variables is based on
likely scenarios, choices, and practices potentially adopted by the large-scale or small-scale
grower. For this study, only qualitative sensitivities were noted by varying the following key
inventory variables (these are discussed in Section 5.4):
fossil fuel component of Scope 2 energy use (because the energy mix may vary from farm-
to farm);
pumped component of water supply (because some irrigation water is gravity fed and
other is pumped);
parameters, such as DNDC input parameters, enterprise budgets, and expert interviews passed
all three DQI tests.
With regards to precision and case study water use, a critical resource component, the Neptune
T-10 water meters have a high precision and are well suited to the flow rates and volumes
expected in small-scale production. The precision of the Neptune T-10 is estimated to be within
the 10% DCIO, as shown in the manufactures literature on Figure 5-19.

AuJn0v
69


raw water component of water supply (because the large-scale format typically uses raw
water and the small-scale format typically uses potable water which may have a higher
embodied energy);
distance from farm gate to market (because transport is perceived be very different
between produce grown under the two formats; and
synthetic fertilizer (N+P+K) use (because the type of amendment or fertilizer is a major
differentiator between the growing formats and synthetic fertilizer has a relatively high
embodied energy and emission factor.
The data are presented under three categories to assist the reader in their own qualitative
sensitivity analysis. These categories are direct emissions from production, indirect emissions
from production, and post-production emissions. Because of the poor participation rate and
low sample size in the case study, yields had an unknown range of certainty and some did not
compare well with published literature. Further, it was unknown if all case study parameters
could be characterized as independent variables for the purposes of calculating the life cycle
impacts. To eliminate a number of potential variables and year-to-year variation in yield, a
separate run was conducted matching urban production yields to those of commercial
production. Please see Section 5.2.5 Management of Limitation of Small Sample Size in the Case
Study for more discussion.
5.2.5 Management of Limitation of Small Sample Size in the Case Study
Only locations managed by Dr. John Brett and Farmyard CSA (n=2) provided whole-season data
that could be used for the study. Others were incomplete. The main ramification of the poor
participation rate is that it creates a very small sample size from which it is difficult to make
statistical inferences for the population as a whole. These include correlations, confidence
intervals, regressions, analysis of variance (ANOVA), analysis of covariance (ANCOVA), and
significance tests such as the t-test and Chi-squared test. Because it is desired to draw
conclusions about a population from a sample, descriptive statistics such as sample mean,
70


sample standard deviation, and standard error are important but not meaningful with a small
sample size. Typically, a sample size of n<30 is considered small. This study uses data from
various sample sizes, but the urban grower data is, in particular, of a very small sample size
(n=2). A special procedure was developed to allow limited application of inferential statistics
and increase the meaningfulness of reported life cycle impacts.
The confidence interval was selected to be reported with sample means. ANOVA, ANCOVA, or
other regression analysis to characterize the interrelationships, if any, between what are
nominally considered "independent variables" was not performed due to the complexity of
properly characterizing data whose sample size is very small. For example, from even cursory
inspection, yield could be seen to be dependent on climate, soil properties, grower practices,
and many others. For this study, the dependent variables were the life cycle impacts and the
nominally independent variables were all operation inputs, climate, soil properties, and yield.
The special procedure (shown on Figure 5-20) starts by utilizing the DQIs that were applied to
the data categories (see Section 5.2.3 Data Quality). Independent variables were grouped into
four categories: DNDC model parameters and its output; enterprise budgets and literature;
expert interviews; and urban measurements (n=2). Only the last category, urban
measurements, failed any one of the DQIs. It was assumed that reporting mean life cycle
impacts based on data sources that passed the DQI test was appropriate and carried relatively
little, albeit uncharacterized quantitatively, uncertainty. Through this process, independent
variables that may contribute significant uncertainty were screened and retained for further
manipulation.
71


Figure 5-20: Managing Small Sample Size in the Case Study
All retained independent variables may not influence a given life cycle impact category. A
dominance analysis was performed on each material flow (independent variable) to identify
which parameters both failed the DQI test and contributed to at least 90% of the life cycle
impact. The dominance analysis was performed using initial values of parameters which were
all means, irrespective of data category. The results of the dominance analysis are presented in
Appendix F and summarized in Table 5-8. In the table, an "X" indicates which top material flow
contributing to at least 90% of life cycle impacts pertains to any given vegetable.
Table 5-8: Dominance Analysis Results
Top Material/Flow Categories that Contribute > 90% of Life Cycle Impacts Urban Crop
Potato Carrot Onion Tomato Turf
PRODUCTION-DIRECT
Mechanized Equip kWh urban
Grow lights kWh X
Vehicle trips gasoline
Mechanized Equip gasoline
72


Top Material/Flow Categories that Contribute > 90% of Life Cycle Impacts Urban Crop
Potato Carrot Onion Tomato Turf
Mechanized Equip diesel
Irrigation pumped water diesel
Land Area X X X X X
Water X X X X X
Impact from DNDC model1 X X X X X
PRODUCTION INDIRECT
Vehicle trips gasoline X X X X
Herbicide/Pesticide
Fungicide X
Soil X
Grow lights electricity X
Mechanized Equipment electricity
Drip Tape or hose X X X
Herbicide/Pesticide
Mechanized Equipment gasoline X X X
Mechanized Equipment diesel
Mechanized Equipment electricity
N ammonium nitrate X
Water
POST-PRODUCTION
Light pickup truck X X
PET clamshell X
PP Polypropylene
LDPE cello" bags
PS polystyrene
Cardboard
20Mbsite,lyr.
Notes:
1. The Denitrification Decomposition (DNDC) model estimates direct emissions and soil organic
carbon (C). The inputs to the model are based on published sources and expert interviews,
meet data quality indicators (DQI) and are therefore excluded from the dominance analysis.
They are presented here for comparison only.
2. Duplicates between Production-Direct and Production-Indirect reflect that Scope 2 and 3 impacts
contribute significantly compared to Scope 1 impacts of the same material/flow.
These selected parameters were then further characterized by expert information to establish
benchmarks for the range and typical values. Because no information was available on the
frequency distribution of a given parameter, a uniform distribution was assumed, but the typical
73


value influenced the selection of upper and lower values to increase confidence and limit some
outliers. Estimates of upper, lower, and typical values for the selected dominant material flows
are presented in Table 5-9.
Table 5-9: Estimated Benchmarks for Dominant Nominally Independent Variables
Category Influencing Factors Upper Estimate Lower Estimate Typical
Water use, gal/lb unless noted
Carrot1 Watering with a timer vs. as-needed determined by grower. Drip assumed to be up to 50% more efficient than spray. Drip vs. broadcast sprinkler irrigation. Commercial irrigation may serve as lower benchmark, because they are generally fine-tuned and sensitive to cost. Overwatering may result for single-tap setups because water is applied to all plants at same time and plants with highest demand dictate irrigation for all plants. 35 16 32
Potato1 26.1 11 18
Onion1 44 22 36
Tomato1 39 19 32
Turf (Kentucky bluegrass)"'" [gal/sqft] Overwatering can occur more readily with use of automatic systems. Turf acreage under institutional management may not mimic turf acreage under residential homeowner management. Applied water at an agronomic rate for Denver assumed to be about 3 feet per year. 1,507,278 669,901 1,004,852
Potting soil use, Ibs/lb unless noted
Carrot
Potato
Onion
Tomato1 Prevalence of starts and starts using soil media is high. Variance in amount of soil used per cell pack can vary 40% 0.042 0.029 0.035
Turf (Kentucky bluegrass) z
Grow-light use, kWh/lb unless noted
Carrot
Potato
Onion
Tomato1 Options in the setup that would influence energy use are limited due to lamp heat generation and space requirements per plant. T12 fluorescent tubes most prevalent. T8 tubes are more efficient than T12. LED estimated at 80% more efficient than fluorescent, but in little use. Management of time and intensity by grower limited in variance by affordable technology and plant health (diurnal cycle must be maintained). 0.72 0.50 0.60
Turf (Kentucky
74


Category Influencing Factors Upper Estimate Lower Estimate Typical
bluegrass) J
Vehicle trips, gasoline gal/lb unless noted
Carrot1 Number of plots per typical CSA limited by economics and operation size. Mile radius of operation dictated by closeness to home- base. Typical operation size is fixed by single owner/operator with few employees. Lower value recognizes the backyard residential grower who grows for him/herself. Number of trips required for growing phase. Number of trips at harvest accounted for by tonmiles below. 0.006 0.000 0.005
Potato1 0.010 0.000 0.008
Onion1 0.008 0.000 0.006
Tomato1 0.006 0.000 0.005
Turf (Kentucky bluegrass) z
Land area, Ib/sqft unless noted
Carrot1 A dependent variable considering growing practices and climate. Assuming typical weather and yields, this parameter assumed independent for this exercise. Yield benchmarks using commercial growing recognizes that harvests usually happen once; plants typically have an assortment of ripeness and the urban grower can harvest accordingly, resulting in slightly higher yield. Yield benchmarks using commercial growing recognize that plant spacing can be limited by needs imposed by mechanized harvesting. Variability in harvest weight due to variety grown can be large, especially between urban and commercial. Typical varieties grown in the urban setting may have fairly uniform yields, excluding other factors. 1.13 0.73 0.94
Potato1 1.05 0.18 0.88
Onion1 1.09 0.5 0.74
Tomato1 0.88 0.59 0.77
Turf (Kentucky bluegrass) z
Drip Tape Ibs/lb unless noted
Carrot1 Lower end represents that drip is optional; there are other methods to irrigate. Typical value represents prevalence of drip tape with little variation in emitters per foot and delivery rate. If any drip tape is used in a plot, typically all plants are put on drip. 0.015 0.000 0.013
Potato1 0.015 0.000 0.013
Onion1 0.019 0.000 0.013
Tomato1 0.015 0.000 0.013
Turf (Kentucky bluegrass) z
Ammonium Nitrate Fertilizer Ibs/lb unless noted
Carrot
Potato
Onion
Tomato
Turf (Kentucky bluegrass)2 [Ibs/ac] Use of fertilizer is typical, but not required. Upper range recognizes that some users will apply more than needed. Typical value derived from institutionally-managed turf. 230 0 192
75


Category Influencing Factors Upper Estimate Lower Estimate Typical
Light Pickup Truck t.mi/lb unless noted
Carrot1 Hauling assumed to be an average of 200 lbs at a distance according to vehicle trips for gasoline (above) Lower estimate reflects the resident grower that is not hauling his/her harvest for sale or transfer. 0.006 0.000 0.002
Potato1 0.006 0.000 0.002
Onion1 0.006 0.000 0.002
Tomato1 0.006 0.000 0.002
Turf (Kentucky bluegrass) ^
PET Clamshells Ibs/lb unless noted
Carrot
Potato
Onion
Tomato Lower estimate reflects no use of clamshell packaging. Packaging optional for both residential backyard gardeners and CSA businesses. Some CSA only harvest/deliver in bulk with reusable containers. 0.067 0.000 0.020
Turf (Kentucky bluegrass) z
Fungicide Ibs/lb unless noted
Carrot
Potato
Onion
Tomato
Turf (Kentucky bluegrass)"'" [Ibs/ac] Fungicide use is optional. Lower estimate reflects no use. Upper estimate reflects over-application compared to institutionally-managed turf (typical). 7.48 0.00 6.20
Mechanized Equipment (gasoline) gal/lb
Carrot1 Gasoline use is associated with tilling. Tilling is optional. Upper estimate reflects tilling all seeded area. 0.001 0.000 0.0005
Potato1 0.001 0.000 0.0005
Onion1 0.001 0.000 0.0005
Tomato1 0.001 0.000 0.0005
Turf (Kentucky bluegrass) [gal/ac] Gasoline use is associated with lawn mowing. There some variability in area mowed per gallon, mowing frequency, species of grass, and overall turf health. Typical value from one mowing per week for approximately 26 weeks. 540 240 360
References:
1. Brett, John. 2014. Phone conversation regarding typical parameters and ranges of values for urban residential
vegetable growing by homeowners and CSA businesses using residential lots. Department of Anthropology,
University of Colorado Denver. Phone: 303-556-8497. E-mail: iohn.brettf5)ucdenver.edu. May 1.
2. Turf water use 2 waterings per week for 9.8 mm/ea in May, Jun, Sep, Oct; 39 mm/eaJul, Aug. Adapted from:
Denver Water. 2011. Sustainable Landscape Conversion Design and irrigation recommendations for converting
bluegrass turf to sustainable low water usage landscapes. August 31.
3. Turf pesticide and fertilizer applications taken from: Pesticide Usage on Turf of West Virginia Golf Courses and
Lawn Care Businesses. 1995. West Virginia University Extension Service.
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With a range and distribution estimated, a Monte-Carlo simulation was carried out for each
selected dominant parameter. Each randomly selected value for each parameter was carried
through the calculations, reporting total life cycle impacts for each impact category,10,000
times. Life cycle impacts were then reported as the mean of 10,000 samples with a 95%
confidence interval calculated from 1.96 times the standard error of the mean. A summary of
the confidence interval is presented in Table 5-10. The results of the special procedure indicate
that the sample mean is representative of the population mean.
Table 5-10: 95 Percent Confidence Intervals for Impacts from Dominant Nominally
Independent Variables
95 Percent Confidence Interval (Cl)a Reported as Meanb Value Shown
Denver Denver Denver Denver Denver
Impact Category Potato Carrot Onion Tomato Turf
Energy (all non-renewable) 9.35 x l'3 6.07 x l'3 8.05 x l'3 2.23 x l'2 6.14 x l'3
Land Use (arable, non-irrigated) 2.60 x l'2 2.74 x l'3 6.11 x l'3 3.16 x l'3 6.25 x l'5
Water (all fresh water sources) 8.56 x l'2 1.07 x 10'1 1.24 x 10'1 1.14 x 10'1 1.09 x 10'1
Total Carbon Dioxide eq 1.19 x l'3 5.40 x l'4 7.39 x l'4 1.39 x l'3 6.06 x l'4
Carbon Dioxide 9.93 x l'4 3.66 x l'4 5.17 xl'4 1.24 x l'3 3.92 x l'4
Nitrous Oxide 2.25 x 1CT5 3.62 x l'6 5.65 x l'6 2.10 x l'4 7.70 x l'5
Methane 4.46 x l'5 2.71 x l'5 3.63 x l'5 7.10 x l'5 2.66 x l'5
TRACI Carcinogens 5.6x l'n 3.39 x l'n 4.53 x l'n 3.41 x l'n 3.83 x l'n
TRACI Non-carcinogens 2.5x l'lu 1.50 x l'lu 2.01 x l'lu 1.55 x 10- 1.71 x l'lu
TRACI Air compartment 2.43 x l'4 2.42 x l'4 3.07 xl'4 2.41 x l'4 2.86 x l'5
TRACI Water compartment 1.88 x l'3 1.13 x l'3 1.51 x l'3 1.18 x l'3 1.29 x l'3
TRACI Soil compartment 4.82 x l'6 2.91 x l'6 3.89 x l'6 1.94 x l'4 3.34 x l'6
Notes:
a. Cl shown applies to dominant, nominally independent variables identified in the dominance analysis.
b. For corresponding means, see Appendix land Appendix J.
Cl =1.96 x (SEM)
SEM Standard Error of the Mean = (STDEV)+(n)y?
STDEV Sample Standard Deviation
n sample size =10,000
The confidence interval and mean are reported in Section 5.3 Impacts Assessment, however the
intermediate LCA step of outputs and emissions factors are described in the following section
and completes the inventory analysis.
77


5.2.6 Outputs and Emission Factors
Direct and indirect inputs detailed in Appendix C were tabulated and manipulated into common
units and the functional unit (conversion factors) for more efficient application of emission
factors and calculation of impacts. As described in Section 5.2.1, some inputs are also outputs
of interest, namely, resource use (e.g. water, fuel, power).
Emission factors were taken from published literature, the U.S. Life-Cycle Inventory Database
(National Renewable Energy Laboratory ([NREL]), and the Carnegie-Mellon Economic input-
output LCAtool as reported in SimaPro software (PRe Consultants 2013). SimaPro was used to
develop emission factors for most upstream materials. Exceptions to this general rule include
potable water (Denver Water), Culiacan electricity, and web hosting. These are detailed in
Appendix G. The DNDC model generated direct impacts. These are explained further in Section
5.3 and shown in Appendix H.
The life cycle inventory yielded a number of outputs that will be the basis for conversion to an
environmental, economic, or social consequence. These, in turn, are used in the impacts
assessment in the next section. As an example, Table 5-11 summarizes inputs and outputs for
one production phase for one vegetable for one growing format (large-scale). In the table,
outputs are fully displayed for plastics; outputs for other inputs are not shown for clarity.
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Table 5-11: Example Input-Output Table for the Growing Phase for the Field Tomato Large-
Scale Growing Format
(Functional Unit [FU]=1 lb vegetable to consumer)
Primary Input Unit(s) Conversion Factor Output(s) Unit(s)
Upstream components Direct fuel diesel (well to wheels) gal $ various 1. various
Upstream components Direct Fuel gasoline (well to wheels) gal $ various 1. various
Upstream components Direct electricity (Scope 1) kWh $ various 1. various
Upstream components Indirect electricity (Scope 2 for Bakersfield, California)2 kWh $ various 1. various
Upstream components kg $ various 1. various
Upstream components kg $ various 1. various
Upstream components kg $ various 1. various
Upstream compon $ kg/$ kg/$ kg/$ kg/$ kg/$ $/$ kg/$ J/$ j/$ short t/$ gal/$ t-km/$ t-km/$ t-km/$ t-km/$ t-km/$ Toxic Releases nitrate compounds methanol hydrochloric acid zinc compounds ammonia Economic Activity employee compensation Greenhouse Gases total C02-e Energy total energy non-fossil energy Hazardous Waste hazardous waste generated Water Use r: domestic by rail domestic by truck domestic by water international by air international by water kg kg kg kg kg $ kg J J short t gal t-km t-km t-km t-km t-km
THIS ROW EXPANDED
ents Plastics (virgin resins)3
Upstream components Cardboard kg $ various 1. various
Upstream components kg $ various 1. various
kg kg/kg Herbicide residual in soil kg
kg kg/kg Herbicide residual on vegetable kg
Upstream components Pesticide kg $ various 1. various
kg kg/kg Pesticide residual in soil kg
kg kg/kg Pesticide residual on vegetable kg
Uostream comoonents


Primary Input Unit(s) Conversion Factor Output(s) Unit(s)
Fungicide $
kg kg/kg Pesticide residual in soil kg
kg kg/kg Pesticide residual on vegetable kg
Upstream components Direct water gal $ various 1. various
Direct land ft2 ft2 Land area devoted to production ft2
Labor hr hr Labor hours hr
hr $/hr of labor Value of wages $
Soil organic carbon (SOC)5 mg/kg A%/scenario Long-term change in SOC %
1.Industry reports and SimaPro were used to determine the outputs that are embodied in each input. These include
economic activity, greenhouse gases, energy, hazardous waste, toxic releases, water use, and transportation. EIO-LCA
was only used if SimaPro or other sources cannot provide bottom up data.
http://www.pre-sustainability.com/
http://www.eiolca.net
2. Nine locales and their energy mixes were identified as the locales of the vegetables grown under the large-scale
format are discovered.
3. The top 5 EIO-LCA results are presented only for clarity. The study will track items that contribute at least 90
percent of the total emission or output and that are relevant to the indicators chosen in the impacts assessment.
SimaPro was set to report output to the 0.1% contribution level.
4. Fuel and water (and associated emissions) related to transportation, not already counted in Energy and Water Use
categories, were included.
$ U.S. Dollars
ft2 square foot
g gram
gal gallons
hr hours
J Joules
kg kilograms
kWh kilowatt-hours
lbs pounds
mt metric ton (1,000 kg)
mg milligram
MW megawatt
short t short ton (2,000 lbs)
t ton
5.3 Impacts Assessment
Midpoint impacts were chosen for the life cycle impacts assessment (LCIA). Examples of
midpoint impacts are global warming potential, resource depletion potential, or 50% lethal
concentration. On the other hand, corresponding endpoint impacts could include global
warming, available water supply, or fish kills. Analysis at a midpoint minimizes the amount of
80


forecasting and effects of modeling incorporated into the LCIA; reduces the complexity of the
modeling; simplifies communication of results; minimizes assumptions and value choices;
reflects a higher level of societal consensus; and be more comprehensive than model coverage
for endpoint estimation (EPA 2006).
LCI data is then paired with an impact category. This step is called classification and may assign
a single LCI item to more than one impact category. Depending on whether an LCI item can act
independently on each impact category, a range of allocation can be applied to each impact
category. If a single LCI item does act independently on each impact category, for example, then
the entire quantity of the LCI item can be assigned to each category.
The impact indicators shown in Table 5-12 were chosen based on their relevance to this study.
These indicators provide useful information to small scale growers, other stakeholders, and
policy makers. The indicators can be broadly grouped under the four categories a)
Environmental Impact; b) Ecosystem Impact; c) Human Health Impacts; and d) Societal Impacts.
Each category is shown with the impact assessment methodology proposed for use.
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Table 5-12: Midpoint impact indicators, classification, and characterization factors
Impact Category and Methodology Impact Indicator Scale Examples of LCI Data (i.e. classification) Characterization Factor
Environmental (TRACI) Global warming Global Gaseous emissions (e.g. carbon dioxide [C02]) Global Warming Potential Converts LCI data to carbon dioxide (C02) equivalents.1
Resource Depletion water use Local Regional Quantity of water used or consumed Resource Depletion Potential Converts LCI data to a ratio of quantity of resource used versus quantity of resource left in reserve.
Resource Depletion fossil fuels Global Regional Quantity of fossil fuels used Resource Depletion Potential Converts LCI data to a ratio of quantity of resource used versus quantity of resource left in reserve.
Ecosystem (TRACI) (DNDC model) Soil health Local Soil Organic Carbon (SOC) N, P, K agronomic percentage Land area changed as a result of increased urban gardening. Carbon Depletion Potential Converts LCI data to a ratio of quantity of SOC versus quantity of ideal SOC.
Land use change2 Local Regional Land area changed as a result of increased urban gardening. Converts the amount of large-scale format land displaced by small- scale format land, corrected for crop yield by format.
Terrestrial ecotoxicity Local Regional Residues and releases of chemicals to soil Converts LC5 data to equivalents; uses multi-media modeling, exposure pathways.
Aquatic ecotoxicity Local Regional Residues and releases of chemicals to water Converts LC5 data to equivalents; uses multi-media modeling, exposure pathways.
Human Health (TRACI) Carcinogeneity Global Regional Local Releases to air, water, soil Converts LC50 data to equivalents; uses multi-media modeling, exposure pathways.
Hazard Index (non-cancer) Local Releases to air, water, soil Converts LC5 data to equivalents; uses multi-media modeling, exposure pathways.
Social3 (UNEP 2009b) Employment Regional Local Hours Wage rate Employment Potential Converts LCI data to full-time equivalents (FTE)
Notes:
1. Global warming potentials can be 50,100, or 500 year potentials.
2. The land use change indicator assumes conversion of large-scale agricultural farmland back to natural
grassland as urban gardening, utilizing the small-scale format, increases. Under this assumption, the small-scale
format displaces the large-scale format in equal areas, weighted by crop productivity per land area. For the small-
scale format, this indicator assumes that a third of the land is converted from previous use as turf, a third as bare
soil, and a third as natural grassland (assumed to represent vacant, weedy areas). Soil organic carbon is highly
related to current and antecedent land use and reaches a steady-state at a 30-year horizon or less (Kim 2009:
Morgan 2010).
3. This is an emerging and developing aspect of life cycle impact assessment. No formal methodologies
exist. In the place of a methodology or model, the following guidance document was used: "Guidelines for Social
Life Cycle Assessment of Products Social and socio-economic LCA guidelines complementing environmental LCA
and Life Cycle Costing, contributing to the full assessment of goods and services within the context of sustainable
development" (UNEP 2009b).
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Sources:
Table adapted from (EPA 2006)
DNDC Denitrification-Decomposition Model, http://www.dndc.sr.unh.edu/
TRACI U.S. Environmental Protection Agency Tool for the Reduction and Assessment of Chemical and Other
Environmental Impacts, http://www.epa.gov/nrmrl/std/sab/traci/
Two indicators that are unique to this study that are not typically found in LCAs are the social
(employment) and soil health (soil organic carbon) indicators. This is owed partly to the nature
of the comparison between large-scale and small-scale growing. The labor indicator is used
because there is a stark and measurable difference between the labor inputs of large-scale and
smaH-scale growing. This can be seen as an opportunity for local employment, but a risk for
corporate efficiency. Since the late 1990s, incorporating any social aspect explicitly in an
otherwise environmental LCA, was unseen. Dreyer (2006) articulated this and recently UNEP
published a guidance document (UNEP 2009b). This study only examined employment and its
potential monetary value to the employee.
The soil organic carbon indicator is used because the land use change that occurs when large-
scale agricultural farmland is converted back to natural grassland as urban gardening, utilizing
the small-scale format, increases. Under this assumption, the small-scale format displaces the
large-scale format in equal areas, weighted by crop productivity per land area. For the small-
scale format, this indicator assumes that a third of the land is converted from previous use as
turf, a third as bare soil, and a third as natural grassland (assumed to represent vacant, weedy
areas). All three scenarios have been studied by a number of researchers (Baird 2011; Churkina
2008; Kim 2009; Morgan 2010; Pouyat 2002; Pouyat 2006; Pouyat 2009; Qian 2010). These
studies make it possible to estimate the long-term state of carbon in soil after a land use change
is enacted. When compiling the results of these studies, one may conclude, In general, the state
83


of carbon is heavily influenced by growing practices, the use of amendments or fertilizers, and
the existence and type of plant cover. Each one of these is starkly different between the
growing formats analyzed in this study, and is therefore interesting to research.
Raw impacts are presented in Appendix I. Impacts are graphically reported in a series of stacked
bar graphs found in Appendix J. As described in Section 5.2.4, the stacked categories are direct
emissions from production, indirect emissions from production, and post-production emissions.
The graphs are arranged by impact and show all crops individually, and the land use change
conversion scenarios associated with them. Land use change is presented on a separate y-axis
(right hand). The land use change results are summarized and presented as percent change in
each impact category and discussed in further detail in Section 5.4.3.
5.4 Interpretation
The interpretation phase of the LCA methodology follows the ISO standard entitled
''Environmental Management Life Cycle Assessment Life Cycle Interpretation/' ISO 14043 (ISO
1998). The components of the interpretation phase are the following:
Identification of the significant issues based on the LCI and LCIA.
Evaluation which considers completeness, sensitivity, uncertainty, and consistency checks.
Conclusions, recommendations, and reporting.
Because of the nature of agricultural product LCAs, inconsistencies can arise from the different
scales of growing, different locations, and sources of data. These have been discussed in Section
5.2.4 and are further discussed below.
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5.4.1 Comparative LCA of Vegetables
The results of the impact assessment are examined for inconsistencies and sensitivities in Table
5-13. The final column in the table indicates where the author found either the magnitude of
sense of the impact to be unexpected. A possible explanation is also offered for each instance.
For relationship to information on the land use change observations, see Section 5.4.3.
Table 5-13: Interpretation of Impacts Assessment
Impact Category Observations for Production (P) Observations for Land Use Change (LUC) Sensitivities & Explanation Un- expected
General (P) GHG emissions are dominated by direct emissions for commercial production. The main contribution for direct emissions is soil/plant emissions (DNDC model), except methane.
(P) GHG emissions are dominated by indirect emissions for urban production. The main contribution for indirect emissions is fossil fuel use associated with vehicle trips.
(P) Energy use is dominated by indirect and post-production sources for commercial production. Packaging and transport dominate potato, onion, and tomato.
(P) Carcinogenicity and non-carcinogenic hazard dominates in indirect and post- production phases. Impacts categorically attributed mostly to fossil fuel combustion.
(P) Overall ecological impacts vary significantly between commercial and urban production in the indirect emissions and post-production phases, respectively. They are dominated by packaging for commercial production and vehicle trips (gasoline) for urban production.
Energy (P) Urban tomato energy use is much higher than the rest. 84% of energy use attributed to electricity use for growing starts. One outlier in this discussion is the urban tomato. If the dominant form of starts used no/low-energy cold frames and hoop houses, urban vegetable production would then be categorically more energy efficient and emit even fewer GHGs than estimated in this study. X
(LUC) Urban instances of tomato result in a net increased specific energy footprint Potato, carrot, and onion result in a net decrease. Energy use from vehicle trips and drip tape amount to 60% and 30%, respectively. X
Land Use (P) Urban potato occupation much larger than the rest. One participant had almost 6 times the specific land area than both the other urban participant and commercial farms. X
(LUC) Urban instances of potato result in a net increased land occupation. All others except carrot A2 A1 result in a net reduced land occupation. Sensitive to potato yield.
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Full Text

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A CASE STUDY OF URBAN AGRICULTURE: A LIFE CYCLE ASSESSMENT OF VEGETABLE PRODUCTION by SF B.S. University of California, Irvine, 1987 M.S. Stanford University, 1989 A thesis submitted to the FacultyoftheGraduateSchoolofthe UniversityofColoradoin partial fulfillment oftherequirementsforthedegreeof Doctor of Philosophy Civil Engineering2014

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2014 STEPHEN FIS HER ALL RIGHTS RESERVED

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This thesis for the Doctor of Philosophy degree by Stephen Fisher has been approved for the Civil Engineering Program by Indrani PalChair Arunprakash Karunanithi, Advisor John Brett Gregory Cronin Balaji Rajagopalan Anuradha Ramaswami ii 12014

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Fisher,Stephen(Ph.D.,Civil Engineering) A Case Stu dy of Urban Agriculture: A Life Cycle Assessment of Vegetable Production Thesis dire cted by Associate Professor Arunprakash Karunanithi ABSTRACT The worlds urb a n population surpassed the non urban population for the first time in 2009. This marks what has been a steady global shift of providing more food to places it is not grown. Because food accounts for over 10 percent of the carbon footprint for the typical American city, this study adopts a social ecological infrastructural systems framework, a large component of which is recognizing urban activities and sectors belonging to infrastructure inside and outside the urban boundary. This is a key way to examine the embodied, life cycle properties of the food we eat in cities. This study develop s a product life cycle assessment (LCA) of a basket of vegetables (product) grown under two different formats. The first format is characterized by the large scale, commercial growers that supply the typical supermarket. The second format is characterized by smallscale growers (less than 1 acre) that use higher land use intensity and less mechanized practices. This second format is typically used by backyard gardeners, operators of neighborhood supported agriculture (NSA) and operators of some community supported agriculture (CSA) businesses. Published data is used for the large scale format; primary, case study data is used for the small scale format. Results of s cenarios of land use chang e and vegetable production for both distant farmland and urban settings found that shifts resulting from urban vegetable production are favorable in terms of greenhouse gas emissions, water use, and soil organic carbon. Surprisingly, urban vegetable production is not categorically favorable for each metric; several key parameters can shift the balance in favor or out of favor for either growing format, and these parameters are distinctly bottom up. The results indicate that state and local policy could remove hurdles to urban agricultural production with these data supporting claims that benefits outweigh costs. The form and content of this abstract are approved. I recommend its publication. Approved: A r unprakash Karunanithi iii

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ACKNOWLEDGEMENTS I would like to acknowledge the followi ng individuals for guidance and inspiration and personal communication in the development of this research proposal. Will Allen Growing Power, Inc. Milwaukee, WI Molly Anderson, Ph.D. Partridge Chair in Food and Sustainable Agriculture Systems College of the Atlantic Bar Harbor, ME John Brett, Ph.D. Professor Department of Anthropol ogy University of Colorado Denver, CO Beth Anne Fisher, PT, DPT, CSCS, CBP Steves wife Stephen Cochenour Common Roo ts CSA Wheat Ridge, CO Roberta Cook Department of Agricultura l and Resource Economics University of California Davis, CA Mike Comazzi Vice President of Procurement FreshPack Pr oduce Denver, CO Greg Cronin, Ph.D. Associate Professor Department of Integra tive Biology University of Colorado Denver, CO Debbie Dalrymple Farmyard CSA Denver, CO James Diekmann Professor Civil and Architectural Engineering University of Colorado Boulder, CO Lisa Rogers Feed Denver Denver, CO Lynn Johnson, Ph.D. Professor Department of Civil Engineering University of Colorado Denver, CO Arunprakash Karunanithi, Ph.D. Associate Professor Department of Civil Engineering University of Colorado Denver, CO Corrie Knapp IGERT Resear cher University of Colorado Denver, CO Sundari Kraft Heirloom Gar dens Denver, CO Debbi Main, Ph.D. Professor Department of Health and Behavioral Scien ces University of Colorado Denver, CO iv

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Pam Sawyer Amirhossein Mehr kesh Student Research assistant University of Colorado University of Colorado Colorado Springs, CO Denver, CO Leigh Tesfatsion, Ph.D. Leslie Miller, Ph.D. Professor IGERT Resear cher Department of Economics University of Colorado University of Iowa Denver, CO Ames, IA Jon and Candice Orlando Dawn Thilmany, Ph.D. Urbiculture C ommunity Farms Professor Denver, CO Department of Agricultura l and Resource Economics Anuradha Ramaswami, Ph.D. Colorado State University Professor Fort Collins, CO Charles M. Denny, Jr ., Chair in Science, Technology, and Public Policy School of Public Affairs Jody Villeco University of Minn esota Whole Foods Boulder, CO Minneapolis, MN National Science Foundation (NSF) Integrative Graduate Education and Research Traineeship (IGERT) Quint Redmond Agriburbia Golden, CO Award No. DG E 0654378 Luann Rudolph IGERT program manager v

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TABLE OF CONTENTS C 1 Introduction...............................................................................................................................1 1.1 The Agri food System....................................................................................................1 1.2 Social Aspects................................................................................................................3 1.3 Urban Sustainability.......................................................................................................4 1.4 Life Cycle Assessment....................................................................................................3 1.5 LCAs of Food and Agriculture........................................................................................4 2 Focus..........................................................................................................................................9 2.1 The Vegetable Basket....................................................................................................9 2.2 Different Growing Formats..........................................................................................112. CharacteristicsofUrbanFarming ......................................................................123 Academic Contribu tion............................................................................................................16 3.1 Methodol ogy...............................................................................................................16 3.2 Primary Case Study Data.............................................................................................16 3.3 Interdisciplinary Impacts Assessment.........................................................................17 3.4 Appropriate Scales for Hybrid LCA..............................................................................17 3.5 Land Use Change.........................................................................................................17 3.6 Relevant Data for Decision makers.............................................................................18 4 Objectives..................................................................................................................... ...........18 5 Methods........................................................................................................................ ..........20 5.1 Goal Definition and Scoping........................................................................................21 5.1.1 Functional Unit.....................................................................................................21 5.1.2 System Boundaries and Components..................................................................22 5.1.3 Small Scale Vegetable Production Format..........................................................24 5.1.4 Large Scale Vegetable Production Format..........................................................32 vi

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5.1.5 Seasonal Sources.................................................................................................34 5.1.6 Scenario Development for Land Use Change......................................................37 5.1.7 Data Quality Objectives.......................................................................................42 5.2 Inventory Analysis........................................................................................................45 5.2.1 Data Sources........................................................................................................45 5.2.1.1 Power..........................................................................................................48 5.2.1.2 Water..........................................................................................................50 5.2.1.3 Other Agricultural Inputs............................................................................55 5.2.2 DNDC Carbon Model............................................................................................57 5.2.2.1 Biogeochemical Processe s..........................................................................57 5.2.2.2 Model Inputs...............................................................................................60 5.2.2.3 Climate Data...............................................................................................60 5.2.2.4 Special Procedure for Estimating Precipitation..........................................61 5.2.3 Data Quality.........................................................................................................67 5.2.4 Sensitivity, Uncertainty, and Variability...............................................................69 5.2.5 Management of Limitation of Small Sample Size in the Case Study...................70 5.2.6 Outputs and Emission Factors.............................................................................78 5.3 Impacts Assessment....................................................................................................80 5.4 Interpretation..............................................................................................................84 5.4.1 Comparative LCA of Vegetabl es...........................................................................85 5.4.2 Water Footprint...................................................................................................88 5.4.3 Land Use Change..................................................................................................90 6 Conclu sion..................................................................................................................... ..........94 6.1 Limitations...................................................................................................................9 5 6.2 Contribut ions...............................................................................................................96 6.3 Further Resear ch.......................................................................................................100 R................................................................................................................................102 vii

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A A. Field Data Collection Form (Example)....................................................................................109 B. Geographi c Sources for Fresh Vegetables..............................................................................110 C. Life Cycle Inventory by Functional Unit..................................................................................111 D. Input Parameters for DNDC....................................................................................................112 E. Article: Nitrous Oxide Emissions .............................................................................................113 F.Dominance Analys is ................................................................................................................114 G. Emission Fac tor Worksh eet.....................................................................................................115 H. DNDC Modeling Output..........................................................................................................116 I. Impacts by Functional Unit......................................................................................................117 J. Impacts Gr aphs........................................................................................................................1 18 viii

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LIST OF TABLES TABLE 1 1. Applications of LCA for evaluating agricultural production systems...........................5 1 2. Impact categories and indicators.................................................................................6 1 3. The potential environmental impacts of vegetables, sugar, and oil............................7 1 4. Greenhouse gas intensity for various vegetables, production on farm.......................8 2 1. Vegetables that can be grown in Colorado................................................................10 2 2. Comparison of qualitative, presumed characteristics of vegetable production grown under large scale and small scale formats.................................................................15 5 1. Production Phases Small scale Format...................................................................28 5 2. Scenario Development for Land Use Change.............................................................40 5 3. Data Types and Quality Objectives.............................................................................43 5 4. Resource Flows and LCI Categories............................................................................48 5 5. Site specific Water Use Paramete rs...........................................................................51 5 6. Water Meter Assignments.........................................................................................55 5 7. Typical Enterprise Budget...........................................................................................56 5 8. Dominance Analysis Results.......................................................................................72 5 9. Estimated Benchmarks for Dominant Nominally Independent Variables.................74 5 10. 95 Percent Confidence Intervals for Impacts from Dominant Nominally Independent Variables.....................................................................................................................7 7 5 11. Example InputOutput Table for the Growing Phase for the Field Tomato Large Scale Growing Format................................................................................................79 5 12. Midpoint impact indicators, classification, and characterization factors..................82 5 13. Interpretation of Impacts Assessmen t.......................................................................85 5 14. Net Change in Impacts from Land Use Conversi on....................................................91 5 15. Net Impacts Including Land Use Change with Alternate Functional Unit..................93 ix

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LIST OF FIGURES FIGURE 1 1. Study Components.......................................................................................................3 1 2. Greenhouse gas emissions summary for Denver in 2005............................................2 2 1. Average U.S. per capita fresh vegetable consumption..............................................11 5 1. Phases of the life cycle assessment............................................................................20 5 2. Life Cycle Inventory Scope..........................................................................................22 5 3. Simplified process flow diagram................................................................................23 5 4. LCA System Boundary Small scale Format..............................................................25 5 5. A Farmyard CSA garden..............................................................................................26 5 6. LCA System Boundary Large scale Format..............................................................32 5 7. Denver Area Supermarket Share................................................................................36 5 8. Supermarket sources and distances by vegetable.....................................................37 5 9. Land Use Change and Conversion Scenarios..............................................................38 5 10. Uncertainty and influence indicate what are key data..........................................44 5 11. Data Sources The hybrid LCA relies on two broad categories of information........47 5 12. Regions of Electricity for Grid Mix Identification......................................................49 5 13. Six Neptune T 10 water meters................................................................................53 5 14. Small scale water metering setup.............................................................................54 5 15. Process Flow for the DNDC Model............................................................................58 5 16. Daily precipitation at Fort Benton, Montana............................................................65 5 17. Cumulative nitrous oxide flux for 1991 and 1996 with daily precipitation...............66 5 18. Cumulative nitrous oxide flux detail for 19 June to 8 July........................................67 5 19. Neptune T 10 water meter accuracy for 5/8inch connections................................69 5 20. Managing Small Sample Size in the Case Study........................................................72 6 1. Data Sources and Emissions Reporting......................................................................98 x

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1 I Our agri food system is the subject of increasing scrutiny from a global to a local scale. More and more people are decrying farming policies and practices that have clear environmental and human health impacts ( Pimentel 1994 ). Issue driven, quantitative indicators are needed by policy makers to prioritize planning and spending of infrastructurein an informed manner. A comparative life cycle assessment could provide quantitative information regarding the benefits and impacts of small scale growing (characteristic of urban areas) compared to large scale commercial growing. Such information is crucial to enable leadership in areas of environmental stewardship, pollution, and energy intensity on an urban scale ( Rochefort 1993 United Nations Environment Programme [UNEP] 2009a Vaughn 2003 Weiss 1989 ). 1.1 The Agri food System Urban garde ning is farming at a small scale. 14% of the U.S. land area is harvested in agriculture everything from big industrial farms producing monocultures to just a few acres producing a variety of organic vegetables, grains, and melons ( U.S. Department of Agriculture [USDA] 2010 ). Under this aggregate classification, 0.9% of all U.S. harvested acres is fresh vegetables ( U.S. Environm ental Protection Agency [EPA] 2010a ). By comparison, 68% of all harvested cropland is planted with one of the four commodity crops corn, cotton, soybean, and wheat ( USDA 2010 ). The USDA categorizes farms as agricultural operations with sales. USDA statistics do not recognize farms of less than 1 acre in size and there is no good estimate of the land area utilized for urban gardening (gardening may or may not generate sales). Yet even with such a tiny amount of land devoted to vegetable production, fruit and vegetables contribute about 8% of greenhouse gas emissions from all food, 4% of water embedded in food products, and 15% of embodied energy ( Institute of Gro cery Distribution [IGD] 2007 ). 1

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Fresh vegetable production at a small scale from urban gardening may possess qualities that belie its seemingly small status in the agri food system. For example, 40% of all produce consumed in the U.S. in 1944 came from Victory Gardens ( Pollan 2008 ). Denver had 41,500 Victory Gardens in 1943, valued at $578,125 ($7,122,319 in 2008 dollars). That is equivalent to an average of about $172 per garden per year. These Victory Gardens ranged in size from a few potted plants on a porch to large, multi acre community gardens. Partly because Colorado has only 354 farms most of those under 10 acres that produce vegetables, and that only 2% of the average Americans caloric intake is from fresh vegetables, only 0.2% of the food we eat in the Denver metro area comes from Colorado ( Masoner 2010 ). Apart from food production, urban gardening has other interesting facets food security, health, recreation, urban resiliency, social justice, and employment, to name a few ( Bellows 2004 ). Under the urban gardening moniker exist community supported agriculture (CSA) and neighborhood supported agriculture, both of which refer to a business model specifically, and relative size of operation generally. CSA is a term generally describing a farm that delivers produce to its shareholders within 100 miles. CSA farms can vary from less than one acre to thousands, and be located within or outside urban areas. These farms, while sometimes large, do not participate in the USDA farm commodity program and are free to plant any crop without penalty ( Raw Earth Living 2010 ). Neighborhood supported agriculture (NSA) is a term describing small businesses that grow vegetables on portions of residential or small commercial properties.Similar to CSAs, shareholders pick up their produce at designated drop points. NSAs can differ from CSAs in size and produce travel distance; NSAs are strictly neighborhood scale. Finally, residents garden either for food or hobby in their own yard or balcony, or their plot in a community garden which 2

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may be collocated with a school or park. This study compares production of vegetables grown under a large scale format and a smallscale format, and these components are depicted on Figure 1 1, the general scope of the research within the context of the agri food system. Figure 1 1: Study Components This study does not examine other parts of the agri food system, including processed food and its packaging, further transport and distribution, cooking, preparation, consumption, waste, or recycling. 1.2 Social Aspects There is a large body of related research that explores the human side of food and agriculture, both large and smallscale. For example, in one article alone, Kloppenburg ( 2000 ) cited 22 3

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peerreviewed articles that discuss it. In that article, Kloppenburg used systems thinking and convened a mediumscale, participatory survey to identify not components of the food system, but the system attributes. This work is one of the most compelling because of the design of the survey and working groups that is perhaps the best combination of ordinary peoples views and those of experts. As a final outcome of the work, 14 attributes of food system sustainability were identified and these are presented below (Kloppenburg 2000 ). Relationship with the land Knowledge & Communication Proximate Profit Participation Justice and ethics Sustainably regulated Sacred human expression Healthful Diversity Culturally nourishing Seasonality Valueoriented economics Relational It is interesting to note that none of the attributes address environmental sustainability and suggests that this is an area of either implicit embrace or possibly ignorance. This study attempts to characterize labor hours and labor wages. 1.3 Urban Sustainability In 2009, the worlds urban population surpassed the non urban population for the first time ( World Resources Institute [WRI] 2010 ). This marks what has been a steady global shift of providing more food to places it is not grown. In the U.S., for example, depending on the vegetable variety and time of year, conventional produce travels, on average, from 500 to over 2,000 miles to terminal markets in the U.S. ( Weber and Matthews 2008 ). The vast majority of American farmland is devoted to the main commodity crops (i.e., corn, cotton, rice, soy, and wheat) that require vast fields, large operations, and economies of scale. However, most is used as animal feed, processed, or exported. Demand for fresh vegetables is met from a surprisingly 4

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small amount of farmland. But constraints on traditional farmland are growing. These include water scarcity, development pressure, degrading soils, nutrient runoff, and cost of inputs. Urban vegetable production is a viable land use change to meet these challenges. There is enough arable land area in the typical American city to support meeting all its fresh vegetable demand many times over. In one study for Denver, Colorado, it was found that there is over 10 times the land available to meet city demand for broccoli, carrot, spinach, tomato, bell pepper, and potato ( Brett, et al. 2013 ). Also, in contrast to commercial farming, urban vegetable production as food, health, and security, is advancing popularly on its own as an advocacy platform, hobby, business, and occupation ( Kloppenburg 2000 ). Some urban areas around the globe provide up to 90 percent of fresh vegetable consumption through urban gardens, others almost none ( Cuba Ministry of Agriculture 2008 ). But the link between the food we eat and urban sustainability exists equally whether the food comes from a farm far away or ones own back yard. What commercial growers provide to supermarkets is a significant cog in the food system and they rely on large contributions of distributed infrastructure from farm to table. In fact, as shown on Figure 1 2, the food urbanites eat ranks significantly with other notable urban sectors, such as transportation, housing, and energy, in terms of fossil fuel consumption, emissions, water use, waste, and economic activity ( Hillman and Ramaswami 2010 Decker 2000 ). 1

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Figure 1 2: Greenhouse gas emissions summary for Denver in 2005 The food sector includes an individuals total diet imported, local, processed, and raw foods. (from Ramaswami, et al. 2008 ). Other notable characteristics of urban food are that it comprises 17 percent of fossil fuels use, 12.7 percent of the post consumer waste stream, and 13 percent of consumer spending ( Heller and Keoleian 2000 Intergovernmental Panel on Climate Change [IPCC] 2008 Ramaswami, et al. 2008 ). Even with the small fraction of all farmland devoted to vegetable production, fruit and vegetables contribute about 8 percent of greenhouse gas emissions from all food, 4 percent of water embedded in food products, and 15 percent of embodied energy ( IGD 2007 ). This paper adopts a social ecological infrastructural systems (SEIS) framework, a large component of which is recognizing urban activities and sectors belonging to infrastructure inside and outside the urban boundary ( Ramaswami, et al. 2012 ). The framework establishes the importance of including WRI Greenhouse Gas Protocol Scope 3 emissions and other indirect, 2

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upstream supply chain footprints when they are a large component of total emissions and footprints of urban activities. This is a key way to examine the embodied, life cycle properties of the food we eat in cities. In counting the impacts of the production, processing, and transport of food, one must extend the boundary however far it exists. ...to address environmental sustainability both in terms of resource use and global pollution impacts, activities and infrastructures within city boundaries must be explicitly integrated with transboundary infrastructures that span hundreds of miles and draw in vast quantities of natural resources, directly or indirectly, to meet city demand... ( Ramaswami, et al. 2012 ). This is a main focus of greenhouse gas (GHG) accounting and reporting for material flows in and out of the [usually jurisdictional] urban boundary.For example, some infrastructure services utilize more fossil fuel outside city boundaries than within ( Ramaswami, et al. 2012 ). 1.4 Life Cycle Assessment The life cycle assessment (LCA) is a framework for identifying impacts of a products manufacture, use, reuse, or disposal. The LCA is typically conducted on manufactured products, but the concept has also been applied to operations research, supply chain analysis, human resources, and a wide variety of capital projects. It has its roots in a multi criteria study done for the Coca Cola Company on their signature beverage. Later, the field of industrial ecology adopted this approach and helped establish the International Standards Organisation (ISO) Series 14000 standards on LCA. The LCA framework and ISO methodology is used for this study because it is a widely recognized and accepted methodology for conducting inventories of resource use, output, and wastes, and 3

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for reporting impacts to human health and environmental indicators. This study performs a type of agricultural LCA, where considerations specific to an agricultural product come into play. The LCA is discussed in more detail in Section 5.0 1.5 LCAs of Food and Agriculture We have seen the food system modeled as an LCA to study material flows, and midpoint and endpoint impacts ( Heller and Keoleian 2000 ). There is a large body of literature that divides the food system into many different components. The Commonwealth of Australias ( 1994 ) and Heller and Keoleians ( 2000 ) reports are life cycle approaches that focus on inputs and measurable outputs, some of which are sustainability indicators, such as health. Molly Andersons work on food systems integrity groups the food system into system functions and spells out indicators and measurements in each ( Anderson 2009 ). Heller and Keoleian ( 2003 ) attempted to quantify the sustainability of the U.S. food system with over 100 environmental, economic, and social indicators corresponding to life cycle assessment and stakeholder groups. A majority of research has been done in Scandinavia and the Low Countries.Miller, in her masters thesis, revealed that little data is available for the U.S. ( Miller 2010 ). Hayashi and Haas both have spearheaded methodology papers for agricultural production systems. Hayashi compiled a list of applications of LCA for agricultural production systems. Table 1 1 shows the eleven studies, none of which has settings in the U.S. or comparable crops to the food basket of this research proposal. 4

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Table 1 1: Applications of LCA for evaluating agricultural production systems Author(s) Issues Alternatives Functional units Cradle to gate Impact Hanegraaf, Biewinga and van der Bijl (1998) Energy crop production in the Netherlands Route+Crop(GAP) 1 GJ and 1 ha Cradle to gate Midpoint Cederberg and Mattsson (2000) Milk production in Germany milk) Conventional and organic farming 1000 kg ECM (energy corrected) Cradle to gate Midpoint Haas, Wetterich and Kopke (2001) Grassland farming in Germany Intensive, extensive, and organic farming 1 ha and 1 t milk Gate to gate Midpoint Frick et al. (2001) Arable crop rotations with clover grass Integrated intensive, integrated extensive, and organic farming 1 ha and 1 kg dry matted Cradle to gate Midpoint Brentrup et al (2001) Sugar beet production in Germany Sugar beet production with calcium ammonium nitrate (solid fertilizer), urea (solid fertilizer), and urea ammonium nitrate solution (liquid fertilizer) 1 t of extractable sugar Cradle to gate Midpoint Eide (2002) Industrial milk production in Norway Small, middle sized, and large dairy 1000 L of drinking milk brought to the consumers Cradle to gave Midpoint Gaillard and Nemecek (2002) Cereal and rape seed production in Switzerland Conventional, integrated intensive, integrated extensive, and organic production 1 ha and 1 kg Cradle to gate Midpoint Bennet et al. (2004) GM sugar beet production in the UK and Germany Conventional and GM herbicide tolerant sugar beet 50000 kg fresh weight of sugar beet Cradle to gate Midpoint Brentrup et al (2004) Winter wheat production in the UK (Nitrogen fertilizer rate) 1 t of grain Cradle to gate Midpoint Basset Mens and van der Werf (2005) Pig production in France (red label), and organic agriculture Conventional GAP, a French quality label 1 ha and 1 kg of pig Cradle to gate Midpoint Anton, Montero and Munoz (2005) Greenhouse tomato production in Spain Soil cultivation, open, and closed hydroponic systems (+3 waste management scenarios) 1 kg of tomatoes Cradle to gate Midpoint Source:From Table 1 ( Hayashi 2006 ) Haas, et al. ( 2000 ) present nuances of LCA methodology applied to agricultural production in terms of scoping, impact categories, functional units, and system boundaries.The setting is Scandinavia and Europe. They also present what they feel are relevant impact categories, shown in Table 1 2. 5

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Table 1 2: Impact categories and indicators Impact category Environmental indicator Resource consumption energy minerals Use of primary energy Use of P & K fertilizer Global warming potential CO2, CH4, N2O emission Soil function/strain grassland of other ecosystems (N eutrophication, acidification) Accumulation of heavy metals NH3, NOx, S02, emission Water quality ground water (nitrate leaching) surface water (P eutrophication) N fertilizing, N farmgate balance, potential of nitrate leaching, P fertilizing, P balance, % of drained area Human and ecotoxicity Application of herbicides and antibiotics, potential of nitrate leaching, NH3, emission Biodiversity Grassland (number of species, date of first cut), hedges & field margins (density, diversity, state, care) Landscape image (aesthetics) Grassland, hedges & field margins (see above), grazing animals (period, breed, alpine cattle keeping), layout of farmstead (regional type, buildings, garden) Animal husbandry (appropriate animal welfare) Housing system & conditions, herd management (e.g. lightness, spacing, grazing season, care) Source: Table 1 ( Haas 2000 ) Mogensens and Carlssons papers represent a range of life cycle approaches from whole food systems to individual raw foods ( Mogensen 2008 ; Carlsson 1997 ). Mogensen has conducted an LCA for a number of individual food items, including potato, onion, carrot, and tomato. Their findings are excerpted in Table 1 3. 6

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Table 1 3: The potential environmental impacts of vegetables, sugar, and oil Impact per kg Sugar Oil Potatoes Carrots Onions Tomatoes GWP 1OO (kg C02 eq) 0.96 3.6 0.22 0.12 0.38 3.5 Acidification potential (g S02 eq) 6.0 31 1.5 1.0 1.5 7.2 Nutrient enrichment (g N03 eq) 12.1 439 14.4 3.6 15.0 24.7 Photochemical smog (g ethane eq) 0.83 2.1 0.14 0.15 0.15 0.84 Land use (m2 year) 0.45 4.5 0.3 0.2 0.3 0.02 Functional unit is 1 kg food ex retail. Source:Table 5.10 ( Mogensen 2008 ). As shown, the impacts categories included global warming potential, acidification potential, nutrient enrichment, photochemical smog, and land use.Data for the study was from the Danish setting. The study quantified global warming potential for a number of raw and processed foods, including potatoes, carrots, onions, and tomatoes.The conclusions of the study were simply that the agricultural production phase of the life cycle of food is often the phase with the most environmental impacts compared to the other phases of transport, processing, and consumer use. Carlssons work does include life cycle inventory information for production of carrot and tomato, and regional transportation after the farm gate, but again, the setting is Europe. Gssling et al. ( 2011 ) presented an article on reducing the carbon foodprint of food consumed in the tourism industry. A part of the study compiled information from a number of sources in Europe on the CO2e footprint for the production of some vegetables.The values shown in Table 1 4 could provide a check against values found for U.S. based production. 7

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Table 1 4: Greenhouse gas intensity for various vegetables, production on farm Vegetable kg CO2 e/k g kg CO2 e/1000 kcal Country Source 0.158 0.247 UK DEFRA, 2007 0.160 0.250 Denmark LCA Food, 2003 0.261 0.274 0.442 0.464 Netherlands Kok et al., 2001a,b 0.073 0.083 0.114 0.130 Sweden Cederberg et al., 2005a Potatoes 0.100 0.156 Sweden Mattsson et al., 2001 0.046 0.144 UK DEFRA, 2007 0.122 0.234 0.381 0.730 Denmark Miljstyrelsen, 2006a,c Carrots 0.036 0.112 Sweden Cederberg et al., 2005 0.060 0.201 Sweden Cederberg et al., 2005 0.079 0.265 UK DEFRA, 2007 Onions 0.382 1.28 Denmark Miljstyrelsen, 2006 Lettuce 0.602 5.46 UK DEFRA, 2007 0.082 (unheated) 0.456 (unheated) Spain Antn, Montero, & Muoz, 2005d 1.30 7.20 Sweden Mller Nielsen, 2007 5.90 28.50 33.00 158.00 UK Williams, Audsley, & Sandars, 2006e Tomatoes (greenhouse) 3.45 4.92 19.10 27.30 Denmark Miljstyrelsen, 2006a Cucumber (greenhouse) 4.37 45.00 Denmark Miljstyrelsen, 2006 System boundary: farm production including all greenhouse gases based on lifecycle analysis. a Lower value: conventional, higher value: organic production. b Own calculation to include other GHGs. c Organic production. d Greenhouse, unheated. e Higher values relate to cocktail tomatoes. Source: Table 1 ( Gssling 2011 ). 8

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2 F This study uses existing research and published data but also makes direct measurements and models agricultural emissions in a very site specific manner. The focus is on the production side of the agri food system inputs required to produce a given vegetable. The processing, packaging, and consumption phases are not included except for bulk transport packaging. A comparative LCA performed on these two growing formats quantifies emissions and growing efficiency in terms of inputs, such as water, fuel, and land area. The setting is the Denver metropolitan area, recognizing that the typical vegetable purchased at the supermarket is, at any given time, likely an imported item grown under a large scale format. 2.1 The Vegeta b le Basket What are the vegeta bles that can be gr own in the Denver metropolitan area and how does that compare with what we consume? This section describes the choice of vegetables on which to perform the LCA. Table 2 1 shows that nearly all vegetables that are shown in USDA national consumption tables can be grown in Colorado.A few common varieties that are not listed on USDA consumption tables can also be grown in Colorado. 9

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Table 2 1: Vegetables that can be grown in Colorado Vegetable variety Vegetable variety Artichokes Kohlrabi Arugula Leeks Beets Lettuce, head Broccoli Lettuce, leaf Brussels Sprouts Mustard Greens Cabbage Onions Carrots Peas, snap Cauliflower Peppers sweet bell Chard Potatoes Collard Greens Radishes Corn, sweet Spinach Cucumbers Squash Eggplants Tomatoes Green Beans Turnips Kale Notes: Table refers to non greenhouse grown Not in USDA national consumption data tables Sources: http://www.ers.usda.gov/data/foodconsumption/ http://eatwhereulive.com/ http://www.grantfarms.com/home.php http://farmyardcsa.com/ The consumption of specific fresh vegetables compared to total fresh vegetable consumption was examined. This revealed that a small set, or basket, of vegetables comprise a majority of the total as shown on Figure 2 1. 10

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Figure 2 1: Average U.S. per capita fresh vegetable consumption. Source: U.S. Department of Agriculture (USDA) Economic Research Service (ERS). 2009 Because consumption quantities by weight and by calorie content are relatively similar, a basket of four vegetables was chosen based on their contribution, by calorie, of the average American diet of fresh vegetables. These are potato 53%; onion 12%; carrot 7%; and tomato 6%. These comprise 78% of the fresh vegetable consumption of the average American. These are found readily in any supermarket, and are all grown in the Denver metropolitan area by operators of NSAs, CSAs, and individuals ( USDA 2009 ). In the lettuce category, there is high variability due to many popular types of lettuce (head and leaf), varieties (e.g. romaine, herbs, iceberg, etc.), and market preference. None of the small scale operations were growing iceberg, yet iceberg is a prevalent crop for the large scale growers. For these reasons, the lettuce category was dropped from the study. 2.2 Different Growing Formats The choice of two different growing formats stems from the desire to draw comparisons between the ways we, as consumers, get our fresh vegetables. In general, there are a number of ways. We can get our produce from grocery stores and supermarkets of all sizes, we can Top 5 Vegetables by Weight Potatoes Onions Tomatoes Lettuce, head Lettuce, leaf The rest Top 5 Vegetables by CaloriesPotatoes Onions Carrots Tomatoes Lettuce, head The rest 11

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grow them ourselves (typically only part of the year), we can mail or internet order, we can drive to a farm, visit a farmers market, or pick up a share at a local CSA drop off location. Among all these ways to acquire our produce, there are two categories all the produce fall under those of the scale of growing. Typically, these categories can be called largescale and smallscale. There is a third category which straddles the gap between large and small, but the majority of our fresh vegetables come from a large scale growing format. Many fewer still come from a smallscale format, but more people engage in it. What is mea nt by format and scale? Table 2 2 compiles a list of characteristics, largely by way of comparison, that, taken together, identify what is a large scale growing format and a smallscale growing format. 2. CharacteristicsofUrbanFarming This section expands on the characteristics found in Table 2 2 with a focus on the grower as a user/owner/operator of urban infrastructure. The growers can be categorized as CSA operators and homeowners. While no attempt was made in the design of this study to fully characterize behavior and motives of urban growers, this section provides this authors general observations through his interactions in the urban gardening communities, associations, networks, and events over more than 5 years. Motives The urban CSA growers appear to have no particular demographic except generally being between the ages of 20 and 45. All urban growers (CSA and homeowners) appear to have diverse backgrounds and vocations, from the sciences to liberal arts. But a common theme 12

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among them is that our commercial food system is broken, if not harmful to the planet, and they can make some kind of living (CSA operators) or hobby (homeowners) from urban vegetable production while providing fresher, more nutritious, potentially less toxic, and more delicious vegetables. There is a fairly universal belief that urban vegetable production can help save the planet. But, for the CSA operators, profit is a concomitant motive. Challenges If profit is a motive, then the challenge is to have enough of it. It would be easy to place the same financial demands of the typical urban resident on the CSA operator. These might include such major expenses such as housing, automobile, utilities, and food. But one must recognize that these expenses are not incurred by all individuals. In this sense, everyone has a slightly different economic situation. If other sources of income are high enough, or certain expenses are low enough, the tolerance for little to no profit is much greater. There are CSAs that fall in this category. Some individuals may have the house paid off, or no automobile. Others may secure grant funding, establishing the CSA as a non profit entity. The author knows no CSA operator that is making a living with the business in a conventional sense, yet CSAs still do prevail and have a track record. A caveat is that Colorado was home to Grant Family Farms (located in Weld County, well outside the urban boundary), one of the largest CSAs in the U.S., growing on about 300 acres. At one point, Grant Family offered at least 4,500 shares, many to Denver residents. It was clearly a forprofit business and had many employees; however, it ultimately went bankrupt owing almost $10 million in debt and liabilities. An additional challenge is that vegetable production is hard work, can involve many hours of manual labor at inconvenient times, and is subject to the vagaries of hail storms, extreme heat, 13

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pests, and, in some cases, uncertainty in land tenure.It is thought that these demands, placed on relatively few owners and employees, explain why four of six initial participants in this study dropped out. In order to replicate or add to this study, data gathering and reporting must be a strong commitment on the part of the grower. Practices Urban growe rs seem to belong to a de facto fellowship defined by common knowledge shared through such fora as the Denver Post gardening section, websites, local networking events, extension service guidance, neighborly encounters, and handme down knowledge. While there are many ways to grow, there is a consistent ethic and this drives consistent growing practices. Most have no interest in organic certification but are, in essence, an organic operation. Notions about the desirability of compost, the aversion to pesticides and herbicides, and maximizing yields seem to be fairly universal. One practice where the author noticed significant variation was in tilling. Some appear to embrace no till practices while others seem to be firm believers in tilling extensively every year. Tilling, or course, provides a short term gain in workability, but destroys humus and soil quality in the long term. Irrigation is an other practice with potentially significant variation. The homeowner is generally aware of the extra water demand and water cost when growing vegetables. The homeowner is in total control of the irrigation setup and application rate, and he directly experiences (for better or for worse) the consequences. In contrast, CSA operators often require the homeowner to provide water and the land for free, in exchange for a CSA share. This could potentially lead to overwatering since the CSA operator is generally in control of the irrigation, but does not necessarily experience the cost. Also, some plots may be over irrigated because 14

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the whole plot is connected to a single timer that is set to meet the demand of the most water thirsty plants. Individual homeowners may be more vigilant and not use automation. Table 2 2: Comparison of qualitative, presumed characteristics of vegetable production grown under largescale and small scale formats Category Large Scale Format1 Small Scale Format1 Business status For profit comp any Hobby; for profit company; non profit company Investment capital High capital and operations and maintenance cos ts for infrastructure Low capital and operations and maintenance cos ts for infrastructure Market Produce whol esalers; vertically integrated retailers (supermarket conglomerates) Direct marketing; farmers markets; bartering; donations; sharing Land area Greater than 1 acre (typically greater than 10acres) Less than 1 acre (typica lly less than 1/4 acre) Setting Rural; peri urba n; distant from consumer Peri urban; urban; local to consumer Fuel Higher fuel use from mechanized equipment Lower fuel use fr om little mechanization Water Furrow (flood ); spray; drip Spray; drip Diversity Monoculture Intercropping, mixed plant varieties Impacts to soil High or comple te tillage Higher propensity for soil erosion Low, minimal, or no tillag e Lower propensity for soil erosion Pesticides and herbicides Heavy use and r eliance of chemicals Low use and re liance of chemicals Productivity Lower harvest pe r unitarea Higher harvest pe r unitarea Fertilizer High inputs of sy nthetic N, P, K Little to no compost use Low inputs of N, P, K Higher compostuse Labor Low labor use pe r unitvegetable High labor use pe r unitvegetable Soil Organic Carbon Lower Higher Certified Organic status Yes and no (thos e that are not organic typically are not managed organically) Yes and no (thos e that are not organic typically are managed organically, but without certification) Transport from farm gate Extensive packa ging; long distance shipping Minimal packaging Notes: 1. Scale is primarily basedon the expedient measure of land area under cultivation. However, the size of the enterprise in terms of assets and sales be it a hobbyistin a residential garden or a corporation also has relevance. The terms large scale and small scale are, therefore, used rather loosely and rely on a compilation of characteristics found in this table. The classification of farms by the value of their product brings together enterprises which really have the same scale of production,regardless of acreage. Accordingly, a highly intensive enterprise on a small tract of land falls into the same group as a relatively extensive enterprise on a large tract; both are actually large scale in terms of production and the employment of hired labour ( Lenin 1917 ). Sources: Partially from ( Grace 2011 ) 15

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3 AC This study offers a comprehensive, life cycle comparison of the two systems behind consumed vegetables for a metropolitan area. While much literature compares large scale growing operations with regards to growing practices, crops, and inputs, none make a direct comparison between large scale and small scale, as defined in this study. With regards to the vegetable for sale through a CSA or NSA or local farmers market (typically grown under the small scale format), no studies exist that couple the inputs to the production side of that vegetable with the yield of its harvest in the Denver metropolitan area. Further contributions are described below.3.1 Methodology The methodology creat ed to characterize urban and commercial vegetable production in a comparative LCA is replicable for any urban area. This is because the study carefully selects data sources that are commensurate with the components of interest. For example, another urban area may have a different mix of supply chains for vegetables sold in grocery stores. Other urban areas may have different climate, soils, and perhaps broadly different growing practices. The methodology in this study captures these important differences. 3.2 Prima ry Case Study Data Primary data, gather ed in a case study of smallscale vegetable growers, in a way that is comparable to published data for large scale vegetable growers is a significant contribution to the literature. 16

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3.3 Interdisciplinary Impacts Assessment Most LCAs, including that found in this study are conducted with interest in environmental impacts. These include resource use (for water, energy, fuel), greenhouse gas emissions, ecotoxicity, and human health impacts. But this study adds additional impact metrics that are less common in the literature such as soil organic carbon (SOC) (an indicator of soil health), land use change, and employment. Soil organic carbon is highly related to current and antecedent land use and reaches a steady state at a 30year horizon or less ( Kim 2009 ; Morgan, et al. 20 10 ). Second, the labor indicator is used because there is a stark and measurable difference between the labor inputs of large scale and small scale growing. This can be seen as an opportunity for local employment, but a risk for corporate efficiency. 3.4 Appr opriate Scales for Hybrid LCA Finally, this study carefully chooses more appropriate, bottom up data sources that match the level of variability and scale of the components of the vegetable production system where many other studies and greenhouse gas accounting reports use national or regional data. For example, local case study data and emissions modeling using local parameters are used instead of one source touted as a national or regional average. In doing so, the accuracy is increased, uncertainty is reduced and meaningful results can inform the transboundary conversation. 3.5 Land Use Change First, the soil organic carbon indicator is used because the land use change that occurs when large scale agricultural farmland is converted back to natural grassland as urban gardening, utilizing the small scale format, increases. Under this assumption, the smallscale format displaces the large scale format in equal areas, weighted by crop productivity per land area. For 17

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18 the small scale format, this indicator assumes that a third of the land is converted from previous use as turf, a third as bare soil, and a third as natural grassland (assumed to represent vacant, weedy areas). These are the basis for a comparison of land use change. 3.6 Relevant Da ta for Decision makers The popularity of urban vegetable production on a small scale is, in part, explained by the way people feel about the food system, and is a response of sorts a response to the existing dominance of large scale production formats. Much of what we know about th e supposed benefits of smallscale production, then, is relative to large scale production. Typically, the belief is that smallscale is better than large scale. However, a quantitative comparison of these two formats using a life cycle approach that could corroborate these beliefs has not been done on a scale that is relevant. Further, to have relevance with a number of municipal climate and sustainability action plans, a life cycle assessment would have to be fairly specific to that locale. This study provides relevant, local data to inform Denver metropolitan area leaders and decision makers.

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19 4 Objectives This study conducts an LCA to compare the environmental and social impact of key vegetables grown in urban settings with that of commercial farming. The LCA will answer questions such as Which vegetable is more environmentally friendly? or Which growing format is more efficient with resources? These are fundamentally unquantified, unanswered questions that could contribute to the policy dialogue with regards to incentives or disincentives to urban gardening. The research plan has four major objectives. The first objective is to compile material and non material flows for the production of four vegetables under two different production formats, including bul k transport to the point of purchase. This information does not exist for the small scale or urban gardening format and will be useful and revealing. For example, few have measured their water use in a vegetable garden and related it to their yield. The data collected during this research will pr ovide a sense of growing efficiency with respect to inputs. The second objective is to conduct an LCA of each format using collected and published data. This will help us develop midpoint metrics that will quantify positive and negative impacts of urban gardening to the environment, ecosystem, human health, and society. Th e third and fourth objectives are to compare the environmental and social impacts of the two growing formats and examine direct and indirect land use change, respectively.

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5 M The LCA was carried out using a modified version of the ISO Standards 14040 through 14043. The method is carried out in four general phases: Goal and scope definition, Inventory analysis, Impacts assessment, and Interpretation, as described below, on Figure 5 1, and in the following sections. Figure 5 1: Phases of the life cycle assessment From ( EPA 2006 ). 1. Goal Definition and Scoping Define and describe the product, process or activity. Establish the context in which the assessment is to be made and identify the boundaries and environmental effects to be reviewed for the assessment. 2. Inventory Analysis Identify and quantify energy, water and materials usage and environmental releases (e.g., air emissions, solid waste disposal, waste water discharges). 3. Impact Asses sment Assess the potential human and ecological effects of energy, water, and material usage and the environmental releases 20

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identified in the inventory analysis. 4. Interpretation Evaluate the results of the inventory analysis and impact assessment to select the preferred product, process or service with a clear understanding of the uncertainty and the assumptions used to generate the results. From ( EPA 2006 ). 5.1 Goal Definition and Scoping As described previously in terms of motivation and objectives, a comparative life cycle assessment provides quantitative information regarding the benefits and impacts of small scale growing (characteristic of urban areas) compared to large scale growing. Such information is crucial to enable leadership in areas of environmental stewardship, pollution, energy intensity, and land use on an urban scale. In addition to these goals, the LCA process: compiles material and non material flows for the production of a basket of four vegetables under two different formats; quantifies positive and negative impacts to the environment and society using midpoint metrics; and draws comparisons between the two formats using quantitative data. 5.1.1 Functional Unit The functional unit for LCA is 1 pound of each type of vegetable, at the point of acquisition consumer. Although the conversion factor for caloric value per pound of produce varies substantially, from 82 to 349 kilocalories per pound [kcal/lb] ( USDA 2009 ), the value of the LCA is a comparison of a given unit of vegetable grown under the large scale format to the same unit of vegetable grown under the small scale format. A number of other units are obtained by simple conversion factors such as per metropolitan area, per household diet, per capita diet, or calorie, for example. 21

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5.1.2 System Boundaries and Components The scope of the research is based primarily on geographic boundaries of population (including many jurisdictional boundaries), but also those of process, time, and enterprise. Figure 5 2 depicts general product life cycle phases and the elements of which the research aims to address. Phases often included in a product life cycle assessment that are not shown and not a part of the scope include post consumption (waste), roundput, and cradleto cradle pathways. Figure 5 2: Life Cycle Inventory Scope The scope of the research could be called cradle to farm gate + transportation. This is depicted on Figure 5 3, a simplified process flow diagram. 22

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Figure 5 3: Simplified process flow diagram This is an emerging and developing aspect of life cycle impact assessment. No formal methodologies exist. In the place of a methodology or model, a guidance document will be used: Guidelines for Social Life Cycle Assessment of Products Social and socio economic LCA guidelines complementing environmental LCA and Life Cycle Costing, contributing to the full assessment of goods and services within the context of sustainable development ( UNEP 2009b ). Because the fate of vegetable after the point of sale is assumed to be identical, the consumption and recycling phase were not included in the study. There is some literature that indicates that a consumer of vegetables grown under the small scale format, with such sources as their own backyard, a CSA, a farmers market, or even an organic section in a supermarket, is somewhat less prone to food waste and tends to recycle more of what waste there is than the consumer of conventional supermarket vegetables. But these are considered minor in the overall process flow diagram. 23

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Because little published data exists for the bulk packaging and processing from farm to truck, a focused literature review was conducted to establish any justification for omitting this phase. The post harvest handling, processing, and packaging of fresh potato, carrot, onion, and tomato vary. Potato, carrot, and onion can all be stored for months after harvest, while tomatoes less so. In addition, tomatoes are much more fragile and require constant cool storage. Despite these differences, available literature was used to estimate that this phase could be omitted without much consequence. The basis for this is using a number of studies of impacts associated with cool storage of the tomatoes after harvest and to the retail store. The most comprehensive study found that this phase contributes a very small fraction of the total environmental impact ( EPA 2010b ). Bulk packaging (paperboard, cardboard, plastics), however, were estimated and included in the LCA. 5.1.3 Small Scale Vegetable Production Format A process and boundary flow diagram was created for the small scale production format as shown on Figure 5 4. 24

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Figure 5 4: LCA System Boundary Small scale Format As discussed previously, a basket of four vegetables was chosen based on their contribution, by calorie, of the average American diet of fresh vegetables. These are potato 53%; onion 12%; carrot 7%; and tomato 6%. These comprise 78% of the fresh vegetable consumption of the average American. These are found readily in any supermarket, and are all grown in the Denver metropolitan area by operators of NSAs, CSAs, and individuals ( USDA 2009 ). For the vegetable grown under the small scale format, local sources and primary data were used. Initially, five growers were contacted and were willing to collect data that characterize inputs and yield. These were Heirloom Gardens, LLC; Stephen Cochenour; Farmyard CSA; Dr. John Brett; and Urbiculture Farms. Growers were given highly accurate water meters and a series of forms to track inputs, wastes, and yields during the growing season. Of these, only 25

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Farmyard CSA and Dr. John Brett followed through with the whole season. One yard is shown on Figure 5 5, below. Only data from these two growers were included in the study, and resulted in a creating a significant limitation of the study. The inventory for the small scale format included the items required for five production phases. These are: Preparation; Growing; Harvest; Cleanup; and Farm Gate to Point of Sale Figure 5 5: A Farmyard CSA garden The single water tap, timer, and water meter can be seen on the figure, the typical arrangement for all yards in the study. 26

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Preparation This phase includes working the plot to remove weeds, spread mulch or amendments, remove debris from the previous season, and install irrigation equipment. This may involve motorized equipment. This phase also involves starts, or seedlings started in small trays. The trays are kept indoors until there is enough heat and light for survival outdoors, in the ground or larger pot. Some vegetables are started outdoors, directly from seed. The needs of the grower for how much and what type of vegetable to be marketed by a target time frame dictates the use of starts. Growing This phase spans the time a seed or start is planted in the plot until the time of harvest. Irrigation and weed control are two of the most important activities during this phase. Other activities include thinning, amending soil, spreading mulch, and pest control. Harvest The harvest phase includes picking and packing for transport to retail sale. A certain amount of waste occurs during this phase as vegetables not suitable for retail sale or consumption are discarded. Typically, these are either returned to the soil upon picking, or are held onsite in a compost heap. Boxes or bins are used to gather harvested produce and transport to a point of sale or distribution, after the farm gate, or plot. Cleanup The cleanup phase is ongoing from the start of the harvest of one vegetable to the final harvest of the last vegetable. During this time, cover crops may be planted in preparation for the next 27

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year. Otherwise, the small scale growers typically keep all harvest residues and stubble in situ for the next season. Farm Gate to Point of Sale The produce is then typically consolidated at the owners property for distributing assortments to bins or boxes for pickup or sale at a farmers market. Transport and packaging is minimal. After what is called the farm gate, the produce travels to its next destination. For the small scale grower, this is typically achieved in non commercial vehicles travelling short distances. This data will be acquired directly for the smallscale growers. There is typically no loss of vegetables. Those that are not sold are either given away or used to make compost. These phases are listed in Table 5 1. The table also formed the basis for data collection through the growing season. Appendix A presents a summary of collected data, compiled from the entire season. Table 5 1: Production Phases Small scale Format Description Unit Other Unit(s) Preparation Phase Land area area used for in ground or potted growing (excluding starter trays) square feet acre Labor labor for all people and activities related to growing all planting phases, vehicle trips, bookkeeping, etc. hours full time equivalent (FTE) Vehicle trips all trips related to growing to/from gardens, stores, distribution points, etc. miles number of trips; average miles per trip Herbicide/Pesticide weed killer or pest control gallons application rate Seed seed used for in ground and starters lbs oz, packets, bags Amendments compost, manure, peat, humus, etc. lbs bags; lbs per bag Starter trays plastic, foam, cardboard trays each 28

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Description Unit Other Unit(s) Starter media rock wool, potting soil, etc. each lbs Grow lights any lighting used in starters kWh number of lights; wattage of lights Water for starts water used to grow starts gallons time at a given flow rate Mechanized Equipment fossil fuel tillers, sod cutters, weed eaters, etc. gallons time of operation; consumption rate Mechanized Equipment electricity mowers, tillers, etc. kWh time of operation; consumption rate Mulches plastic, wood, stone used in bed preparation square feet square yards; feet of set width; cubic feet; cubic yards Drip Tape or hose tape, tube, emitters, etc. feet lbs; each Other Irrigation Equip hoses, sprinklers, etc. each feet Tarpaulin poly, canvas, etc. used to cover tools or areas prior to planting square feet acre Wastes any waste to unclassified trash or dumpster such as hose, tape, bags, wood scraps, etc. lbs cubic feet Growing Phase Labor labor for all people and activities related to growing all planting phases, vehicle trips, bookkeeping, etc. hours full time equivalent (FTE) Vehicle trips all trips related to growing to/from gardens, stores, distribution points, etc. miles number of trips; average miles per trip Herbicide/Pesticide weed killer or pest control gallons application rate Mechanized Equipment fossil fuel tillers, sod cutters, weed eaters, heat, etc. gallons time of operation; consumption rate Mechanized Equipment electricity mowers, tillers, heat, etc. kWh time of operation; consumption rate Mulches plastic, wood, stone used in bed preparation square feet square yards; feet of set width; cubic feet; cubic yards 29

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Description Unit Other Unit(s) Amendments compost, manure, peat, humus, etc. lbs cubic feet; cubic yards Fertilizer N, P, K, etc. lbs cubic feet; cubic yards Water water used for growing gallons time at a given flow rate Wastes any waste to unclassified trash or dumpster such as hose, tape, bags, wood scraps, etc. lbs cubic feet Planted area by type square feet devoted to a given crop square feet number of plants; area per plant Harvest Phase Labor labor for all people and activities related to growing all planting phases, vehicle trips, bookkeeping, etc. hours full time equivalent (FTE) Vehicle trips all trips related to growing to/from gardens, stores, distribution points, etc. miles number of trips; average miles per trip Mechanized Equipment fossil fuel loaders, lifts, etc. gallons time of operation; consumption rate Mechanized Equipment electricity lifts, mowers, etc. kWh time of operation; consumption rate Production packaging boxes, plastic, etc. (exclude bins that are reused) square feet each; square yards; lbs Water water use in preparing for transport or production packaging gallons time at a given flow rate Harvest by type (weight, bushel, etc.) gross amount of produce picked lbs bushel; box; each; bag Wastes any waste to unclassified trash or dumpster such as hose, tape, bags, wood scraps, etc.; ALSO track produce unsuitable for sale or consumption. lbs cubic feet Cleanup Phase Labor labor for all people and activities related to growing all planting phases, vehicle trips, bookkeeping, etc. hours full time equivalent (FTE) Vehicle trips all trips related to growing to/from gardens, miles number of trips; 30

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Description Unit Other Unit(s) stores, distribution points, etc. average miles per trip Composting any consumables used to create, process, or store compost. lbs each; feet; square feet; cubic feet Mechanized Equipment fossil fuel tillers, mowers, blowers, etc. gallons time of operation; consumption rate Mechanized Equipment electricity tillers, mowers, blowers, etc. kWh time of operation; consumption rate Water water use in leaving site for next season gallons time at a given flow rate Wastes any waste to unclassified trash or dumpster such as hose, tape, bags, wood scraps, etc. lbs cubic feet Labor labor for all people and activities related to growing all planting phases, vehicle trips, bookkeeping, etc. hours full time equivalent (FTE) Transport trips for hauling to any distribution point or point of sale. miles number of trips; average miles per trip Mechanized Equipment fossil fuel refrigeration, lifts gallons time of operation; consumption rate Mechanized Equipment electricity refrigeration, storage facility utilities kWh time of operation; consumption rate Packaging packaging required for final sale (only that in addition to Production Packaging) square feet each; square yards; square feet; lbs Retail Sale any consumables used in sale lbs each; square yards; square feet; lbs Wastes any waste to unclassified trash or dumpster such as hose, tape, bags, wood scraps, etc. lbs cubic feet 31

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5.1.4 Large Scale Vegetable Production Format A process and boundary flow diagram was created for the large scale production format as shown on Figure 5 6. Figure 5 6: LCA System Boundary Large scale Format Published enterprise budgets were adapted to match, as the data allowed, the phases identified for the small scale growing format. The inventory for the large scale format includes the items required for five production phases. These are: Preparation; Growing; Harvest; 32

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Cleanup; and Farm Gate to Point of Sale Preparation This phase includes working the field to remove weeds, spread mulch or fertilizer, pesticides or herbicides, remove debris from the previous season, and install irrigation equipment. This typically involves heavy, diesel powered farm machinery. Seed is sown through the use of a mechanized seed drill. Growing This phase spans the time a seed is planted in the plot until the time of harvest. Irrigation and weed control are two of the most important activities during this phase. Other activities may include thinning, pesticide application, or herbicide application. Harvest The harvest phase includes manual or mechanized picking and packing for transport to retail sale. A certain amount of waste occurs during this phase as vegetables not suitable for retail sale or consumption are discarded. Typically, these are either returned to the soil upon picking, or are held onsite in a compost heap. Boxes or bins are used to gather harvested produce and transport to a point of sale or distribution, after the farm gate, or plot. Cleanup The cleanup phase may include tilling, burning, or mowing after the final harvest.During this time, cover crops may be planted in preparation for the next year. Otherwise, growers typically keep all harvest residues and stubble in situ for the next season. 33

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Farm Gate to Point of Sale The produce is then typically consolidated at the field for shipment to a distribution center or directly to a customer. After what is called the farm gate, the produce travels to its next destination. Produce harvested from large scale operations typically experience some minimal packaging to transport it in bulk. From the farm gate, the produce can travel to a wholesale or retail warehouse or directly to the retail point of sale. Determining how much produce arrives from where is not a trivial procedure and is important for the study. It is required to compare what are called food miles between locally grown produce (under the smallscale format) and the [often] imported produce that stocks supermarkets. 5.1.5 Seasonal Sources In the past, terminal market hubs in major metropolitan areas such as Chicago, Los Angeles, Dallas, and Atlanta, served as intermediate wholesale clearinghouses before produce was then transported to other wholesale markets and retailers ( Pirog 2001 ). One wholesale market in Denver was the Denargo Market, in what is now called the RiNo district. Over time, the terminal markets ceased operations and the supply chain evolved to where each retailer, or group of retailers in a single metropolitan area, source dedicated truck shipments directly from the farm. The USDAs Agricultural Marketing Service (AMS) tracked the movement of produce between a number of terminal markets within the U.S. Pirog carried out detailed food miles calculations for produce whose final consumption location was Des Moines, Iowa ( Pirog 2001 ). Many of his factors for fuel and emissions are still valid and available today, updated. But the tracking of the movement of produce within the country to that level of detail is no longer supported by the 34

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AMS. So, replicating his work for a different point of consumption for a particular vegetable is not possible using his sources. At an even more aggregated level, Weber et al. conducted a food miles study in 2007 ( Weber and Matthews 2008 ). In this study, a different data set, currently maintained by the Leopold Center ( Leopold Center 2011 ) and the USDAs AMS, was used to calculate food miles and environmental impacts of classes of food, from overall movements between states and from international sources. The data at the Leopold Center could also be used per vegetable variety to produce an aggregate estimate of food miles based on all sources (by state and country) of all shipments to and within the U.S. For greater accuracy, an investigation of the food miles of a given vegetable that retails in the Denver metropolitan area is preferable to a national average. Such an investigation includes determining if retailers that comprise the majority of the sale of vegetables grown under the large scale format are representative and similar in where and when they source potato, carrot, onion, and tomato. The Figure 5 7 shows a market share report that reveals that relatively few retail supermarket chains dominate a large sector in the Denver area. Just four retailers represent about 84% of the market. 35

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Denver Area Supermarket Share (No. Stores) Counties include Adams, Arapahoe, Broomfield, Clear Creek, Denver, Douglas, Elbert, Gilpin, Jefferson, and Park King Soopers (74) 38% Safeway (56) 19% Wal-Mart Supercenter (21) 19% SuperTarget (15) 8% Albertsons (14) 5% All Others (62) 11% Sources: Nielsen Company-Trade Dimensions 2009 Shelby Report 2009 Figure 5 7: Denver Area Supermarket Share As an indicator of practices for these top four retailers, a member of the wholesale produce community was contacted ( Comazzi 2012 ). From this interview, it was determined that wholesale produce sourcing and movement is similar between the food service companies supplying restaurants and institutions, and grocery stores. To meet fresh vegetable demand year round, retailers have an array of different sources across the seasons. A simple calculation of weight and distance (commonly called ton miles or ton kilometers) can be accomplished through any web based map directions calculator and the payload capacity of refrigerated tractortrailers. These data can then be converted to fuel use and other midpoint impacts from published data. Figure 5 8 presents the sources and distances for potato, carrot, onion, and tomato in Denver metropolitan area supermarkets. Appendix B contains the complete seasonal contribution by location, coupled with the typical harvest and storage practices. 36

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Figure 5 8: Supermarket sources and distances by vegetable 5.1.6 Scenario Development for Land Use Change It is assumed that land area representing the functional unit of a given vegetable can be put into service or out of service based on the incidence of vegetable production elsewhere. A number of researchers have characterized SOC and agricultural emissions for various long term land uses and as it relates to land use change. This study sets urban gardening as a type of land use that displaces existing land uses. Conversion to urban garden involves displacement from one of three land uses, namely: 1. Large scale commercial farmland each new instance of urban gardening is assumed to displace an equal amount of commercial farmland, adjusted for differences in land productivity related to the functional unit. 37

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2. Neglected and degraded urban areas in the existing urban setting, a new urban garden can purpose these areas unused areas that typically have the poorest soil health and lowest SOC levels. 3. Residential turf grass in the existing urban setting, the demand for space for an urban garden may force conversion from turf grass. These land uses and scenarios are shown on Figure 5 9. Although common land uses exist in the urban setting, they are assumed to be similar in the degree of dense plant matter, cultivation, and richly maintained soil that urban vegetable gardening has. Figure 5 9: Land Use Change and Conversion Scenarios The array and variety of vegetable and production formats combined with land use scenarios results in 19 unique vegetable land use combinations. 38

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These existing land uses are paired with the urban garden land use to form the following scenarios in Table 5 2. 39

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Table 5 2: Scenario Development for Land Use Change Scenario No. Scenario Code Location Starting Land Use (years) Crop(s) Ending Land Use (years) Crop(s) 1 1a1 Terreton, ID 2 1a2 Alamosa, CO Potato 3 1b1 Bakersfield, CA 4 1b2 Greeley, CO Carrot 5 1c1 Mission, TX 6 1c2 Deming, NM 7 1c3 Greeley, CO 8 1c4 Ontario, OR Onion 9 1d1 Culiacn, SIN 10 1d2 Bakersfield, CA 11 1d3 Punta Gorda, FL Large scale commercial farm (30) Tomato Native perennial grassland (30) Perennial grass 12 2a Potato 13 2b Carrot 14 2c Onion 15 2d Denver, CO Neglected urban land (30) n/a Urban garden (30) Tomato 16 3a Potato 17 3b Carrot 18 3c Onion 19 3d Denver, CO Turf lawn (30) Cool season perennial grass Urban garden (30) Tomato Notes: Scenario codes example: 1 c 3 conversion code crop location (crop specific) Crop codes: a. Potato b. Carrot c. Onion d. Tomato Conversions are shown on Figure 5 5. Locations for large scale commercial sources of vegetables sold in Denver are shown represented regionally and represent at least 75% of all sources at various times of the year. All locations for ending land uses are set to be the same as the starting land uses. As shown in the table, a given land use is modeled for 30 years. For example, Scenario No. 10 indicates that land used for tomato production in Bakersfield, California would return to native grassland. Scenarios 15 and 19, for example, convert two common existing land uses (neglected urban land and turf lawn, respectively) into urban tomato production. The 30year time frame was chosen because this amount of time is sufficient to achieve steady state values for the parameters of interest (soil organic carbon, CH4, CO2, N2O emissions), and is a time frame used for environmental analysis. 40

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Direct Land Use Change Under this assumption, the amount of land required to grow 1 pound of tomato in Denver, for example, would displace the amount of land required to grow 1 pound of tomato in Punta Gorda, Florida. Because yields (pounds per square foot) are different for each vegetable and production format, 1 square foot of land in Denver would not necessarily displace 1 square foot in Punta Gorda. Indirect Land Use Change Indirect land use change occurs when land use is changed in response to a land use elsewhere. In our example above, Scenario No. 10 could be considered as indirect land use change because an urban grown tomato would displace land used for tomato production in Bakersfield, California and return it to native grassland. In this example, native grassland is the indirect land use change. Other potential land uses, such as for growing biofuels or biomass, were not considered because the regions in which vegetable production occurs have come to be such regions because they are well suited for such production. This cannot be said for any particular replacement crop such as biofuels or biomass. In practice, the reduced demand for, say, tomatoes from Bakersfield, California, resulting from an increase in tomato production in Denver either would not be felt in the market supply chain, or would be transferred to another specialty crop (vegetable). Supply and demand market forces are beyond the scope of this study. Also, even if significant urban fresh vegetable production would displace commercial production, this could only be the case seasonally; for 6 8 months of the year, fresh vegetables would still have to be obtained from the same areas in production. 41

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Comparisons between commercial and urban production formats may be drawn by looking at the respective steady state values in Year 30. For example, a comparison between commercially and urban grown tomatoes can be drawn by looking at the 30year value for Scenarios 9, 10, and 11 and the 30year value for Scenarios 15 or 19. 5.1.7 Data Quality Objectives Data sources include direct measurement (primary data), peer reviewed literature, industry publications, government publications or databases, lifecycle databases, and engineering judgment. The time frame for data collection is approximately April through November for the smallscale growers, and year round for the large scale growers. Data is standardized to one growing season or rotation of a given vegetable crop. Table 5 3 describes the data quality objectives (DQO) for the study. 42

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Table 5 3: Data Types and Quality Objectives Category DQO Measured material (gallons, each, lbs, hours) The target precision will be % Site specific data Primary: direct measurement Secondary: Allocation according to ISO 14041: 6.5.3 Allocation Procedure Aggregated data Allocation according to ISO 14041: 6.5.3 Allocation Procedure Estimated data Interview based and subject to professional judgment. May be proved with subsequent data collection of the same item or with past collected data. Data format Participants field notebooks and notes. Researcher transcription of live and phone conversations. Data coverage A minimum of 80% of the material and energy inputs will be accounted for in the LCI. Data origin Denver metropolitan area and states and countries from which growing occurs. All large scale format data will be U.S. based averages. No such averages will be taken from Europe. Published data, when available, were used as data quality indicators.These included benchmark values from other similar components of the smallscale format or the large scale format. Differences are noted but no attempt was made to reconcile them due to many independent variables. A sensitivity screening (or focusing exercise per the European Commission [2010] ) was not conducted for this study. The results later show that some data and their contributions to impacts are indeed insignificant. However at the outset, this was not known; every effort was made to include all direct and indirect inputs, and Scope 1, 2, and 3 resources and impacts. Later, qualitative sensitivity was conducted on a few key variables, as those were discovered to either have a large influence on the results, or if their data quality was suspect. The framework established by the European Commission ( European Commission 2010 ) was employed to identify these key variables. This framework is shown on Figure 5 10. If used in conjunction with an uncertainty analysis to indicate data quality, the interpretation of LCA results can be 43

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greatly enhanced by being able to indicate the prevalence of high quality/high sensitivity LCI data. These data are most useful for drawing conclusions for decision makers. Figure 5 10: Uncertainty and influence indicate what are key data Once key data are identified, other data may be dropped from the sensitivity analysis because of their weak influence on impacts or reliance on data quality. This is an iterative process. From Figure 26. ( European Commission 2010 ). Uncertainty and variability were not quantified and is traditionally problematic in LCAs that include many inputs and processes. However, qualitative means were used to characterize uncertainty and variability. These include: careful planning and execution of the LCI process leading up to final results; identifying outliers by using information from circumstances and published benchmarks; formal and informal reviews of the LCI data development process; focusing data quality efforts on values that significantly influence LCI results; and ensuring that sample sizes for key data values are as large as possible, thus increasing the reliability of the estimates. From ( EPA 1995 ). 44

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Sources of uncertainty are typically categorized as (1) random error in measurement and sampling methods, (2) systematic error in measurement and sampling methods, (3) natural variability, and (4) approximation in modeling ( EPA 1995 ). For this study, natural (random) variability and approximation in modeling are thought to be the most likely and relevant sources of uncertainty. 5.2 Inventory Analysis The LCI follows the product phases within the system boundary. In general, the inputs shown in the background system are characterized by four direct resources and their functional units water (gallons), fuel (gallons), energy (kWh), and labor (hours). A number of inputs with little differentiation (such as any manner or shape of plastics, paperboard, pesticides, etc.) are converted to a generic mass quantity. Typically, the smallscale format requires primary data gathering, while published farm enterprise budgets are used as representative for any given large scale grower. Startup costs are an item worth addressing, since infrastructure requirements between the two growing formats vary greatly and a number of fixed assets (e.g., tractors, pumps, etc.) have longer term depreciations and salvage dates. For this reason, the LCA only focuses on consumables and activities for operations after startup. For the purposes of the study, a consumable is something that is used up or replaced between 0 and 5 years, following generally accepted return periods for fixed asset depreciation used in tax accounting. 5.2.1 Data Sources Data sources range within two broad categories of information top down, and bottom up. Top down information is efficient to use and is published, replicable, and comparable. It is 45

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appropriate for materials or resources that are not sensitive to location or scale, but there may be a loss of precision and variability if applied to much smaller scales, locations, or components of systems. Generally, manufactured items fall under this category. It is useful for LCA background systems, indirect uses and emissions, and for projects where WRI Scope 2 or 3 are reported. Bottom up information is more time consuming and unique, but has the ability to be more accurate and representative of local and small scale characteristics of components within the system boundary. The disadvantage of bottom up information is that it is not necessarily comparable or replicable with other studies. This study uses a combination of information and therefore is called a hybrid LCA. The specific sources are shown on Figure 5 11 and feed into the hybrid LCA. 46

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Figure 5 11: Data Sources The hybrid LCA relies on two broad categories of information. The inventory is composed of all the direct and indirect inputs to the system boundary as defined in Section 5.1.2 through 5.1.4. These are shown in Table 5 4 and fully expanded in Appendix C These inputs are described in more detail in the following sections. 47

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Table 5 4: Resource Flows and LCI Categories Category Specific Materials Region Unit Fossil Fuel Diesel, gasoline (well to pump WTP) U.S. L Electricity Electricity (Scope 1,2) eGrid region kWh Soil Bagged potting soil organic U.S. kg Fertilizer N, P, K, Zn, Mn, Mg, Cu, gypsum, sulfur, lime U.S. kg Plastics (virgin resins) HDPE, LDPE, PP, PS, PET U.S. kg Paper Cardboard U.S. kg Chemicals Herbicide, pesticide, fungicide U.S. kg Water Raw irrigation water Site specific L Water Potable irrigation water Denver, CO L Transport Refrigerated tractor / 17 ton trailer Varies km Transport Light pickup truck Denver, CO km Web Hosting 20 Mb site, 1 yr. U.S. year 5.2.1.1 Power Direct electrical power use (Scope 1 and 2) was used to estimate total energy demand (see Section 5.3 Impact Assessment). In addition, Scope 3 electricity required the use of U.S. EPAs Emissions & Generation Resource Integrated Database (eGRID) scheme ( EPA 2012 ). The eGRID conveniently breaks down the U.S. electrical grid into subregions of connectivity and similar grid mix of fuel sources to generate electricity. The eGrid reports also estimate grid losses from generating station to end use ( EPA 2012 ). For electrical power use in Culiacn, Mexico, comparable data were compiled from several published sources as shown on Figure 5 12. Grid mix fuel sources and subregions are important because each commercial grower belongs to a distinct subregion, and these data are needed for emissions and upstream LCI. 48

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Figure 5 12: Regions of Electricity for Grid Mix Identification 49

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5.2.1.2 Water The agricultural extension office for each region growing vegetables sourced from Denver was contacted to characterize the type of water, pumping depth (if any), type of irrigation, and application rates. These data supplemented or superseded any applied water depths reported in the enterprise budgets. In addition, fuel and electricity demands associated with pumping or conveying irrigation water was noted. These data are summarized in Table 5 5. Denver Waters energy intensity was calculated from total enterprise energy use (including treatment pumping) per total potable water sales. Even though Denver Water generates a significant amount of its own electricity, this was not accounted for since this would not be a comparable energy intensity. 50

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Table 5 5: Site specific Water Use Parameters Cooperative Extension Office / Source Crop Irrigation Type Energy Intensity Applied Pumping (kWh/AF) Water Head (or as eGRID (ft) (ft) noted) Region Alamosa County 1899 E. Hwy 160 Monte Vista 81144 (719) 852 7381 Representing: Alamosa, CO David Holm Potato Pumped Center pivot from shallow well 1.75 6.5 12.8 RMPA 50% pumped drip from canal 2.5 10 20 CAMX Tomato 50% pumped drip from well 2.5 160 320 CAMX 25% pumped drip from canal 2.5 10 20 CAMX Kern County 1031 S. Mount Vernon Ave Bakersfield, CA 93307 (661) 868 6200 Representing: Bakersfield, CA Joe Nuez Carrot 75% pumped drip from well 2.5 160 320 CAMX Luna County 210B Poplar St. Deming, NM 88030 (575) 546 8806 Representing: Deming, NM Jack Blanford Onion Pumped drip from well 4 300 591 AZNM Carrot Gravity ditch furrow 2 Weld County 525 North 15th Ave Greeley 80631 2049 (970) 304 6535 Representing: Greeley, CO Thaddeus Gourd Onion Gravity ditch furrow 3.5 Hidalgo County 410 N 13th Ave Edinburg, TX 78541 3582 (956) 383 1026 Representing: Mission, TX Brad Callen Onion Gravity ditch furrow 2.8 50% pumped drip from ditch 2.67 5 3.7 gal diesel/ac Malheur County 710 SW 5th Ave Ontario, OR 97914 (541) 881 1417 Representing: Ontario, OR Jim Clouser 208 741 7154 Onion 50% gravity ditch furrow 4 51

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Cooperative Extension Office / Source Crop Irrigation Type Applied Water (ft) Pumping Energy Intensity (kWh/AF) (or as Head eGRID (ft) noted) Regio n Jefferson County Rigby, ID 83442 Representing: Terreton, ID Bill Bohl Potato Pumped Center pivot from 1.67 150 296 NWPP Courthouse (208) 745 6685 well 60% pumped drip from well 1.67 150 36 gal diesel/ac Charlotte County 25550 Harbor View Rd #3 (941) 764 4340 furrow 4 Port Charlotte, FL 33980 Representing: Punta Gorda, FL Gene McAvoy Tomato 40% gravity ditch Denver Water Representing: Denver, CO Personal communication with Alicia K. Andersen Shyam, (303) 628 6653 and the 2012 Comprehensive Annual Financial Report. Tomato Potato Onion Carrot Potable tap Variable 119 (365 per Mgal) RMPA Industry Practice Representing: Culiacn, Sinaloa, Mexico Tomato Pumped drip from ditch 2.5 5 3.7 gal diesel/ac Direct water use was measured in the small scale operations using Neptune, residential utility water meters. The meters are shown on Figure 5 13. 52

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Six Neptune T 10 water meters the water supply to the gardens and case study participants noted their water use in a manner similar to that depicted on Figure 5 14. Figure 5 13: These were attached to 53

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Figure 5 14: Small scale Water meters were w et assigned to the individuals shown in Table 5 6. Only locations managed by Farmyard CSA pro on data that could be the study. te. The main ramification of the poor participation rate is that it creates a ater m ering setup Dr. John Brett and vided wholeseas used for Others were incomple very small sample size from which it is difficult to make statistical inferences. This is discussed further in Section 5.2.5. 54

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Table 5 6: Water Meter Assignments Phone First Name Last Name Bus/other Name Meter No. Meter Address (303) 949 xxxx Jon & Candice Orlando Urbiculture Community Farms 78221855 2xxx S Hazel Ct, Denver, CO 80219 (303) 250 xxxx Stephen Cochenour Stephen Cochenour 75389823 11xxx W 38th Ave, Wheat Ridge, CO 80033 (303) 733 xxxx Debbie Dalrymple Farmyard CSA 46989710 1xxx S Fairfax St, Denver, CO 80222 (303) 556 xxxx John Brett John Brett 77669616 2xxx Bellaire St, Denver, CO 80207 (303) 956 xxxx Sundari Kraft Heirloom Gardens 74083435 3xxx Dover St. Wheat Ridge, CO 80033 (303) 733 xxxx Debbie Dalrymple Farmyard CSA 74083439 1xxx W Virginia Ave, Denver, CO 80223 5.2.1.3 Other Agricultural Inputs For the large scale format, enterprise budgets will be used as representing all large scale growers included in the study. A typical budget, for onion for example, shown in Table 5 7. is 55

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T able 5 7: Typical Enterprise Budget Quantity/ Price or Value or Item Acre Unit Cost/Unit Cost/Acre GROSS RETURNS Red Onion 650 box 8.00 5,200 OPERATING COSTS Fertilizer: 15 15 15 1,500.00 lb 0.22 326 Irrigation: DripTape 5 mil 13,755.00 foot 0.01 165 Water Pumped 40.00 acin 4.83 193 Seed: Onion Transplants 73.00 thou 5.00 365 Custom: Transplant Onion Labor 13,750.00 foot 0.02 220 Bagging Labor (harvest) 650.00 each 0.50 325 Herbicide: Dacthal W 75 7.00 lb 18.85 132 Fungicide: Ridomil Gold Bravo 2.00 lb 25.06 32 Carton: Onion Bags (harvest) 650.00 each 0.50 325 Boxes 40 lb 650.00 each 1.10 715 Labor (machine) 19.51 hrs 12.42 242 Labor (non machine) 51.20 hrs 9.32 477 Fuel Gasoline 10.41 gal 2.55 27 Fuel Diesel 47.54 gal 2.00 95 Lube 18 From Table 2 ( UC Davis 2006 ). Costs and returns per acre to produce onion San Joaquin Valley For the small scale growers, bookkeeping forms were used to document actual inputs throughout the season. These forms contain the information shown in Table 5 1. An example of field forms was previously presented in Appendix A 56

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5.2.2 DN Carbon Model DC T itrificatio osit ) ess o m ion model of carbon and gen biogeochemistry initially devel continually Changshen and the mid U w he Den n Decomp ion (DNDC model is a proc riented co puter simulat nitro in agroecosystems oped and refined by Dr. g Li others since 1990s ( niversity of Ne H H tute for t udy of eans and Sp ampshire [UN ] Insti he St Earth, Oc ace 2012 ). It was chosen a arb els, because it is rame b parameters inter this research Oland Kozy mong other c on mod suited for the pa ters availa le and generates of est for ( er and Haugen ra 2011 ) widely, is userfriendly, and has a track record in the research community. biogeochemical processes shown on Figure 5 15 that are of interest for this study are discussed in more detail below. Carbon Dioxide Processes It is also used 5.2.2.1 Biogeochemical Processes The process flow diagram for the DNDC model is shown on Figure 5 15. Selected Plants can give off up to half of the carbon dioxide (CO2) that they absorb through oxidation (use) of sugars they manufactured from photosynthesis. In photosynthesis, CO2 and water is converted to other carbon containing compounds and stored up in plant tissues. When parts of the plant die (or the entire plant dies), most of this stored carbon is released again into the atmosphere as CO2 and other carbon containing compounds through chemical processes. This is carried out by soil bacteria, fungi, and insects. In the overall carbon cycle, only a very small portion of the carbon in the plant incorporates itself into the soil. The process, also known as humification, takes years to substantially build up the soil in terms of soil organic carbon (see 57

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below). Ext CO2 is emitted from the soil ra when tilli ng occurs and these emissions are unrelated to cover cropping or crop residues ( Ohio State University [OSU] 2013 ). F 15: Proc igure 5 es s Flow for the DNDC Mo (reproduced from UNH 2009 del ) n Processes Soil Organic Carbo Soil matter is about 60% by weight carbon ( OSU 2013 organic ). As described above, soil organic insects, r uilibrium with the soil/plant biome, soil organic carbon is a fairly stable soil property. But when soils are tilled, much organic matter is matter is created by the cycling of carbon containing compounds in plants (dead animals, and microorganisms in the same ecosystem also contribute). As organic matte decomposes more fully, it starts to form humus, a material that itself provides a carbon and energy source for soil microbes and plants. When in eq 58

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expose to rapid oxidation and d de composition by being a food source of other bacteria and microbes. This loss of humus is tantamount to soil erosion because, without humus, the soil structure begins to be cohesionless like sand, and fails to support most plant and microbial communities. Tilling and plowing are major practices to control weeds. For the commercial farmer that wants to conserve soil organic carbon and humus, the consequence is often increased herbicide use to reduce the need to till ( OSU 2013 ). Methane Pro cesses CH4 is emitted when soils are wet enough to enable anaerobic decomposition of organic matter. This is similar to nitrous oxide, except that nitrous oxide production requires N nutrient. Insects intercepted that feed on organic matter also produce methane, but direct emissions are often and oxidized in the presence of soil microorganisms ( Brevik 2012 ). Nitrous Oxide Processes N2O is produced when soil is saturated enough to create anaerobic conditions that allow soil or ive bacteria to convert NO3 to NO, N2O, or N2. Typically, this occurs after achieving field capacity the capacity of the soil to hold water before that water percolates beyond the reach of plant roots. The presence of NO3 is a direct result of applied N, and, to a smaller degree, atmospheric N2. In a general sense for the agricultural context, the presence of moisture and nutrients dr the production of N2O ( Brevik 2012 ). Nitrous oxide remains in the atmosphere for about 120 years before being transformed by chemical reactions or removed by a sink ( EPA 2013 ). 59

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5.2.2.2 Model Inputs DNDC was selected, in part, because it has already characterized many parameters based on published literature for the crops of interest in this study. Site or crop specific parameters were overridden manually when needed. These data are tabulated for each of the 19 scenarios in Appendix D Climate data required a high level of effort to prepare and was very site specific. Precipitation, in particular, required design of a statistical procedure to preserve wetness, dryness, and randomness, while being representative of long term averages. These are discussed in the following sections. 5.2.2.3 Climate Data Although DNDC demands daily climatological data, it allows for a number of levels of detail for the data. In general, the fewer the climate data, the more assumptions the model must make about daily atmospheric and soil conditions. Given the body of data readily available from web based government agencies and commercial weather sites, the level of detail selected in the model contain arameters. These are the following: 1. Daily maximum temperature (C) 2. Daily minimum temperature (C) 3. Daily precipitation (cm) s a high number of preferred p 4. Daily average wind speed (m/s) 5. Relative humidity (%) One other parameter can be used by DNDC, solar radiation (MJ/m2/d), but if omitted can be estimated within the model, based on latitude. Solar radiation was not chosen as an input because relatively few data sources record daily solar radiation. 60

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Climate data was acquired from the National Oceanic and Atmospheric Administration (NOAA) and WeatherUnderground (WUnderground), a website that compiles data on individual weat stations as well as those operated or administered by local, state, or federal agencies. Altho long term averages and normals are the standard choice for projecting climate parameters in the future, a relatively recent period of record (generally 1995 to present) was used for th DNDC model. There are several reasons for this choice of perio her ugh e d of record. First, the period of record could be consistent because it is available for all meteorological stations. Second, few stations report longer term daily normals for the required input parameters. Third, some experience climate change. Fourth, the gas emissions that DNDC calculates occur as a result of minor, daily events of temperature, soil wetting, and soil drying. A shorter period of record is advantageous to capture this phenomenon, explained in the following section. 5.2.2.4 Special Procedure for Estimating Precipitation DNDC can use a separate climate input file for each year modeled. These inputs can be all years, or a separate file for any given year or group of years. For this study, a single climate parameters file was used for each station, for each scenario. Selection of a single climate file for all future years avoids the complicated exercise of making predictions either by stochastic postulate that the period of record for long term normals may be increasingly obsolete as we historical or predictive. For this study, the historical period of record was used to represent likely future conditions. The period of record was used to average, on a daily basis, each climatological parameter. I did not attempt to agree with long term, published normals with this approach because the value of the study is a relative comparison of scenarios, not necessarily the absolute quantities estimated by the model resulting from these scenarios. The modeling period (30 years) would require either one climate parameters file used for 61

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methods or climatologi cal statistics, if available. The relatively short periods of record (ranging the data set is essential. The single climate file contains the average of each parameter for all days in the period of record. For example, the average precipitation for all from 5 to 15 years) allow for a reasonable sense of average conditions, while preserving daily variations, especially in terms of precipitation events.Because the gas emissions that DNDC calculates occur as a result of minor, daily events of temperature, soil wetting, and soil drying, daily variation in June 2nds is the average of each June 2nd for every year in the period of record. In other words Pn i n i 1 where Pi is the parameter per year n. The following paragraphs further explain why the daily precipitation is a key yet problematic s, crop production in product life cycle assessment studies, carbon accounting, and corporate social res causing variability in nitrous oxide emissions are rec portant factor rainfall is not particularly suited for mere ari ates at any scale should take into account the stochastic and tion. them ( Li 2000 model input. Soil nitrous oxide emissions display a high degree of sensitivity and variability to the day of fertilizer application and daily rainfall distribution. At nearly all geographic and temporal scale this variability appears to be unrecognized when modeled emissions are reported for ponsibility reporting. Although many factors ognized, we show that one im thmetic averaging, and reliable estim periodic nature of precipita Variations in on farm parameters such as agricultural practices, soil types, and precipitation result in some degree of variation in dependent variables tied to ). For example, it 62

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is known that nitrous oxide (N2O) emissions are produced when sufficient nitrogen (N) and moisture are present in soil ( Li 2000 ; EPA 2010c ; IPCC 2006 ). Nitrification is the aerobic oxidation of ammonia to nitrate, and denitrification is the anaerobic reduction of nitrate to nitrogen gas (N2). In this process, N2O is released. Inorganic N has a controlling influence on these reactions while soil moisture and depth of N in the soil column dictate whether release N2O is a result of aerobic or anaerobic processes, both of which can occur at the same time within a given soil profile ( Kuenen and Robertson 1994 of ). Denitrification is associated with wat in the soil pore space and nitrification is associated with air in the pore space ( Davidson et er al. 1986 ). 2 The combined effects of moisture, inorganic N, depth, temperature, and plant metabolism in the root zone result in varying degrees of NO flux. A study by Fisher focused on the coincidence of N application and rainfall events that influence soil moisture and therefore N2O flux ( Fisher 2013 ). Since N application is usually controlled and quan tifiable, it is rainfall that introduces As a means to illustrate one potentially significant source of uncertainty in N2O reporting, a simple comparison was drawn using the Denitrification Decomposition (DNDC) model to estimate N2O emissions from dryland spring wheat production in two separate years near Fort Benton, Montana ( Fisher 2013 uncertainty in N2O estimates. ). The only parameters that were allowed to change were historical, daily rainfall events and the timing of a single, annual application of manure. It was hoped that the comparison would expose the singular effect and potential source of variability of coinciding rainfall and N application events. The model was set to run for 365 days, once for 1991 and once for 1996. These two years have almost identical precipitation (331 and 342 millimeters [mm] respectively) to the annual average of 341 mm for the period 1981 to 2010. 63

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As hoped, the distribution of precipitation is unique and different for each year, as shown on Figure 5 16. The results are shown on a daily basis on Figure 5 17. The large spike in N2O emissions seen on July 1 and 2 coincides with the application of manure on July 1 (day 182 of 365). The peak daily N2O emissions for 1991 was 934 g per ha and for 1996, 229 g per ha, both on July 1. Since all other parameters were identical, we focused our attention on rainfall differences around the spike. As shown on Figure 5 18, the 1991 simulation had relatively significant antecedent rainfall; on June 29, 7.1 mm of rainfall occurred and on June 30, 18.3 mm occurred. In 1996, the first antecedent rainfall event was 10.7 mm on June 26. The results confirm that the strong relationship between the coincidence of N application and soil moisture can result in the largest daily emissions of N2O of the year. Please see Appendix E for the complete study. 64

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Figure 5 16: Daily precipitation at Fort Benton, Montana Years shown have nearly identical total annual rainfall. 65

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Figure 5 17: Cumulative nitrous oxide flux for 1991 and 1996 with daily precipitation. 66

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Figure 5 18: Cumulative nitrous oxide flux detail for 19 June to 8 July. .2.3 Data Quality Much of the data in the LCI for this study was not based on large sample sizes. There are only five growing locations and other data is reported in summary from potentially a large sample size, but this is unknown. Depending on the data source and LCI item, raw data may be in the form of an aggregate, reported number or a range of measured values. For example, the enterprise budgets for the large scale format are reported as single data points and not even ranges. Because of these data limitations, non parametric statistical procedures are best suited for uncertainty analysis and identifying key data, as described above. To improve data quality primary case study data, and first hand industry data was used together with consistent background LCA information from SimaPro ( PR Consultants 2013 5 ). SimaPro is one of a handful 67

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of internationally recognized life cycle assessment tools and compiles data sets that have been in use world wide. Overall, the data quality is considered good. Data quality was estimated against the following data quality indicators (DQI): Precision Data are either measured or modeled based on primary, case study information or are derived from SimaPro databases. Precision is difficult to estimate due to the differences in these sources, or the unavailability of the information. Completeness As described in Section 5.1.2 through 5.1.4, every component except bulk, post harvest packaging and preservation were included. Comparability Uniformity of methodology is high compared to other LCA studies. Some consistency with other LCA studies may be lower due to the hybrid approach using primary, case study data. Repeatability None of the data are proprietary and the method and data sources are easily replicated. Representativeness Temporal representativeness is high. All LCI processes belong to industries where technological innovation is relatively slow and the usefulness of its data lasts production whose practices also do not change rapidly. The model was run for 30 years, and each model run shows that asymptotic behavior dominates by 30 years of the same agricultural practice(s). It was convenient for later analysis to group certain data sources together addressing comparability, repeatability, and representativeness. These three DQI were paired with the data categories in Table 5 3. It was determined that aggregated data and estimated data relied most on published literature and expert interviews. These were deemed average values with little uncertainty. It was determined that measured material (gallons, each, lbs, hours) and site specific data were not necessarily to be trusted because of the poor participation rate of the urban growers and therefore small sample size. For these reasons, parameters reported by the urban growers failed the DQI tests for comparability, repeatability, and representativeness. How this failure was managed in the impacts assessment is discussed in Section 5.2.5. All other a number of years. The DNDC model affords a site specific evaluation of agricultural 68

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parameters, such as DNDC input parameters, enterprise budgets, and expert interviews passed all three DQI tests. With regards to precision and case study water use, a critical resource component, the Neptune T 10 water meters have a high precision and are well suited to the flow rates and volumes expected in small scale production. The precision of the Neptune T 10 is estimated to be within the % DQO, as shown in the manufacturers literature on Figure 5 19. Figure 19: Neptun 5 e T 10 water meter accuracy for 5/8 inch connections Source ( Neptune 2008 ). 5.2.4 Sensitivity, Uncertainty, and Variability d on fossil fuel component of Scope 2 energy use (because the energy mix may vary from farm pumped component of water supply (because some irrigation water is gravity fed and other is pumped); A sensitivity analysis allows key variables and assumptions to be changed to test their influence on the results of the impact assessment. The preliminary choice of these variables is base likely scenarios, choices, and practices potentially adopted by the large scale or smallscale grower. For this study, only qualitative sensitivities were noted by varying the following key inventory variables (these are discussed in Section 5.4): to farm); 69

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raw water component of water supply (because the large scale format typically uses raw embodied en water and the smallscale format typically uses potable water which may have a higher ergy); distance from farm gate to market (because transport is perceived be very different between produce grown under the two formats; and e growing formats and synthetic fertilizer has a relatively high factor. se co se pr Study 5.2.5 Management of Limitation of Small Sample Size in the Case Study synthetic fertilizer (N+P+K) use (because the type of amendment or fertilizer is a major differentiator between th embodied energy and emission The data are presented under three categories to assist the reader in their own qualitative nsitivity analysis. These categories are direct emissions from production, indirect emissions from production, and post production emissions. Because of the poor participation rate and low sample size in the case study, yields had an unknown range of certainty and some did not mpare well with published literature. Further, it was unknown if all case study parameters could be characterized as independent variables for the purposes of calculating the life cycle impacts. To eliminate a number of potential variables and year to year variation in yield, a parate run was conducted matching urban production yields to those of commercial oduction. Please see Section 5.2.5 Management of Limitation of Small Sample Size in the Case for more discussion. Only locations managed by Dr. John Brett and Farmyard CSA (n=2) provided wholeseason data that could be used for the study. Others were incomplete. The main ramification of the poor participation rate is that it creates a very small sample size from which it is difficult to make statistical inferences for the population as a whole. These include correlations, confidence intervals, regressions, analysis of variance (ANOVA), analysis of covariance (ANCOVA), and significance tests such as the t test and Chisquared test. Because it is desired to draw conclusions about a population from a sample, descriptive statistics such as sample mean, 70

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sample standard deviation, and standard error are important but not meaningful with a small sample size. Typica lly, a sample size of n<30 is considered small.This study uses data from various sample sizes, but the urban grower data is, in particular, of a very small sample size ANCOVA, or other regression analysis to characterize the interrelationships, if any, between what are nominally considered "independent variables" was not performed due to the complexity of properly characterizing data whose sample size is very small. For example, from even cursory inspection, yield could be seen to be dependent on climate, soil properties, grower practices, and many others. For this study, the dependent variables were the life cycle impacts and the nominally independent variables were all operation inputs, climate, soil properties, and yield. The special pr 5 20) starts by utilizing the DQIs that were applied to the data categories (see Section 5.2.3 Data Quality). Independent variables were grouped into four categories: DNDC model parameters and its output; enterprise budgets and literature; expert interviews; and urban measurements (n=2). Only the last category, urban measurements, failed any one of the DQIs. It was assumed that reporting mean life cycle impacts based on data sources that passed the DQI test was appropriate and carried relatively little, albeit uncharacterized quantitatively, uncertainty. Through this process, independent variables that may contribute significant uncertainty were screened and retained for further manipula (n=2). A special procedure was developed to allow limited application of inferential statistics and increase the meaningfulness of reported life cycle impacts. The confidence interval was selected to be reported with sample means. ANOVA, ocedure (shown on Figure tion. 71

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Figure 5 20: Managing Small Sample Size in the Case Study All retained independent variables may not influence a given life cycle impact category. A dominance analysis was performed on each material flow (independent variable) to ide which parameters both failed the DQI test and contributed to at least 90% of the life cycle impact. The dominance ana ntify lysis was performed using initial values of parameters which were all means, irrespective of data category. The results of the dominance analysis are presented in Appendix F and summarized in Table 5 8. In the table, an X indicates which top material flow contributing to at least 90% of life cycle impacts pertains to any given vegetable. Table 5 8: Dominance Analysis Results Urban Crop Top Material/Flow Categories that Contribute 90% of Life Cycle Impacts Potato Carrot Onion Tomato Turf PRODUCTION DIRECT Mechanized Equip kWh urban Grow lights kWh X Vehicle trips gasoline Mechanized Equip gasoline 72

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Urban Crop Top Material/Flow Categories that Contribute 90% of Life Cycle Impacts Potato Carrot Onion Tomato Turf Mechanized Equip diesel Irrigation pumped water diesel Land Area X X X X X Water X X X X X Impact from DNDC model1 X X X X X PRODUCTION INDIRECT Vehicle trips gasoline X X X X Herbicide/Pesticide Fungicide X Soil X Grow lights electricity X Mechanized Equipment electricity Drip Tape or hose X X X Herbicide/Pesticide Mechanized Equipment gasoline X X X Mechanized Equipment diesel Mechanized Equipment electricity N ammonium nitrate X Water POST PRODUCTION Light pickup truck X X PET clamshell X PP Polypropylene LDPE cello bags PS polystyrene Cardboard 20 Mb site, 1 yr. Notes: 1. The Denitrification Decomposition (DNDC) model estimates direct emissions and soil organic meet data quality indicators (DQI) and are therefore excluded from the dominance analysis. They are presented here for comparison only. 2. Duplicates between Production Direct and Production Indirect reflect that Scope 2 and 3 impacts contribute significantly compared to Scope 1 impacts of the same material/flow. carbon (C). The inputs to the model are based on published sources and expert interviews, These selected parameters were then further characterized by expert information to establish benchmarks for the range and typical values. Because no information was available on the frequency distribution of a given parameter, a uniform distribution was assumed, but the typical 73

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value influenced the selection of upper and lower values to increase confidence and limit some outliers. Estimates of upper, lower, and typical values for the selected dominant material flows are presented in Table 5 9. Table 5 9: Estimated Benchmarks for Dominant Nominally Independent Variables Upper Lower Category Influencing Factors Estimate Estimate Typical Water use, gal/lb unless noted Carrot1 35 16 32 Potato1 26.1 11 18 Onion1 44 22 36 Tomato1 Watering with a timer vs. as needed determined by grower. Drip assumed to be up to 50% more efficient than spray. Drip vs. broadcast sprinkler irrigation. Commercial irrigation may serve as lower benchmark. because they are generally fine tuned and sensitive to cost. Overwatering may result for single tap setups because water is applied to all plants at same time and plants with highest demand dictate irrigation for all plants. 39 19 32 Turf (Kentucky [gal/sqft] atic Turf acreage under institutional management may not management. be about 3 feet per year. 4,852 Overwatering can occur more readily with use of autom systems. bluegrass)2, 3 mimic turf acreage under residential homeowner Applied water at an agronomic rate for Denver assumed to 1,507,278 669,901 1,00 Potting soil use, lbs/lb unless noted Carrot Potato Onion Tomato1 Prevalence of starts and starts using soil media is high. 0.042 0.029 0.035 Variance in amount of soil used per cell pack can vary 40% Turf (Kentucky bluegrass) Grow light use, kWh/lb unless noted Carrot Potato Onion Tomato1 Options in the setup that would influence energy use are and space p T8 tubes are more efficien at 80% fluorescent, bt in little use and intensit by grower lited in nce by affordable technology plant heth (diurnal t be maintained). 0.72 0.50 0.60 limited due to lamp heat generation requirements per plant. T12 fluorescent tubes most revalent. t than T12. LED estimated more efficient than Management of time u y mi varia and al cycle mus Turf ( Kentucky 74

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Cat egory Influencin g Factors Upp er Est Lower im ate Es tima te Typical blueg rass) Vehi cle trips, ga gal/lb ess noted soline unl Carrot 0.006 0.00 0.005 1 0 Potato 0.010 0.00 0.008 1 0 Onion 0.008 0.00 0.006 1 0 Tomato Number of plots per typical CSA limited by ecoics and operation size. Mile radius of operation dictated closeness home ion size is fixed by le owner/erator with few employees. e recognizes the back residenti wer ows for him/herself. r of trips required for growng phase. Number of trips at harvest accoued for by tonmiles below. 0.006 0.000 0.005 1 Lower valu nom by to base. Typical operat sing op yard al gro who gr Numbe i nt Turf (Ke blueg ntucky rass) La nd area, /sqft unles noted lb s Carrot 1.13 0.73 0.94 1 Potato 1.050.18 0.88 1 Onion 1.09 0.5 0.74 1 Toma endent variable considerin rowing pras and Assuming typical weathe and yields, t parameter assumed independent for this exercise. g commer growing rgnizes happen once plants typicy have an and the uran grower can harvest slightly hher yield. chmarks using commercial growing regnize that plant spacing can be limited by needs imposed y mechanized harvesting. Variability in harvest weight be especially between urban ad commerc ical varieties grown in the urban setting may have fairly niform yields, excluding other faors. 0.880.59 0.77 to1 A dep g g ctice climate. r his Yield benchmarks usin cial eco that harvests usually ; all assortment of ripeness accordingly, resulting in b ig Yield ben co b due to variety grown can large, n ial. Typ u ct Turf ( blueg Kentucky rass) Drip Tape unlessed lbs/lb not Carrot 0.0150.000 0.013 1 Potato 0.0150.000 0.013 1 Onion 0.019 0.000 0.013 1 Toma Lower end represents that drip is ional; there are other methods to irrigate. l value represents prevalen of drip tap with little variation in emitters per foot and delivery rate. 0.013 to1 If any drip tape is used in a plot, typically all plants are put on drip. 0.015 0.000 opt Typica ce e Turf (Kentu bluegrass) cky Ammonium Nitrate Fertilizer lbs/lb unless noted Carrot Potato Onion Tomato Turf (Kentucky [lbs/ac] Use of fertilizer is typical, but not required. than needed. Typical value derived from institutionally managed turf. bluegrass)2 Upper range recognizes that some users will apply more 230 0 192 75

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Upper Lower Category Influencing Factors Estimate Estimate Typical Light Pickup Truck tmi/lb unless noted Carrot1 0.006 0.000 0.002 Potato1 0.006 0.000 0.002 Onion1 0.006 0.000 0.002 Tomato1 to be an average of 200 lbs at a distance vehicle trips for gasoline (above) Lower estimate reflects the resident grower that is not hauling his/her harvest for sale or transfer. 0.006 0.000 0.002 Hauling assumed according to Turf (Kentuc ky bluegrass) PET Clamshells lbs/lb unless noted Ca rrot Potato Onion Tomato timate reflects no use of clamshell packaging. s 0.067 0.000 0.020 Packaging optional for both residential backyard gardener Lower es and CSA businesses. Some CSA only harvest/deliver in bulk with reusable containers. Turf (Kentucky bluegrass) Fungicide lbs/lb unless noted Carrot Potato Onion Tomato Turf (Kentucky bluegrass)2, 3 Lower estimate refl Upper estimate reflects over application compared to institutionally managed turf (typical). 7.48 0.00 6.20 [lbs/ac] Fungicide use is optional. ects no use. Mechanized Equipment (gasoline) gal/lb Carrot1 0.0010.000 0.0005 Potato1 0.001 0.000 0 .0005 Onion 10.001 0.000 0.0005 Tomato1 Gasoline use is associated with tilling. Tilling is optional. Upper estimate reflects tilling all seeded area. 0.0010.000 0.0005 Turf (Ke bluegra ntucky ss)2, 3 Gasoline use is ass There some variability in area mowed per gallon, mowing frequency, species of grass, and overall turf health. Typical value from one mowing per week for approximately 26 weeks. 540240 360 [gal/ac] ociated with lawn mowing. References: 1. Brett, John. 2 eters and ranges of values for urban residential vegetable grow businesses using residential lots. Department of Anthropology, University o E mail: john.brett@ucdenver.edu 014. Phone conversation regarding typical param ing by homeowners and CSA f Colorado Denver. Phone: 303 556 8497. May 1. water 9 mm/ Aug. Ad from Denver Wate and irrigation recommendations for converting bluegrass t 3. Turf pesticide of West Virginia Golf Courses and Lawn Care Bus University Extension Service. 2. Turf use 2 waterings per week for 9.8 mm/ea in May, Jun, Sep, Oct; 3 r. 2011. Sustainable Landscape Conversion Design ea Jul, dapte : urf to sustainable low water usage landscapes. August 31. and fertilizer applications taken from: Pesticide Usage on Turf inesses. 1995. West Virginia 76

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With a range and distribution estimated, a Monte Carlo simulation rriedr each s minant parameter. Each randomly selected value for each parameter was carried t the ca imp tego ,000 t cyc ,000 samples with % confidence inte of the mean.A summary of t ence in ble 5 10. The results of the s procedure indi that the sampl n. Table 5 10: 95 Percent Confiden Dominant Nominally I was ca out fo elected do hrough lculations, reporting total life cycle impacts for each act ca ry, 10 imes. Life le impacts were then reported as the mean of 10 a 95 rval calculated from 1.96 times the standard error he confid terval is presented in Ta pecial cate e mean is representative of the population mea ce Intervals for Impacts from ndependent Variables 95 Percent Confidence Interva l (CI)a Reported anb V Show as Me alue n Impact C ategory Denver Potato Denver Carrot Denver Onion Denver Denver Tomato Turf Energy (all non re 8.05 10 3 2.23 10 2 6.14 10 3 newable) 9.35 10 3 6.07 10 3 Land Use (arab 3 3.16 10 3 6.25 10 5 le, non irrigated) 2.60 10 2 2.74 10 3 6.11 10 Water (al l fresh w 1.24 10 1 10 1 09 1 ater sources) 8.56 10 2 1.07 10 1 1.14 1. 0 1 Total Carbon Diox 3 4 10 4 1.39 10 3 6.06 10 ide eq 1.19 10 5.40 10 7.39 4 Carbon Dioxid 10 4 1.24 10 3 3.92 10 4 e 9.93 10 4 3.66 10 4 5.17 Nitrous Oxide 6 2.10 10 4 7.70 10 5 2.25 10 3.62 10 5.65 10 5 6 Methane 10 3.63 10 5 7.10 10 5 2.66 10 5 4.46 10 5 2.71 5 TRACI Carcino gens 5.60 10 11 3.39 10 11 4.53 10 11 3.41 10 11 3.83 10 11 TRACI No n carcinogens 2.50 10 10 1.50 10 10 2.01 10 10 1.55 10 10 1.71 10 10 TRACI Air compartment 2. 7 10 4 2.41 10 4 2.86 10 5 43 10 4 2.42 10 4 3.0 TRACI Water c 3 10 329 ompartment 1.88 10 3 1.13 10 3 1.51 10 1.18 1. 10 3 TRACI Soil compa 10 2.91 10 3.89 10 6 10 434 rtment 4.82 6 6 1.94 3. 10 6 Notes : a. CI shown applie s identified in the dominance analysis. rresp s to dominant, nominally independent variable b. For co onding means, see Appendix I and Appendix J M) nda Error of the Mean = (STDEV)(n) STDEV Sample Standard D e = 10,000 CI = 1.96 (SE SEM Sta rd eviation n sample siz T fidence interval and mean are reported in Section 5.3 Impacts Assessment, however the intermediate LCA rs are described in the following section a letes th analysis. he con step of outputs and emissions facto nd comp e inventory 77

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5.2.6 Outputs and Emission Factors Direct indirect inputs detailed and in Appendix C were tabulated and manted com u the fu appn o sion f calculation of impacts. As described in Section 5.2.1, some s are outp o namely, resource use (e.g. water, fuel, power). Emission factors were taken from published literature, the U.S. LifeCycle Inventory Database (National Rene egie Mellon Economic input output LCA too tants 2013 ipula into mon nits and n ore efficient ctional unit (conversion factors) for m licatio f emis actors and input also uts f interest, wable Energy Laboratory ([NREL]), and the Carn l as reported in SimaPro software ( PR Consul ). SimaPro was used to d in factors for most upstream materials. Exceptions to this general rule include potable water (Denver Water), Culia osting. These are detailed in Aix G evelop emiss o cn electricity, and web h ppend The DNDC model generated direct impacts. These are explained further Section 5 in Appendix H in .3 and shown T in the basis for conversion to an environmental, economic, or so used in the impacts assessment in Table 5 11 summarizes s an ut o ction phase for one vegetable for one growing format (large In ble o l not shown for clarity. he life cycle ventory yielded a number of outputs that will be cial consequence. These, in turn, are the next section. As an example, input d outp s for ne produ scale) the ta utputs are fu ly displayed for plastics; outputs for other inputs are 78

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Table 5 11: Example Input Output Table for the Growing Phase for the Field Tomato La (Functional Unit [FU] = 1 lb vegetable to consumer) rge Scale Growing Format Conversion Primary Input Unit(s) Factor Output(s) Unit(s) Upstream components Direct fuel diesel (well to wheels) gal $ various 1. various Upstream components Direct Fuel gasoline (well to wheels) gal $ various 1. various Upstream components Direct electricity (Scope 1) kWh $ various 1. various Upstream components Indirect electricity (Scope 2for kWh $ various 1. Bakersfield, California)2 various Upstream components kg various 1. various N fertilizer $ Upstream components kg $ P fertilizer various 1. various U pstream components K fertilizer kg s $ various 1. variou Upstream compon ents Plas tics (virgin resins) 3 $ Toxic Re leases nitrate c kg kg kg kg kg $ kg J J short t kg /$ ompou nds methanol hydrochlori k g/$ k g/$ c a cid zinc compo k g/$ und s ammonia Economic A k g/$ $ /$ k g/$ J/$ j/$ s hort t/$ g al/$ t km/$ t km/$ t km/$ t km/$ t km/$ cti vity employee c om pensation Greenhous e G ases total CO2 e Energy total eneg ry non fossil e ne rgy Hazardous Wa ste hazardous w as te generated Water Use water use Transportation4 domestic by rail domestic by truck domestic by water international by air international by water gal t km t km t km t km t km Upsar kg $ various 1. various tream components C dboard kg $ various 1. various kg kg/kg Herbicide residual in soil kg Upstream components kg kg/kg Herbicide residual on vegetable kg Herbicide kg $ various 1. variou s kg kg/kg Pesticide residual in soil kg Upstream components kg/kg Pesticide residual on vegetable kg Pesticide kg Upstream components kg various 1. various THIS ROW EXPANDE D 79

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Conversion Primary Input Unit(s) Factor Output(s) Unit(s) $ kg kg/kg Pesticide residual in soil kg Fungicide kg kg/kg Pesticide residual on vegetable kg Upstream components gal $ various 1. var Direct water ious Direct land ft2 ft2 Land area devoted to production ft2 hr hr Labor hours hr Labor hr $/hr of labor Value of wages $ Soil organic carbon (SOC)5 mg/kg %/scenario Long term change in SOC % 1. Industry reports and SimaPro were used to determine the outputs that are embodied in each input. These include was only used if SimaPro or other sources cannot provide bottom up data. economic activity, greenhouse gases, energy, hazardous waste, toxic releases, water use, and transportation. EIO LCA http://www.pre sustainability.com/ http://www.eiolca.net 2. Nine locales and their energy mixes were identified as the locales of the vegetables grown under the large scale 3. The top 5 EIO LCA results are presented only for clarity. The study will track items that contribute at least 90 percent of the total emission or output and that are relevant to the indicators chosen in the impacts assessment. SimaPro was set to report output to the 0.1% contribution level. 4. Fuel and water (and associated emissions) related to transportation, not already counted in Energy and Water Use $ U.S. Dollars format are discovered. categories, were included. ft2 square foot g gram J Joules kWh kilowatt hours mg milligram short t short ton (2,000 lbs) gal gallons hr hours kg kilograms lbs pounds mt metric ton (1,000 kg) MW megawatt t ton 5.3 Impacts Assessment Midpoint impacts were chosen for the life cycle impacts assessment (LCIA). Examples of midpoint impacts are global warming potential, resource depletion potential, or 50% lethal concentration. On the other hand, corresponding endpoint impacts could include global warming, available water supply, or fish kills. Analysis at a midpoint minimizes the amount of 80

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forecasting and effects of modeling incorporated into the LCIA; reduces the complexity of the modeling; sim ions and value choices; reflects a higher level of societal consensus; an comprehensive than model coverage ation ( EPA 2006 plifies communication of results; minimizes assumpt d be more for endpoint estim ). aired with an impac gor step is d classification and may more than one im category. Depending on whether an LCI item can act impact category, a range of allocation can be applied to each impact If a single LCI item does ac dependently on each impact category, for example, then y of the LCI item ca e assigned to each category. The impact indicators shown in Table 5 12 we osen bas nce to this study. These indicators provide useful information t ll scale g akeholders, a policy makers. The indicators can be broadly uped under Environmental Impact; b) Ecosystem Impact; man Hea and d) Societal Impacts. wn with the imp assessment method d for use. LCI data is then p t cate y. This calle assign a single LCI item to independently on each pact category. t in the entire quantit n b re ch ed on their releva o sma rowers, other st nd g ro the four catego ries a) c) Hu lth Impacts; Each category is sho act ology prop ose 81

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Table 5 12: Midpoint impact indicators, clas and characterization factors sification, Impact Impact Category and Indicator Exam f LCI Data ples o Methodology (i.e. classification) Scale Characterization Factor Global warming Global Ga issions ( carbon dioxide [CO2]) Global Warming Potential a to carbon dioxide ts.1 seous em e.g. Converts LCI dat (CO2) equivalen Resource Depletion Local Regio Quantity us co rce Depletion Potentia LCI data to a ratio used ves left in reserve. water use nal of water nsumed ed Resou Conve or l of rts quantity of resource quantity of resource rsu Envir onmental quantity of resource left in reserve. (TRACI) Resource Depletion fossil fuels Global Regional Quantity of fossil fuels used Resource Depletion Potential Converts LCI data to a ratio of quantity of resource used versus Soil health Local N, P, K agronomic percentage Converts LCI data to a ratio of Soil Organic Carbon (SOC) Land area changed as a Carbon Depletion Potential quantity of SOC versus quantity of ideal SOC. result of increased urban gardening. Land use Local Land area ch Converts the amount of large scal change Regional gardening. scale format land, corrected for crop yield by format. 2anged as a result of increased urban e format land displaced by small Terrestrial otoxicity Local Regional Residues and releases of chemicals to soil Converts LC50 data to equivalents; uses multi media modeling, exposure pathways. ec Ecosystem (TRACI) (DNDC model) Aquatic ecotoxicity Local Regional Residues and releases of chemicals to water Converts LC50 data to equivalents; uses multi media modeling, exposure pathways. Carc inogeneity Global Regional Local Releases to air, water, soil Converts LC50 data to equivalents; uses multi media modeling, exposure pathways. Hum Health an ACI) ) Local Releases to air, water, soil Converts LC50 data to equivalents; uses multi media modeling, exposure pathways. (TR Hazard Index (non cancer Soci al ( UNEP 009b t Regional Local Hours Wage rate Employment Potential Converts LCI data to full time equivalents (FTE) 3 2 ) Employmen Notes: ls can be 50, 100, or 500 year potentials. cator assumes conversion of large scale agricultural farmland back to natural grassland as urban gardening, utilizing the small scale format, increases. Under this assumption, the small scale small bare soil, and a third as natural grassland (assumed to represent vacant, weedy areas). Soil organic carbon is highly 1. Global warming potentia 2. The land use change indi format displaces the large scale format in equal areas, weighted by crop productivity per land area.For the scale format, this indicator assumes that a third of the land is converted from previous use as turf, a third as related to current and antecedent land use and reaches a steady state at a 30 year horizon or less ( Kim 2009 ; Morgan 2010 ). ies Life Cycle Assessment of Products Social and socio economic LCA guidelines complementing environmental LCA development ( UNEP 2009b 3. This is an emerging and developing aspect of life cycle impact assessment. No formal methodolog exist. In the place of a methodology or model, the following guidance document was used: Guidelines for Social and Life Cycle Costing, contributing to the full assessment of goods and services within the context of sustainable ). 82

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Table adapted from ( Sources: EPA 2006 ) DNDC Denitrification Decomposition Model. http://www.dndc.sr.unh.edu/ Environmental Impacts. http://www.epa.gov/nrmrl/std/sab/traci/ TRACI U.S. Environmental Protection Agency Tool for the Reduction and Assessment of Chemical and Other Two indicators that are unique to this study that are not typically found in LCAs are the social (employment) and soil health (soil organic carbon) indicators. This is owed partly to the nature of the comparison between large scale and smallscale growing. The labor indicator is used because there is a stark and measurable difference between the labor inputs of large scale and smallscale growing. This can be seen as an opportunity for local employment, but a risk for corporate efficiency. Since the late 1990s, incorporating any social aspect explicitly in an otherwise environmental LCA, was unseen. Dreyer ( 2006 ) articulated this and recently UNEP published a guidance document ( UNEP 2009b ). This study only examined employment and it potential monetary value to the employee. s The soil organic carbon indicator is used because the land use change that occurs when large scale agricultural farmland is converted back to natural grassland as urban gardening, utilizing the small scale format, increases. Under this assumption, the small scale format displaces the large scale format in equal areas, weighted by crop productivity per land area. For the small scale format, this indicator assumes that a third of the land is converted from previous use as turf, a third as bare soil, and a third as natural grassland (assumed to represent vacant, weedy areas). All three scenarios have been studied by a number of researchers ( Baird 2011 ; Churkina 2008 ; Kim 2009 ; Morgan 2010 ; Pouyat 2002 ; Pouyat 2006 ; Pouyat 2009 ; Qian 2010 ). These studies make it possible to estimate the long term state of carbon in soil after a land use change is enacted. When compiling the results of these studies, one may conclude, In general, the state 83

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of carbon is heavily influenced by growing practices, the use of amendments or fertilize rs, and t ence an of plant cover. Each one of these is starkly different between the g ats analyzed in th y, a terestin Raw impacts are d in Appendix I he exist rowing form d type is stud nd is therefore in g to research. presente Impacts are graphically cked b hs foun ndix J reported in a series of sta ar grap d in Appe As desc r 5.2.4, the emissions from ind is a ions. The graphs are arranged by impact and show all crops individually conversion scenarios associated with the ge is p axis (right hand). The land use change results re each category and discussed in fu l in Section 5.4.3. 5.4 Interpretation The interpretation phase of the LCA methodology follows the ISO Environmental ent e Cycle O 14043 ( ISO ibed in Section stacked categories are direct production, irect em sions from production nd post production emiss and the land use change m. Land use chan resented on a separate y sented as percent change in are summarized and p rther detai impact standard entitled Interpretation, IS Managem Life Cycl Assessment Life 1998 ). The com ta phase are the follow Identification of the significant issues based on the LCI and on ide le itivity, uncer cks. Conclusions, recommendations, and reporting. Because t s n 5 ponents of the interpre tion ing: LCIA. Evaluati which cons rs com p teness, sens tainty, and consistency che of the nature of agricultural product LCAs, inconsistencies can arise from the differen cales of growing, different locations, and sources of data. These have been discussed in Sectio .2.4 and are further discussed below. 84

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5.4.1 Comparative LCA of Vegetables The results of the impact assessment are examined for inconsistencies and sensitivities in Table 5 sense of the impact to be unexpected. A possible explanation is also offered for each instance. 13. The final column in the table indicates where the author found either the magnitude of For relationship to information on the land use change observations, see Section 5.4.3. Table 5 13: Interpretation of Impacts Assessment Impact Observations for Production (P) Un Category Observations for Land Use Change (LUC) Sensitivities & Explanation expected (P) GHG emissions are dominated by direct emissions for commercial production. The main contribution for direct emissions is soil/plant emissions (DNDC model), except methane. (P) GHG emissions are dominated by The main contribution for indirect emissions indirect emissions for urban production. is fossil fuel use associated with vehicle trips. (P) Energy use is dominated by indirect commercial production. Packaging and transport dominate potato, and post production sources for onion, and tomato. (P) Carcinogenicity and non carcinogenic production phases. hazard dominates in indirect and post Impacts categorically attributed mostly to fossil fuel combustion. General (P) Overall ecological impacts vary significantly between commercial and urban production in the indirect respectively. They are dominated by packaging for commercial production and vehicle trips emissions and post production phases, (gasoline) for urban production. (P) Urban tomato energy use is much higher than the rest. 84% of energy use attributed to electricity use for growing starts. One outlier in this dominant form of starts used no/low energy vegetable production would then be categorically more energy efficient and emit even fewer GHGs than estimated in this study. discussion is the urban tomato. If the cold frames and hoop houses, urban x Energy in a net increased specific energy result in a net decrease. Energy use from vehicle trips and drip tape x (LUC) Urban instances of tomato result footprint. Potato, carrot, and onion amount to 60% and 30%, respectively. (P) Urban potato occupation much larger specific land area than both the other urban x than the rest. One participant had almost 6 times the participant and commercial farms. Land Use a net increased land occupation. All others except carrot 2 1 result in a Sensitive to potato yield. (LUC) Urban instances of potato result in net reduced land occupation. 85

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Impact Observations for Production (P) Un Category Observations for Land Use Change (LUC) Sensitivities & Explanation expected (P) Instances of urban potato and carrot their respective commercial variety. Use of automatic timer can be correlated with increased water use; one timer is used different water needs; the same emitter growing consumed more water than Onion and tomato consumed less. for a garden containing different plants and (drip tape) or broadcast spray is used for all plants. x Water (LUC) Conversion to urban garden water use, except for potato and carrot farmland and also turf. resulted in a net decrease in specific ( 2 1). This conversion scenario fallows commercial x (LUC) All conversions result in a net lights) dominates tomato to the extent that impact. decrease in total GWP. The urban tomato energy use (from grow conversion is practically no net negative Total below Carbon Dioxide eq. (GWP) Individual components are discussed (P) Urban production had a greater contribution of CO2 from indirect Most of the emissions can be attributed to sources. fuel combustion (vehicle trips and tilling) (P) Commercial production had a greater production phases. Over 60% of CO2 for commercial onion and direct emissions. contribution of CO2 from direct and post tomato production is related to agricultural x Carbon The urban tomato energy u Dioxide (LUC) All conversions result in a net decrease in total GWP. se (from grow lights) dominates tomato to the extent that conversion is practically no net negative impact. (P) N2O domina tes heavily in commercial production compared to urban Generally, over 70% of N2O emissions can be attributed to agricultural (plant/soil) production. emissions. (P) The urban tomato is a distinct NO The use of compostor manure based 2sink. potting soil is responsible for this behavior. x Nitrous (P) Urban turf has more NO emissions than urban vegetables. Urban turf results in about 100 times more attributed to nitrogen fertilize Oxide 2N2O than urban vegetables, overwhelmingly r. (P) Urban production is characterized by Fossil fuel use is responsible indirect methane entirely compared to other emissions. for this behavior, except for tomato, where Scope 2 electricity dominates fuel use. x Methane packaging x (P) Commercial production has significant components of post Most of the emissions stem from production methane emissions. and transport. (P) Urban turf has the highest carcinogenicity of all crops, urban or Use of fu this behavior. commercial. ngicide and fertilizer is the cause of (P) Carcinogenicity for urban production Generally, over 60% of carcinogenicity is is entirely dominated by indirect sources. related to fuel combustion. TRACI (P) Carcinogenicity for commercial post production phases. Similar to urban production, the majority of irrigation pumps) production is split between indirect and carcinogenicity is attributed to fuel combustion (transport and use of diesel x Health gens n vegetable results in a net reduction in carcinogenicity; other conversions increase it. The intensive use of gasoline combustion in turf maintenance compared to urban production is the cause of this. x Human Carcino (LUC) Conversion from turf to urba TRACI Human (P) Hazard for urban production is generated in the indirect phase almost entirely. Fuel use strongly dominates this impact for all urban vegetables. For tomato, about half is attributed to electricity use. 86

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Impac t Observations for Production (P) Un Category Observations for Land Use Change (LUC) Sensitivities & Explanation expected (P) Hazard for urban production is production phases. Commercial onion, potato, and tomato have distances (tomato over 80%). generated in the indirect and post significant contributions from transport (P) Hazard for the commercial carrot is very high. This is caused by the use of magnesium fertilizer, relatively unique to carrots. x Health carcino (Hazard) production results in a net reduction in The intensive use of gasoline combustion in production is the cause of this. It is assumed Non gens (LUC) All conversions to urban vegetable hazard. turf maintenance compared to urban that better urban potato yields would then make conversion to potato less hazardous. (P) Urban and commercial production both have releases to air mostly from the The overwhelming majority of releases to air d by use of plastic drip tape. x indirect phase. are cause TRACI Ecological to Air C (LUC) Conversion to urban carrot and onio The cause of this is the unusual contribution of magnesium fertilizer to ecological toxicity (carrot o Releases ompart ment n results in a net reduction in releases to air. All others increase. nly). x (P) Releases to water from urban production o ccur primarily in the indirect st phase; there is almost no direct, or po production contribution. Approximately 60% of releases to water originate from electricity consumption, in the case of urban tomato. The remainder, true for all other urban production, is due to gasoline combustion. (P) Releases to water from commercial production occur mainly in the post is generally production phase for onion and tomato, while indirect contributions make up the majority of releases for carrot and potato. Transport and fuel combustion more for onion and tomato, and less for carrot and potato. TRACI Ecological Releases Compart ment ction to Water (LUC) All conversions to urban production result in fewer releases to water. Elimination of indirect and post produ components for commercial growing is responsible for this behavior. (P) For urban pr oduction, there are almost no releases to the soil, and, in the case of tomato, restoration of soil (negative release) takes place. l abstention of This is consistent with the genera of synthetic fertilizer and pesticide use for urban production. The strongly negative contribution from tomato is caused by use potting soil/amendment. (P) Releases to soil for commercial production are entirel y dominated by the post production phase. Releases to soil from the post production phase of commercial production are attributed mainly to cardboard packaging. x TRACI Ecological Releases to Soil (LUC) All instances of urban production result in a net reduction of releases to if rdboard. Compart ment soil. Urban production uses comparably less, any, ca (P) SOC for all urban prod uction is much approach means there is always organic year round. less than for commercial production. Turf outperforms vegetables in the 0 10 cm zone. This is as reported by the DNDC model. It is believed that the disparity is caused by the short, single growing season in one locale (Denver), compared to crops and cover cropping systems implemented year round for commercial production. Also, for commercial production, the aggregated material being generated x Soil organic (SOC) uction appears to have a net depletion of SOC. See previous response. Also, after commercial farmland is left fallow, SOC develops slower and at a deeper root zone than those reported (0 10 cm depth) x carbon (LUC) Conversion to urban prod 87

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Impact Category Observations for Production (P) Observations for Land Use Change (LUC) Sensitivities & Explanation Un expected (P) Labor required for urban production is much greater than for commercial production. Mechanization is the cause of this disparity. Employ ment hours employment hours. ast ion. (LUC) Conversion to urban production results in categorical net gains in Urban production generally requires at le 50 times more labor than commercial product (P) Specific wages ($/lb) are much hig for urban production than for commercial production. her Lack of mechanization is the cause of this times to over 350 times that of commercial production. disparity. Specific wages ($/lb) for urban production range from 14 x Laborer pay turf n that n. on. eq. (LUC) Conversion to urban productio results in higher specific wages than of commercial productio See previous response. As a point of reference, on an equivalent area basis, urban turf has the lowest wage of urban producti x CONVERSION 2 1 Conversions aded urban land to urban garden ( The en a Conversions from urban turf to urban garden ( 3) displac displacement results in com 1). KEY: from degr 2) displace demand on large commercial farms. displacem 3 1 t results in commercial farmland to be left f llow ( 1). e demand on large commercial farms. The mercial farmland to be left fallow ( 5.4.2 Water Footprint oot ommercially grown veget very distinct from urban grown vegetables. Appendices C and H for water footprint In all cases, tomato, po on are grown w rms typically use untreated ch e a large collateral e on the p ter is typically en transport rces may ploy n. pes of on also cally t in Denver ls to t gravity, but th otable. By the time water y ha ng cycle before fina Water f prints for c ables are See values per functional unit. ith irrigation. Large scale fa tato, carrot, and oni (raw) pumped groundwater or dit water. Pumped groundwater can hav nergy footprint, depending umping head and flow rate. Ditch wa ergyneutral, having been ed from its origin by gravity. Both sou be em irrigati trave ed for spray, flood, or drip irrigatio In the urban setting, these three ty exist, but the source is typi he treatment plant by reated potable water. Potable water en energy is required to make it p arrives at the point of use, it ma l storage. ve also gone through another pumpi 88

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A LCI m bodied water. Many data sources provide ous l of th mpt otp withdrawals. Consumption refers to irreversible use of fresh beneficial uses it origi nsp aporation, emi ptiv water use, ple be considered a consumptive use if, after sewage treatment, that water to signific sometimes listed for power plant or industrial c typically much large t s. as requ is also a rce w o table water for irrigation when raw water e ethical potable water in ur eas is easily orders of magnitu raw water. d, so a nd, since e ready handily meet fresh vegetable dem d water supplies to urban he pers ge. In the conte LCA, although potable water c footprint, u s it than the mm ch ry and ion efficiency is the subject of the study. lmost all aterials and flows involve em numer consu classifications for water, not al em use. For this study, only water ion was counted in the water fo rint. Other water types fall under water that renders it unsuitable for the iration, power plant cooling tower ev nally had. For example, plant tra and ch for exam returned cal waste streams are all consum would not e uses.On the other hand, household a fresh water body without ant impairment. Water withdrawals are ooling processes. Because these are ant to exclude these tabulated value r than consumptive use, it is impor Water h resou other qualities than the energy ith many competing uses. The use ired to extract, treat, or deliver it. It f high quality po could be used presents som ban ar considerations. For one, the cost of de more expensive than the cheapest ter rights have been secured. Seco and, why tax stresse Indee farms al areas. T me water is truly free, if the right w larg se issues can be put into proper pective using LCA and land use chan xt of arries with it more of an energy and emissions e much less water per functional un component of the emissions invento rban vegetable gardens typically u ercial farms. A comparison of ea large co irrigat 89

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In ntext recognized that the land devoted to vegetab tion gr d ubiqutous cro take tables grown on t of land. an expected water use for urban vege y 1) a single tap and timer le for all plants, gardless of crop specific demand; 3) some yards use broadcast spray. This is similar to ed water use with lawn sprinkler timers. Land Use Change In review, it is assumed that land area representing the functional unit of a given vegetable can be put into service or out of service based on the incidence of vegetable production elsewhere. ng displace an equal amount of commercial farmland, adjusted for differences in land nversion scenarios are summarized in Table 5 14 for each impact category. the co of land use change, it must be le produc urban has a likelihood of displacing turf ass, the single most water intense an i p. It is not uncommon that turf he same amount s several times more water than vege Larger th tables is thought to be caused b is used in the urban yards; 2) a sing drip tape emitter size is used re increas 5.4.3 A number of researchers have characterized SOC and agricultural emissions for various lo term land uses and as it relates to land use change.This study sets urban gardening as a type of land use that displaces existing land uses. Conversion to urban garden involves displacement from one of three land uses, namely: 1. Large scale commercial farmland each new instance of urban gardening is assumed to productivity related to the functional unit. 2. Neglected and degraded urban areas in the existing urban setting, a new urban garden can purpose these areas unused areas that typically have the poorest soil health and lowest SOC levels. 3. Residential turf grass in the existing urban setting, the demand for space for an urban garden may force conversion from turf grass. These land uses and co 90

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Table 5 14: Net Change in Impacts from Land Use Conversion Conversions from Commercial Production to Urban Producti on Impact Category 2 1 Potato 2 1 Carrot 2 1 On ion 2 1 Tomato 3 1 Potato 3 1 Carrot 3 1 On ion 3 1 Tomato Energy (MJ/lb) -27% -56% -62% 235% -124% -135% -115% 196% Land Use (ft /lb) 281% 2 130% -11% -15% 167% -71% -71% -75% Water (gal/lb) 64% 177% -24% -10% -156% -95% -82% -87% Global Warming Potential (CO2e/lb) -4% -35% -71% -13% -119% -143% -103% -37% Carbon Dioxide (CO2 as CO2e/lb) -12% -33% -69% 2% -105% -130% -98% -17% Nitrous Oxide (N2O as CO2e/lb) -93% -96% -97% -214% -127% -124% -105% -223% Methane (CH4 as CO2e/lb) 101% -53% -77% 511% -179% -170% -180% 403% TRACI Human Health 303% Carcinogens (CTUh/lb) 126% 42% 21% -320% -406% -217% -207% TRACI Human Health Non -3% -79% -30% -24% -162% -133% -171% -121% carcinogens (CTUh/lb) TRACI Ecological Releases to 8% -72% -31% 172% -6% -76% -39% 142% Air (CTUe/lb) TRACI Ecological Releases to -30% -69% -52% -56% -145% -149% -149% -106% Water (CTUe/lb) TRACI Ecological Releases to Soil (CTUe/lb) -98% -98% -99% -333% -102% -104% -101% -335% Soil Organic Carbon (SOC) (kg -125% -22% -116% -109% -243% -195% -156% -154% Change in kgC/kg soil per year) 25,882% 66,698% 2,350% 11,617% 24,918% 59,461% 2,046% 10,279% Labor Hours (hours/lb) Labor Pay (U$S/lb) 14,747% 38,070% 1,300% 6,596% 11,992% 17,393% 433% 2,773% Shaded cells denote reduced resource use or improvements to human health, environment, and society. Values shown based on mean values from Appendix I Unshaded cells denote the opposite. Please refer to Table 5 10 for confidence intervals. 2 1: Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). displacement results in commercial farmland to be left fallow ( 1). CTUh Equivalent Comparative Toxicity Units for human health receptors. Defined as the estimated increase in Lb Pounds 3 1: Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The CTUe Equivalent Comparative Toxicity Units for ecological receptors. Defined as the potentially affected fraction of species (PAF) integrated over time and volume per unit mass. morbidity in the total human population per unit mass of a chemical emitted. MJ Megajoules 91

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As an aide to understanding impacts, a summary table was generated that converts the functional unit from 1 lb of consumer vegetable to the total weight of all demand in the Denver BroomfieldAurora Metropolitan Statistical Area. The values in Table 5 15 may be more user friendly and give the reader a sense of scale. 92

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93 Table 5 15: Net Impacts Including Land Use Change with Altern ate Functional Unit Conversions from Commercial Production to Urban Production Impact Category (all units per weight of annual demand of Denver Aurora Broomfield metropolitan statistical area) 2 1 Potato 2 1 Carrot 2 1 Onion 2 1 Tomato 3 1 Potato 3 1 Carrot 3 1 Onion 3 1 Tomato Energy (MJ) [Mu ltiple of Colorado usehold use]1 average annual ho -3. 61E+07 [-333] -1. 99E+07 [ -183.03] -7.7 70E+0 [-709 ] 3. 63E+08 [3,344] -1.6 4E+08 [1,513] -4.7 6E+07 [-438] -1.43 E+08 [-1, 3.0 313] 3E+08 [ 2,793] Land Use (ft2) [a c] 2.1 0E+08 [ 4,824] 1. 20E+07 [275] -8.6 41E+0 [-193 ] -1. 01E+07 [-233] 1.2 5E+08 [2,861] -6.53 E+06 [-150] -5.22E +07 [-1, 198] -5.00 E+07 [1,149] Water (ac ft) [Multiple of D average an enver nual household water 6.2 use]2 0E+08 [4,757] 2.9 8E+08 [2,289] -4.8 54E+0 [-3,487] -1. 32E+08 [-1,011] -1.5 0E+09 [-11,517] -1.6 1E+08 [-1,233] -1.54 E+09 [-11,821] -1.12 E+09 [-8,606] Global Warming Pote ntial (kg CO2e) -5.78E+05 -1.24E+06 -1. 97E+07 -4. 74E+06 -1.65E+07 -5.10E+06 -2.88E+07 -1.30E+07 Carbon Dioxide (kg CO2 as CO 2e) -1. 38E+06 -9. 60E+05 -1.7 57E+0 5. 33E+05 -1.2 1E+07 -3.7 9E+06 -2.23 E+07 -5.5 2E+06 Nitrous Oxide (kg N2O as CO2e) -1. 69E+06 -5. 13E+05 -4. 60E+06 -7.34E+06 -2.30E+06 -6.62E+05 -4.94E+06 -7.66E+06 Methane (kg CH4 as CO2 e) 3.91E+05 -6.08E+04 -2.51E+05 1.42E+06 -6.89E+05 -1.96E+05 -5.86E+05 1.12E+06 TRACI Human Health Carcin (CTUh) ogens 3.6 5E-01 3. 84E-02 6.17E-0 2 3. 18E-02 -3. 85E-01 -1. 24E-01 -3.2 2E-01 -3. 18E-01 TRACI Human Health Non carcinogens (C TUh) -6.4 5E-02 -1. 06E+00 -3.64E-01 -3.81E-01 -3.43E +00 -1.79 E+00 -2.09E +00 -1.9 5E+00 TRACI Ecological Releases to Air (CTUe) 1.4 0E+05 -1. 00E+06 -5.5 32E+0 6. 54E+05 -1.0 8E+05 -1.0 6E+06 -6.59E+05 5.3 8E+05 TRACI Ecological Releases to Water (CTUe) -6. 75E+06 -4. 69E+06 -6.6 92E+0 -1. 35E+07 -3.2 2E+07 -1.0 2E+07 -2.00 E+07 -2. 54E+07 TRACI Ecological Releases to So (CTUe) il -1. 73E+06 -2. 23E+05 -1.71E+0 6 -5. 21E+06 -1.8 0E+06 -2.3 8E+05 -1.75 E+06 -5. 24E+06 Soil Organic Carbon (SOC ) (kg il per year) Change in kgC/kg so -1.11E+04 -2.83E+02 -1.54E+04 -1. 19E+04 -2. 15E+04 -2.07E+04 -1. 53E+03 -2. 68E+04 Labor Hours (hours) 1.36E+07 1.01E+06 2.00E+06 2.05E+06 1.31E+07 8.98E+05 1.74E+06 1.81E+06 Labo r Pay (U$S) 1.08E+0 8 8.05E+06 1.55E+07 1.63E+07 8.79E+07 3.68E+06 5.17E+06 6.84E+06 Shaded cells denote reduced resource use or improvements to human health, environment, and society. Unshaded cells denote the opposite. Functional unit converted to total annual demand for Denver Aurora Broomfield metropolitan statistical area (MSA) (U.S. Census Bureau, 2008 2012 American Community Survey). MSA total population, 2010, all sexes, l ag vail total pita/year). s 200 Energy Consumption Survey. www.eia.gov/consumption/residential/ al es: 2,554,243. Total households: 1,000,849. Demand estimated as weight to consumer (Calculated by ERS/USDA based on data from various sources (see http://www.ers.usda.gov/data products/food availability (per capita) data system/loss adjusted food aability documentation.aspx). Consumer defined as average U.S. person suitable for use with unqualified n values.Potato 33; Carrot 7.14; Onion 16.9; Tomato 15.4 (lbs/ca populatio1 EIA 9 Residential Wate http://www.denverwater. org/AboutUs/KeyFacts/ 2Denver r 2014. 2 1: Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 1: Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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6 C Urban vegetable production, whose practices are dictated by small scale, whether it be on a residential or larger lot, has clear benefits and few costs to the environment, the urban grower, and the urban consumer. This study limited itself to certain life cycle impacts, but the context around which the boundary of the LCA was drawn is an important point and merits further discussion. The mostly beneficial effect of urban vegetable production was presented in terms of a comparison with the commercially grown vegetable. The LCA boundary mimicked the boundary set by the trend among cities to account for GHGs and supply chains beyond their political borders. With many impact metrics, the urban accountant can claim net reductions in most emissions and net gains in employment. This pairs well with many other beneficial attributes of urban vegetable production such as food access, nutrition, social fabric, and local economy. The same cannot be said for the commercial vegetable production that participates in the grocery store supply chain. But what if ones boundary was simply the home or the individual CSA business? If not for urban vegetable production, there is not necessarily any comparable land use that provides the social benefits of labor and nutrition. Except for turf, growing vegetables can have a greater long term carbon fixing capability than other land uses.Water use would be higher, but one is growing food in exchange for potable water use. When paired with the logistical fact that potable water is available practically everywhere in the urban boundary, vegetables can be produced and consumed locally, not only foregoing the transportation costs of vegetables grown in the hinterlands but also at least some trips the grocery store. 94

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Similarly, what if the boundary was simply the commercial farm operation? Commercia l vegetable production would shrink ly few vegetable because of the highly mechanized nature of the ope e los te agriculturaluc co a e the field goes fallow. If the irrigation tappe same cessation thdrawals would eventually flow downstream to the next user. In the arid west, where ers commercial vegetables are grown, little groundwater or surface is not d. c l residential development where turf becomes the main crop. tions discuss the t ch needed. sa f a g variabil n t li sim ars of gro strongly i ally friendly than others. Data een in response to less demand. This would result in relative job d of rations. Also, with th s lost per poun s of in nsive prod tion, particularly when ver crops are used and tilling is n ot perfo rmed, c rbon fixi ng woul d be a m uch slow er proc ss when water w ere grou ndwater the ce ssation o f withdr awals could be a benefit to others of wi d into the aquifer. If surface water were used, most of Denv eventually beneficially use These b enefits ould be potentia lly nega ted, how ever, if and availability spawns lowdensity The following sec limitations of study academic contribu tions, and fur her resear 6.1 Limitations Generally, hybrid LCAs are ddled w ith signi icant lim itations in terms of reco nciling sc ales, dat sources, and trackin ity and u ncertai ty. Strict quanti ative er ror and probabi ty distributions were not possible, although qualitative screening methods and Monte Carlo ulation enabled an estimated mean and confidence interval. The study could have benefitted from a larger sample size of instances of urban production, spanning several ye wing seasons. Because yields affect all impacts linearly, variations in yield can nfluence whether one growing format is more environment collection could have been more vigilant and more commitment and communication betw 95

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th e author and growers would have benefitted the amount and accuracy of the primary, case study data. s gnificantly different resource and environmental impact footprints. In addition, urban production values found in this study may not be representative of other urban growers in other areas of the country. While the study method is replicable and repeatable, its findings are not necessarily comparable or representative to any other metropolitan area in the country. Due to the unique supply chains that exist for every retailer in every major market, commercially produced vegetable may have si 6.2 Contributions The results of the LCA complement other urban metabolism sustainability reports (e.g., Ramaswami 2008 ) and potentially establish urban food in general as an important sector on the civic agenda. The LCA information reveals that there are some aspects of small scale growing only stop job loss but that not only reduce environmental impacts, but reverse them. For example, practices that not create jobs; or that urban growing results in not merely a low or net zero carbon footprint but a net positive one. s : soil organic carbon In review, the study provided an interdisciplinary impacts assessment, a hybrid LCA using appropriate scales, examined net impacts after transboundary land use changes were taken into account, and provided relevant data for decision makers. Most LCAs are conducted with interest in greenhouse gas accounting. This study contribute additional valuable metrics such as resource use (for water, energy, fuel), ecotoxicity, and human health impacts. Most unusual are the additional impact metrics 96

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(SOC) as an indicator of soil health, land use change, and employment. Soil organic carbo highly related to current and antecedent land use and reaches a steady state at a 30year horizon or less ( Kim 2009 n is ; Morgan 2010 ). Second, the labor indicator is used because stark and measurable difference between the labor inputs of large scale and small scale growing. This can be seen as an opportunity for local employment, but a risk for corporate efficiency. This study carefully chooses appropriate data sources that match the level of variability and scale of the components of the vegetable production system where many other studies greenhouse gas accounting reports use national or regional data. In doing so, the accuracy is there is a and increased, uncertainty is reduced and meaningful results can inform the transboundary conversation. Most other food LCA studies use national, or even global IPCC Tier 1 data. Reporting of emissions can draw from any one or more of the intermediate sources from raw data to proprietary software tools, as depicted in Figure 6 1. Each use can support far reaching imp decisions on the part of government and industry ( Grubb 1995 and actful policy ; Smith et al. 2008 ; Lynch et al. 2011 ; Ramaswami et al. 2012 ; Zborel et al. 2012 ). Yet unless explicitly described, variability and uncertainty in emissions are generally not carried forward beyon raw data phase. Even variability and uncertainty in the raw data are scarce ( Commonwealth d the of Australia 2009 ). Although some standards exist for quantifying and reporting uncertain variability in emission data and emission factors (e.g., ISO 2006 ty and ; GHG Protocol 2013 ; IPCC 2006 ; EPA 2013 ), the relative contributions and effects of each emission factor on each compone a life cycle inventory is generally not reported in life cycle assessment studies ( Commonwea nt of lth of Australia 2009 ). 97

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Figure 6 1: Data Sources and Emissions Reporting estimated, and modeled data. The examination of N2O in this study showed that N2O emissions have generally been overestimated in the past, have an unreported component of variability, and can be greatly influenced by management practices (e.g Pathways of agricultural production emissions reporting can include measured, N application amounts and times). The implications of this overestimation and uncertainty could be significant, depending on ones point of intersection with the life cycle and supply chain of wheat or its main food product, bread. For example, Korsaeth et al. ( 2012 ) conducted a life cycle assessment of cereal and bread production in Norway. The study estimated a total global warming potential per kg of bread at study for 0.95 kg CO2equivalent. In the agricultural production phase, N2O emissions accounted for about 50% of the total global warming potential for spring wheat emissions. Although the used the Norwegian Farm Accountancy Survey (including farm specific soil and weather data 98

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1,000 farms across Norway) to represent 93 chosen farms, N2O emissions were still estimated from the IPCC Tier 1 approach. The lack of proper characterization of agricultural production emissions may also be an impediment to the voluntary, market driven reporting of corporate social responsibility (CSR) reports and individual product life cycle assessments. Market forces could be a relatively untapped GHG mitigative strategy if more products containing wheat, for example, were able to (GRI) quantify and report the contribution of agricultural practices to its overall GHG emissions portfolio. With emissions known, the market may then be able to influence the selection of production regions, practices, and even individual farms. For example, the largest bread producer in the U.S. and the world, Grupo Bimbo, publishes a Global Reporting Initiative checked and registered CSR report ( Grupo Bimbo 2011 ). Unfortunately, it does not repor t any type of GHG emissions from agricultural production. Another large agri food company in the emissions per pound of product by 20% by 2015, while engaging suppliers through a variety of initiatives including an information database and regional dashboards sharing best practices and field/crop input and yield benchmarks ( ConAgra 2011 U.S., ConAgra Foods, also publishes a GRI CSR report. ConAgra says it aims to reduce GHG ). At the time of this writing, no further opolitan area leaders and decision makers. information was available. Finally, beliefs (at least of the author) about the magnitude of supposed benefits of small scale urban production were sometimes proved wrong. The quantitative comparison of the two growing formats using a life cycle approach made this possible. This study contributes to the body of information and cultural sense we have about urban agriculture, and lends relevance to inform Denver metr 99

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6 .3 Further Research This study clearly points to a need for more primary data collection for urban production. As a more representative database is compiled, urban sustainability could be better evaluated. Corresponding to this knowledge is the needed characterization of the larger food system and the capacity of cities to meet their food demand, not just seasonally, but year round. The results of the LCA provide backcasting information that could be used, for example, in a three step process of indicator selection, an agent based model (ABM), and quantitative indicators. It could be useful for evaluating the sustainability of any aspect of the urban metabolism, from clothing to water to personal electronic devices. It may be particularly useful for evaluating urban activities that became popular for other reasons than sustainability, as a tool to determine if such activities should be further promoted, incentivized, or discourage on su owing: Agent based models of agri food systems Choice of a relevant suite of sustainability indicators Local underutilized properties or brownfield surveys d the basis of stainability. Other junctures in this research that may be suited for further research include the foll Modeling methods Validation methods Food system LCA data How to measure the food supply other than by weight, volume, or calories Local gardening preferences surveys Terminal markets and movement of food supply Behavioral theory for urban gardening Comparisons between cities in developed and developing countries with respect to urban agriculture and urban gardening 100

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Uncharacterized variability in agricultural emissions reporting and modeling Stochastic methods for re and consecutive wet days presentative daily rainfall that preserve consecutive dry days 101

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REFERENCES Anderson, Molly D. 2009. Defining Sus tainable Food Systems through Indicators and Standards. Food Systems Integrity. January. Brevik, Eric C. 2012. Soils and Climate Change: Gas Fluxes and Soil Processes. Soil Horizons. Vol. 53 No. 4, p. 1223. Accessed online at: https://www.agronomy.org/publications/sh/articles/53/4/12 Brett, John., et al. 2013. A Methodology for Determining the Production Potential of Sustainable Urban Agriculture. Journal of Agriculture, Food Systems, and Community e Golf Course Superintendents Association of America 2011 Education Conference, Orlando, Security. The Community Food Security Coalition's North American Initiative on Urban Development (under review). Baird, James. 2011. Up date on Carbon Sequestration Science. Session presentation at th Florida, Feb 7 11. Bellows, Anne C. et al. 2004. Health Benefits of Urban Agriculture Public Health and Food Agriculture. Accessed online at: http://www.foodsecurity.org/UAHealthArticle.pdf IMES/EESS Report No. 24. Department of Environ mental and Energy Systems Studies, d, Sweden. March. Churkina, Galina.2008. Modeling the carbon cycle of urban systems. Ecological Modeling. 216 Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of sold in Denver metropolitan area major 1 Bannock St., Denver, CO 80216 1850, Phone: bsite: www.freshpoint.com E mail: mike.comazzi@freshpoint.com Cm 1994. Australias Food and Nutrition. Australian Institute of Cm in Agriculture. lopment Corporation. Publication No. 09/029. March. C gra Foods, Inc., One ConAgra Drive, Cuba m/agriculture today.htm Carlsson. Annika. 1997. Greenhouse Gas Emissions in the Life Cycle of Carrots and Tomatoes. Lund University, Lun (107). selected fres h vege tables imported and supermarkets.FreshPoint of Denver, 515 303 382 1742. We September 5. omonwealth of Aust ralia. Health and We lfare. Lester, Ian H. omonwealth of Au st ralia. 2009. A Literature Review of Life Cycle Assessment Rural Industries Researc h and Deve onAgra. 2011. Corporate Responsibility Report. ConA Omaha, Nebraska 68102 5001. Ministry of Agriculture.2008. Agriculture in Cuba Today. Accessed online at: http://www.cubaagricult ure.co 102

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Dd and Decker, Ethan H. et al. 2000. Energy and Material Flow Through the Urban Ecosystem.Annual Review of Energy and the Environment Vol. 25: 685 740. Dreyer, Louise C., et al. 2006. A Framework for Social Life Cycle Impact Assessment. International Journal of Life Cycle Assessment. 11 (2) 88. European Commission. 2010. International Reference Life Cycle Data System (ILCD) Handbook General guide for Life Cycle Assessment Detailed guidance. First edition March 2010. EUR 24708 EN. Luxembourg. Joint Research Centre, Institute for Environment and Sustainability. Publications Office of the European Union. March Fisher, Stephen W. 2013. Unrecognized variation in nitrous oxide emissions reported for a global dryland staple crop. Journal of Environmental Management (submitted 21 Jan 14) Gssling, Stefan, et al. 2011. Food management in tourism Reducing tourisms carbon foodprint. Tourism Management Vol. 32: 534 543. Grace Communications Foundation (Grace). 2011. Sustainable Table Sustainable vs. industrial: A comparison. Accessed online at: http://www.sustainabletable.org/intro/comparison/ avison, E.A., Swank, W.T., Perry, T.O. 1986. Distinguishing between Nitrification Denitrification as Sources of Gaseous Nitrogen Production in Soil. Applied and Environmental Microbiology. December. pp. 1280 1286. Greenhouse Gas Protocol. 2013. Quantitative inventory uncertainty. World Resources Institute and the World Business Council for Sustainable Development. Accessed online at: http://www.ghgprotocol.org/files/ghgp/tools/ Quantitative%20Uncertainty%20Guidanc e.pdf Grubb, M. 1995. Seeking Fair Weather: Ethics and the International Debate on Climate Change. International Affairs 71(3): 463 496. Grupo Bimbo. 2011. Integrated Annual Report. Corporative Bimbo, S.A. de C.V., Prolongacin Paseo de la Reforma No. 1000, Colonia Pea Blanca Santa Fe, Delegacin lvaro Obregn, Mexico City 01210. Haas, Guido, et al. 2000. Life Cycle Assessment Framework in Agriculture on the Farm Level. International Journal of Life Cycle Assessment, Vol. 5 No. 6, 345 348. Hayashi, Kiyotada, et al. 2006. Life Cycle Assessment of Agricultural Production Systems. Proceedings of the International Seminar on Technology Development for Good Agricultural Practice (GAP) in Asia and Oceania. Heller, Martin C. and Keoleian, Gregory A. 2000. Life Cycle Based Sustainability Indicators for Assessment of the U.S. Food System. Center for Sustainable Systems. Report No. CSS00 04. 6 December. 103

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C. and Keoleian, Gregory A. 2003. Assessing the sustainability of the US food system. Agricultural Systems 76 1007. Hillman, ha. 2010. Greenhouse Gas Emission Footprints and Energy Use Benchmarks for Eight U.S. Cities. Environmental Science and Technology 44, Intergov National Greenhouse Gas Inventories. Accessed online at: http://www.ipcc nggip.iges.or.jp/public/2006gl/ Heller, Martin Timothy and Ramaswami, Anurad 1902. ernmental Panel on Climate Change (IPCC).2006. Guidelines for IPCC. 2 008. Climate Change 2007: Synthesis Report. Fourth Assessment report on Climate Change. Geneva, Switzerland. Institute of Grocery Distribution (IGD). 2007. Embedded Water in Food Production.Accessed online at: http://www.igd.com/inde x.asp?id=1&fid=1&sid=5 ternational Standards Organization (ISO). 1998. Environmental Management Life Cycle Kim, Hy me Unexplored Variables. Environmental Science and Technology. 43 (961). Kloppen f ent, Ordinary People. Human Organization, Vol. 59, No. 2. Korsaet en, A.Z., Roer, A. G., Henriksen, T.M., Sonesson, U., Bonesmo, H., Skjelvg, A.O., Strmman, A.H. 2012. Environmental life cycle assessment of cereal and bread Kuenen icrobiological Societies (FEMS) Microbiology Reviews 15: 109. hers.St. Petersburg, Russia. ww.leopold.iastate.edu/ resources/fruitveg/fruitveg.php In Assessment Life Cycle Interpretation ISO 14043. ungtae, et. al. 2009. Biofuels, Land Use Change, and Greenhouse Gas Emissions: So burg, Jack Jr. et al. 2000. Tasting Food, Tasting Sustainability: Defining the Attributes o an Alternative Food System with Compet h, A., Jacobs production in Norway. Acta Agriculturae Scandinavica Section A Animal Science 62 (4): 242. J.G., Robertson, L.A. 1994. Combined nitrificationdenitrification processes. Federation of European M Lenin, V.I. 1917. New Data on the Laws Governing the Development of Capitalism in Agriculture. Zhizn i Znaniye Publis Leopold Center For Sustainable Agriculture. 2011. Where do your fresh fruits and vegetables come from? Accessed online at: http://w Li, C.S. 2000. Modeling trace gas emissions from agricultural ecosystems. Nutrients Cycling in Agroecosystems 58: 259 276. ial Impacts ergy Constrained World? Sustainability 3: 322 362. Lynch, D.H., MacRae, R., Martin, R.C. 2011. The Carbon and Global Warming Potent of Organic Farming: Does It Have a Significant Role in an En 104

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r, Barbara. 2010. Personal communication regarding Denver historical instance victory gardens. January 31. Masone s of ply he Food Industry. Wiley Blackwell. March. Morgan eptune Technology Group, Inc. (Neptune). 2008. T 10 Product Sheet. 1600 Alabama Highway at Miller, Leslie. 2010. Masters thesis. University of Colorado Denver, Department of Civil Engineering. Mogensen, Lisbeth, et al. 2008. Chapter 5 Life Cycle Assessment across the Food Sup Chain. From BALDWIN, Cheryl J. ed. 2009. Sustainability in t Jack A., et al. 2010. Carbon sequestration in agricultural lands of the United States. Journal of Soil and Water Conservation. 65(1). JanuaryFebruary. N 229, Tallassee, AL 36078, Tel: (800) 645 1892, Fax: (334) 283 7299. Accessed online http://neptunetg.com/userfiles/file/products/T 10%20Small/10NTG 544%20PS%20T 10% 20Small%2007_10.pdf Ohio State University (OSU). 2013. Extension Fact Sheet Soil Carbon Sequestration Fundamentals. AEX 510 05. Accessed online at: http ://ohioline.osu.edu/aex fact/0510.html Olande ing Biogeochemical Process Models to l Management Projects. Technical Working Group on Agrigicultural Greenhouse Gases (T AGG) Supplemental Report. Pimente id and Giampietro, Mario.1994. Food, Land, Population and the U.S. Economy. Carrying Capacity Network. November 21. Accessed online at: r, Lydia P. and Haugen Kozyra, Karen. 2011. Us Quantify Greenhouse Gas Mitigation from Agricultura Nicholas Institute for Environmental Policy Solutions Report NI R 11 03. Duke University. March. l, Dav http://www.dieoff.com/page55.htm irog, Rich. 2001. Food, Fuel, and Freeways: An Iowa perspective on how far food travels, fuel Pollan, f. New York Times. October 12. Accessed online at: http://www.nytimes.com/2008/10/12/magazine/12policy t.html P usage, and greenhouse gas emissions. Leopold Center for Sustainable Agriculture. June Michael.2008. Farmer in Chie R., et al. 2002. Soil carbon pools and fluxes in urban ecosystems. Environmental Pouyat, Pollution. 116(S107 S118). Pouyat, R., et al. 2006. Carbon Storage by Urban Soils in the United States. Journal of Environmental Quality. 35:1566 1575. Pouyat, R., et al. 2009. A comparison of soil organic carbon stocks between residential turf grass and native soil. Urban Ecosystems. 12:45 62. 105

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P R Consultants. 2013. SimaPro Life Cycle Analysis version 7.3 (software). Qian, Ya Urban Turfgrasses. Soil Science Society of America Journal. 74:366. Ramasw Gas Inventories. Environmental Science & Technology, Vol. 42, No. 17. Ramasw Interdisciplinary Study of Sustainable City Systems. Journal of Industrial Ecology 16: aw Earth Living. 2010. Farmers Face Severe Penalties When Trying to Convert Commodity farmers face severe ling, et al. 2010. Soil Organic Carbon Input from ami, Anuradha, et al. 2008. A Demand Centered, Hybrid LifeCycle Methodology for City Scale Greenhouse ami, A., et al. 2012. A Social EcologicalInfrastructural Systems Framework for 801. R Designated Farm Land to Organic Fruit or Vegetable Production. March 13. Accessed online at: http://rawearthliving.wordpress.com/2010/03/13/ penalties when trying to convert commodity designated farm land to organic fruit or vegetable production/ ochefort, David A. and Cobb, Roger W. 1993. Problem Definition, Agenda Access, and Policy helby Publishing, Inc.2009. The Shelby Report of the Southwest. 517 Green Street, NW. Smith, P Z., Gwary, D., Janzen, H., Kumar, P., McCarl, B., Ogle, S., O'Mara, F., Rice, C., Scholes, B., Sirotenko, O., Howden, M., McAllister, T., Pan, G., Romanenkov, V., nited Nations Environment Programme (UNEP). 2009a. Integrated Assessment: NEP. 2009b. Guidelines for Social Life Cycle Assessment of Products Social and socio ting, context of sustainable development. ISBN: 978 92 807 3021 0. Univers sion. 2006. Sample Costs To Produce Onions Red and Space. August 15. du/ R Choice. Policy Studies Journal Vol. 21, No. 1, 5671. S Gainesville, GA 30501. November. ., Martino, D., Cai, Schneider, U., Towprayoon, S., Wattenbach, M., Smith, J. 2008. Greenhouse gas mitigation in agriculture. Philosophical Transactions of the Royal Society of London B 363: 789. U Mainstreaming sustainability into policymaking A guidance manual.August. U economic LCA guidelines complementing environmental LCA and Life Cycle Cos contributing to the full assessment of goods and services within the ity Of California Cooperative Exten Onion. ON VS 06. University of New Hampshire.2009. User's Guide for the DNDC Model (Version 9.3). Institute for the Study of Earth, Oceans University of New Hampshire (UNH). 2012. User's Guide for the DNDC Model (Version 9.5). Institute for the Study of Earth, Oceans and Space. Accessed online at: http://www.dndc.sr.unh.e 106

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U.S. Department of Agriculture (USDA) Economic Research Service (ERS). 2009. Food Availability (Per Capita) Data Syste m. Accessed online at: http://www.ers.usda.gov/data/foodconsumption/ RS. 2010. Stat USDA E e Fact Sheets: United States. Accessed online at: http://www.ers.usda.gov/statefacts/us.htm ironmental Protection Agency (EPA). 1995. Guidelines for Assessing the Quality of Life Cycle Inventory Ana U.S. Env lysis. Office of Solid Waste and Emergency Response. EPA530 R 95 010. April. EPA. 19 1. March. EPA. 20 d online at: http://www.epa.gov/oecaagct/ag101/cropmajor.html 97. Guiding Principles for Monte Carlo Analysis. EPA/630/R 97/00 EPA. 2006. Life Cycle Assessment: Principles and Practice. EPA/600/R 06/060. May. 10a. Major Crops Grown in the United States. National Agriculture Compliance Assistance Center. Accesse 10b. Evaluating the Environmental Impacts of Packaging Fresh Tomatoes Usin Cycle Thinking & EPA. 20 g Life Assessment: A Sustainable Materials Management Demonstration Project Final Report. EPA530 R 11005. October 29. EPA. 20 10c.Methane and Nitrous Oxide Emissions from Natural Sources. EPA 430 R 10001. April. Accessed online at: http://www.epa.gov/outreach/pdfs/Methane and Nitrous Oxide Emissions From NaturalSources.pdf PA. 2012. The Emissions & Generation Resource Integrated Database for 2012 (eGRID 2012) EPA. 20 s Emissions and Sinks: 1990 2011. EPA 430 R 13 001. April 12. Vaughn, Administrative Appeals. Annual meeting of the Western Political Science Association. eber, Christopher L. and Matthews, Scott H. 2008. Food Miles and the Relative Climate gy 42 work. tal population. Accessed online at: http://earthtrends.wri.org/searchable_db/index.php?theme=4 E Technical Support Document. April 13. Inventory of U.S. Greenhouse Ga Jacqueline. 2003. Show Me The Data! Wildfires, Healthy Forests And Forest Service 27 March. W Impacts of Food Choices in the United States. Environmental Science and Technolo (10), pp 3508. Weiss, Janet A. 1989. The powers of problem definition: The case of government paper Policy Sciences 22:97 121. World Resources Institute (WRI). 2010. Population, Health and Human Well being Urban and Rural Areas: Urban population as a percent of to 107

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Z borel, T., Holland, B., Thomas, G., Baker, L., Calhoun, K., Ramaswami, A. 2012. Translating Research to Policy for Sustainable Cities. Journal of Industrial Ecology 16: 78688. 108

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APPENDIX A FIELD DATA COLLECTION FORM (EXAMPLE)

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start – 27 Feb p. 1 Check Phase: Prep Grow Harvest Cleanup To Market Water Meter Readings start 15430; 10 May 15430; 5 Aug 21286; 24 Oct 25696 Category Example Description Example Units Item Unit Quantity Vegetable Notes Area area used for inground or potted growing (excluding starter trays); square feet devoted to a given crop square feet; acre; number of plants; area per plant sqft 32 carrot onion sqft 16 potato tomato Amendments compost, manure, peat, humus, etc. lbs; bags; lbs per bag; cubic feet; cubic yards cuyd 0.50 carrot applied 14 Mar onion cuyd 0.25 potato tomato Composting any consumables used to create, process, or store compost. lbs; each; feet; square feet; cubic feet carrot onion potato tomato Drip Tape, tube, pipe tape, tube, emitters, etc. feet; lbs; each carrot onion potato tomato Electricity lighting, mowers, tillers, blowers, heat, loaders, lifts, refrigeration, storage facility utilities, etc. kWh; time of operation; consumption rate hr 1 carrot 14 Mar 12A motor onion hr 0.5 potato tomato Fertilizer N, P, K, etc. lbs; cubic feet; cubic yards carrot onion potato tomato Fossil fuel tillers, sod cutters, gallons; time carrot

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start – 27 Feb p. 2 Category Example Description Example Units Item Unit Quantity Vegetable Notes (gasoline, diesel) weed eaters, heat, loaders, lifts, refrigeration, etc. of operation; consumption rate onion potato tomato Grow lights any lighting used in starters kWh; number of lights; wattage of lights carrot onion potato kWh 110.4 tomato Harvest by type (weight, bushel, etc.) gross amount of produce picked lbs; bushel; box; each; bag lb 3.0+3.8+1.0 +15.5 carrot onion 1.8+15.0 potato lb 2.5+3.0+10. 2+9.0+6.0+1 .0+5.0+20.0 tomato Herbicide, Pesticide, Fungicide weed killer or pest control gallons; application rate carrot onion potato tomato Labor labor for all people and activities related to growing all planting phases, vehicle trips, bookkeeping, etc. hours; full time equivalent (FTE) hr 2.6+0.5 carrot 14 Mar + 1 Aug onion 1.4 potato tomato Mulches plastic, wood, stone used in bed preparation square feet; square yards; feet of set width; cubic feet; cubic yards carrot onion potato tomato Packaging boxes, plastic, etc. (exclude bins that are square feet; each; square carrot onion

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start – 27 Feb p. 3 Category Example Description Example Units Item Unit Quantity Vegetable Notes reused); packaging required for final sale yards; lbs potato tomato Retail any consumables used in sale lbs; each; square yards; square feet; lbs carrot onion potato tomato Sale value of sold produce $; $/share; No. shares, share contents carrot onion potato tomato Seed seed used for inground and starters lbs; oz; packets; bags carrot onion potato tomato Starter media rock wool, potting soil, etc. each; lbs carrot onion potato tomato Starter trays plastic, foam, cardboard trays each carrot onion potato tomato Tarpaulin poly, canvas, etc. used to cover tools or areas prior to planting square feet; acre carrot onion potato tomato Vehicle trips all trips related to miles; number carrot

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start – 27 Feb p. 4 Category Example Description Example Units Item Unit Quantity Vegetable Notes growing to/from gardens, stores, distribution points, point of sale, etc. of trips; average miles per trip onion potato tomato Wastes Lost vegetables due to pests, injury, irregularity (do NOT include natural disaster) lbs; cubic feet; barrels, dumpsters carrot onion potato tomato Wastes to cardboard/paper recycling lbs; cubic feet; barrels, dumpsters carrot onion potato tomato Wastes to plastics recycling lbs; cubic feet; barrels, dumpsters carrot onion potato tomato Wastes plant material returned to ground or composting (do NOT include lost vegetables) lbs; cubic feet; barrels, dumpsters carrot onion potato tomato Wastes any waste to unclassified trash or dumpster such as hose, tape, bags, wood scraps, etc. lbs; cubic feet; barrels, dumpsters carrot onion potato tomato Water water used to irrigate gallons; time at a given flow rate carrot onion potato tomato

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start – 27 Feb p. 5 Category Example Description Example Units Item Unit Quantity Vegetable Notes Water water used for other than irrigation; water use in preparing for transport or production packaging; water use in leaving site for next season gallons; time at a given flow rate carrot onion potato tomato Other specify -carrot onion potato tomato Other specify -carrot onion potato tomato Other specify -carrot onion potato tomato

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A PPENDIX B GEOGRAPHIC SOURCES FOR FRESH VEGETABLES

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MONTHLY DISTRIBUTION OF SUPERMARKET VEGETABLES BY LOCATION Potato MonthMonthStart DayDenver1TerretonAlamosaDenver1TerretonAlamosa Jan11100%100% Feb232100%100% Mar360100% Apr491100%100%100% May5121100%100%100%100% Jun6152100%100%100%100% Jul7182100%100%100%100% Aug8213100%100%100%100% Sep9244100%100%100%100%100% Oct10274100%100%100%100%100% Nov11305100%100% Dec12335100%100% Onion MonthMonthStart DayDenver1MissionDemingGreeleyOntarioDenver1MissionDemingGreeleyOntario Jan11100%100%100% Feb232100%100% Mar360100%100%100% Apr491100% 100% May5121100%100%100%100% Jun6152100%100%100%100% Jul7182100%100%100%100% Aug8213100%100%100%100%100% Sep9244100%100% Oct10274100%100% Nov11305100%100% Dec12335100%100% Carrot MonthMonthStart DayDenver1BakersfieldGreeleyDenver1BakersfieldGreeley Jan11100% Feb232100%100% Mar360100%100% Apr491100%100%100%100% May5121100%100%100%100% Jun6152100%100%100%100% Jul7182100%100%100% Aug8213100%100%100%100%100% Sep9244100%100%100%100%100% Oct10274100%100%100%100%100% Nov11305100%50%50%100% Dec12335100% Tomato MonthMonthStart DayDenver1CuliacanPunta GordaBakersfieldDenver1CuliacanPunta GordaBakersfield Jan1150%50% Feb23250%50%100%100%100% Mar36050%50%100%100%100% Apr49150%50%100%100%100% May5121100%100%100%100%100% Jun6152100%100%100%100%100% Jul7182100%100%100%100% Aug8213100%100%100%100%100% Sep9244100%100%100%100%100% Oct10274100%50%50%100%100% Nov1130550%50%100%100% Dec1233550%50% Notes: 1100% shown nominal. In reality, vegetables are under production for 3-6 months, and are harvested in 1 month. Source: Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported an d sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 80216-1850, Phone: 303-382-1742. Website: www.freshpoint.com E-mail: mike.comazzi@freshpoint.com September 5. Market AvailabilityGrowing Season Market AvailabilityGrowing Season Market AvailabilityGrowing Season Market AvailabilityGrowing Season

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Geographic Sources of Fresh Vegetables Consumed in Denver Metropolitan Area Annual AveragesSmall-ScaleVegetableRepresentative LocationAnnual Share, %LatElev Avg Annual Rainfall (in)Climate File N deposition 1998 (kg/ha) Total Precip 1998 (in) N conc. Precip (mg/L) PotatoDenver, CO10039.77534315.37DenverMayfair.txt1.515.930.370716372 http://www.nws.noaa.gov/climate/local_data.php?wfo=bou http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?co0130 http://www.wrcc.dri.edu/summary/ CarrotDenver, CO10039.77534315.37DenverMayfair.txt1.515.930.370716372 OnionDenver, CO10039.77534315.37DenverMayfair.txt1.515.930.370716372 TomatoDenver, CO10039.77534315.37DenverMayfair.txt1.515.930.370716372Large ScalePotatoTerreton, ID5043.84478813.03Terreton.txt1.513.80.427935638 Alamosa, CO5037.4375397.05Alamosa.txt0.86.870.458457977 http://www.colorado.gov/cs/Satellite?blobcol=urldata&blobheader=a p CarrotBakersfield, CA8335.394226.17Bakersfield.txt3.513.31.036054704 Greeley, CO1740.44465914.2Greeley.txt2.511.50.855871277 http://www.weldcounty150.org/AgricultureinWeldCounty/index.html OnionMission, TX1626.3212321.13Mission.txt3.5190.725238293 Deming, NM1732.1242508.33Deming.txt17.80.504744599 http://www.ipmcenters.org/cropprofiles/docs/nmonions.pdf Greeley, CO4240.44465914.2Greeley.txt2.511.50.855871277 http://www.ipmcenters.org/pmsp/pdf/WesternONION.pdf Ontario, OR2544.0221929.68Ontario.txt1.5130.454270139 TomatoCuliacn, Sinaloa, Mexico2524.7610526.27Culiacan.txt3.525.10.548985162 Bakersfield, CA5035.394226.17Bakersfield.txt3.513.31.036054704 http://www.epa.gov/oppefed1/models/water/met_ca_tomato.htm Punta Gorda, FL2526.922357.02PuntaGorda.txt3.5520.264990915 http://www.epa.gov/oppefed1/models/water/met_fl_tomato.htm Reference: Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 80216-1850, Phone: 303-382-1742. Website: www.freshpoint.com E-mail: mike.co mazzi@freshpoint.com September 5.

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APPENDIX C LIFE CYCLE INVENTORY BY FUNCTIONAL UNIT

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VEGETABLE PRODUCTION DATA COLLECTION FOR LIFE CYCLE ASSESSMENT (LCA) USE PER FUNCTIONAL UNIT = 1 LB 5col DNDC4754674567col DNDC8910111213141516171819 electricity1012676483295 water473739424443414540384648 Location Brett Denver Brett Denver Brett Denver Farmyard Denver Farmyard Denver Farmyard Denver Farmyard Denver Denver CODenver CODenver CODenver COTerreton IDAlamosa COBakersfield CAGreeley COMission TXGreeley CODeming NMOntario ORCuliac n SINBakersfield CAPunta Gorda FLDenver CO Crop carrotpotatotomatocarrotpotatooniontomatopotatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatot urf FactorLCI row Item Descripti onQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuant ityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnit QuantityUnitQuantityUnit Nominal Specific yield 0.728 lbs/sqft 1.050 lbs/sqft 0.591 lbs/sqft 0.935 lbs/sqft 0.179 lbs/sqft 0.738 lbs/sqft 0.882 lbs/sqft 0.218 lbs/sqft 0.882 lbs/sqft 0.738 lbs/sqft 0.786 lbs/sqft 1.148 lbs/sqft 1.148 lbs/sqft 2.020 lbs/sq ft 2.020 lbs/sqft 0.597 lbs/sqft 0.597 lbs/sqft 0.597 lbs/sqft 0.597 lbs/sqft 0.597 lbs/sqft 0.597 lbs/sqft 0.597 lbs/sqft Wastage rate post-harvest to consumer (applies to l a 0.105 0.105 0.080 0.080 0.158 0.158 0.158 0.158 0.282 0.282 0.282 Specific Yield w/ waste to POS (applies to large sc a 0.728 lbs/sqft 1.050 lbs/sqft 0.591 lbs/sqft 0.935 lbs/sqft 0.179 lbs/sqft 0.738 lbs/sqft 0.882 lbs/sqft 0.218 lbs/sqft 0.882 lbs/sqft 0.738 lbs/sqft 0.786 lbs/sqft 1.027 lbs/sqft 1.027 lbs/sqft 1.859 lbs/sq ft 1.859 lbs/sqft 0.503 lbs/sqft 0.503 lbs/sqft 0.503 lbs/sqft 0.503 lbs/sqft 0.429 lbs/sqft 0.429 lbs/sqft 0.429 lbs/sqft 1Land area for in1.373 sqft 0.952 sqft 1.693 sqft 1.069 sqft 5.600 sq ft 1.355 sqft 1.134 sqft 4.583 sqft 1.133 sqft 1.355 sqft 1 .272 sqft 0.871 sqft 0.871 sqft 0.495 sqft 0.495 sqft 1.675 sq ft 1.675 sqft 1.675 sqft 1.675 sqft 1.675 sqft 1.675 sqft 1.67 5 sqft 43,560.000 sqft 1Labor all people 0.133 hours 0.083 hours 0.106 hours 0.034 hr 0.18 3 hr 0.048 hr 0.035 hr 0.161 hr 0.055 hr 0.048 hr 0.052 h r 0.001 hr 0.001 hr 0.000 hr 0.000 hr 0.002 hr 0.002 hr 0.002 hr 0.002 hr 0.000 hr 0.000 hr 0.000 hr 261.000 hr/ ac 1Labor acre 0.009 per ac 0.009 per ac 0.001 per ac 0.001 per ac 0.028 per ac 0.028 per ac 0.028 per ac 0.028 per ac 0.006 pe r ac 0.006 per ac 0.006 per ac 10,440.000 $/ac/yr 1Labor average $ 8.00 $ 8.00 $ 8.00 $ 8.00 $ 8.00 $ 8.00 $ 8.00 $ 8.00 $ 8.00 $ 8.00 $ 8.00 $ 14.00 per hour $ 14.00 per hour $ 14.00 per hour $ 14.00 per hour $ 14.00 pe r hour $ 14.00 per hour $ 14.00 per hour $ 14.00 per hour $ 14.00 per hour $ 14.00 per ho ur $ 14.00 per hour $ 40.00 per hour 1 12 Vehicle trips gasoline related to 0.005 gal 0.022 gal 0.007 gal 0.005 gal 0.017 gal 0. 004 gal 0.007 gal 0.004 gal 0.45454531Herbicide/Pesticide or pest 0.006 lbs 0.006 lbs 0.011 lbs 0.011 lbs 0.000 lbs 0.000 lbs 0.000 lbs 3.505 lbs/ac/yr 0.45454532Fungicide control 0. 000 lbs 0.000 lbs 0.000 lbs 0.000 lbs 0.000 lbs 0.000 lbs 0.000 lbs 0.000 lbs 0.000 lbs 6.237 lbs/ac/yr 0.45454533Soil organic lbs lbs 0.035 lbs 0.024 lbs lbs lbs lbs 0.027 lbs 1 5 Grow lights electricity lighting 1.947 kWh 0.599 kWh kWh kWh kWh 0.933 kWh 1 5 Mechanized Equipment electricity mowers, tillers, etc. 0.062 kWh 0.043 kWh 0.45454525Drip Tape or hose emitters, 0.012 lbs 0.063 lbs 0.015 lbs 0.013 lbs 0.049 lbs 0.0 09 lbs 0.015 lbs 0.010 lbs 0.008 lbs 0.008 lbs 0.008 lbs 0.008 lbs 0.45454531Herbicide/Pesticide or pest 0.000 lbs 0.000 lbs 0.000 lbs 0.000 lbs 0.000 lbs 0.000 lbs 0.000 lbs 0.000 lbs 1 12 Mechanized Equipment gasoline cutters, 0.001 gal 0.006 gal 0.001 gal 0.001 gal 0.004 gal 0.00 1 gal 0.001 gal 0.001 gal 0.000 gal 0.000 gal 0.000 gal 0.000 gal 0.000 gal 0.000 gal 0.000 gal 0.000 gal 0.000 gal 360.036 gal/ac/yr 1 11 Mechanized Equipment diesel cutters, 0.001 gal 0.001 gal 0.001 gal 0.001 gal 0.002 gal 0.002 gal 0.002 gal 0.002 gal 0.002 gal 0.002 gal 0.002 gal 1 5 Mechanized Equipment electricity tillers, 0.001 kWh 0.001 kWh 0.001 kW h 0.001 kWh 0.45454513Fertilizer N 0.001 lbs 0.001 lbs 0.003 lbs 0.003 lbs 0.009 lbs 0.009 lbs 0.009 lbs 0.009 lbs 0.003 lbs 0.003 lbs 0.003 lbs 192.389 lbs/ac/yr 0.45454515Fertilizer P 0.001 lbs 0.001 lbs 0.001 lbs 0.001 lbs 0.009 lbs 0.009 lbs 0.009 lbs 0.009 lbs 0.004 lbs 0.004 lbs 0.004 lbs 12.648 lbs/ac/yr 0.45454516Fertilizer K 0.003 lbs 0.003 lbs 0.001 lbs 0.001 lbs 0.009 lbs 0.009 lbs 0.009 lbs 0.009 lbs 0.003 lbs 0.003 lbs 0.003 lbs 48.097 lbs/ac/yr 0.45454521Fertilizer Gypsum 0.015 lbs 0.015 lbs 0.154 lbs 0.154 lbs 0.154 lbs 0.45454522Fertilizer Sulfur 0.0 00 lbs 0.000 lbs 0.45454517Fertilizer Zinc 0.000 lbs 0.000 lbs 0.000 lbs 0.000 lbs 0.45454520Fertilizer Copper 0.000 lbs 0.000 lbs 0.45454518Fertilizer Manganes e 0.000 lbs 0.000 lbs 0.45454517Fertilizer Magnesium 0.002 l bs 0.002 lbs 0.45454523Fertilizer Lime 0.007 lbs 0.007 lbs 1 11 Diesel pumped water 0.000 gal 0.000 gal 0.001 gal Electricity pumped and/or treated water 0.010 kWh 0.007 kWh 0.012 kWh 0.011 kWh 0.012 kWh 0.014 kWh 0.011 kWh 0.011 kWh 0.011 kWh 0.014 kWh 0.012 kWh 0.006 kWh 0.000 kWh 0.003 kWh 0.023 kWh 0.007 kWh 1 48 Water water used for growing 31.899 gallons 22.120 gal 39.325 gal 35.069 gal 40.143 gal 44.452 gal 37.453 gal 36.200 gal 34.399 gal 44.452 gal 37.917 gal 10.883 gal 11.404 gal 9.257 gal 7.405 gal 35.089 gal 43.862 gal 50.128 gal 41.794 gal 32.608 gal 31.330 gal 32.608 gal 1,004,852.598 gal/ac/yr Harvest by type (weight, bushel, etc.) gross amount of produce picked 23.300 lbs 16.800 lbs 56.700 lbs 87.000 lbs 60.000 lbs 62.000 lbs 172.000 lbs 76.800 lbs 110.300 lbs 62.000 lbs 228.700 lbs 50,000.000 lbs ######### lbs 88,000.000 lbs ######### lbs 26,000.000 lbs 26,000.000 lbs 26,000.000 lbs ######### lbs ######### lbs 26,000.000 lbs 26,000.000 lbs ----------Farm Gate---------1.6454235Transport 17-ton 0.300 tmi 0.114 tmi 0.274 tmi 0.015 t mi 1.092 tmi 0.050 tmi 0.630 tmi 0.805 tmi 1.398 tmi 0.928 tmi 1.807 tmi 1.4636Transport truck / 0.005 tmi 0.005 tmi 0.005 tmi 0.005 tmi 0.033 tmi 0.023 tmi 0.040 tmi 0.011 tmi Mechanized Equipment fossil fuel refrigeratio n, lifts Mechanized Equipment electricity refrigeratio n, storage facility utilities 0.45454529Packaging clamshell 0.06 7 lbs lbs lbs lbs 0.050 lbs 0.45454527Packaging PP poly cello bags 0.002 lbs 0.002 lbs 0.001 lbs 0.001 lb s 0.004 lbs 0.004 lbs 0.004 lbs 0.004 lbs 0.45454526Packaging PE 0.003 lbs 0.003 lbs 0.003 lbs 0.45454528Packaging PS 0.001 lbs 0.001 lbs 0.001 lbs 0.45454524Packaging d/cardboar 0.029 lbs 0.029 lbs 0.017 lbs 0.017 lb s 0.057 lbs 0.057 lbs 0.057 lbs 0.057 lbs 0.057 lbs 0.057 lbs 0.057 lbs 149Web Hosting 0.013 $/yr 0.062 $/yr 0.017 $/yr 0.013 $/yr 0.048 $/yr 0.011 $ /yr 0.017 $/yr 0.010 $/yr

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APPENDIX D INPUT PARAMETERS FOR DNDC

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Table D 1 Input Parameters for Scenario 1a1 Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Terreton, ID 43.84 43.84 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air te mperature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0 43 0.43 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field dry grassla nd/pasture Topsoil texture (7) silty clay loam (7) silty clay loam Bulk density (g/cm 3 Mg/m 3 ) 1.3 1.3 pH 7 7 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0. 013 Depth of topsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0.8 0.8 Slope (m/m) 0 0 Soil salinity index 50 50 CROPPING TAB Number of cropping systems during simulated no of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 2 1 Crop type (18) Potato ; (4) Legume Hay (12) Perennial grass Is it a cover crop? no ; yes no Planting month and day 1 May ; 1 Nov 1 Apr Harvest month and day 30 Oct ; 30 Apr 1 Nov Harvest mode (1 same year; 2 next year; etc.) 1 ; 2 1 Fraction of leaves and stems left in field after harvest 0.10; 0.5 1.0 Max imum biomass production grain (kgC/ha/yr) default 38 Water demand (g water/g dry matter) default 150 TILLAGE TAB Number of tillage applications in this year 1 0 Tilling month and day 1 May 1 Jan Tilling method ( 3) Plough 10 cm (1) No Till FERTILI ZATION TAB Application type Precision automatic ; Manual (none) Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 1 0 Application month and day 1 Nov 1 Jan Manure type (4) slurry animal waste (3) straw

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kg C per ha 500 0 C/N ratio 5 n/a kg N per ha 100 n/a Application method surface spread surface spread IRRIGATION TAB Input mode index 0.95 0.00 Irrigation method sprinkler sprinkler FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none none Number of applications 0 0 From month and day n/a n/a To month and day n/a n/a Covered fraction n/a n/a GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/ a n/a Cut fraction n/a n/a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University o f New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2 006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 13371347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Ra in. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Jo urnal of Range Management v 37(2).

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Table D -2 Input Parameters for Scenario 1a 2 Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Alamosa, CO 37. 43 37.43 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temperature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0.4 58 0.4 58 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field dry grass land/pasture Topsoil texture loam y sand loamy sand Bulk density (g/cm 3 Mg/m 3 ) 1.43 1.43 pH 7 7 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.013 Depth of topsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 8 0. 8 Slope (m/m) 0 0 Soil salinity index 50 50 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 2 1 Crop type (18) Potato; (4) Legume Hay (12) Perennial grass Is it a cover crop? no; yes no Planting month and day 1 May; 1 Nov 1 Apr Harvest month and day 30 Oct; 30 Apr 1 Nov Harvest mode (1 same year; 2 next year; etc.) 1; 2 1 Fraction of leaves and stems left in field after harvest 0.10; 0.5 1.0 Maximum biomass production grain (kgC/ha/yr) default 38 Water demand (g water/g dry matter) default 150 TILLAGE TAB Number of tillage applications in this year 1 0 Tilling month and day 1 May 1 Jan Tilling method (3) Plough 10 cm (1) No Till FERTILIZATION TAB Application type Precision automatic; Manual (none) Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 1 0 Application month and day 1 Nov 1 Jan Manure type (4) slurry animal waste (3) straw

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kg C per ha 500 0 C/N ratio 5 n/a kg N per ha 100 n/a Application method surface spread surface spread IRRIGATION TAB Input mode index 0.95 0.00 Irrigation method sprinkler sprinkler FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none none N umber of applications 0 0 From month and day n/a n/a To month and day n/a n/a Covered fraction n/a n/a GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/a n/a Cut fr action n/a n/a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshi re [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Wa ter Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://w ebsoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United King dom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Rang e Management v 37(2).

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Table D -3 Input Parameters for Scenario 1 b1 Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Bakersfield C A 35.39 35.39 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temperature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg /L) 1.036 1.036 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field dry g rassland/pasture Topsoil texture sandy loam sandy loam Bulk density (g/cm 3 Mg/m 3 ) 1.46 1.46 pH 7 7 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.013 Depth of topsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 8 0.8 Slope (m/m) 0 0 Soil salinity index 50 50 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 2 1 Crop type (19) Beet 2x (surrogate for carrot) (12) Perennial grass Is it a cover crop? no no Planting month and day 1 Feb; 1 Jul 1 Feb Harvest month and day 30 Jun; 30 Nov 1 Dec Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 0.10 1.0 Maximum biomass p roduction grain (kgC/ha/yr) default 38 Water demand (g water/g dry matter) default 150 TILLAGE TAB Number of tillage applications in this year 2 0 Tilling month and day 1 Feb; 1 Jul 1 Jan Tilling method (3) Plough 10 cm (1) No Till FERTILIZATION TAB Application type Precision automatic Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 2 0 Application month and day 1 Feb ; 1 Jul 1 Jan Manure type (4) slurry animal waste (3) straw kg C per ha 500 0

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C/N ratio 5 n/a kg N per ha 100 n/a Application method surface spread surface spread IRRIGATION TAB Input mode index 0.95 0.00 Irrigation method furrow sprinkler FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none none Number of applications 0 0 From month and day n/a n/a To month and day n/a n/a Covered fraction n/a n/a GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/a n/a Cut fractio n n/a n/a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [U NH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water C haracteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoi lsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil a nd E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Man agement v 37(2).

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Table D -4 Input Parameters for Scenario 1 b2 Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Greeley C O 40.44 40.44 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temperature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0.856 0.856 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field dry grass land/pasture Topsoil texture loam loam Bulk density (g/cm 3 Mg/m 3 ) 1.43 1.43 pH 7 7 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.013 Depth of topsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 8 0.8 Slope (m/m) 0 0 Soil salinity index 50 50 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration o f each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 3 1 Crop type (19) Beet 2x (surrogate for carrot) ; (4) Legume Hay (12) Perennial g rass Is it a cover crop? no ; no; yes no Planting month and day 1 Apr; 15 Jul ; 1 Nov 1 Apr Harvest month and day 15 Jul; 30 Oct ; 30 Mar 1 Nov Harvest mode (1 same year; 2 next year; etc.) 1; 1; 2 1 Fraction of leaves and stems left in field after harve st 0.10; 0.10; 0.5 1.0 Maximum biomass production grain (kgC/ha/yr) default 38 Water demand (g water/g dry matter) default 150 TILLAGE TAB Number of tillage applications in this year 2 0 Tilling month and day 1 Apr; 15 Jul 1 Jan Tilling method (3 ) Plough 10 cm (1) No Till FERTILIZATION TAB Application type Precision automatic Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 3 0 Application month and day 1 Apr ; 1 5 Jul; 1 Nov 1 Jan Manure type (4) slurry animal waste (3) straw

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kg C per ha 500 0 C/N ratio 5 n/a kg N per ha 100 n/a Application method surface spread surface spread IRRIGATION TAB Input mode index 0.95 0.00 Irrigation method furrow sprinkler FLOODING TAB Times field is flooded (irrigation me thod) 0 0 PLASTIC TAB Method none none Number of applications 0 0 From month and day n/a n/a To month and day n/a n/a Covered fraction n/a n/a GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting mo nth and day n/a n/a Cut part n/a n/a Cut fraction n/a n/a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ S axton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarke ts. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com Sept ember 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Jour nal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2).

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Table D -5 Input Parameters for Scenario 1 c1 Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Mission TX 26.32 26.32 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temperature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0. 725 0.725 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field dry grass land/pasture Topsoil texture sandy loam sandy loam Bulk density (g/cm 3 Mg/m 3 ) 1.46 1.46 pH 7 7 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.013 Depth of topsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 8 0.8 Slope (m/m) 0 0 Soil salinity index 50 50 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 2 1 Crop type (31) Onion 2x (12) Perennial grass Is it a cover crop? no no Planting month and day 1 Jan; 30 Sep 1 5 Jan Harvest month and day 1 Apr; 31 Dec 15 Nov Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 0.10 1.0 Maximum biomass production grain (kgC/ha /yr) default 38 Water demand (g water/g dry matter) default 150 TILLAGE TAB Number of tillage applications in this year 2 0 Tilling month and day 1 Jan; 30 Sep 1 Jan Tilling method (3) Plough 10 cm (1) No Till FERTILIZATION TAB Application type Precision automatic Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 2 0 Application month and day 1 Jan ; 30 Sep 1 Jan Manure type (4) slurry animal waste (3) straw kg C per ha 500 0 C/N ratio 5 n/a

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kg N per ha 100 n/a Appl ication method surface spread surface spread IRRIGATION TAB Input mode index 0.95 0.00 Irrigation method furrow sprinkler FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none none Number of applications 0 0 F rom month and day n/a n/a To month and day n/a n/a Covered fraction n/a n/a GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/a n/a Cut fraction n/a n/a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the S tudy of Earth, Oceans and Space. 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/a pp/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone : 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson 1995. Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2).

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Table D -6 Input Parameters for Scenario 1 c2 Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Deming NM 32.12 32.12 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temp erature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0 .505 0.505 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field dry grassl and/pasture Topsoil texture silty clay loam silty clay loam Bulk density (g/cm 3 Mg/m 3 ) 1.3 1.3 pH 7 7 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.013 De pth of topsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 8 0.8 Slope (m/m) 0 0 Soil salinity index 50 50 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 2 1 Crop type (31) Onion 2x (12) Perennial grass Is it a cover crop? no no Planting month and day 1 Mar; 1 Sep 1 Feb Harvest month and day 1 Jul; 1 Dec 15 Nov Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 0.10 1.0 Maximum biomass production grain (kg C/ha/yr) default 38 Water demand (g water/g dry matter) default 150 TILLAGE TAB Number of tillage applications in this year 2 0 Tilling month and day 1 Mar; 1 Sep 1 Jan Tilling method (3) Plough 10 cm (1) No Till FERTILIZATION TAB Application ty pe Precision automatic Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 2 0 Application month and day 1 Ma r ; 1 Sep 1 Jan Manure type (4) slurry animal waste (3) straw kg C per ha 500 0 C/N ratio 5 n/a

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kg N per ha 100 n/a A pplication method surface spread surface spread IRRIGATION TAB Input mode index 0.95 0.00 Irrigation method furrow sprinkler FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none none Number of applications 0 0 From month and day n/a n/a To month and day n/a n/a Covered fraction n/a n/a GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/a n/a Cut fraction n/a n/a N otes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estima tes by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.go v/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Ph one: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPher son. 1995. Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2).

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Table D -7 Input Parameters for Scenario 1 c3 Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Greeley, CO 40.44 40.44 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air te mperature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0. 856 0.856 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field dry gras sland/pasture Topsoil texture loam loam Bulk density (g/cm 3 Mg/m 3 ) 1.43 1.43 pH 7 7 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.013 Depth of topsoil wit h uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 8 0.8 Slope (m/m) 0 0 Soil salinity index 50 50 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 2 1 Crop type (31) Onion ; (4) Legume Hay (12) Perennial grass Is it a cover crop ? no ; yes no Planting month and day 1 May ; 2 Sep 1 Apr Harvest month and day 1 Sep ; 30 Apr 1 Nov Harvest mode (1 same year; 2 next year; etc.) 1 ; 2 1 Fraction of leaves and stems left in field after harvest 0.10; 0.5 1.0 Maximum biomass production g rain (kgC/ha/yr) default 38 Water demand (g water/g dry matter) default 150 TILLAGE TAB Number of tillage applications in this year 1 0 Tilling month and day 1 May 1 Jan Tilling method (3) Plough 10 cm (1) No Till FERTILIZATION TAB Application t ype Precision automatic Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 2 0 Application month and day 1 May 1 Jan Manure type (4) slurry animal waste (3) straw kg C per ha 500 0

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C/N ratio 5 n/a kg N per ha 100 n/a Applicat ion method surface spread surface spread IRRIGATION TAB Input mode index 0.95 0.00 Irrigation method furrow sprinkler FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none none Number of applications 0 0 From month and day n/a n/a To month and day n/a n/a Covered fraction n/a n/a GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/a n/a Cut fraction n/a n/a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Gu ide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1 995. Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2).

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Table D -8 Input Parameters for Scenario 1 c4 Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Ontario O R 44.02 44.02 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air te mperature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0. 454 0.454 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field dry gras sland/pasture Topsoil texture silty loam silty loam Bulk density (g/cm 3 Mg/m 3 ) 1.38 1.38 pH 7 7 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.013 Depth of topsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 8 0.8 Slope (m/m) 0 0 Soil salinity index 50 50 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 2 1 Crop type (31) Onion ; (4) Legume Hay (12) Perennial grass Is it a cover crop? no ; yes no Planting month and day 1 May ; 2 Sep 1 Mar Harvest month and day 1 Sep ; 30 Apr 15 Nov Harvest mode (1 same year; 2 next year; etc.) 1 ; 2 1 Fraction of leaves and stems left in field after harvest 0.10; 0.5 1.0 Maximum biomass p roduction grain (kgC/ha/yr) default 38 Water demand (g water/g dry matter) default 150 TILLAGE TAB Number of tillage applications in this year 1 0 Tilling month and day 1 May 1 Jan Tilling method (3) Plough 10 cm (1) No Till FERTILIZATION TAB Application type Precision automatic Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 2 0 Application month and day 1 May; 2 Sep 1 Jan Manure type (4) slurry animal waste (3) straw kg C per ha 500 0

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C/N ratio 5 n/a kg N per ha 100 n/a Application method surface spread surface spread IRRIGATION TAB Input mode index 0.95 0.00 Irrigation method furrow sprinkler FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none none Number of applications 0 0 From month and day n/a n/a To month and day n/a n/a Covered fraction n/a n/a GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/a n/a Cut fraction n/a n /a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Ins titute for the Study of Earth, Oceans and Space. 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characte ristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey .nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 8 02161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andara nayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. G regory McPherson. 1995. Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2).

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Table D -9 Input Parameters for Scenario 1 d1 Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Culiacn, Sinaloa, Mxico 24.76 24.76 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temperature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precip itation (mg/L) 0.549 0.549 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop f ield dry grassland/pasture Topsoil texture sandy clay loam sandy clay loam Bulk density (g/cm 3 Mg/m 3 ) 1.5 1.5 pH 6.5 6.5 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C /kg) 0.013 0.013 Depth of topsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 8 0.8 Slope (m/m) 0 0 Soil salinity index 50 50 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 3 1 Crop type (45) Tomato 3x (12) Perennial grass Is it a cover crop? no no Planting month and day 1 Feb; 1 6 Apr; 2 Aug 15 Jan Harvest month and day 15 Apr; 1 Aug; 30 Nov 15 Dec Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 0.10 1.0 Maximum biomass production grain (kgC/ha/yr) default 38 Water demand (g water/g dry matter) default 150 TILLAGE TAB Number of tillage applications in this year 3 0 Tilling month and day 1 Feb; 1 6 Apr; 2 Aug 1 Jan Tilling method (3) Plough 10 cm ( 1) No Till FERTILIZATION TAB Application type Precision automatic Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 3 0 Application month and day 1 Feb ; 1 6 Apr ; 2 Aug 1 Jan Manure type (8) poultry waste (3) straw kg C per ha default 0 C/N ratio default n/a

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kg N per ha default n/a Application method incorporation surface spread IRRIGATION TAB Input mode index 0.95 0.00 Irrigation method drip sprinkler FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method film mulch none Number of applications 3 0 From month and day 1 Feb; 1 6 Apr; 2 Aug n/a To month and day 15 Apr; 1 Aug; 30 Nov n/a Covered fraction 0.90 n/a GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/a n/a Cut fraction n/a n/a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed t o be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accesse d online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mik e.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amend ed with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atm ospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. W ater Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2).

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Table D 10 Input Parameters for Scenario 1 d2 Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Bakersfield, CA 35.39 35.39 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum ai r temperature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (m g/L) 1.036 1.036 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field dry grassland/pasture Topsoil texture sandy loam sandy loam Bulk density (g/cm 3 Mg/m 3 ) 1.48 1.48 pH 7 7 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.013 Dept h of topsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 8 0.8 Slope (m/m) 0 0 Soil salinity index 50 50 CROPPING TAB Number of cropping systems during simulated no. of year s 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 2 1 Crop type (45) Tomato ; (4) Legume Hay (12) Perennial grass I s it a cover crop? no ; yes no Planting month and day 15 May ; 16 Sep ; 1 Feb Harvest month and day 15 Sep ; 14 May 1 Dec Harvest mode (1 same year; 2 next year; etc.) 1 ; 2 1 Fraction of leaves and stems left in field after harvest 0.10; 0.5 1.0 Maximum b iomass production grain (kgC/ha/yr) default 38 Water demand (g water/g dry matter) default 150 TILLAGE TAB Number of tillage applications in this year 1 0 Tilling month and day 15 May 1 Jan Tilling method (3) Plough 10 cm (1) No Till FERTILIZATIO N TAB Application type Precision automatic Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 2 0 Application month and day 15 May; 16 Sep 1 Jan Manure type (8) poultry waste (3) straw kg C per ha default 0

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C/N ratio default n/a kg N per ha default n/a Application method incorporation surface spread IRRIGATION TAB Input mode index 0.95 0.00 Irrigation method drip sprinkler FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method film mul ch none Number of applications 1 0 From month and day 15 May n/a To month and day 15 Sep n/a Covered fraction 0.90 n/a GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut par t n/a n/a Cut fraction n/a n/a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: Universi ty of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5 151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Age ncy United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 13371347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nation s Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2).

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Table D -11 Input Parameters for Scenario 1 d3 Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Punta Gorda, FL 26.92 26.92 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum ai r temperature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (m g/L) 0.265 0.265 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field dry grassland/pasture Topsoil texture sand sand Bulk density (g/cm 3 Mg/m 3 ) 1.43 1.43 pH 7 7 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.013 Depth of topsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 8 0.8 Slope (m/m) 0 0 Soil salinity index 50 50 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 3 1 Crop type (45) Tomato (12) Perennial grass Is it a cover crop? no no Pl anting month and day 1 Feb; 1 6 Apr; 2 Aug 15 Jan Harvest month and day 15 Apr; 1 Aug; 30 Nov 15 Dec Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 0.10 1.0 Maximum biomass production grain ( kgC/ha/yr) default 38 Water demand (g water/g dry matter) default 150 TILLAGE TAB Number of tillage applications in this year 3 0 Tilling month and day 1 Feb; 16 Apr; 2 Aug 1 Jan Tilling method (3) Plough 10 cm (1) No Till FERTILIZATION TAB Appl ication type Precision automatic Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 3 0 Application month and day 1 Feb ; 1 6 Apr ; 2 Aug 1 Jan Manure type (8) poultry waste (3) straw kg C per ha default 0 C/N ratio default n/a

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kg N per ha default n/a Application method incorporation surface spread IRRIGATION TAB Input mode index 0.95 0.00 Irrigation method drip sprinkler FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method film mulch none Number of applications 3 0 From month and day 1 Feb; 1 6 Apr; 2 Aug n/a To month and day 15 Apr; 1 Aug; 30 Nov n/a Covered fraction 0.90 n/a GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month a nd day n/a n/a Cut part n/a n/a Cut fraction n/a n/a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining lay ers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal o f Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plan ts in Central Oklahoma. Journal of Range Management v 37(2).

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Table D -12 Input Parameters for Scenario 2a Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Denver, CO 39.77 39.77 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temp erature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0 371 0. 371 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) dry grassland/pasture upland crop field Topsoil texture clay loam clay loam Bulk density (g/cm 3 Mg/m 3 ) 1. 39 1.39 pH 7.8 7.8 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.03 Depth of t opsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 80 0.95 Slope (m/m) 0 0 Soil salinity index 25 25 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 1 1 Crop type (12) Perennial grass (18) Potato Is it a cover crop? n o no Planting month and day 1 Apr 15 Apr Harvest month and day 1 Nov 15 Oct Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 1.0 0. 10 Maximum biomass production grain (kgC/ha/yr) 38 default Water demand (g water/g dry matter) 150 default TILLAGE TAB Number of tillage applications in this year 0 1 Tilling month and day 1 Jan 15 Apr Tilling method (1) No Till (1) No Till FERTILIZATION TAB Application type Manual (none) Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 0 2 Application month and day 1 Jan 15 Apr; 16 Oct Manure type (3) straw (5) compost; (2) green manure Application method surface spread incorporation; surface

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spread IRRIGATION TAB Input mode index 0.00 0.95 Irrigation method sprinkler drip FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none film mulch Number of applications 0 1 From month and day n/a 15 Apr To month and day n/a 15 Oct Covered fraction n/a 0.90 GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/a n/a Cut fraction n/a n/a Notes: a default value a. Parameters with default values base d on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for t he DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions S oil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012 Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Residential Gree nspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2). Self, James. 2012. Estimate of Soil Parameters Denver Metropo litan Area Table created by author and populated by Mr. Self.

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Table D -13 Input Parameters for Scenario 2b Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Denver, CO 39.77 39.77 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temp erature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0 371 0. 371 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) dry grassland/pasture upland crop field Topsoil texture clay loam clay loam Bulk density (g/cm 3 Mg/m 3 ) 1. 39 1.39 pH 7.8 7.8 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.03 Depth of t opsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 80 0.95 Slope (m/m) 0 0 Soil salinity index 25 25 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 1 1 Crop type (12) Perennial grass (19) Beet (surrogate for carrot) Is it a cover crop? no no Planting month and day 1 Apr 15 Apr Harvest month and day 1 Nov 15 Oct Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 1.0 0.10 Maximum biomass production grain (kg C/ha/yr) 38 default Water demand (g water/g dry matter) 150 default TILLAGE TAB Number of tillage applications in this year 0 1 Tilling month and day 1 Jan 15 Apr Tilling method (1) No Till (1) No Till FERTILIZATION TAB Application type Manual ( none) Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 0 2 Application month and day 1 Jan 15 Apr; 16 Oct Manure type (3) straw (5) compost; (2) green manure

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Application method surface spread incorporation; surface spread IRR IGATION TAB Input mode index 0.00 0.95 Irrigation method sprinkler drip FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none film mulch Number of applications 0 1 From month and day n/a 15 Apr To month and da y n/a 15 Oct Covered fraction n/a 0.90 GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/a n/a Cut fraction n/a n/a Notes: a default value a. Parameters wit h default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012 User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hyd rologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone c onversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organ ic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in U rban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2). Self, James. 2012. Estimate of Soil Parame ters Denver Metropolitan Area. Table created by author and populated by Mr. Self.

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Table D -14 Input Parameters for Scenario 2c Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Denver, CO 39.77 39.77 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temp erature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0 371 0. 371 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) dry grassland/pasture upland crop field Topsoil texture clay loam clay loam Bulk density (g/cm 3 Mg/m 3 ) 1. 39 1. 39 pH 7.8 7.8 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.03 Depth of t opsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 80 0.95 Slope (m/m) 0 0 Soil salinity index 25 25 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 1 1 Crop type (12) Perennial grass (31) Onion Is it a cover crop? no no Planting month and day 1 Apr 1 May Harvest month and day 1 Nov 31 Aug Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 1.0 0.10 Maximum biomass production grain (kgC/ha/yr) 38 default Wa ter demand (g water/g dry matter) 150 default TILLAGE TAB Number of tillage applications in this year 0 1 Tilling month and day 1 Jan 1 May Tilling method (1) No Till (1) No Till FERTILIZATION TAB Application type Manual (none) Manual (none) MAN URE MANAGEMENT TAB Number of applications in this year 0 2 Application month and day 1 Jan 15 Jun; 1 Sep Manure type (3) straw (5) compost; (2) green manure Application method surface spread incorporation; surface

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spread IRRIGATION TAB Input mode index 0.00 0.95 Irrigation method sprinkler drip FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none film mulch Number of applications 0 1 From month and day n/a 1 May To month and day n/a 31 Aug Covered frac tion n/a 0.90 GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/a n/a Cut fraction n/a n/a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for the DN DC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil S ci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfg rass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Residential Greenspac e. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2). Self, James. 2012. Estimate of Soil Parameters Denver Metropolitan Area. Table created by author and populated by Mr. Self.

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Table D -15 Input Parameters for Scenario 2d Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Denver, CO 39.77 39.77 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temp erature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0 371 0. 371 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) dry grassland/pasture upland crop field Topsoil texture clay loam clay loam Bulk density (g/cm 3 Mg/m 3 ) 1.25 1.25 pH 7.8 7.8 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.013 0.03 Depth of t opsoil with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 80 0.95 Slope (m/m) 0 0 Soil salinity index 25 25 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 1 1 Crop type (12) Perennial grass (45) Tomato Is it a cover crop? n o no Planting month and day 1 Apr 15 May Harvest month and day 1 Nov 15 Sep Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 1.0 0. 10 Maximum biomass production grain (kgC/ha/yr) 38 default Water demand (g water/g dry matter) 150 default TILLAGE TAB Number of tillage applications in this year 0 1 Tilling month and day 1 Jan 15 May Tilling method (1) No Till (1) No Till FERTILIZATION TAB Application type Manual (none) Manual (none) MANURE MANAGEMENT TAB Number of applications in this year 0 2 Application month and day 1 Jan 15 May ; 16 Sep Manure type (3) straw (5) compost ; (2) green manure Application method surface spread incorporation; surface

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spread IRRIGATION TAB Input mode index 0.00 0.95 Irrigation method sprinkler drip FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none film mulch Number of applications 0 1 From month and day n/a 15 May To month and day n/a 15 Sep Covered fraction n/a 0.90 GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/a n/a Cut fraction n/a n/a Notes: a default value a. Parameters with default values base d on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for t he DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions S oil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012 Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Residential Gree nspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2). Self, James. 2012. Estimate of Soil Parameters Denver Metropo litan Area. Table created by author and populated by Mr. Self.

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Table D -16 Input Parameters for Scenario 3a Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Denver, CO 39.77 39.77 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temp erature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0 371 0. 371 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field upland crop field Topsoil texture clay loam clay loam Bulk density (g/cm 3 Mg/m 3 ) 1. 39 1.39 pH 7.8 7.8 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.03 0.03 Depth of topsoi l with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0. 80 0.95 Slope (m/m) 0 0 Soil salinity index 25 25 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Du ration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 1 1 Crop type (12) Perennial grass modified for Kentucky bluegrass/fescu e turf (18) Potato Is it a cover crop? no no Planting month and day 1 Apr 15 Apr Harvest month and day 1 Nov 15 Oct Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 1.0 0.10 Maximum biomass pr oduction grain (kgC/ha/yr) 120 default Water demand (g water/g dry matter) default default Root depth (m) 0.37 default TILLAGE TAB Number of tillage applications in this year 0 1 Tilling month and day 1 Jan 15 Apr Tilling method (1) No Till (1) N o Till FERTILIZATION TAB Application type manual Manual (none) Number of applications this year 2 n/a Application month and day 1 Mar; 30 Nov n/a Application depth surface n/a

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Fertilizer type (NH 4 ) 2 SO 4 n/a Application rate (kg N/ha) 108 n/a MANUR E MANAGEMENT TAB Number of applications in this year 0 2 Application month and day 1 Jan 15 Apr; 16 Oct Manure type (3) straw (5) compost ; (2) green manure Application method surface spread incorporation; surface spread IRRIGATION TAB Input mode index 0.95 0.95 Irrigation method sprinkler drip FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none film mulch Number of applications 0 1 From month and day n/a 15 Apr To month and day n/a 15 Oct Covered frac tion n/a 0.90 GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 20 0 Cutting month and day weekly for 2 0 weeks n/a Cut part leaf n/a Cut fraction 0.25 n/a Notes: a default value a. Parameters with defau lt values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User 's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversa tion regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carb on Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Re sidential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2). Self, James. 2012. Estimate of Soil Parameters Denver Metropolitan Area. Table created by author and populated by Mr. Self.

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Table D -17 Input Parameters for Scenario 3b Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Denver, CO 39.77 39.77 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temp erature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0 371 0. 371 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field upland crop field Topsoil texture clay loam clay loam Bulk density (g/cm 3 Mg/m 3 ) 1. 39 1. 39 pH 7.8 7.8 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.03 0.03 Depth of topsoi l with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0.80 0.95 Slope (m/m) 0 0 Soil salinity index 25 25 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Du ration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 1 1 Crop type (12) Perennial grass modified for Kentucky bluegrass/fescu e turf (19) Beet (surrogate for carrot) Is it a cover crop? no no Planting month and day 1 Apr 15 Apr Harvest month and day 1 Nov 15 Oct Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 1.0 0.10 Maximum biomass production grain (kgC/ha/yr) 120 default Water demand (g water/g dry matter) default default Root depth (m) 0.37 default TILLAGE TAB Number of tillage applications in this year 0 1 Tilling month and day 1 Jan 15 Apr Tilling met hod (1) No Till (1) No Till FERTILIZATION TAB Application type manual Manual (none) Number of applications this year 2 n/a Application month and day 1 Mar; 30 Nov n/a Application depth surface n/a

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Fertilizer type (NH 4 ) 2 SO 4 n/a Application rate (kg N/ha) 108 n/a MANURE MANAGEMENT TAB Number of applications in this year 0 2 Application month and day 1 Jan 15 Apr; 16 Oct Manure type (3) straw (5) compost; (2) green manure Application method surface spread incorporation; surface spread IRRIGATI ON TAB Input mode index 0.95 0.95 Irrigation method sprinkler drip FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none film mulch Number of applications 0 1 From month and day n/a 15 Apr To month and day n/a 15 Oct Covered fraction n/a 0.90 GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 20 0 Cutting month and day weekly for 24 weeks n/a Cut part leaf n/a Cut fraction 0.25 n/a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2). Self, James. 2012. Estimate of Soil Parameters Denver Metropolitan Area. Table created by author and populated by Mr. Self.

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Table D -18 Input Parameters for Scenario 3c Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Denver, CO 39.77 39.77 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temp erature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0 371 0. 371 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field upland crop field Topsoil texture clay loam clay loam Bulk density (g/cm 3 Mg/m 3 ) 1. 39 1.39 pH 7.8 7.8 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.03 0.03 Depth of topsoi l with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0.80 0.95 Slope (m/m) 0 0 Soil salinity index 25 25 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Du ration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 1 1 Crop type (12) Perennial grass modified for Kentucky bluegrass/fescu e turf (31) Onion Is it a cover crop? no no Planting month and day 1 Apr 1 May Harvest month and day 1 Nov 31 Aug Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 1.0 0.10 Maximum biomass prod uction grain (kgC/ha/yr) 120 default Water demand (g water/g dry matter) default default Root depth (m) 0.37 default TILLAGE TAB Number of tillage applications in this year 0 1 Tilling month and day 1 Jan 1 May Tilling method (1) No Till (1) No T ill FERTILIZATION TAB Application type manual Manual (none) Number of applications this year 2 n/a Application month and day 1 Mar; 30 Nov n/a Application depth surface n/a

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Fertilizer type (NH 4 ) 2 SO 4 n/a Application rate (kg N/ha) 108 n/a MANURE M ANAGEMENT TAB Number of applications in this year 0 2 Application month and day 1 Jan 1 May; 1 Sep Manure type (3) straw (5) compost; (2) green manure Application method surface spread incorporation; surface spread IRRIGATION TAB Input mode ind ex 0.95 0.95 Irrigation method sprinkler drip FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none film mulch Number of applications 0 1 From month and day n/a 1 May To month and day n/a 31 Aug Covered fraction n /a 0.90 GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 20 0 Cutting month and day weekly for 24 weeks n/a Cut part leaf n/a Cut fraction 0.25 n/a Notes: a default value a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User's Gui de for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solut ions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversation r egarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbon Cha nges in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Resident ial Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2). Self, James. 2012. Estimate of Soil Parameters Denver Metropolitan Area. Table created by author and populated by Mr. Self.

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Table D -19 Input Parameters for Scenario 3 d Parametera Starting Land Use Ending Land Use CLIMATE TAB Latitude Denver, CO 39.77 39.77 Simulated No. of Years (30 per each land use) 30 30 Meteorological Data Julian day number; daily maximum air temp erature (C); daily minimum air temperature (C); daily precipitation (cm); daily average wind speed (m/s); daily average relative humidity (%) See Appendix B Meteorological Data See Appendix B Meteorological Data N concentration in precipitation (mg/L) 0 371 0. 371 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmospheric CO2 concentration (ppm/yr) 0 0 SOIL TAB Land use type(s) upland crop field upland crop field Topsoil texture clay loam clay loam Bulk density (g/cm 3 Mg/m 3 ) 1. 39 1.39 pH 7.8 7.8 Macro pores no no Depth of water retention layer (m) b 9.99 9.99 Waterlogging problem no no SOC at surface soil, 0 5 cm (kg C/kg) 0.03 0.03 Depth of topsoi l with uniform SOC content (m) 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0.80 0.95 Slope (m/m) 0 0 Soil salinity index 25 25 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Du ration of each cropping system (years) 30 30 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTICES CROP TAB Number of new crops planted in this year 1 1 Crop type (12) Perennial grass modified for Kentucky bluegrass/fescu e turf (45) Tomato Is it a cover crop? no no Planting month and day 1 Apr 15 May Harvest month and day 1 Nov 15 Sep Harvest mode (1 same year; 2 next year; etc.) 1 1 Fraction of leaves and stems left in field after harvest 1.0 0.1 Maximum biomass pro duction grain (kgC/ha/yr) 120 default Water demand (g water/g dry matter) default default Root depth (m) 0.37 default TILLAGE TAB Number of tillage applications in this year 0 1 Tilling month and day 1 Jan 15 May Tilling method (1) No Till (1) No Till FERTILIZATION TAB Application type manual Manual (none) Number of applications this year 2 n/a Application month and day 1 Mar; 30 Nov n/a Application depth surface n/a

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Fertilizer type (NH 4 ) 2 SO 4 n/a Application rate (kg N/ha) 108 n/a MANURE MANAGEMENT TAB Number of applications in this year 0 2 Application month and day 1 Jan 15 May; 16 Sep Manure type (3) straw (5) compost; (2) green manure Application method surface spread incorporation; surface spread IRRIGATION TAB Input mode index 0.95 0.95 Irrigation method sprinkler drip FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none film mulch Number of applications 0 1 From month and day n/a 15 May To month and day n/a 15 Sep Covered fract ion n/a 0.90 GRAZING OR CUTTING TAB Number of grazing applications 0 0 Number of biomass cuttings 20 0 Cutting month and day weekly for 24 weeks n/a Cut part leaf n/a Cut fraction 0.25 n/a Notes: a default value a. Parameters with defaul t values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. Sources: University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space. 2012. User' s Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ Saxton, K. E. and W. J. Rawls 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions Soil Sci ence Soc iety of Am erica J ournal. 70:1569 1578. NRCS. 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ Comazzi, Michael. 2012. Phone conversat ion regarding the temporal geographic sources of selected fresh vegetables imported and sold in Denver metropolitan area major supermarkets. FreshPoint of Denver, 5151 Bannock St., Denver, CO 802161850, Phone: 3033821742. Website: www.freshpoint.com E mail: mike.comazzi@freshpoint.com September 5. The Environment Agency United Kingdom. 2011. Think Manure A Guide to Manure Management. Romany, Joan, et. al. 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. B andaranayakea, W., et. al. 2003. Estimation of Soil Organic Carbo n Changes in Turfgrass Systems Using the CENTURY Model. Agronomy Journal v. 95 pp. 558 563. National Atmospheric Deposition Program 2000. Nitrogen in the Nations Rain. Jo, Hyun Kil and E. Gregory McPherson. 1995. Carbon Storage and Flux in Urban Res idential Greenspace. Journal of Environmental Management v 45 pp. 109 133. Hake, D.R., et. al. 1984. Water Stress of Tallgrass Prairie Plants in Central Oklahoma. Journal of Range Management v 37(2). Self, James. 2012. Estimate of Soil Parameters D enver Metropolitan Area. Table created by author and populated by Mr. Self.

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APPENDIX E ARTICLE: NITROUS OXIDE EMISSIONS Fisher, Stephen W. and Karunanithi, Arunprakash. 2013. Unrecognized variation in nitrous oxide emissions reported for a global dryland staple crop. Journal of Environmental Management (submitted 21 Jan 14).

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Elsevier Editorial System(tm) for Journal of Environmental Management Manuscript Draft Manuscript Number: Title: Unrecognized variation in nitrous oxide emissions reported for a globa l dryland staple crop Article Type: Research Paper Keywords: agricultural practices; life cycle assessment; emission factors; supply chain; nitrous oxide; stochastics Corresponding Author: Mr. Stephen Fisher, Corresponding Author's Institu tion: University of Colorado Denver First Author: Stephen Fisher Order of Authors: Stephen Fisher; Arunprakash Karunanithi

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Stephen Fisher a *, Arunprakash Karunanithi b a. Center for Sustainable Infrastructure Systems University of Colorado, Denver, Colorado, 1200 Larimer Street, NC 3027, Campus Box 113, P.O. Box 173364, Denver, CO 80217 stephen.fisher@ucdenver.edu b. Department of Civil Engineering, University of Colorado, Denver, Colorado, 1200 Larimer Street, NC 3027 Campus Box 113, P.O. Box 173364, Denver, CO 80217 arunprakash.karunanithi@ucdenver.edu Corresponding Author. Tel.: +1 303 312 8849; fax: +1 303 295 2818. E mail address: stephen.fisher@ucdenver.edu Dear Editor(s): This research article was submitted first to Agriculture, Ecosystems and Environment on 15 Dec 13 A ter an initial evaluation the editors felt the manuscript was not appropriate for that journal. After reviewing the eligible journals in the journal transfer service Agricult ural Systems, Journal of Environmental Management, and Science of the Total Environment all st oo d out as appropriate venues. We have chosen Journal of Environmental Management. The Journal of Environmental Management was chosen because its focus areas app ear to span most of the disciplines and topics in the article: agricultural practices (N application) meteorological data, greenhouse gas emissions and modeling, and sustainability reporting. The article is a direct result of the use of the soil carbon an d emissions model DNDC in a life cycle assessment of vegetable production. The large variation of nitrous oxide emissions resulting from the coincidence of N application and soil moisture ( rainfall or irrigation ) was noticed almost by accident and led to the investigation of how emissions inventories are developed and applied by policy makers in the halls of large corporations and government. We are grateful for the opportunity to submit this article. Sincerely, Stephen Fisher, Corresponding Author Cover Letter

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Stephen Fisher a *, Arunprakash Karunanithi b a. Center for Sustainable Infrastructure Systems University of Colorado, Denver, Colorado, 1200 Larimer Street, NC 3027, Campus Box 113, P.O. Box 173364, Denver, CO 80217 stephen.fisher@ucdenver.edu b. Department of Civil Engineering, University of Colorado, Denver, Colorado, 1200 Larimer Street, NC 3027 Campus Box 113, P.O. Box 173364, Denver, CO 80217 arunprakash.karunanithi@ucdenver.edu Corresponding Author. Tel.: +1 303 312 8849; fax: +1 303 295 2818. E mail address: stephen.fisher@ucdenver.edu Nitrous oxide emissions from the cult ivation of a major global dryland staple crop can v ary by hundreds of percent depending on the daily coincidence and magnitude of rainfall and nitrogen application. E xtrapolations of measured discrete emissions or estimated daily rainfall to larger areas even if modest, paint a potentially incomplete and inaccurate picture of nitrous oxide emissions. When p olicy and corporate actors use aggregated nitrous oxide emissions inventories in GHG reporting and planning they carry an unquantified component of uncertainty and variability. Agricultural nitrous oxide emissions may be generally over estimated unless periods of wetness and dryness and rainfall randomness are replicated in modeled weather data, an area for further, cross discipline research. Highlights (for review) Click here to view linked References

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1 Unrecognized variation in nitrous oxide emissions reported for a global dryland staple crop Stephen Fisher a *, Arunprakash Karunanithi b a. Center for Sustainable Infrastructure Systems University of Colorado Denver, Colorado, 1200 Larimer Street, NC 302 7, Campus Box 113, P.O. Box 173364, Denver, CO 80217 stephen.fisher@ucdenver.edu b. Department of Civil Engineering, University of Colorado, Denver, Colorado, 1200 Larimer Street, NC 3027 Campus Bo x 113, P.O. Box 173364, Denver, CO 80217 arunprakash.karunanithi@ucdenver.edu Corresponding Author. Tel.: +1 303 312 8849; fax: +1 303 295 2818. E mail address: stephen.fisher@ucdenver.edu ABSTRACT Soil nitrous oxide e missions display a high degree of sensitivity and variability to the day of fertilizer application and daily rainfall distribution. A t nearly all geographic and temporal scales t h is variability appears to be unrecognized when modeled emissions are reported for staple crop production in product life cycle assessment studies, carbon accountin g and corporate social responsibility reporting. Although many factors causing va riability in nitrous oxide emissions are recognized, we show that one important factor rainfall is not particularly suited for mere arithmetic averaging, and reliable estimates at any scale should take into account the stochastic and periodic nature of precipitation. Rainfall data that are not well characterized using probabilistic methods such as Markov series, maximum entropy, or disaggregation could result in poor estimation of nitrous oxide emissions. More research is needed to define the appropri ate temporal and geographic scales for rainfall data sets used as inputs to process models. Application of widely used inventories that are based on data specific to a particular region, time period, crop, food, or agricultural practice should be carefull y evaluated before they are used in broader analysis Keywords : agricultural practices ; life cycle assessment; emission factors ; supply chain; nitrous oxide; stochastics 1.0 INTRODUCTION Variations in on farm parameters such as agricultural practices, soil types, and precipitation result in some degree of variation in dependent variables tied to them (Li 2 000). For example, it is known that nitrous oxide (N 2 O) emissions are produced when sufficient nitrogen (N) and moisture are present in soil (Li 2 000 ; EPA 2 010 ; IPCC 2 006) Nitrification is the aerobic oxidation of ammonia to nitrate, and denitrification is the anaerobic reduction of nitrate to nitrogen gas (N 2 ). In this process, N 2 O is released Inorganic N has a controlling influence on these re actions while soil moisture and depth *Manuscript Click here to download Manuscript: Unrecognized variation of nitrous oxide emissions v6 for J Env. Mgmt.doc Click here to view linked References

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2 of N in the soil column dictate whether release of N 2 O is a result of aerobic or anaerobic processes, both of which can occur at the same time within a given soil profile (Kuenen and Robertson 1 994). D enitrificatio n is associated with water in the soil pore space and nitrification is associated with air in the pore space (Davidson et al. 1 986). The combined effects of moisture, inorganic N, depth, temperature, and plant metabolism in the root zone result in varyin g degrees of N 2 O flux. This study focuses on the coincidence of N application and rainfall events that influence soil moisture and therefore N 2 O flux. Since N application is usually controlled and quantifiable, it is rainfall that introduces uncertainty in N 2 O estimates Unless recorded rainfall at a discrete location is used in N 2 O estimates, rainfall must also be estimated for that discrete location. One convenient way is to interpolate between two neighboring weather stations. Because thunderstorm s release large quantities of rainfall in an uneven manner, such a rainfall event may not even register at a weather station. As we s cal e up and couple agricultural production with average rainfall data, we completely lose the spatially and temporally discr ete thunderstorm event. Yet it is exactly at this scale of granularity that the biogeochemical processes are responsive. The current study illustrates this effect by performing a comparison of results with aggregated and non aggregated rainfall data usin g the Denitrification Decomposition (DNDC) model (Li 2 000) to estimate N 2 O emissions from dryland spring wheat production near Fort Benton, Montana. 1.1 Significance of Staple Crops in Agricultural Emissions The sheer magnitude of land area devoted to the production of staple crops (such as wheat) alone indicate s that even small variations in on farm parameters may significantly contribute to variability in reporting aggregated dependent variables over whole crop or food sectors. This is significant be cause some parameters may not be well suited for regional or sector defined averages. Nitrous oxide emission is one such dependent variable In the U.S., agricultural subsidy and global market forces have resulted in about 83 million hectares ( Mha ) be ing devoted to just three crops: corn (34 Mha), soybean (30 Mha), and wheat (18.5 Mha). These crops, two of them mostly grown to feed animals, occupy about 60% of total U.S. harvested farmland (EPA 2 013 a ). T he fact that most of the harvest of these thr ee crops is not used for direct human consumption is overshadowed by the fact that they comprise, indirectly, the majority of human caloric intake in the form of animal products and processed foods. T hese three crops and their derivatives in the food supp ly chain contribute over 65% of kilocalories (kcal) to the average daily per capita U.S. diet of about 2,600 kcal (USDA 2 0 07 ). Corn is found in about 28 % of the food we eat, wheat in about 25 % and soy in about 12 % (EAERE 2 009 ; FAO 2 0 0 3). Particular s taple foods, of course, have predominant amounts of these crops, most notably breads and pastas. Typical plain wheat bread formulations, for example, obtain about 85 % of their calories from wheat flour (USDA 2 013).

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3 1.2 Quantifying Agricultural Emissions Emissions from agriculture are globally significant. It is estimated that they comprise about 14 % of global greenhouse gas (GHG) emissions and about 8 % of U S GHG emissions (EPA 2 013) Within the food and agriculture sector, an average of 80 % of its emissions occurs o n the farm and during packaging (Weber and Matthews 2 008). Further, a griculture represents about 50 % of anthropogenic methane emissions and 60 % of anthropogenic N 2 O emissions with CH4 and N 2 O having about 23 and 296 times the global w arming potential (GWP) of carbon dioxide respectively ( Olander and Haugen Kozyra 2 011 ; EPA 2 006a ; EPA 2 006b) The significant contribution of agricultural emissions to global climate change has spawned interest in research related to their accurate qu antification and mitigation strategies. Some of the most impactful mitigation strategies can occur with farm level agricultural practices (Smith et al. 2 008). Quantifying the effects of such measures at the local scale and then accurately applying them to larger areas and sectors is challenging. For example, t he Intergovernmental Panel on Climate Change (IPCC) developed a framework that recognizes increasing granularity in emission factors (IPCC 2 006). IPCC provides Tier 1 project emission factors for estimating N 2 0 emissions at the national and average annual levels, currently 1% of applied N (IPCC 2 006). Tier 1 is understandably insensitive to numerous other agricultural and environmental conditions besides N application. Tier 2 emission factors a pply to intermediate or regional scales and often utilize empirical models to estimate emissions. Tier 3 is useful for field site or farm scale estimates and typically utilizes process models or even field measurements. Since field measurement of G HGs is often infeasible, validated process models offer a fairly ideal path to match mitigation measures with net outcomes. Because some emissions are responsive to daily, site specific conditions, several process models have been developed for estimating GHGs from biogeochemical processes at a daily and site specific scale. Some popular models are Daily Century ( DAYCENT ) DNDC, and Erosion Policy Impact Climate ( EPIC ) / Agricultural Policy E x tender ( APEX ) (Olander and Haugen Kozyra 2 011). Numerous studie s have compared and validated estimates from process models with field data and exhibit a usable degree of accuracy (Smith et al. 2 010; Tonitto et al. 2 007; Del Grosso et al. 2 005 2 010; Li 2 011; Barton et al. 2 008; Biswas et al. 2 008). The models h ave been used, in two notable examples, to estimate absolute GHG emissions for large regions or continents (EPA 2 013 ; Smith et al. 2 010), but they are also suitable for farm level evaluations. Inventory of Greenhouse Gas Emissions and Sinks (EPA 2 013) aggregate emissions attributed to a large class of land is estimated from a body of Tier 3 emissions derived using the DAYCENT model with parameters from 380,956 points (representing approximately 21 ha each) in agricult ural land for the conterminous United States and Hawaii The authors used a scaling factor to bring the area represented by the thousands of discrete points to amount to total farmed area ( Del Grosso et al. 2 005 ). The uncertainty reported for this appro ach is discussed in Section 2.

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4 A similar study for 462 ecodistricts across Canada recognized that soil emissions are very sensitive to climate and fertilizer application rates and much variability in N 2 O estimates exists ( Smith et al. 2 010 ). The granul arity represented by the ecodistricts can be Canadian example, using DNDC, reported more detailed management scenarios, crops, and crop rotations, but did not repor t any uncertainty or characterize variability. 1 3 Reporting Agricultural Emissions Agricultural emissions are tabulated and reported in various forms and fora such as research journals, government and quasi governmental publications (e.g. EPA 2 013), no n governmental organizations (e.g. Carbon Disclosure Project, World Resources Institute Greenhouse Gas Protocol [Bhatia et al. 2 004] ), public repositories for emissions data (e.g. N ational Renewable Energy Laboratory [NREL] U.S. Life Cycle Inventory Dat abase [NREL 2 012] ), and proprietary software (e.g. Simapro [Pre Consultants 2 013] GaBi [PE International 2 013] and EcoInvent [Swiss Centre for Life Cycle Inventories 2 013] ). Also, more and more companies are disclosing their emissions in product lif e cycle assessments and corporate social responsibility reports (CSR) (GRI 2 012). Reporting of emissions can draw from any one or more of the intermediate sources from raw data to proprietary software tools, as depicted in Figure 1. Each use can suppo rt far reaching and impactful policy decisions on the part of government and industry (Grubb 1 995 ; Smith et al. 2 008 ; Lynch et al. 2 011 ; Ramaswami et al. 2 012 ; Zborel et al. 2 012). Yet unless explicitly described, variability and uncertainty in emiss ions are generally not carried forward beyond the raw data phase. Even variability and uncertainty in the raw data are scarce (Australia 2 009). Although some standards exist for quantifying and reporting uncertainty and variability in emission data and emission factors (e.g. ISO 2 0 06 ; GHG Protocol 2 013; IPCC 2 006; EPA 2 013), the relative contributions and effects of each emission factor on each component of a life cycle inventory is generally not reported in life cycle assessment studies (Australia 2 009) 2 .0 VARIABILITY AND UNCERTAINTY OF NITROUS OXIDE EMISSIONS Issues of scale, variability, accuracy, and uncertainty have been well studied in the environmental sciences (Duxbury et al. 1 993; Groffman et al. 1 992; Kachanoski 1 988 ). Although ma ny have recognized these issues, quantification of uncertainty and variability in the data sets have not been conveyed, at least consistently, as they are used by various disciplines (Heuvelink 1 998; Wagenet 1 998). For example, countries that report emi ssions under the Kyoto Protocol may do so electing the use of IPCC default uncertainty values or they may use their own expert assessments. Even then, one cannot be sure that the largest contributors of uncertainty (e.g. appropriate rainfall data sets) ha ve been taken into account. The range, then, of uncertainty methodologies and scope makes decision making and progress evaluation difficult, but this is a subject of improvement for GHG reporting, especially for N 2 O emissions (Rydpal and Winiwarter 2 001) For instance, in a study of N 2 O emissions attributed to animal production

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5 systems, it was found that the uncertainty range in global N 2 O estimates was by itself larger than the 50 to 60% reductions in emissions by 2030 proposed in the Kyoto Protocol (Oe nema et al. 2 005). The relationship between direct field measurements and estimated emissions has also been studied previously (Barton 2 008; Li et al. 2 008 2 012; Stehfest and Bouwman 2 006) Stehfest and Bouwman (2006) asserted that, despite a 20 % in crease in the number of N 2 O measurements in agricultural soils, uncertainty in calculated estimates remain ed high; the average 95 percent confidence interval for global emissions from agricultural soils was still 51 to +107 % (Stehfest and Bouwman 2 006). The authors cited continued poor representation of environmental parameters and management practices as a cause for the poor precision. This indicates that, while models may be validated with field measurements, the characterization of environmental par ameters pose problems for forward looking estimates. Direct field measurements and process modeling have documented very well the variability of N 2 O emissions from cropping systems and their soils (Barton 2 008) For example, Barton, et al. (2008) condu cted field measurements on a dryland wheat cropping system in Western Australia for one year. The measurements were taken on an intra day basis and thus the response of soil emissions to external factors was highly rated that d aily N 2 O emissions ranged from 1.8 to + 7.3 grams ( g ) N 2 O N per ha and amounted to 0.11 kilograms ( kg ) N 2 O N per ha annually from soil fertilized at a rate of 100 kg N per ha per year. This is about 60 times less than the IPCC default value fo r fertilized cropland. The data also displayed spikes of N 2 O emissions in response to natural rainfall events and that the annual sum is a Li et al. (2012) followed Bart on et al. (2008) by using intra day data for two consecutive years to calibrate and estimate, with a process model (W ater and Nitrogen Management Model [Li et al. 2 007] ), emissions from the same location in Western Australia. It used historical precipita tion records and fertilizer application dates and amounts, and as expected the model estimated emissions were in very good agreement with field measurements. Although the data was available, the study unfortunately did not characterize the daily variabi lity of precipitation events, so accuracy and uncertainty in forward looking estimates was not described. The second part of the study used the calibrated model. Simulations were carried out for 37 past years using annual average input parameters, but th e distribution that was used relative to fertilizer application dates was not described. In the simulations, annual N 2 O losses ranged from 118 to 274 g N per ha (range = 156 g N per ha). A similar simulation for the same time period but in a wetter locat ion in southeast Australia yielded a range of annual N 2 O losses of 530 g N per ha (120 to 650 g N per ha), confirming that variability exists between different locations with different annual precipitation amounts, soils, and production practices (Li, et a l 2 008). Some of this variability could be due to differences in annual rainfall amounts, but it remains to be seen how variability in emissions are attributed to the coincidence of daily precipitation events and N application. When inherent variabilit y at one scale is not characterized for a larger scale, this can lead to significant uncertainty.

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6 When such data is used in life cycle assessment, emissions should take into account the right geographical and temporal scales and be reported accordingly an d some have set out to expose the importance of this. For example, Biswas et al. (2008) used the data from Western Australia (Li et al. 2 012) to conduct a life cycle assessment of wheat production at different points in the supply chain, from cradle to farm gate to shipping port. With N 2 O contributing a large share of total GHG emissions in wheat production, the study found that using site specific data decreased the total GHG emissions calculated for the production of wheat by 38% and decreased calculat ed soil N 2 O emissions by almost 85% compared to using the standard IPCC emission factor (N 2 O emissions estimated at 1% of applied N). The Australian Rural Industries Research and Development Corporation (RIRDC) conducted a review of 70 articles, reports a nd journal papers in agricultural life cycle literature (Australia 2 009) It concluded that the greatest uncertainty in agricultural life cycle assessments is in N 2 O emissions. T he U.S. GHG Emissions and Sinks document used as an inventory source for m any life cycle assessment studies, reports a combined uncertainty for direct soil N 2 O emissions from 18 % below to 40 % above the total U.S. CO 2 equivalent emissions in 2011, and combined uncertainty for indirect soil N 2 O emissions from 50 % below to 151 % abo ve the total (Del Grosso et al. 2 005). Del Grosso et al. (2010) later developed a methodology to quantify uncertainty for small grain crops, such as wheat, and reported a range of uncertainties from % to +50 % for all U.S. agricultural land Uncertain ty increases to over 100% of the emissions estimate for regional or smaller scales (Del Grosso et al. 2 010). Although probability distribution functions and Monte Carlo analysis were used for several key input parameters, the probability of the coinciden ce of N application with precipitation events on a daily basis was not considered. 2.1 Variability Due to Coincidence of N Application with Rainfall As a means to illustrate one potentially significant source of uncertainty in N 2 O reporting, a simple com parison was drawn using the Denitrification Decomposition (DNDC) model to estimate N 2 O emissions from dryland spring wheat produc tion in two separate years near Fort Benton, Montana. The only parameters that were allowed to change were historical, daily r ainfall events and the timing of a single, annual application of manure. It was hoped that the comparison would expose the singular effect and potential source of variability of coinciding rainfall and N application events. The basis for this comparison was drawn from the disciplines of hydrology and meteorology. Simulated data sets of rainfall that mimic the randomness and periodicity of rainfall has been a subject of research for many (Katz 1 977; Stern 1 980; Schoof and Pryor 2 008; Sun et al. 2 006; Koutsoyiannis 2 006; Kottegoda et al. 2 004). Some have also applied this research to agricultural settings and the N cycle (e.g. Yemenu and Chemeda 2 013) and Oenema et al. highly stochastic, both in sp et al. (2005) confirmed that seasonally averaged weather data yield poor process model results compared to observed weather

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7 data and their use is not recommended. The body of research suggests that simulating the randomness and peri odicity of rainfall is important and more useful in certain applications than mere averages. To illustrate this, consider the daily rainfall totals for two weeks from Fort Benton, Montana in two years, 1991 and 1996 (WRCC 2 013), as shown in Table 1. Thi s is compared to the daily average for the periods 1895 2012 and 1981 2010. By inspection, one can see that rainfall is different and variable on a daily basis when compared intra year and between years. Also, the average daily rainfall is always more th an zero. The periods of consecutive dry days (CDD) and consecutive wet days (CWD) are different, as are the rainfall totals. Average daily rainfall significantly underestimates CDDs and overestimates both CWDs and overall rainfall. This small sampling i s illustrative for rainfall data in general. From this review, it was concluded that t he timing and amount of N application and rainfall has never been characterized so that they can be correlated statistically or stochastically with actual practices eve nts, and the resulting real emissions. 2.1.1 Design Chouteau County, Montana, ranks high in the U.S. for area devoted to spring, winter, and durum wheat and was selected as the setting for the model (USDA 2 012). It was also important to select an are a that practiced dryland farming in order to avoid the complications of applied water amount and timing in the model. Aside from a few default parameters, site specific data were used for climate and soil. We set planting to May 1 and harvest on Aug 20. We set one round of tillage to 10 centimeters (cm) on May 1, and a single manure application of 148 kg N per ha on July 1. These and all other input parameters and their references are found in Appendix A. The model was set to run for 365 days, once fo r 1991 and once for 1996. These two years have almost identical precipitation (331 and 342 millimeters [mm] respectively) to the annual average of 341 mm for the period 1981 to 2010. As hoped, the distribution of precipitation is unique and different for each year, as shown on Figure 2. Average daily temperature, as required by the climate input module, was 19 C on July 1, 1991 and 18 C on July 1, 1996; this difference for all days was considered negligible. 2.2.2 Results The model estimated about 2 .3 and 0.46 kg N 2 O N per ha for 1991 and 1996, respectively, or over a 5 fold difference. The results are shown on a daily basis on Figure 3. The large spike in N 2 O emissions seen on July 1 and 2 coincides with the application of manure on July 1 (day 18 2 of 365). The peak daily N 2 O emissions for 1991 was 934 g per ha and for 1996, 229 g per ha, both on July 1. Since all other parameters were identical, we focused our attention on rainfall differences around the spike. As shown on Figure 4 and in Appen dix B, the 1991 simulation had relatively significant antecedent rainfall; on June 29, 7.1 mm of rainfall occurred and on June 30, 18.3 mm occurred. In

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8 1996, the first antecedent rainfall event was 10.7 mm on June 26. This study could not benefit from fi eld measurements of real antecedent moisture conditions in the soil but we would expect, given the rainfall data, that their effects on N 2 O emissions compared to the effects of rainfall events on and before that date to be minimal, as modeled in DNDC. 2.2.3 Interpretation The results appear to indicate that the coincidence of N application and precipitation events in dryland farming result in the largest daily emissions of N 2 O of the year, compared to days where there is either no precipitation or no N application. The data show an N 2 O emission response to precipitation events throughout the year, but these appear to be limited by available inorganic soil N at various growth stages (N uptake) of the crop at different times of the year, as expected. I nterestingly, the portion of N 2 O emissions occurring on and the day following N application contribute similarly to the total for both years. In 1991, about 75% of the annual N 2 O emissions occurred on July 1 and 2; in 1996 about 71% occurred (Figure 4). This indicates that N application may result in large portions of the annual flux of N 2 O around the date of application, regardless of rainfall, but the presence of rainfall and wetter soils can increase the annual flux itself. Although soil moisture throu ghout the soil column was not known, it is a fair approximation that, in the near term, it is highly correlated with rainfall events. However, more research is needed to tie soil column moisture profiles and their corresponding aerobic and anaerobic N p rocesses to rainfall, its random distribution, and its random intensity. These processes may be embedded in the models, but practioners should insist on using as inputs appropriate, simulated rainfall data that is representative of the amount and distribu tion at a particular location. Rainfall and its random nature has been studied extensively by hydrologists and meteorologists, but perhaps not adopted by practitioners in agricultural emissions estimation. The Markov chain simulation was first used by Ga briel and Neumann (1962) where the probability of rainfall on the current day is conditioned on Roldn and Woolhiser (1982) found that the Markov chain, two state simulation was more accurate than an alternating rene wal process for five stations in the United States. First order (i.e. dependent on only the previous day) Markov chain simulations are appropriate for many applications ( Katz 1 977; Richardson 1 981; Wilks 1 992 ; Kottegoda et al. 2 004 ), and other research ers find better simulations at second and third orders when strong, regional topographic and convective forces, such as for monsoons or El Nios, are at play (Wilks 1 999; Wan et al. 2 005 ; Schoof and Pryor 2 008 ) Others researchers have proposed other frameworks, such as entropy ma ximization (Koutsoyiannis 2 006), as a more accurate alternative than the conventional Markov chain model. Because rainfall data sets are often lacking for a specific application, researchers are evaluating the use of widely available gridded data sets (NOAA 2 013) for disaggregation or aggregation in time and space using Poisson techniques (Koutsoyiannis and Onof 2 001) and climate models such as GFDL CM and CCSM3 (Sun et al. 2 rainfall that preserves important characteristics such as periodicity and intensity would be extremely

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9 helpful in agricultural N 2 O emissions models because there would be a justification to use a single year of data that represents all years in forward loo king estimations. 3.0 DISCUSSION A great deal of interest has been focused on agricultural production practices as a way to mitigate global GHG emissions (Duxbury et al. 1 993 ; Oenema et al. 1 998 ; Romany et al. 2 012) As we have seen, field measureme nts and process models of agricultural production have characterized N 2 O emissions, their variability, and uncertainty quite well when using historical data. The study did not attempt to validate a biogeochemical process model as others have already done, but used it as a tool to illustrate the significant effect on daily N 2 O emissions from N application, rainfall, and their coincidence. Because the variability of rainfall has been poorly characterized in data sets used in forward looking estimations, we tend to see that N 2 O emissions have generally been overestimated in the past, have an unreported component of variability, and can be greatly influenced by management practices (e.g. N application amounts and times). The implications of this overestimati on and uncertainty could be significant, depending food product, bread. For example, Korsaeth et al. (201 2 ) conducted a life cycle assessment of cereal and bread prod uction in Norway. The study estimated a total global warming potential per kg of bread at 0.95 kg CO 2 equivalent In the agricultural production phase, N 2 O emissions accounted for about 50% of the total global warming potential for spring wheat emissions Although the study used the Norwegian Farm Accountancy Survey (including farm specific soil and weather data for 1,000 farms across Norway) to represent 93 chosen farms, N 2 O emissions were still estimated from the IPCC Tier 1 approach. The lack of prop er characterization of agricultural production emissions may also be an impediment to the voluntary, market driven reporting of corporate social responsibility (CSR) reports and individual product life cycle assessments. Market forces could be a relativel y untapped GHG mitigative strategy if more products containing wheat, for example, were able to quantify and report the contribution of agricultural practices to its overall GHG emissions portfolio. With emissions known, the market may then be able to inf luence the selection of production regions, practices, and even individual farms. For example, the largest bread producer in the U.S. and the world, Grupo Bimbo, publishes a Global Reporting Initiative (GRI) checked and registered CSR report (Grupo Bimbo 2 011). Unfortunately, it does not report any type of GHG emissions from agricultural production. Another large agri food company in the U.S., ConAgra Foods, also publishes a GRI CSR report. ConAgra says it aims to reduce GHG emissions per pound of prod uct by 20 % by 2015, while engaging suppliers through a variety of initiatives including an information database and regional dashboards sharing best practices and field/crop input and yield benchmarks 2 011). At the time of this writing, no fur ther information was available.

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10 To address the hurdles of data gathering and reporting by corporations, the Carbon Disclosure Project studied agricultural production of tomatoes and potatoes and the foods Unearthed: Agricultura l Emissions in the Corporate Supply Chain looked at how effective, robust, and accessible it was for corporations to gather data from agriculture suppliers (CDP 2 011) requests for better data management and recording parameters of the farm operation can be a burden, yet both government resource agencies and commodity purchasers are demanding it.

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11 REFERENCES Austrailia, 2009. A Literature Review of Life Cycle Assessment in Agriculture. Rural Industries Rese arch and Development Corporation. Publication No. 09/029. March. Barton, L., Kiesew, R., Gatterz, D.D., Butterbach Bahlw, K., Buck, R., Hinz, C., Murphy, D.V., 2008. Nitrous oxide emissions from a cropped soil in a semi arid climate. Global Change Bio logy 14: 177 192. Bhatia, P., Ranganathan, J., World Business Council for Sustainable Development (WBCSD), 2004. The Greenhouse Gas Protocol A Corporate Accounting and Reporting Standard (Revised Edition). March. Biswas, W.K., Barton, L., Carter, D., 2008. Global warming potential of wheat production in Western Australia: a life cycle assessment. Water and Environment Journal 22: 206 216. Carbon Disclosure Project, 2011. Unearthed: Agricultural Emissions in the Corporate Supply Chain. Carbon Discl osure Project, c/o RPA, 6 W 48th Street, 10th Floor, New York, New York 10036. ConAgra, 2011. Corporate Responsibility Report. ConAgra Foods, Inc., One ConAgra Drive, Omaha, Nebraska 68102 5001. Dailey, A.G., Smith, J.U., Whitmore, A.P., 2005. Weekly Weather Generation for a Nitrogen Turnover Model. Nutrient Cycling in Agroecosystems 73(2 3): 257 266. Davidson, E.A., Swank, W.T., Perry, T.O., 1986. Distinguishing between Nitrification and Denitrification as Sources of Gaseous Nitrogen Production in Soil. Applied and Environmental Microbiology. December. pp. 1280 1286. Del Grosso, S.J., Mosier, A.R., Parton, W.J., Ojima, D.S., 2005. DAYCENT Model Analysis of Past and Contemporary Soil N2O and Net Greenhouse Gas Flux for Major Crops in the USA. S oil Tillage and Research 83: 9 24. Del Grosso, S.J., Ogle, S.M, Parton, W.J., Breidt, F.J., 2010. Estimating Uncertainty in N 2 O Emissions from US Cropland Soils. Global Biogeochemical Cycles 24. Duxbury, J.M., Harper, L.A., Mosier, A.R., 1993. Contrib utions of agroecosystems to global climate change, in: Harper, L.A., Mosier, A.R., Duxbury, J.M., Rolston, D.E (Eds.), Agroecosystem Effects on Radiatively Important Trace Gases and Global Climate Change. Special Publication No. 55. American Society of Agronomy. Madison, WI. pp. 1 18. EPA, 2006a. Global Anthropogenic Non CO 2 Greenhouse Gas Emissions: 1990 2020. U.S.

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12 Environmental Protection Agency, Washington, DC. EPA, 2006b. Global Mitigation of Non CO 2 Greenhouse Gases. U.S. Environmental Protectio n Agency, Washington, DC. EPA, 2010. Methane and Nitrous Oxide Emissions from Natural Sources. EPA 430 R 10 001. April. Accessed online at: h ttp://www.epa.gov/outreach/pdfs/Methane and Nitrous Oxide Emissions From Natural Sources.pdf EPA, 2013a. Ag 101 Major Crops Grown in the United States. Accessed online at: http://www.ep a.gov/oecaagct/ag101/cropmajor.html EPA, 2013b. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 2011. EPA 430 R 13 001. April 12. European Association of Environmental and Resource Economists (EAERE), 2009. World supply and demand of food commodity calories. January 26. United Nations Food and Agriculture Organization (FAO), 2003. Agricultural Commodities: Profiles and Relevant World Trade Organization Negotiating Issues Basic Foodstuffs. Accessed online at: http://www.fao.org/docrep/006/y4343e/y4343e02.htm Gabriel, K. R., Neumann, J., 1962. A Markov chain model for daily rainfall occurrence at Tel Aviv. Quarterly Journal of the Royal Meteorological Society 88: 90 9 5. Global Reporting Initiative (GRI), 2012. Annual Report 2011/12. Accessed online at: https://www.globalreporting.org/resourcelibrary/GRI Annual Report 2011 2012.pdf Greenhouse Gas Protocol, 2013. Quantitative inventory uncertainty. World Resources Institute and the World Business Council for Sustainable Development. Accessed online at: http://www.ghgprotocol.org/files/ghgp/tools/Quantitative%20Uncertainty%20Guidance.p df Groffman, P.M., Tiedje, J.M., Mokma, D.L., Simkins, S., 1992. Regional scale estimates of denitrification in north temperate forest so ils. Landscape Ecology 7: 45 53. Grubb, M., 1995. Seeking Fair Weather: Ethics and the International Debate on Climate Change. International Affairs 71(3): 463 496. Grupo Bimbo, 2011. Integrated Annual Report. Corporative Bimbo, S.A. de C.V., Prolong acin Paseo de la Reforma No. 1000, Colonia Pea Blanca Santa Fe, Delegacin lvaro Obregn, Mexico City 01210.

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13 Heuvelink, G.B.M, 1998. Uncertainty analysis in environmental modelling under a change of spatial scale. Nutrient Cycling in Agroecosystems 5 0(1 3): 255 264. Intergovernmental Panel on Climate Change (IPCC), 2006. Guidelines for National Greenhouse Gas Inventories. Accessed online at: http://www.ipcc nggip.iges.or.jp/public/2006 gl/ International Organization for Standardization (ISO), 2006. ISO 14043: Environmental Management Life Cycle Assessment Life cycle interpretation. Second Edition, July. Kachanoski, R.G., 1988. Processes in Soils From Pedon to Landscape, in: Ro sswall, T., Woodmansee, R.G., Risser, P.G., (Eds.), Scales and Global Change: Spatial and Temporal Variability in Biospheric and Geospheric Processes, John Wiley and Sons, New York. pp. 153 179. Katz, R.W., 1977. Precipitation as a chain dependent proces s. Journal of Applied Meteorology 16: 671 676. Korsaeth, A., Jacobsen, A.Z., Roer, A. G., Henriksen, T.M., Sonesson, U., Bonesmo, H., Skjelvg, A.O., Strmman, A.H., 2012. Environmental life cycle assessment of cereal and bread production in Norway. Act a Agriculturae Scandinavica Section A Animal Science 62 (4): 242 253. Kottegoda, N.T., Natale, L., Raiteri, E., 2004. Some considerations of periodicity and persistence in daily rainfalls. Journal of Hydrology 296: 23 37. Koutsoyiannis, D. Onof, C., 2001. Rainfall disaggregation using adjusting procedures on a Poisson cluster model. Journal of Hydrology 246: 109 122. Koutsoyiannis, D., 2006. An entropic stochastic representation of rainfall intermittency: The origin of clustering and persistence Water Resources Research 42: W01401. Kuenen, J.G., Robertson, L.A., 1994. Combined nitrificationdenitrification processes. Federation of European Microbiological Societies (FEMS) Microbiology Reviews 15: 109 117. Li, C.S., 2000. Modelling trace gas emissions from agricultural ecosystems. Nutrients Cycling in Agroecosystems 58: 259 276. Li, C.S., 2011. Mitigating Greenhouse Gas Emissions from Agroecosystems: Scientific Agr Series. Washington, D.C.

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14 Li, Y., White, R., Chen, D., Zhang, J., Li, B., Zhang, Y., Huang, Y., Edis, R., 2007. A spatially referenced water and nitrogen management model (WNMM) for (irrigated) intensive cropping systems in the North China Plain. Ecological Modelling 203:395 423. Li, Y., Chen, D., Barker Reid, F., Eckard, R., 2008. Simulation of N 2 O emissions from rain fed wheat and the impact of climate variation in southeaster n Australia. Plant and Soil 309:239 251. Li, Y., Barton, L., Chen, D., 2012. Simulating response of N 2 O emissions to fertiliser N application and climatic variability from a rain fed and wheat cropped soil in Western Australia. Journal of the Science of Food and Agriculture 92: 1130 1143. Lynch, D.H., MacRae, R., Martin, R.C., 2011. The Carbon and Global Warming Potential Impacts of Organic Farming: Does It Have a Significant Role in an Energy Constrained World? Sustainability 3: 322 362. National Re newable Energy Laboratory (NREL), 2012. U.S. Life Cycle Inventory Database. Accessed November 19, 2012: https://www.lcacommons.gov/nrel/search Oenema, O., Gebauer, G., Rodriguez, M., Sapek, A., Ja rvis, S.C., Corr, W.J., Yamulki, S., 1998. Controlling nitrous oxide emissions from grassland livestock production systems. Nutrient Cycling in Agroecosystems 52(2 3): 141 149. Oenema, O., Wrage, N., Velthof1, G.L., van Groenigen, J.W., Dolfing, J., Ku ikman, P.J., 2005. Trends in Global Nitrous Oxide Emissions from Animal Production Systems. Nutrient Cycling in Agroecosystems 72(1): 51 65. Olander, L.P., Haugen Kozyra, K., 2011. Using Biogeochemical Process Models to Quantify Greenhouse Gas Mitigati on from Agricultural Management Projects. Nicholas Institute for Environmental Policy Solutions, Report NI R 11 03. March. PE International, 2013. GaBi Sustainability Software. Accessed online at: http://w ww.gabi software.com/ Pre Consultants, 2013. SimaPro Life Cycle Assessment Software. Accessed online at: http://www.pre sustainability.com/all about simapro Ramaswami, A., Weible, C., Main, D., Heikkila, T., Siddiki, S., Duvall, A., Pattison, A., Bernard, M., 2012. A Social Ecological Infrastructural Systems Framework for Interdisciplinary Study of Sustainable City Systems. Journal of Industrial Ecology, 16: 801 813. Richardson, C.W., 1981. Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resources Research 17(1): 182 190.

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15 Roldn, J., Woolhiser, D.A., 1982. Stochastic daily precipitation models A comparison of occurrence processes. Water Resources Resarch 18: 1451 1459. Romany, J., Arco, N., Sol Morales, I., Armengot, L., Sans, F.X., 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Qualit y v. 41 pp. 1337 1347. Rypdal, K., Winiwarter, W., 2001. Uncertainties in greenhouse gas emission inventories evaluation, comparability and implications. Environmental Science & Policy 4: 107 116. Schoof, J.T., Pryor, S.C., 2008. On the proper orde r of Markov chain model for daily precipitation occurrence in the contiguous United States. Journal of Applied Meteorology and Climatology, v 47. September. Smith, P., Martino, D., Cai, Z., Gwary, D., Janzen, H., Kumar, P., McCarl, B., Ogle, S., O'Mara, F., Rice, C., Scholes, B., Sirotenko, O., Howden, M., McAllister, T., Pan, G., Romanenkov, V., Schneider, U., Towprayoon, S., Wattenbach, M., Smith, J., 2008. Greenhouse gas mitigation in agriculture. Philosophical Transactions of the Royal Society of L ondon B 363: 789 813. Smith, W.N., Grant, B., Desjardins, R., Worth, D., Li, C., Boles, S.H., Huffman, E.C., 2010. A tool to link agricultural activity data with the DNDC model to estimate GHG emission factors in Canada. Agriculture, Ecosystems and Envi ronment 136: 301 309. Stehfest, E., Bouwman, L., 2006. N 2 O and NO emission from agricultural fields and soils under natural vegetation: summarizing available measurement data and modeling of global annual emissions. Nutrient Cycling in Agroecosystems 74 : 207 228. Stern, R.D., 1980. The Calculation of Probability Distributions for Models of Daily Precipitation. Archives for Meteorology Geophysics and Bioclimatology Series B 28: 137 147. Sun, Y., Solomon, S., Dai, A., Portmann, R., 2006. How often doe s it rain? Journal of Climate 19: 916 934. Swiss Centre for Life Cycle Inventories, 2013. Ecoinvent Life Cycle Inventory Database. Accessed online at: http://www.ecoinvent.ch/ Tonitto, C., David, M.B., Li, C. Drinkwater, L.E., 2007. Application of the DNDC model to tile drained Illinois agroecosystems: model comparison of conventional and diversified rotations. Nutrient Cycling in Agroecosystems DOI 10.1007/s10705 006 9074 2.

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16 USDA, 2007. Nutrient Content of the U.S. Food Supply, 1909 2004 A Summary Report. Center for Nutrition Policy and Promotion Home Economics, Research Report Number 57. February. USDA, 2012. National Agricultural Statistics Service Agriculture Counts Durum/Spring/Winter Wheat 20 12 Harvested Acres by County for Selected States. Accessed online at: http://www.nass.usda.gov/Charts_and_Maps/Crops_County/ USDA, 2013. National Nutrient Database for Standard Refe rence Release 25. Agricultural Research Service Nutrient Data Laboratory. Accessed online at: http://www.ars.usda.gov/main/site_main.htm?modecode=12 35 45 00 Wagenet, R.J., 1998. Scale issues in agroecological research chains. Nutrient Cycling in Agroecosystems 50: 23 34. Wan, H., Zhang, W., Barrow, E.M., 2005. Stochastic modelling of daily precipitation for Canada. Atmosphere Ocean 43(1): 23 32. Weber, C.L., Matthews, H.S., 2008. Food Miles and the Relative Climate Impacts of Food Choices in the United States. Environmental Science and Technology 42: 3508 3513. Western Regional Climate Center at Desert Research Institute, 2013. Weather data for Coop Station No. 2431 13, Fort Benton, Montana. Accessed online at: http://www.wrcc.dri.edu/cgi bin/cliMAIN.pl?mt3113 Wilks, D.S., 1992. Adapting stochastic weather generation algorithms for climate change st udies. Climatic Change 22: 67 84. Wilks, D.S., 1999. Interannual variability and extreme value characteristics of several stochastic daily precipitation models. Agricultural and Forest Meteorology 93: 153 169. Yemenu, F., Chemeda, D., 2013. Dry and W et Spell Analysis of the Two Rainy Seasons for Decision Support in Agricultural Water Management for Crop production in the Central Highlands of Ethiopia. Journal of Biology, Agriculture and Healthcare 3 (11). Zborel, T., Holland, B., Thomas, G., Baker, L., Calhoun, K., Ramaswami, A., 2012. Translating Research to Policy for Sustainable Cities. Journal of Industrial Ecology 16: 786 788.

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Raw Data Field measurement / Modeling Life Cycle Inventories U.S. EPA Inventory of GHG Emissions / NREL U.S. Life Cycle Inventory Database IPCC Tier 1 IPCC Tier 3 IPCC Tier 2 Life Cycle Tools Ecoinvent/ GaBi/ Simapro/ Quantis/ LCA Food (Denmark) Emissions Reporting Global Reporting Initiative / Carbon Disclosure Project / ISO / Climate registri es / GHG Protocol Abbreviations: U.S. Environmental Protection Agency (E PA); International Organization for Standardization (ISO); Intergovernmental Panel on Climate Change (IPCC); life cycle assessment (LCA); National Renewable Energy Laboratory (NREL) Figure 1

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0 0.5 1 1.5 2 2.5 3 121416181101121141161181201221241261281301321341361 Day of YearPrecipitation (cm) Precip 1991 Precip 1996 Figure 2

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0 0.5 1 1.5 2 2.5 3 050100150200250300350400 Day of YearN2O flux (kg N/ha)0 0.5 1 1.5 2 2.5 3Precipitation (cm) N2O flux 1991 N2O flux 1996 Precip 1991 Precip 1996 Figure 3

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0 0.5 1 1.5 2 2.5 3 170172174176178180182184186188190 Day of YearN2O flux (kg N/ha)0 0.5 1 1.5 2 2.5Precipitation (cm) N2O flux 1991 N2O flux 1996 Precip 1991 Precip 1996 Figure 4

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Figure 1 Pathways of agricultural production emissions reporting can include measured, estimated, and modeled data Figure 2 Daily precipitation at Fort Benton, Montana for two different years of very similar total annual rainfall. Figure 3 Cumulative n i trous o xide f lux for 1991 and 1996 with daily precipitation Figure 4 Cumulative n itrous o xide f lux detail for 19 June to 8 July Figure Captions

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Appendix B DNDC Results N2O flux 1991N2O flux 1996 N2O flux 1991 N2O flux 1996 Precip 1991Precip 1996 DateDay(kg N/ha/day)(kg N/ha/day)(kg N/ha)(kg N/ha)(cm)(cm) Jun 191700.000 0.000 0.014 0.003 0.406 Jun 201710.000 0.000 0.015 0.003 2.032 Jun 211720.000 0.000 0.015 0.003 0.127 Jun 221730.000 0.000 0.015 0.003 0.076 0.635 Jun 231740.000 0.000 0.015 0.004 Jun 241750.000 0.000 0.015 0.004 0.508 Jun 251760.000 0.000 0.016 0.004 0.203 0.178 Jun 261770.000 0.000 0.016 0.004 1.067 Jun 271780.000 0.001 0.016 0.005 Jun 281790.000 0.000 0.016 0.005 Jun 291800.000 0.000 0.016 0.005 0.711 Jun 301810.001 0.000 0.017 0.005 1.829 Jul 011820.934 0.279 0.950 0.284 0.203 Jul 021830.825 0.045 1.775 0.329 Jul 031840.347 0.006 2.122 0.336 0.229 Jul 041850.090 0.002 2.212 0.337 Jul 051860.018 0.001 2.230 0.338 0.025 Jul 061870.003 0.000 2.234 0.338 Jul 071880.001 0.000 2.235 0.339 Jul 081890.001 0.000 2.235 0.339 Appendix B

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Appendix A DNDC Input Parameters Parameter a [ref] Year: 1991 Year: 1996 CLIMATE TAB Latitude Fort Benton, Montana 4 7 .4 9 47.49 Simulated No. of Years 1 1 Meteorological Data [1 0 ] Julian day number; daily average air temperature (C); daily precip itation (cm) See reference [1 0 ] See reference [1 0 ] N concentration in precipitation (mg/L) [ 3 ] 0. 29 0. 29 Atmospheric background NH 3 concentration (g/m 3 )* 0.06 0.06 Atmospheric background CO 2 concentration (ppm)* 350 350 Annual increase rate of atmosph eric CO 2 concentration (ppm/yr)* 0 0 SOIL TAB Land use type(s) upland crop field upland crop field Topsoil texture [ 4, 6 ] (8 ) cla y loam ( 8 ) clay loam Bulk density (g/cm 3 Mg/m 3 ) [ 4, 6 ] 1.3 5 1.35 pH [ 4 ] 7 7 Macro pores* no no Depth of water retenti on layer (m) b 9.99 9.99 Waterlogging problem* no no SOC at surface soil, 0 5 cm (kg C/kg) [ 1 ] 0.013 0.013 Depth of topsoil with uniform SOC content (m)* 0.05 0.05 SOC decrease rate below topsoil, 0.5 5.0 m 0.7 0.7 Microbial activity index 0.8 0.8 Slope (m/m) [ 4 ] 0 .02 0.02 Soil salinity index [ 4 ] 0 0 CROPPING TAB Number of cropping systems during simulated no. of years 1 1 Duration of each cropping system (years) 1 1 Years of a cycle within each cropping system 1 1 FARMING MANAGEMENT PRACTI CES CROP TAB Number of new crops planted in this year 1 1 Crop type ( 6 ) Spring Wheat (6) Spring Wheat Is it a cover crop? no no Planting month and day [ 9 ] 1 May 1 May Harvest month and day [ 9 ] 20 Aug 20 Aug Harvest mode (1 = same year ) [ 9 ] 1 1 Fraction of leaves and stems left in field after harvest 0.5 0.5 Maximum biomass production grain (kgC/ha/yr) default default Water demand (g water/g dry matter) default default TILLAGE TAB Number of tillage applications in this year 1 1 Tilling m onth and day 1 May 1 May Tilling method (3) Plough 10 cm (3) Plough 10 cm FERTILIZATION TAB Application type Manual Manual Number of applications none none MANURE MANAGEMENT TAB Number of applications in this year [2] 1 1 Application month and d ay [2] 1 Jul 1 Jul Manure type (4) slurry animal waste (4) slurry animal waste kg C per ha [ 5 7 ] 500 500 C/N ratio [ 5 7 ] 3.38 3.38 kg N per ha [ 2 5 7 ] 148 148 Appendix A

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Application method surface spread surface spread IRRIGATION TAB Input mode index 0 .00 0.00 Irrigation method n/a n/a FLOODING TAB Times field is flooded (irrigation method) 0 0 PLASTIC TAB Method none none Number of applications 0 0 From month and day n/a n/a To month and day n/a n/a Covered fraction n/a n/a GRAZING OR CUT TING TAB Number of grazing applications 0 0 Number of biomass cuttings 0 0 Cutting month and day n/a n/a Cut part n/a n/a Cut fraction n/a n/a Notes: a default value from [ 8 ] a. Parameters with default values based on other user assigned values not shown. Initial conditions shown. b. All soils assumed to be deep soils with no confining layers. References: 1. Hake, D.R., Powell, J ., McPherson, J.K., Claypool, P.L., Dunn, G.L., 1984. Water Stress of Tallgrass Prairie Plants in Central Oklaho ma Journal of Range Management 37(2). 2. Montana State University Extension Service 2012. Practices to Increase Wheat Grain Protein. Report EB0206. May. 3. National Atmospheric Deposition Program 4. N atural Resources Conse rvation Service, 2013. Web Soil Survey. Accessed online at: http://websoilsurvey.nrcs.usda.gov/app/ 5. Romany, J Arco, N., Sol Morales, I., Armengot, L., Sans F.X., 2012. Carbon and Nitrogen Stocks and Nitrogen Mineralization in Organically Managed Soils Amended with Composted Manures. Journal of Environmental Quality v. 41 pp. 1337 1347. 6. Saxton, K.E. Rawls W.J 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrolo gic Solutions. Soil Science Society of America Journal. 70:1569 1578. 7. The Environment Agency United Kingdom. 2011 Think Manure A Guide to Manure Management. 8. University of New Hampshire [UNH] Institute for the Study of Earth, Oceans and Space 2012. User's Guide for the DNDC Model (Version 9.5). Accessed online at: http://www.dndc.sr.unh.edu/ 9. U S Department of Agriculture, 2010. Field crops usual planting and harvesting dates. National Agricultural Statis tics Service Agricultural Handbook No. 628. October. 10. Western Regional Climate Center at Desert Research Institute Coop Station No. 243113, Fort Benton, Montana. Accessed online at: http://www .wrcc.dri.edu/cgi bin/cliMAIN.pl?mt3113

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APPENDIX F DOMINANCE ANALYSIS

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DOMINANCE OF INPUT ON: Denver CODenver CODenver CODenver COTerreton ID A lamosa C O Bakersfield C Greeley COMission TXGreeley CODeming NMOntario ORCuliacn SI N Bakersfield C Punta Gord a Denver CO Energy(all non-renewable) potatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatoturf PRODUCTION DIRECT % in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in To t Mechanized Equip kWh urbanGrow lights kWh0.18 Mechanized Equip kWh commercial0.00 0.00 0.00 0.00 Vehicle trips gasoline0.03 0.03 0.03 0.00 Mechanized Equip gasoline0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 Mechanized Equip diesel0.01 0.01 0.00 0.01 0.00 0.01 0.00 0.01 0.00 0.01 0.00 Irrigation pumped water diesel0.00 0.00 0.00 PRODUCTION INDIRECT % in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in To t Vehicle trips gasoline0.50 0.52 0.53 0.03 Herbicide/Pesticide0.28 0.39 0.46 0.65 0.01 0.01 0.01 0.00 Fungicide0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.01 Soil(0.00) Grow lights electricity0.67 Mechanized Equipment electricityDrip Tape or hose0.32 0.28 0.28 0.02 0.07 0.16 0.07 0.09 Herbicide/Pesticide0.01 0.01 0.00 0.00 0.01 0.01 0.01 0.01 Mechanized Equipment gasoline0.13 0.10 0.12 0.01 0.01 0.01 0.02 0.03 0.01 0.02 0.00 0.00 0.00 0.85 Mechanized Equipment diesel0.11 0.15 0.08 0.11 0.07 0.17 0.07 0.09 0.07 0.09 0.05 Mechanized Equipment electricity0.00 0.01 0.00 0.00 N ammonium nitrate0.01 0.01 0.04 0.05 0.06 0.13 0.06 0.07 0.02 0.03 0.02 0.08 P triple superphosphate0.01 0.01 0.01 0.01 0.03 0.07 0.03 0.04 0.01 0.02 0.01 0.00 K potash0.01 0.01 0.00 0.00 0.01 0.02 0.01 0.01 0.00 0.00 0.00 0.00 Sulfur0.00 0.00 Zinc0.00 0.00 0.00 0.00 Copper0.00 0.00 Manganese0.00 0.00 Magnesium0.01 0.01 Lime0.00 0.00 Irrigation pumped water diesel0.00 0.01 0.03 Water0.01 0.04 0.03 0.00 0.06 0.01 0.04 0.25 0.00 0.01 0.05 0.03 POST-PRODUCTION % in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in To t Refrigerated tractor / 17-ton trailer0.34 0.18 0.28 0.02 0.58 0.06 0.33 0.50 0.71 0.60 0.73 Light pickup truck0.00 0.02 0.01 0.00 0.01 PET clamshell0.09 PP – Polypropylene0.04 0.05 0.02 0.03 0.03 0.07 0.03 0.04 LDPE “cello” bags0.03 0.03 0.02 PS – polystyrene0.01 0.02 0.01 Cardboard0.13 0.17 0.06 0.09 0.11 0.25 0.11 0.13 0.11 0.14 0.08 20 Mb site, 1 yr.0.00 0.00 0.00 0.00 0.00

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DOMINANCE OF INPUT ON: Denver CODenver CODenver CODenver COTerreton IDAlamosa COBakersfield C Greeley COMission TXGreeley CODeming NMOntario ORCuliacn SI N Bakersfield C Punta GordaDenver CO Land Use(arable, non-irrigated) potatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatoturf PRODUCTION DIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Mechanized Equip kWh urbanGrow lights kWhMechanized Equip kWh commercialVehicle trips gasolineMechanized Equip gasolineMechanized Equip dieselIrrigation pumped water dieselLand Area0.97 0.97 0.96 0.97 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.99 PRODUCTION INDIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Vehicle trips gasoline0.01 0.01 0.01 0.00 Herbicide/Pesticide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Fungicide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 SoilGrow lights electricity0.00 Mechanized Equipment electricityDrip Tape or hose0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Herbicide/Pesticide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mechanized Equipment gasoline0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 Mechanized Equipment diesel0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mechanized Equipment electricity0.00 0.00 0.00 0.00 N ammonium nitrate0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 P triple superphosphate0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 K potash0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfur0.00 0.00 Zinc0.00 0.00 0.00 0.00 Copper0.00 0.00 Manganese0.00 0.00 Magnesium0.00 0.00 Lime0.00 0.00 Irrigation pumped water diesel0.00 0.00 0.00 Water0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POST-PRODUCTION % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Refrigerated tractor / 17-ton trailer0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Light pickup truck0.00 0.00 0.00 0.00 0.00 PET clamshell0.00 PP – Polypropylene0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 LDPE “cello” bags0.00 0.00 0.00 PS – polystyrene0.00 0.00 0.00 Cardboard0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 20 Mb site, 1 yr.0.03 0.02 0.03 0.02 0.13

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DOMINANCE OF INPUT ON: Denver CODenver CODenver CODenver COTerreton IDAlamosa COBakersfield C Greeley COMission TXGreeley CODeming NMOntario ORCuliacn SI N Bakersfield C Punta GordaDenver CO Water(all fresh water sources) potatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatoturf PRODUCTION DIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Mechanized Equip kWh urbanGrow lights kWhMechanized Equip kWh commercialVehicle trips gasolineMechanized Equip gasolineMechanized Equip dieselIrrigation pumped water dieselLand AreaWater0.99 1.00 1.00 0.98 0.97 0.98 0.97 0.97 0.98 0.99 0.98 0.98 0.97 0.98 0.97 0.99 PRODUCTION INDIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Vehicle trips gasoline0.01 0.00 0.00 0.00 Herbicide/Pesticide0.01 0.00 0.01 0.01 0.00 0.00 0.00 0.00 Fungicide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Soil(0.00) Grow lights electricity0.01 Mechanized Equipment electricityDrip Tape or hose0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Herbicide/Pesticide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mechanized Equipment gasoline0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 Mechanized Equipment diesel0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mechanized Equipment electricity0.00 0.00 0.00 0.00 N ammonium nitrate0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 P triple superphosphate0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 K potash0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfur0.00 0.00 Zinc0.00 0.00 0.00 0.00 Copper0.00 0.00 Manganese0.00 0.00 Magnesium0.00 0.00 Lime0.00 0.00 Irrigation pumped water diesel0.00 0.00 0.00 Water0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 POST-PRODUCTION % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Refrigerated tractor / 17-ton trailer0.01 0.00 0.01 0.00 0.01 0.00 0.01 0.01 0.02 0.01 0.02 Light pickup truck0.00 0.00 0.00 0.00 0.00 PET clamshell0.00 PP – Polypropylene0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 LDPE “cello” bags0.00 0.00 0.00 PS – polystyrene0.00 0.00 0.00 Cardboard0.01 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20 Mb site, 1 yr.0.00 0.00 0.00 0.00 0.00

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DOMINANCE OF INPUT ON: Denver CODenver CODenver CODenver COTerreton ID A lamosa C O Bakersfield C Greeley COMission TXGreeley CODeming NMOntario ORCuliacn SI N Bakersfield C Punta Gord a Denver CO Total Carbon Dioxide eq potatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatoturf PRODUCTION DIRECT % in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in To t Mechanized Equip kWh urbanGrow lights kWhMechanized Equip kWh commercialVehicle trips gasolineMechanized Equip gasolineMechanized Equip dieselIrrigation pumped water dieselLand AreaImpact from DNDC0.31 0.29 0.24 0.05 0.35 0.35 0.28 0.57 0.50 0.80 0.47 0.68 0.62 0.77 0.50 0.27 PRODUCTION INDIRECT % in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in To t Vehicle trips gasoline0.45 0.40 0.45 0.05 Herbicide/Pesticide0.15 0.21 0.27 0.24 0.00 0.00 0.00 0.00 Fungicide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Soil(0.07) Grow lights electricity0.85 Mechanized Equipment electricityDrip Tape or hose0.07 0.05 0.06 0.01 0.01 0.01 0.01 0.01 Herbicide/Pesticide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mechanized Equipment gasoline0.11 0.08 0.10 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.64 Mechanized Equipment diesel0.08 0.12 0.07 0.06 0.04 0.04 0.04 0.03 0.03 0.02 0.03 Mechanized Equipment electricity0.00 0.00 0.00 0.00 N ammonium nitrate0.01 0.02 0.06 0.05 0.06 0.06 0.05 0.04 0.02 0.01 0.02 0.09 P triple superphosphate0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 K potash0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfur0.00 0.00 Zinc0.00 0.00 0.00 0.00 Copper0.00 0.00 Manganese0.00 0.00 Magnesium0.01 0.01 Lime0.00 0.00 Irrigation pumped water diesel0.00 0.00 0.01 Water0.05 0.17 0.15 0.03 0.07 0.01 0.05 0.21 0.00 0.01 0.02 0.00 POST-PRODUCTION % in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in Tot% in To t Refrigerated tractor / 17-ton trailer0.23 0.13 0.22 0.01 0.30 0.01 0.14 0.16 0.27 0.14 0.38 Light pickup truck0.00 0.01 0.00 0.00 0.01 PET clamshell0.07 PP – Polypropylene0.01 0.01 0.01 0.00 0.01 0.01 0.01 0.00 LDPE “cello” bags0.00 0.00 0.00 PS – polystyrene0.00 0.00 0.00 Cardboard0.08 0.11 0.04 0.04 0.05 0.05 0.04 0.04 0.04 0.03 0.04 20 Mb site, 1 yr.0.00 0.00 0.00 0.00 0.00

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DOMINANCE OF INPUT ON: Denver CODenver CODenver CODenver COTerreton IDAlamosa COBakersfield C Greeley COMission TXGreeley CODeming NMOntario ORCuliacn SI N Bakersfield C Punta GordaDenver CO Carbon Dioxide potatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatoturf PRODUCTION DIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Mechanized Equip kWh urbanGrow lights kWhMechanized Equip kWh commercialVehicle trips gasolineMechanized Equip gasolineMechanized Equip dieselIrrigation pumped water dieselLand AreaImpact from DNDC0.39 0.35 0.30 0.05 0.25 0.34 0.25 0.50 0.46 0.81 0.40 0.69 0.55 0.77 0.50 0.37 PRODUCTION INDIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Vehicle trips gasoline0.37 0.32 0.36 0.03 Herbicide/Pesticide0.16 0.21 0.27 0.30 0.00 0.00 0.00 0.00 Fungicide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Soil(0.00) Grow lights electricity0.82 Mechanized Equipment electricityDrip Tape or hose0.08 0.05 0.06 0.01 0.01 0.01 0.01 0.01 Herbicide/Pesticide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mechanized Equipment gasoline0.09 0.06 0.08 0.01 0.00 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.58 Mechanized Equipment diesel0.10 0.13 0.07 0.08 0.05 0.05 0.04 0.03 0.04 0.02 0.03 Mechanized Equipment electricity0.00 0.00 0.00 0.00 N ammonium nitrate0.00 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.00 0.01 0.04 P triple superphosphate0.00 0.01 0.01 0.01 0.02 0.02 0.02 0.01 0.01 0.00 0.00 0.00 K potash0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfur0.00 0.00 Zinc0.00 0.00 0.00 0.00 Copper0.00 0.00 Manganese0.00 0.00 Magnesium0.01 0.01 Lime0.00 0.00 Irrigation pumped water diesel0.00 0.00 0.02 Water0.06 0.21 0.19 0.03 0.09 0.02 0.05 0.25 0.00 0.01 0.02 0.00 POST-PRODUCTION % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Refrigerated tractor / 17-ton trailer0.28 0.14 0.25 0.01 0.35 0.01 0.18 0.18 0.34 0.14 0.39 Light pickup truck0.00 0.01 0.01 0.00 0.01 PET clamshell0.06 PP – Polypropylene0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00 LDPE “cello” bags0.00 0.00 0.00 PS – polystyrene0.00 0.00 0.00 Cardboard0.09 0.12 0.05 0.05 0.06 0.06 0.05 0.04 0.04 0.03 0.04 20 Mb site, 1 yr.0.00 0.00 0.00 0.00 0.00

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DOMINANCE OF INPUT ON: Denver CODenver CODenver CODenver COTerreton IDAlamosa COBakersfield C Greeley COMission TXGreeley CODeming NMOntario ORCuliacn SI N Bakersfield C Punta GordaDenver CO Nitrous Oxide potatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatoturf PRODUCTION DIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Mechanized Equip kWh urbanGrow lights kWhMechanized Equip kWh commercialVehicle trips gasolineMechanized Equip gasolineMechanized Equip dieselIrrigation pumped water dieselLand AreaImpact from DNDC0.74 0.73 0.62 (0.02) 0.88 0.69 0.56 0.81 0.76 0.79 0.83 0.74 0.94 0.85 0.75 0.08 PRODUCTION INDIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Vehicle trips gasoline0.20 0.22 0.30 (0.00) Herbicide/Pesticide0.05 0.14 0.12 0.05 0.00 0.00 0.00 0.00 Fungicide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Soil1.02 Grow lights electricityMechanized Equipment electricityDrip Tape or hoseHerbicide/Pesticide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mechanized Equipment gasoline0.05 0.04 0.07 (0.00) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 Mechanized Equipment diesel0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mechanized Equipment electricityN ammonium nitrate0.05 0.13 0.30 0.13 0.22 0.20 0.15 0.24 0.05 0.12 0.18 0.87 P triple superphosphate0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 K potash0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfur0.00 0.00 Zinc0.00 0.00 0.00 0.00 Copper0.00 0.00 Manganese0.00 0.00 Magnesium0.00 0.00 Lime0.00 0.00 Irrigation pumped water diesel0.00 0.00 0.00 Water0.00 0.00 0.00 (0.00) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POST-PRODUCTION % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Refrigerated tractor / 17-ton trailer0.01 0.01 0.01 0.00 0.01 0.00 0.00 0.01 0.01 0.01 0.04 Light pickup truck0.00 0.01 0.01 (0.00) 0.00 PET clamshellPP – PolypropyleneLDPE “cello” bagsPS – polystyreneCardboard0.01 0.03 0.01 0.00 0.01 0.01 0.00 0.01 0.00 0.01 0.01 20 Mb site, 1 yr.0.00 0.00 0.00 (0.00) 0.00

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DOMINANCE OF INPUT ON: Denver CODenver CODenver CODenver COTerreton IDAlamosa COBakersfield C Greeley COMission TXGreeley CODeming NMOntario ORCuliacn SI N Bakersfield C Punta GordaDenver CO Methane potatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatoturf PRODUCTION DIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Mechanized Equip kWh urbanGrow lights kWhMechanized Equip kWh commercialVehicle trips gasolineMechanized Equip gasolineMechanized Equip dieselIrrigation pumped water dieselLand AreaImpact from DNDC(0.19) (0.24) (0.21) (0.02) (0.14) (0.36) (0.18) (0.08) (1.46) (0.37) (0.16) (0.28) (0.27) (1.14) (0.32) (0.13) PRODUCTION INDIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Vehicle trips gasoline0.62 0.71 0.68 0.04 Herbicide/Pesticide0.39 0.61 0.60 0.77 0.02 0.03 0.01 0.01 Fungicide0.00 0.00 0.01 0.01 0.00 0.00 0.01 0.02 0.01 0.01 SoilGrow lights electricity0.78 Mechanized Equipment electricityDrip Tape or hose0.41 0.39 0.37 0.02 0.31 0.31 0.08 0.18 Herbicide/Pesticide0.01 0.01 0.00 0.01 0.02 0.02 0.01 0.01 Mechanized Equipment gasoline0.16 0.14 0.15 0.01 0.01 0.01 0.06 0.06 0.02 0.04 0.01 0.01 0.01 1.06 Mechanized Equipment diesel0.14 0.22 0.09 0.12 0.25 0.25 0.06 0.15 0.13 0.22 0.11 Mechanized Equipment electricity0.01 0.01 0.00 0.01 N ammonium nitrate0.01 0.01 0.03 0.04 0.13 0.13 0.03 0.08 0.03 0.04 0.02 0.06 P triple superphosphate0.00 0.01 0.01 0.01 0.07 0.07 0.02 0.04 0.02 0.03 0.01 0.00 K potash0.01 0.02 0.00 0.00 0.04 0.04 0.01 0.02 0.01 0.01 0.01 0.00 Sulfur0.00 0.00 Zinc0.00 0.00 0.00 0.00 Copper0.00 0.00 Manganese0.00 0.00 Magnesium0.01 0.01 Lime0.00 0.00 Irrigation pumped water diesel0.01 0.01 0.05 Water0.00 0.00 0.00 0.00 0.14 0.03 0.16 0.67 0.00 0.00 0.49 0.00 POST-PRODUCTION % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Refrigerated tractor / 17-ton trailer0.25 0.15 0.19 0.01 1.14 0.05 0.16 0.49 0.77 0.85 0.85 Light pickup truck0.00 0.00 0.00 0.00 0.00 PET clamshell0.17 PP – Polypropylene0.05 0.07 0.02 0.03 0.11 0.11 0.03 0.07 LDPE “cello” bags0.07 0.12 0.06 PS – polystyrene0.03 0.06 0.03 Cardboard0.13 0.20 0.06 0.08 0.31 0.31 0.08 0.18 0.16 0.27 0.14 20 Mb site, 1 yr.0.00 0.00 0.00 0.00 0.00

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DOMINANCE OF INPUT ON: Denver CODenver CODenver CODenver COTerreton IDAlamosa COBakersfield C Greeley COMission TXGreeley CODeming NMOntario ORCuliacn SI N Bakersfield C Punta GordaDenver CO TRACI Carcinogens potatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatoturf PRODUCTION DIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Mechanized Equip kWh urbanGrow lights kWhMechanized Equip kWh commercialVehicle trips gasolineMechanized Equip gasolineMechanized Equip dieselIrrigation pumped water dieselLand AreaImpact from DNDCPRODUCTION INDIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Vehicle trips gasoline0.72 0.76 0.75 0.58 Herbicide/Pesticide0.23 0.31 0.39 0.58 0.01 0.01 0.00 0.00 Fungicide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 Soil(0.00) Grow lights electricity0.19 Mechanized Equipment electricityDrip Tape or hose0.09 0.08 0.08 0.07 0.08 0.16 0.09 0.09 Herbicide/Pesticide0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.00 Mechanized Equipment gasoline0.18 0.15 0.17 0.13 0.04 0.05 0.09 0.16 0.10 0.10 0.02 0.02 0.01 0.98 Mechanized Equipment diesel0.15 0.21 0.11 0.16 0.08 0.15 0.09 0.09 0.08 0.11 0.07 Mechanized Equipment electricity0.00 0.00 0.00 0.00 N ammonium nitrate0.01 0.01 0.03 0.05 0.04 0.07 0.04 0.04 0.02 0.02 0.01 0.01 P triple superphosphate0.03 0.04 0.05 0.08 0.13 0.25 0.15 0.15 0.06 0.08 0.05 0.00 K potash0.01 0.01 0.00 0.01 0.01 0.02 0.01 0.01 0.00 0.01 0.00 0.00 Sulfur0.00 0.00 Zinc0.00 0.00 0.00 0.00 Copper0.01 0.01 Manganese0.00 0.00 Magnesium0.01 0.01 Lime0.00 0.00 Irrigation pumped water diesel0.00 0.01 0.03 Water0.01 0.00 0.02 0.10 0.00 0.00 0.03 0.00 POST-PRODUCTION % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Refrigerated tractor / 17-ton trailer0.38 0.19 0.32 0.03 0.50 0.04 0.33 0.42 0.71 0.61 0.74 Light pickup truck0.00 0.00 0.00 0.00 0.00 PET clamshell0.03 PP – Polypropylene0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 LDPE “cello” bags0.00 0.00 0.00 PS – polystyrene0.01 0.01 0.01 Cardboard0.11 0.15 0.06 0.09 0.07 0.14 0.09 0.09 0.08 0.11 0.07 20 Mb site, 1 yr.0.00 0.00 0.00 0.00 0.00

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DOMINANCE OF INPUT ON: Denver CODenver CODenver CODenver COTerreton IDAlamosa COBakersfield C Greeley COMission TXGreeley CODeming NMOntario ORCuliacn SI N Bakersfield C Punta GordaDenver CO TRACI Non-carcinogens potatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatoturf PRODUCTION DIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Mechanized Equip kWh urbanGrow lights kWhMechanized Equip kWh commercialVehicle trips gasolineMechanized Equip gasolineMechanized Equip dieselIrrigation pumped water dieselLand AreaImpact from DNDCPRODUCTION INDIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Vehicle trips gasoline0.80 0.83 0.82 0.36 Herbicide/Pesticide0.18 0.21 0.12 0.13 0.01 0.01 0.01 0.00 Fungicide0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.00 Soil(0.00) Grow lights electricity0.55 Mechanized Equipment electricityDrip Tape or hose0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Herbicide/Pesticide0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 Mechanized Equipment gasoline0.20 0.17 0.18 0.08 0.01 0.01 0.04 0.10 0.05 0.05 0.01 0.01 0.01 0.98 Mechanized Equipment diesel0.12 0.15 0.03 0.04 0.12 0.26 0.13 0.14 0.11 0.14 0.09 Mechanized Equipment electricity0.00 0.00 0.00 0.00 N ammonium nitrate0.00 0.00 0.00 0.00 0.03 0.06 0.03 0.03 0.01 0.01 0.01 0.01 P triple superphosphate0.01 0.01 0.00 0.00 0.05 0.10 0.05 0.06 0.02 0.02 0.01 0.00 K potash0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 Sulfur0.00 0.00 Zinc0.25 0.30 0.03 0.03 Copper0.04 0.05 Manganese0.00 0.00 Magnesium0.70 0.76 Lime0.00 0.00 Irrigation pumped water diesel0.01 0.01 0.04 Water0.01 0.00 0.01 0.16 0.00 0.00 0.03 0.00 POST-PRODUCTION % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Refrigerated tractor / 17-ton trailer0.22 0.10 0.07 0.00 0.56 0.06 0.36 0.48 0.65 0.55 0.69 Light pickup truck0.00 0.01 0.00 0.00 0.00 PET clamshell0.00 PP – Polypropylene0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 LDPE “cello” bags0.00 0.00 0.00 PS – polystyrene0.01 0.01 0.01 Cardboard0.14 0.16 0.03 0.03 0.18 0.39 0.20 0.21 0.17 0.21 0.14 20 Mb site, 1 yr.0.00 0.00 0.00 0.00 0.00

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DOMINANCE OF INPUT ON: Denver CODenver CODenver CODenver COTerreton IDAlamosa COBakersfield C Greeley COMission TXGreeley CODeming NMOntario ORCuliacn SI N Bakersfield C Punta GordaDenver CO TRACI Air compartmen t potatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatoturf PRODUCTION DIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Mechanized Equip kWh urbanGrow lights kWhMechanized Equip kWh commercialVehicle trips gasolineMechanized Equip gasolineMechanized Equip dieselIrrigation pumped water dieselLand AreaImpact from DNDCPRODUCTION INDIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Vehicle trips gasoline0.00 0.00 0.00 0.00 Herbicide/Pesticide0.34 0.34 0.16 0.16 0.05 0.05 0.05 0.02 Fungicide0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.05 0.05 0.05 Soil(0.00) Grow lights electricity0.12 Mechanized Equipment electricityDrip Tape or hose1.00 1.00 1.00 0.81 0.61 0.61 0.61 0.61 Herbicide/Pesticide0.01 0.01 0.00 0.00 0.01 0.01 0.01 0.01 Mechanized Equipment gasoline0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 Mechanized Equipment diesel0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 Mechanized Equipment electricity0.00 0.00 0.00 0.00 N ammonium nitrate0.03 0.03 0.04 0.04 0.21 0.21 0.21 0.21 0.32 0.32 0.32 0.89 P triple superphosphate0.01 0.01 0.00 0.00 0.04 0.04 0.04 0.04 0.07 0.07 0.07 0.01 K potash0.01 0.01 0.00 0.00 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.01 Sulfur0.00 0.00 Zinc0.38 0.38 0.03 0.03 Copper0.10 0.10 Manganese0.00 0.00 Magnesium0.75 0.75 Lime0.00 0.00 Irrigation pumped water diesel0.00 0.00 0.01 Water0.00 0.00 0.00 POST-PRODUCTION % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Refrigerated tractor / 17-ton trailerLight pickup truck0.00 0.00 0.00 0.00 0.00 PET clamshell0.08 PP – Polypropylene0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 LDPE “cello” bags0.00 0.00 0.00 PS – polystyreneCardboard0.12 0.12 0.02 0.02 0.12 0.12 0.12 0.12 0.48 0.49 0.48 20 Mb site, 1 yr.0.00 0.00 0.00 0.00 0.00

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DOMINANCE OF INPUT ON: Denver CODenver CODenver CODenver COTerreton IDAlamosa COBakersfield C Greeley COMission TXGreeley CODeming NMOntario ORCuliacn SI N Bakersfield C Punta GordaDenver CO TRACI Water compartment potatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatoturf PRODUCTION DIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Mechanized Equip kWh urbanGrow lights kWhMechanized Equip kWh commercialVehicle trips gasolineMechanized Equip gasolineMechanized Equip dieselIrrigation pumped water dieselLand AreaImpact from DNDCPRODUCTION INDIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Vehicle trips gasoline0.79 0.82 0.81 0.32 Herbicide/Pesticide0.43 0.58 0.60 0.84 0.01 0.02 0.01 0.01 Fungicide0.01 0.01 0.01 0.03 0.01 0.01 0.02 0.03 0.02 0.02 Soil(0.00) Grow lights electricity0.60 Mechanized Equipment electricityDrip Tape or hose0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Herbicide/Pesticide0.01 0.01 0.00 0.01 0.01 0.05 0.01 0.01 Mechanized Equipment gasoline0.20 0.16 0.18 0.07 0.01 0.01 0.03 0.11 0.03 0.03 0.00 0.01 0.00 0.97 Mechanized Equipment diesel0.14 0.19 0.08 0.11 0.12 0.52 0.13 0.15 0.09 0.12 0.07 Mechanized Equipment electricity0.00 0.00 0.00 0.00 N ammonium nitrate0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 P triple superphosphate0.00 0.00 0.00 0.00 0.01 0.06 0.02 0.02 0.00 0.01 0.00 0.00 K potash0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sulfur0.00 0.00 ZincCopper0.00 0.00 Manganese0.00 0.00 MagnesiumLime0.00 0.00 Irrigation pumped water diesel0.01 0.01 0.03 Water0.01 0.00 0.03 0.26 0.00 0.01 0.06 0.00 POST-PRODUCTION % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Refrigerated tractor / 17-ton trailer0.38 0.19 0.27 0.02 0.81 0.17 0.53 0.76 0.83 0.74 0.85 Light pickup truck0.00 0.02 0.01 0.01 0.01 PET clamshell0.00 PP – Polypropylene0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 LDPE “cello” bags0.00 0.00 0.00 PS – polystyrene0.01 0.02 0.01 Cardboard0.01 0.01 0.00 0.00 0.01 0.04 0.01 0.01 0.01 0.01 0.01 20 Mb site, 1 yr.0.00 0.00 0.00 0.00 0.00

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DOMINANCE OF INPUT ON: Denver CODenver CODenver CODenver COTerreton IDAlamosa COBakersfield C Greeley COMission TXGreeley CODeming NMOntario ORCuliacn SI N Bakersfield C Punta GordaDenver CO TRACI Soil compartmen t potatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatoturf PRODUCTION DIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Mechanized Equip kWh urbanGrow lights kWhMechanized Equip kWh commercialVehicle trips gasolineMechanized Equip gasolineMechanized Equip dieselIrrigation pumped water dieselLand AreaImpact from DNDCPRODUCTION INDIRECT % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Vehicle trips gasoline0.80 0.83 0.82 (0.00) Herbicide/Pesticide0.02 0.02 0.06 0.06 0.00 0.00 0.00 0.01 FungicideSoil1.01 Grow lights electricityMechanized Equipment electricityDrip Tape or hoseHerbicide/Pesticide0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mechanized Equipment gasoline0.20 0.17 0.18 (0.00) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.90 Mechanized Equipment diesel0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Mechanized Equipment electricityN ammonium nitrate0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 P triple superphosphate0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.01 K potash0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 Sulfur0.00 0.00 ZincCopperManganese0.00 0.00 MagnesiumLime0.00 0.00 Irrigation pumped water diesel0.00 0.00 0.00 WaterPOST-PRODUCTION % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t % in To t Refrigerated tractor / 17-ton trailerLight pickup truckPET clamshellPP – PolypropyleneLDPE “cello” bagsPS – polystyreneCardboard0.97 0.97 0.93 0.93 0.99 0.99 0.99 0.99 0.99 0.99 0.99 20 Mb site, 1 yr.0.00 0.00 0.00 (0.00) 0.00

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APPENDIX G EMISSION FACTOR WORKSHEET

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LIFE CYCLE INVENTORY EMISSION FACTORS AND THEIR SOURCESResource Use Indirect NoteCategory Specific Material Source Location or RegionE n e r g y ( a l l n o n r e n e w a b l e ) U n i t L a n d U s e ( a r a b l e n o n i r r i g a t e d ) U n i t W a t e r ( a l l f r e s h w a t e r s o u r c e s ) U n i t T o ta l C a r b o n D i o x i d e e q U n i t C a r b o n D i o x i d e U n i t N i t r o u s O x i d e U n i t M e t h a n e U n i t T R A C I C a r c i n o g e n s U n i t T R A C I N o n -c a r c i n o g e n s U n i t T R A C I A i r c o m p a r t m e n t U n i t T R A C I W a t e r c o m p a r t m e n t U n i t T R A C I S o i l c o m p a r t m e n t U n i tSoil organic carbonUnit Employm ent hoursUnit Laborer rateUnit c, gElectricityElectricity (Scope 1,2)1 kWh Electricity, at eGrid, RMPA, 2008/RNA U (of project USLCI)Alamosa, CO1.3E+01MJ/kWh5 .7E-05ft2/kWh1.3E+00gal/kWh9.3E-01kg CO2 eq8.8E-01kg CO2 eqkg CO2 eq4.9E-02kg CO2 eq1.1E-09CTUh2.4E-08CTUh3.9E-03CTUe2.2E-01CTU eCTUe c, gElectricityElectricity (Scope 1,2)1 kWh Electricity, at eGrid, CAMX, 2008/RNA U (of project USLCI)Bakersfield, CA7.3E+00MJ/ kWh1.5E-04ft2/kWh5.1E-01gal/kWh4.4E-01kg CO2 eq3.9E-01kg CO2 eq2.0E-03kg CO2 eq5.0E-02kg CO2 eq1.8E-09CTUh2.4E-08CTUhCTUe5.7E-0 1CTUeCTUe c, dElectricityElectricity (Scope 1,2)1 kWh Electricity NAPP 2005, Sinaloa powerplants onlyCuliacn, Sin.1.0E+01MJ/kWh3.4E-05ft 2/kWh1.6E-01gal/kWh7.4E-01kg CO2 eq7.4E-01kg CO2 eq3.3E-06kg CO2 eq8.3E-06kg CO2 eq2.3E-09CTUh3.1E-08CTUh8.6E-04CTUe5.9E-01CTUe CTUe c, gElectricityElectricity (Scope 1,2)1 kWh Electricity, at eGrid, AZNM, 2008/RNA U (of project USLCI)Deming, NM7.5E+00MJ/kWh9. 7E-05ft2/kWh1.0E+00gal/kWh4.9E-01kg CO2 eq4.5E-01kg CO2 eq1.4E-03kg CO2 eq4.3E-02kg CO2 eq1.4E-09CTUh2.1E-08CTUhCTUe4.2E-01CTUe CTUe c, gElectricityElectricity (Scope 1,2)1 kWh Electricity, at eGrid, RMPA, 2008/RNA U (of project USLCI)Denver, CO1.3E+01MJ/kWh5. 7E-05ft2/kWh1.3E+00gal/kWh9.3E-01kg CO2 eq8.8E-01kg CO2 eqkg CO2 eq4.9E-02kg CO2 eq1.1E-09CTUh2.4E-08CTUh3.9E-03CTUe2.2E-01CTUe CTUe c, gElectricityElectricity (Scope 1,2)1 kWh Electricity, at eGrid, RMPA, 2008/RNA U (of project USLCI)Greeley, CO1.3E+01MJ/kWh5 .7E-05ft2/kWh1.3E+00gal/kWh9.3E-01kg CO2 eq8.8E-01kg CO2 eqkg CO2 eq4.9E-02kg CO2 eq1.1E-09CTUh2.4E-08CTUh3.9E-03CTUe2.2E-01CTU eCTUe c, gElectricityElectricity (Scope 1,2)1 kWh Electricity, at eGrid, ERCT, 2008/RNA U (of project USLCI)Mission, TX1.1E+01MJ/kWh1 .2E-04ft2/kWh9.4E-01gal/kWh6.8E-01kg CO2 eq6.2E-01kg CO2 eq1.7E-03kg CO2 eq5.4E-02kg CO2 eq1.8E-09CTUh2.8E-08CTUh5.3E-04CTUe5.2 E-01CTUeCTUe c, gElectricityElectricity (Scope 1,2)1 kWh Electricity, at eGrid, NWPP, 2008/RNA U (of project USLCI)Ontario, OR6.9E+00MJ/kWh6 .1E-05ft2/kWh6.2E-01gal/kWh5.0E-01kg CO2 eq4.7E-01kg CO2 eq4.8E-04kg CO2 eq2.8E-02kg CO2 eq7.0E-10CTUh1.4E-08CTUh1.9E-03CTUe1.5 E-01CTUeCTUe c, gElectricityElectricity (Scope 1,2)1 kWh Electricity, at eGrid, FRCC, 2008/RNA U (of project USLCI)Punta Gorda, FL1.0E+01MJ/ kWh1.4E-04ft2/kWh8.0E-01gal/kWh6.8E-01kg CO2 eq6.3E-01kg CO2 eq1.7E-03kg CO2 eq5.4E-02kg CO2 eq2.3E-09CTUh3.1E-08CTUh8.6E-04CTU e5.9E-01CTUeCTUe c, gElectricityElectricity (Scope 1,2)1 kWh Electricity, at eGrid, NWPP, 2008/RNA U (of project USLCI)Terreton, ID6.9E+00MJ/kWh 6.1E-05ft2/kWh6.2E-01gal/kWh5.0E-01kg CO2 eq4.7E-01kg CO2 eq4.8E-04kg CO2 eq2.8E-02kg CO2 eq7.0E-10CTUh1.4E-08CTUh1.9E-03CTUe1. 5E-01CTUeCTUe a, b, kEnergy directFuel – diesel (well to pump – WTP) + stationary combustion1 gal* Diesel, at refinery/l/US + combustion (o f project USLCI)U.S.1.6E+02MJ/gal1.6E+00ft2/gal1.9E+01gal/gal1.3E+01kg CO2 eq1.2E+01kg CO2 eq2.3E-02kg CO2 eq6.2E-01kg CO2 eq1. 9E-07CTUh2.6E-06CTUh6.0E-02CTUe3.3E+01CTUe7.6E-02CTUe a, b, kEnergy directFuel – gasoline (well to pump – WTP) + stationary combustion1 gal* Diesel, at refinery/l/US + combustion (of project USLCI)U.S.1.5E+02MJ/gal1.6E+00ft2/gal1.9E+01gal/gal1.5E+01kg CO2 eq9.8E+00kg CO2 eq7.6E-02kg CO2 eq6.8E-01kg CO2 eq 9.8E-07CTUh4.4E-06CTUh1.2E-02CTUe3.3E+01CTUe8.5E-02CTUe FertilizerN ammonium nitrate1 kg Ammonium nitrate, as N, at regional storehouse/RER S (of project Ecoinvent system processes)Eu rope (RER)5.6E+01MJ/kg2.0E-03ft2/kg3.9E+00gal/kg8.6E+00kg CO2 eq2.8E+00kg CO2 eq5.6E+00kg CO2 eq1.5E-01kg CO2 eq4.2E-08CTUh2.6E -07CTUh2.1E+00CTUe4.1E-01CTUe3.2E-02CTUe Fertilizer N urea 1 kg Urea, as N, at regional storehouse/RER S (of project Ecoinvent system processes)Europe (RER)6.2E+01MJ/kg1.3E-03ft2/kg2.0E+ 00gal/kg3.3E+00kg CO2 eq3.1E+00kg CO2 eq1.2E-02kg CO2 eq2.0E-01kg CO2 eq2.7E-08CTUh1.7E-07CTUh1.5E+00CTUe2.7E-01CTUe2.8E-02CTUe FertilizerP triple superphosphate1 kg Triple superphosphate, as P2O5, at regional storehouse/RER S (of project Ecoinvent system processes)Europe (RER)3.0E+01MJ/kg1.8E-03ft2/kg2.7E+01gal/kg2.0E+00kg CO2 eq1.9E+00kg CO2 eq1.3E-02kg CO2 eq7.7E-02kg CO2 eq1. 5E-07CTUh4.7E-07CTUh4.0E-01CTUe1.8E+00CTUe5.7E-02CTUe Fertilizer K potash 1 kg Potassium chloride, as K2O, at regional storehouse/RER S (of project Ecoinvent system processes)Europe (RER)8.4E+00MJ/kg4. 6E-04ft2/kg1.9E+00gal/kg5.0E-01kg CO2 eq4.4E-01kg CO2 eq1.1E-02kg CO2 eq4.4E-02kg CO2 eq1.2E-08CTUh6.1E-08CTUh1.2E-01CTUe1.2E-0 1CTUe8.2E-03CTUe FertilizerZinc1 kg Zinc monosulphate, ZnSO4.H2O, at plant/RER S (of project Ecoinvent system processes)Europe (RER)3.0E+01MJ/kg 7.2E-04ft2/kg8.5E+00gal/kg1.8E+00kg CO2 eq1.7E+00kg CO2 eq3.9E-03kg CO2 eq1.0E-01kg CO2 eq1.5E-08CTUh7.6E-05CTUh8.3E+01CTUeCTUe CTUe FertilizerManganese1 kg Manganese oxide (Mn2O3), at plant/CN S (of project Ecoinvent system processes)China (CN)3.1E+01MJ/kg1.6 E-03ft2/kg4.0E+00gal/kg2.4E+00kg CO2 eq2.3E+00kg CO2 eq1.5E-02kg CO2 eq8.1E-02kg CO2 eq2.3E-08CTUh1.4E-07CTUh2.1E-01CTUe2.6E-01 CTUe4.8E-02CTUe FertilizerMagnesium1 kg Magnesium sulphate, at plant/RER S (of project Ecoinvent system processes)Europe (RER)4.9E+00MJ/kg8.8E05ft2/kg6.2E-01gal/kg3.0E-01kg CO2 eq2.8E-01kg CO2 eq2.8E-03kg CO2 eq1.2E-02kg CO2 eq2.5E-09CTUh2.0E-08CTUh2.6E-02CTUe3.5E-02CT Ue7.9E-03CTUe FertilizerCopper1 kg Copper oxide, at plant/RER S (of project Ecoinvent system processes)Europe (RER)3.0E+01MJ/kg8.8E-03ft2/kg1 .4E+01gal/kg1.9E+00kg CO2 eq1.8E+00kg CO2 eq5.8E-02kg CO2 eq8.6E-02kg CO2 eq1.7E-07CTUh1.2E-05CTUh2.1E+01CTUe6.2E-01CTUeCTUe FertilizerGypsum1 kg Gypsum, mineral, at mine/CH S (of project Ecoinvent system processes)Switzerland (CH)3.0E-02MJ/kg1.1E-05ft 2/kg2.1E-03gal/kg2.0E-03kg CO2 eq1.9E-03kg CO2 eq9.1E-05kg CO2 eq9.7E-02kg CO2 eq2.6E-11CTUh1.0E-10CTUh1.6E-04CTUe2.6E-04CTUe5. 4E+00CTUe FertilizerSulfur1 kg Secondary sulphur, at refinery/RER S (of project Ecoinvent system processes)Europe (RER)4.9E+00MJ/kg7.4E-0 5ft2/kg3.2E-01gal/kg3.1E-01kg CO2 eq3.1E-01kg CO2 eq8.0E-04kg CO2 eq5.4E-03kg CO2 eq1.1E-09CTUh1.3E-08CTUh5.5E-02CTUe1.6E-02CTU e3.1E-03CTUe FertilizerLime1 kg Lime, from carbonation, at regional storehouse/CH S (of project Ecoinvent system processes)Switzerland (CH)1 .9E-01MJ/kg1.5E-05ft2/kg1.2E-02gal/kg1.2E-02kg CO2 eq1.1E-02kg CO2 eq4.9E-05kg CO2 eq4.0E-04kg CO2 eq1.6E-10CTUh7.6E-10CTUh7.8E -04CTUe1.7E-03CTUe1.3E-03CTUe PaperCardboard1 kg Packaging, corrugated board, mixed fibre, single wall, at plant/RER S (of project Ecoinvent system processes )Europe (RER)1.7E+01MJ/kg8.9E-01ft2/kg5.8E+00gal/kg1.1E+00kg CO2 eq1.1E+00kg CO2 eq2.2E-02kg CO2 eq5.3E-02kg CO2 eq1.3E-08CTUh2 .8E-07CTUh1.8E-01CTUe1.8E-01CTUe1.5E+00CTUe ePlastics (virgin resins)HDPE drip tape1 kg HDPE resin E (of project Industry data 2.0) U.S.7.4E+01MJ/kg7.8E-05ft2/kg8.6E-01gal/kg1.9E+00kg CO2 eq1.6E+00kg CO2 eqkg CO2 eq3.5E-01kg CO2 eq9.8E-08CTUh2.0E-09CTUh6.3E+0 0CTUe7.6E-03CTUeCTUe ePlasticsLDPE “cello” bags1 kg LDPE resin E (of project Industry data 2.0) U.S.7.3E+01MJ/kg1.3E-06ft2/kg7.3E-01gal/kg2.1E+00kg CO2 eq1.7E+00kg CO2 eqkg CO2 eq4.1E-01kg CO2 eq2.7E-11CTUh3.4E-09CTUh5.2E-0 4CTUe1.4E-03CTUeCTUe ePlasticsPP – Polypropylene1 kg Polypropylene resin E (of project Industry data 2.0)U.S.7.2E+01MJ/kg1.3E-04ft2/kg1.2E+00gal/kg2 .0E+00kg CO2 eq1.7E+00kg CO2 eqkg CO2 eq3.0E-01kg CO2 eq1.4E-11CTUh1.6E-09CTUh1.2E-04CTUe1.9E-03CTUeCTUe c, e, fPlasticsPS – polystyrene1 kg High impact polystyrene resin, at plant/RNA (of project USLCI)U.S.9.4E+01MJ/kg2.4E-06ft2/kg 1.8E+00gal/kg2.7E+00kg CO2 eq2.2E+00kg CO2 eqkg CO2 eq4.5E-01kg CO2 eq5.4E-08CTUh6.4E-07CTUhCTUe1.4E+01CTUeCTUe ePlasticsPET clamshell1 kg PET (bottle grade) E (of project Industry data 2.0)U.S.7.9E+01MJ/kg1.4E-04ft2/kg1.2E+00gal/kg3.4E+00 kg CO2 eq2.9E+00kg CO2 eqkg CO2 eq4.7E-01kg CO2 eq8.5E-09CTUh2.6E-09CTUh1.2E-01CTUe6.4E-04CTUeCTUe HerbicideUnspecified1 kg Herbicides, at regional storehouse/RER S (of project Ecoinvent system processes)U.S.1.9E+02MJ/kg2.0E-0 3ft2/kg2.2E+01gal/kg1.0E+01kg CO2 eq8.8E+00kg CO2 eq5.7E-01kg CO2 eq8.1E-01kg CO2 eq1.4E-07CTUh1.8E-06CTUh1.9E+00CTUe5.7E+01CTU e1.5E-01CTUe PesticideUnspecified1 kg Pesticide unspecified, at regional storehouse/RER S (of project Ecoinvent system processes)Europe (RER )1.8E+02MJ/kg1.9E-03ft2/kg2.0E+01gal/kg1.0E+01kg CO2 eq8.7E+00kg CO2 eq5.6E-01kg CO2 eq7.7E-01kg CO2 eq1.4E-07CTUh1.7E-06CTUh2. 4E+00CTUe4.7E+01CTUe1.4E-01CTUe FungicideUnspecified1 kg Fungicides, at regional storehouse/RER S (of project Ecoinvent system processes)Europe (RER)1.8E+02MJ/ kg1.8E-03ft2/kg1.6E+01gal/kg1.1E+01kg CO2 eq9.0E+00kg CO2 eq8.0E-01kg CO2 eq7.4E-01kg CO2 eq1.3E-07CTUh1.8E-06CTUh3.7E+00CTUe8. 9E+01CTUeCTUe SoilBagged potting soil – organic1 kg Manure for vegetables ( from farming on sandy soil) (of project LCA Food DK)Denmark (DK)1.1E-01MJ/kg-2.0E-02gal/kg-6.3E+00kg CO2 eq-8.0E-03kg CO2 eq-6.3E+00kg CO2 eqkg CO2 eq-4.8E-11CTUh-4.8E-10CTUh-9.9E-03CTUe-9.3E -04CTUe-5.8E+00CTUe c, h, lTransportRefrigerated tractor / 17-ton trailer1 tkm Transport, combination truck, diesel powered/US (of project USLCI)U. S.1.3E+00MJ/tkm1.6E-03ft2/tkm2.6E-01gal/tkm9.3E-02kg CO2 eq8.9E-02kg CO2 eq6.1E-04kg CO2 eq2.8E-03kg CO2 eq1.3E-09CTUh1.2E-0 8CTUhCTUe2.3E-01CTUeCTUec, hTransportLight pickup truck1 tkm Transport, single unit truck, gasoline powered/US (of project USLCI)U.S.2.4E+00MJ/tkm1.6E -03ft2/tkm2.6E-01gal/tkm1.3E-01kg CO2 eq1.3E-01kg CO2 eq1.7E-03kg CO2 eq7.1E-04kg CO2 eq2.4E-09CTUh2.3E-08CTUh1.1E-04CTUe4.5E -01CTUeCTUe iWater directRaw irrigation water1 ac Agricultural extension enterprise budgets PotatoAlamosa, CO3.6E+02MJ/ac5.8E-03ft2/ac1 .3E+02gal/ac9.4E+01kg CO2 eq8.9E+01kg CO2 eq0.0E+00kg CO2 eq4.9E+00kg CO2 eq1.1E-07CTUh2.4E-06CTUh3.9E-01CTUe2.3E+01CTUe0.0E+00 CTUe iWater directRaw irrigation water1 ac Agricultural extension enterprise budgets TomatoBakersfield, CA4.6E+03MJ/ac2.0E-01ft2 /ac6.6E+02gal/ac5.7E+02kg CO2 eq5.0E+02kg CO2 eq2.5E+00kg CO2 eq6.4E+01kg CO2 eq2.3E-06CTUh3.0E-05CTUh0.0E+00CTUe7.3E+02CTUe0.0 E+00CTUe iWater directRaw irrigation water1 ac Agricultural extension enterprise budgets CarrotBakersfield, CA6.7E+03MJ/ac2.8E-01ft2 /ac9.5E+02gal/ac8.2E+02kg CO2 eq7.2E+02kg CO2 eq3.6E+00kg CO2 eq9.2E+01kg CO2 eq3.4E-06CTUh4.4E-05CTUh0.0E+00CTUe1.1E+03CTUe0.0 E+00CTUe iWater directRaw irrigation water1 ac Agricultural extension enterprise budgets TomatoCuliacn, Sin.5.9E+02MJ/ac5.7E-03ft2/ ac9.6E+01gal/ac1.2E+02kg CO2 eq1.2E+02kg CO2 eq5.4E-04kg CO2 eq1.4E-03kg CO2 eq3.7E-07CTUh5.1E-06CTUh1.4E-01CTUe9.7E+01CTUe0.0E +00CTUe iWater directRaw irrigation water1 ac Agricultural extension enterprise budgets OnionDeming, NM2.6E+04MJ/ac7.1E-01ft2/ac7.3 E+03gal/ac3.6E+03kg CO2 eq3.3E+03kg CO2 eq1.0E+01kg CO2 eq3.1E+02kg CO2 eq1.0E-05CTUh1.5E-04CTUh0.0E+00CTUe3.1E+03CTUe0.0E+00CT Ue iWater directRaw irrigation water1 ac Agricultural extension enterprise budgets CarrotGreeley, CO0.0E+00MJ/ac0.0E+00ft2/ac0 .0E+00gal/ac0.0E+00kg CO2 eq0.0E+00kg CO2 eq0.0E+00kg CO2 eq0.0E+00kg CO2 eq0.0E+00CTUh0.0E+00CTUh0.0E+00CTUe0.0E+00CTUe0.0E+00 CTUe iWater directRaw irrigation water1 ac Agricultural extension enterprise budgets OnionGreeley, CO0.0E+00MJ/ac0.0E+00ft2/ac0. 0E+00gal/ac0.0E+00kg CO2 eq0.0E+00kg CO2 eq0.0E+00kg CO2 eq0.0E+00kg CO2 eq0.0E+00CTUh0.0E+00CTUh0.0E+00CTUe0.0E+00CTUe0.0E+00C TUe iWater directRaw irrigation water1 ac Agricultural extension enterprise budgets OnionMission, TX0.0E+00MJ/ac0.0E+00ft2/ac0. 0E+00gal/ac0.0E+00kg CO2 eq0.0E+00kg CO2 eq0.0E+00kg CO2 eq0.0E+00kg CO2 eq0.0E+00CTUh0.0E+00CTUh0.0E+00CTUe0.0E+00CTUe0.0E+00C TUe iWater directRaw irrigation water1 ac Agricultural extension enterprise budgets OnionOntario, OR3.0E+02MJ/ac3.0E+00ft2/ac7. 0E+01gal/ac4.7E-02kg CO2 eq4.5E-02kg CO2 eq8.4E-05kg CO2 eq2.3E-03kg CO2 eq7.2E-10CTUh9.7E-09CTUh2.2E-04CTUe1.2E-01CTUe2.8E-04C TUe iWater directRaw irrigation water1 ac Agricultural extension enterprise budgets TomatoPunta Gorda, FL3.5E+03MJ/ac3.5E+01ft2 /ac4.1E+02gal/ac2.8E-01kg CO2 eq2.7E-01kg CO2 eq4.9E-04kg CO2 eq1.3E-02kg CO2 eq4.2E-09CTUh5.7E-08CTUh1.3E-03CTUe7.1E-01CTUe1.6 E-03CTUe iWater directRaw irrigation water1 ac Agricultural extension enterprise budgets PotatoTerreton, ID5.2E+03MJ/ac8.7E-02ft2/ac 8.9E+02gal/ac7.2E+02kg CO2 eq6.8E+02kg CO2 eq6.9E-01kg CO2 eq4.0E+01kg CO2 eq1.0E-06CTUh2.0E-05CTUh2.7E+00CTUe2.2E+02CTUe0.0E+0 0CTUe jWater directPotable irrigation water1 gal Denver Water and direct measurement Denver, CO1.4E-03MJ/gal2.2E-08ft2/gal4.9E-04gal/gal7.8E-04kg CO2 eq7.7E-04kg CO2 eq1.3E-08kg CO2 eq2.1E-08kg CO2 eq Web Hosting20 Mb site, 1 yr.1 M$ Data processing, hosting, and related services (of project USA Input Output Database)U.S.2.0E+ 00TJ/$M2.7E+06ft2/$M8.5E+05gal/$M6.1E+03kg CO2 eq6.1E+03kg CO2 eq5.9E+01kg CO2 eq8.8E+00kg CO2 eq2.9E-07CTUh8.0E-08CTUh7.0E-02C TUe6.3E+00CTUe2.0E-02CTUe NOTESAll emissions and impacts limited to the top 0.1% contributions to total using PR Consultants. 2013. SimaPro Life Cycle Analysis version 7.3 (software), except for Denver Water emissions reported by its Carbon Registry submittal. All land use from Ecoinvent Europe (RER) under categories: Occupation, arable, non-irrigated; permanent crop, fruit, intensive; shrub land, sclerophyllous a18.8 kg water/L diesel. From Gerbens-Leenes, P.W., Hoekstra, A.Y. ‘Business water footprint accounting: a tool to assess how production of goods and services impacts on freshwater resources worldwide’ Value of Water Research Series No. 27, UNESCO-IHE, 20 bTRACI from USLCI diesel (at plant), all others GREET diesel or gasoline, respectively (WTP) cEnergy summed from anthracite hard coal 33 MJ/kg; lignite brown coal 15.4 MJ/kg; crude oil in ground 46.3 MJ/kg; natural gas i n ground 0.0364 MJ/L; unspecified coal 26.4 MJ/kg dCuliacn grid 0.1% diesel, 99.9% oil; global warming values from NAPP 2005; all others modeled after eGrid FRCC reported for d iesel and oil scaled accordingly eLand use from Ecoinvent Europe (RER) fFresh water consumption from Ecoinvent Europe (RER) under categories: Water, lake; river; unspecified natural origin; and well in ground gLand use and fresh water consumption per eGrid 2009 mix component compiled from Ecoinvent Europe (RER): Hard coal (PL); Brown coal (PL); Fuel oil (DE); Natural gas (DE); Hydroelectric (PL) hLand use and fresh water consumption estimated using Ecoinvent 1 tkm Transport, tractor and trailer/CH S iDirect water factored without upstream system losses; according to fuel type or power source. Amounts derived from agricultura l extension published enterprise budgets and interviews with extension agents in locations representing crops jDirect water factored without upstream system losses; Denver Water only includes distribution system losses between treatment plant and customer meter, 2012 Scope 1 & 2 reported only kLand use for gasoline and diesel from EIO-LCA petroleum refining purchaser price 2002 adjusted 21% consumer purchase to wholes ale purchases at refinery lRefrigerated tractor values adjusted from 12.7% increase of cost per ton-mile between “5-axle 48’ Dry Van” and “5-axle 58’ Ref rigerated Van” per Exhibit F.1 Estimates of 1995 Costs for Truckload Operations. Adapted from DOT BTS 1995 Characteristics an d changes in freight transportation demand. Conversion from N2O reported as N multiplied by 477 to get CO2e Conversion from CO2 reported as C multiplied by 3.66 to get CO2e Conversion from CH4 reported as C multiplied by 45.33 to get CO2e

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APPENDIX H DNDC MODELING OUTP UT

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DNDC CROP MODELLING OUTPUT PotatoCarrotOnionTomatoPotatoCarrotOnionTomatoTurf SOC 0-10cm DenverDenverDenverDenverTerretonAlamosaBakersfiel d GreeleyMissionGreeleyDemingOntarioCuliacnBakersfiel d Pta GordaDenver kgC/kg Beginning0.03310.03310.0330.0330.01460.01390.0140.01440.01430.01410.01490.01410.0140.01380.0140.03 End0.03310.0330.03320.03360.01820.01660.01530.02060.01670.02540.01890.02770.01630.02820.01620.0333 Cumulative0.99030.98820.99130.99870.51240.46660.44280.55220.47240.64250.52970.67680.47530.68590.46240.9573 Average0.033010.032940.0330430.033290.017080.0155530.014760.0184070.0157470.0214170.0176570.022560.0158430.0228630.0154130.0319 1 Net Change0-1E-040.00020.00060.00360.00270.00130.00620.00240.01130.0040.01360.00230.01440.00220.0033 Net Change %0-0.0030210.0060610.0181820.247 0.1942450.0928570.4305560.1678320.8014180.2684560.9645390.1642861.0434780.1571430.11 Change per year(0.000) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00048 0.000 0.00011 Soil CO2Beginning1716.941710.51794.21802.72973.671337.092811.142638.633064.682228.392742.582469.682917.672611.662802.141355.54 kgC/ha/yr End1147.661131.971122.41285.491,2771143.162154.254216.383046.396205.322982.136929.14418.239705.374369.651628.33 Cumulative34801.2834396.1534336.5437879.2736,25836336.667631.9512371292552.04177279.387792.59199830.2127056.7279038.5128355.846 416.93 Average1160.0431146.5381144.5511262.6421,2091211.222254.3984123.7343085.0685909.3112926.426661.0064235.2239301.2844278.5261547. 231 Net Change-569.28-578.53-671.8-517.23303.32-193.93-656.891577.75-18.293976.93239.554459.421500.567093.711567.51272.79 Net Change %-0.331567-0.338223-0.374429-0.2869160.312 -0.14504-0.233670.597943-0.005971.7846650.0873451.8056670.5143012.7161690.5593970.201240834 Soil CH4Beginning-1.79-1.82-2.17-1.97-1.96-3.23-5.34-1.97-7.87-2.33-3.51-2.89-1.93-6.07-3.03-2.02 kgC/ha/yr End-1.94-1.98-2.4-2.18-1.85-3.08-5.22-1.81-7.74-1.93-3.43-2.47-2.5-5.87-3.22-1.62 Cumulative-57.47-58.8-71.17-64.73-55.98-92.71-157.16-54.51-232.27-58.68-103.36-74.85-68.7-175.53-94.72-50.7 Average-1.915667-1.96-2.372333-2.157667-1.866-3.09033-5.23867-1.817-7.74233-1.956-3.44533-2.495-2.29-5.851-3.15733-1.69 Net Change-0.15-0.16-0.23-0.210.110.150.120.160.130.40.080.42-0.570.2-0.190.4 Net Change %0.0837990.0879120.1059910.106599(0.056) -0.04644-0.02247-0.08122-0.01652-0.17167-0.02279-0.145330.295337-0.032950.062706-0.198019802 N2O fluxBeginning0.190.160.150.23.771.675.6419.238.87.3211.896.5612.423.552.860.07 kgC/ha/yr End0.230.20.170.246.71.735.5118.28.5510.514.067.9115.936.013.40.29 Cumulative7.156.155.177.16194.7954.56168.03550.1255.58295.7408.01229.12458.83167.0398.286.62 Average0.2383330.2050.1723330.2386676.4931.8186675.60118.336678.5193339.85666713.600337.63733315.294335.5676673.2760.220666667 Net Change0.040.040.020.042.930.06-0.13-1.03-0.253.182.171.353.512.460.540.22 Net Change %0.2105260.250.1333330.20.777 0.035928-0.02305-0.05356-0.028410.4344260.1825060.2057930.2826090.6929580.1888113.142857143 SOCkgC/kg soil per year0-3.33E-066.67E-062E-050.000120.000094.33E-050.0002070.000080.0003770.0001330.0004537.67E-050.000487.33E -050.00011 CO2kgC/ha/yr 1160.0431146.5381144.5511262.6421,2091211.222254.3984123.7343085.0685909.3112926.426661.0064235.2239301.284427 8.5261547.231 CH4kgC/ha/yr -1.915667-1.96-2.372333-2.157667-1.866-3.09033-5.23867-1.817-7.74233-1.956-3.44533-2.495-2.29-5.851-3.15733-1. 69 N2OkgN/ha/yr 0.2383330.2050.1723330.2386676.4931.8186675.60118.336678.5193339.85666713.600337.63733315.294335.5676673.2760. 220666667 SOCkgC/kg soil per year0-3.33E-066.67E-062E-050.000120.000094.33E-050.0002070.000080.0003770.0001330.0004537.67E-050.000487.33E -050.00011 CO2e TotkgCO2e/ha/yr 4272.6044205.2694163.7234637.3087436.045160.48410685.3123757.0915004.1126241.0417041.8827909.1922692.5 136433.2517078.945691.51576 CO2kgCO2e/ha/yr 4245.7564196.334189.0584621.2714423.4654433.0658251.09815092.8711291.3521628.0810710.724379.2815500.9134042 .715659.415662.86546 CH4kgCO2e/ha/yr -86.83717-88.8468-107.5379-97.80703-84.58578-140.085-237.469-82.3646-350.96-88.6655-156.177-113.098-103.806 -265.226-143.122-76.6077 N2OkgCO2e/ha/yr 113.68597.78582.203113.8443097.161867.5042671.6778746.594063.7224701.636487.3593643.0087295.3972655.7771562 .652105.258 SOCkgC/kg soil per year0-3.33E-066.67E-062E-050.000120.000094.33E-050.0002070.000080.0003770.0001330.0004537.67E-050.000487.33E -050.00011 CO2e TotkgCO2e/ac/yr 1729.0991701.8491685.0361876.6933009.3242088.4194324.2849614.3636072.0810619.66896.75411294.699183.531 14744.336911.752303.324873 CO2kgCO2e/ac/yr 1718.2341698.2321695.2881870.2031790.1521794.0373339.1746107.9994569.5468752.7634334.5599866.166273.1341377 6.896337.2742291.730255 CH4kgCO2e/ac/yr -35.14252-35.95581-43.51998-39.58196-34.2314-56.6915-96.1023-33.3325-142.032-35.8824-63.2039-45.7703-42.009 6-107.335-57.9206-31.00271145 N2OkgCO2e/ac/yr 46.0076939.5730533.267146.072041253.404351.07411081.2133539.6961644.5661902.7242625.3981474.3052952.4071074 .778632.396642.59732902 SOCkgC/kg soil per year0-3.33E-066.67E-062E-050.000120.000094.33E-050.0002070.000080.0003770.0001330.0004537.67E-050.000487.33E -050.00011 CO2e TotkgCO2e/ft2/yr 0.0396950.0390690.0386830.0430830.0690850.0479440.0992720.2207150.1393960.2437930.1583280.2592910.210 8250.3384830.1586720.052877063 CO2kgCO2e/ft2/yr 0.0394450.0389860.0389180.0429340.0410960.0411850.0766570.140220.1049020.2009360.0995080.2264960.1440110.3 162740.1454840.052610887 CH4kgCO2e/ft2/yr -0.000807-0.000825-0.000999-0.000909-0.000786-0.0013-0.00221-0.00077-0.00326-0.00082-0.00145-0.00105-0.000 96-0.00246-0.00133-0.000711724 N2OkgCO2e/ft2/yr 0.0010560.0009080.0007640.0010580.0287740.008060.0248210.081260.0377540.0436810.0602710.0338450.0677780.02 46740.0145180.0009779 NOTES Conversion from N2O reported as N multiplied by 477 to get CO2e Conversion from CO2 reported as C multiplied by 3.66 to get CO2e Conversion from CH4 reported as C multiplied by 45.33 to get CO2e

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Soil Organic Carbon Potato0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 1357911131517192123252729313335373941434547495153555759 YearSoil Organic Carbon 0-10 cm (kgC/kg) 1-S01TerretonID\ 2-S02AlamosaCO\ 12-S12DenverCO\ 16-S16DenverCO\

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Soil Organic Carbon Carrot0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 1357911131517192123252729313335373941434547495153555759 YearSoil Organic Carbon 0-10 cm (kgC/kg) 3-S03BakersfieldCA\ 4-S04GreeleyCO\ 13-S13DenverCO\ 17-S17DenverCO\

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Soil Organic Carbon Onion0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 1357911131517192123252729313335373941434547495153555759 YearSoil Organic Carbon 0-10 cm (kgC/kg) 5-S05MissionTX\ 6-S06DemingNM\ 7-S07GreeleyCO\ 8-S08OntarioOR\ 14-S14DenverCO\ 18-S18DenverCO\

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Soil Organic Carbon Tomato0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 1357911131517192123252729313335373941434547495153555759 YearSoil Organic Carbon 0-10 cm (kgC/kg) 9-S09CuliacanSIN\ 10-S10BakersfieldCA\ 11-S11PuntaGordaFL\ 15-S15DenverCO\ 19-S19DenverCO\

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Annual Soil Carbon Dioxide Emission Potato0 200 400 600 800 1000 1200 1400 1600 1800 2000 1357911131517192123252729313335373941434547495153555759 YearAnnual Soil CO2 Emission (kgC/ha/yr) 1-S01TerretonID\ 2-S02AlamosaCO\ 12-S12DenverCO\ 16-S16DenverCO\

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Annual Soil Carbon Dioxide Emission Carrot0 500 1000 1500 2000 2500 3000 3500 4000 4500 1357911131517192123252729313335373941434547495153555759 YearAnnual Soil CO2 Emission (kgC/ha/yr) 3-S03BakersfieldCA\ 4-S04GreeleyCO\ 13-S13DenverCO\ 17-S17DenverCO\

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Annual Soil Carbon Dioxide Emission Onion0 1000 2000 3000 4000 5000 6000 7000 1357911131517192123252729313335373941434547495153555759 YearAnnual Soil CO2 Emission (kgC/ha/yr) 5-S05MissionTX\ 6-S06DemingNM\ 7-S07GreeleyCO\ 8-S08OntarioOR\ 14-S14DenverCO\ 18-S18DenverCO\

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Annual Soil Carbon Dioxide Emission Tomato0 2000 4000 6000 8000 10000 12000 1357911131517192123252729313335373941434547495153555759 YearAnnual Soil CO2 Emission (kgC/ha/yr) 9-S09CuliacanSIN\ 10-S10BakersfieldCA\ 11-S11PuntaGordaFL\ 15-S15DenverCO\ 19-S19DenverCO\

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Annual Soil Methane Emission Potato-4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 1357911131517192123252729313335373941434547495153555759 YearAnnual Soil CH4 Emission (kgC/ha/yr) 1-S01TerretonID\ 2-S02AlamosaCO\ 12-S12DenverCO\ 16-S16DenverCO\

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Annual Soil Methane Emission Carrot-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1357911131517192123252729313335373941434547495153555759 YearAnnual Soil CH4 Emission (kgC/ha/yr) 3-S03BakersfieldCA\ 4-S04GreeleyCO\ 13-S13DenverCO\ 17-S17DenverCO\

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Annual Soil Methane Emission Onion-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1357911131517192123252729313335373941434547495153555759 YearAnnual Soil CH4 Emission (kgC/ha/yr) 5-S05MissionTX\ 6-S06DemingNM\ 7-S07GreeleyCO\ 8-S08OntarioOR\ 14-S14DenverCO\ 18-S18DenverCO\

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Annual Soil Methane Emission Tomato-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1357911131517192123252729313335373941434547495153555759 YearAnnual Soil CH4 Emission (kgC/ha/yr) 9-S09CuliacanSIN\ 10-S10BakersfieldCA\ 11-S11PuntaGordaFL\ 15-S15DenverCO\ 19-S19DenverCO\

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Annual Soil Nitrous Oxide Emission Potato0 1 2 3 4 5 6 7 8 1357911131517192123252729313335373941434547495153555759 YearAnnual Soil N2O Emission (kgN2O/ha/yr) 1-S01TerretonID\ 2-S02AlamosaCO\ 12-S12DenverCO\ 16-S16DenverCO\

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Annual Soil Nitrous Oxide Emission Carrot0 5 10 15 20 25 1357911131517192123252729313335373941434547495153555759 YearAnnual Soil N2O Emission (kgN2O/ha/yr) 3-S03BakersfieldCA\ 4-S04GreeleyCO\ 13-S13DenverCO\ 17-S17DenverCO\

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Annual Soil Nitrous Oxide Emission Onion0 2 4 6 8 10 12 14 16 1357911131517192123252729313335373941434547495153555759 YearAnnual Soil N2O Emission (kgN2O/ha/yr) 5-S05MissionTX\ 6-S06DemingNM\ 7-S07GreeleyCO\ 8-S08OntarioOR\ 14-S14DenverCO\ 18-S18DenverCO\

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Annual Soil Nitrous Oxide Emission Tomato0 2 4 6 8 10 12 14 16 18 1357911131517192123252729313335373941434547495153555759 YearAnnual Soil N2O Emission (kgN2O/ha/yr) 9-S09CuliacanSIN\ 10-S10BakersfieldCA\ 11-S11PuntaGordaFL\ 15-S15DenverCO\ 19-S19DenverCO\

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Annual Irrigation Water Potato0 200 400 600 800 1000 1200 1357911131517192123252729313335373941434547495153555759 YearAnnual Irrigation Water (mm) 1-S01TerretonID\ 2-S02AlamosaCO\ 12-S12DenverCO\ 16-S16DenverCO\

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Annual Irrigation Water Carrot0 100 200 300 400 500 600 700 800 900 1000 1357911131517192123252729313335373941434547495153555759 YearAnnual Irrigation Water (mm) 3-S03BakersfieldCA\ 4-S04GreeleyCO\ 13-S13DenverCO\ 17-S17DenverCO\

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Annual Irrigation Water Onion0 100 200 300 400 500 600 700 800 900 1000 1357911131517192123252729313335373941434547495153555759 YearAnnual Irrigation Water (mm) 5-S05MissionTX\ 6-S06DemingNM\ 7-S07GreeleyCO\ 8-S08OntarioOR\ 14-S14DenverCO\ 18-S18DenverCO\

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Annual Irrigation Water Tomato0 200 400 600 800 1000 1200 1400 1600 1357911131517192123252729313335373941434547495153555759 YearAnnual Irrigation Water (mm) 9-S09CuliacanSIN\ 10-S10BakersfieldCA\ 11-S11PuntaGordaFL\ 15-S15DenverCO\ 19-S19DenverCO\

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0.016 0.015 0.020 0.019 0.017 0.017 0.022 0.021 0.014 0.014 0.014 0.014 0.016 0.016 0.016 0.016 0.032 0.032 0.032 0.032 0.033 0.033 0.033 0.033 0.005 0.010 0.015 0.020 0.025 0.030 0.035 PotatoCarrotOnionTomatokgC/kgCropDirect Land Use Change -Soil Organic Carbon (SOC) (all values 30-year average) Farm Fallow Degraded Urban Urban Garden Urban Turf Urban Garden Conversion 1 Conversion 2 Conversion 3Legend Explanation Conversion 1 30 years of commercial farmland (weighted average of all locations supplying Denver metropolitan area) followed by 30 years of fallow farmland. Conversion 2 30 years of degraded urban land (e.g. vacant, uncultivated) in Denver displaced by 30 years of urban gardening. Conversion 3 30 years of urban turf (Kentucky bluegrass coolseason grass) displaced by 30 years of urban gardening.

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1,210 2,572 5,138 6,779 100 812 829 974 232 232 232 232 954 945 964 1,037 1,547 1,547 1,547 1,547 1,160 1,147 1,145 1,263 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 PotatoCarrotOnionTomatokgC/ha/yrCropDirect Land Use Change -Carbon Dioxide (CO2) Flux from Soil (all values 30-year average) Farm Fallow Degraded Urban Urban Garden Urban Turf Urban Garden Conversion 1 Conversion 2 Conversion 3Legend Explanation Conversion 1 30 years of commercial farmland (weighted average of all locations supplying Denver metropolitan area) followed by 30 years of fallow farmland. Conversion 2 30 years of degraded urban land (e.g. vacant, uncultivated) in Denver displaced by 30 years of urban gardening. Conversion 3 30 years of urban turf (Kentucky bluegrass coolseason grass) displaced by 30 years of urban gardening.

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(2.5) (4.7) (3.3) (4.3) (3.4) (6.9) (5.2) (5.9) (3.7)(3.7)(3.7)(3.7) (2.2) (2.3) (2.7) (2.4) (1.7)(1.7)(1.7)(1.7) (1.9) (2.0) (2.4) (2.2) (8.0) (7.0) (6.0) (5.0) (4.0) (3.0) (2.0) (1.0) PotatoCarrotOnionTomatokgC/ha/yrCropDirect Land Use Change -Methane (CH4) Flux from Soil (all values 30-year average) Farm Fallow Degraded Urban Urban Garden Urban Turf Urban Garden Conversion 1 Conversion 2 Conversion 3Legend Explanation Conversion 1 30 years of commercial farmland (weighted average of all locations supplying Denver metropolitan area) followed by 30 years of fallow farmland. Conversion 2 30 years of degraded urban land (e.g. vacant, uncultivated) in Denver displaced by 30 years of urban gardening. Conversion 3 30 years of urban turf (Kentucky bluegrass coolseason grass) displaced by 30 years of urban gardening.

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4.16 7.77 9.72 7.43 0.04 0.02 0.25 0.05 0.06 0.06 0.06 0.06 0.09 0.08 0.08 0.13 0.22 0.22 0.22 0.22 0.24 0.21 0.17 0.24 2.00 4.00 6.00 8.00 10.00 12.00 PotatoCarrotOnionTomatokgN/ha/yrCropDirect Land Use Change -Nitrous Oxide (N2O) Flux from Soil (all values 30-year average) Farm Fallow Degraded Urban Urban Garden Urban Turf Urban Garden Conversion 1 Conversion 2 Conversion 3Legend Explanation Conversion 1 30 years of commercial farmland (weighted average of all locations supplying Denver metropolitan area) followed by 30 years of fallow farmland. Conversion 2 30 years of degraded urban land (e.g. vacant, uncultivated) in Denver displaced by 30 years of urban gardening. Conversion 3 30 years of urban turf (Kentucky bluegrass coolseason grass) displaced by 30 years of urban gardening.

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730 764 526 1,129 87 47 24 193 938 938 938 938 171 121 38 260 200 400 600 800 1,000 1,200 PotatoCarrotOnionTomatomm/yrCropDirect Land Use Change -Applied Irrigation (excluding precipitation) (all values 30-year average) Farm Fallow Degraded Urban Urban Garden Urban Turf Urban Garden Conversion 1 Conversion 2 Conversion 3Legend Explanation Conversion 1 30 years of commercial farmland (weighted average of all locations supplying Denver metropolitan area) followed by 30 years of fallow farmland. Conversion 2 30 years of degraded urban land (e.g. vacant, uncultivated) in Denver displaced by 30 years of urban gardening. Conversion 3 30 years of urban turf (Kentucky bluegrass coolseason grass) displaced by 30 years of urban gardening.

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0.002 0.004 0.004 0.120 0.001 0.003 0.003 0.097 0.020 0.040 0.060 0.080 0.100 0.120 0.140 PotatoCarrotOnionTomatokgC/kgCropIndirect Land Use Change -Soil Organic Carbon (SOC) (all values derived from 30-year averages) 2 – 1 3 – 1Legend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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(388) (1,047) (3,578) (150,025) (1,498) (2,161) (4,712) (182,700) (200,000) (180,000) (160,000) (140,000) (120,000) (100,000) (80,000) (60,000) (40,000) (20,000) PotatoCarrotOnionTomatokgC/ha/yrCropIndirect Land Use Change -Carbon Dioxide (CO2) Flux from Soil (all values derived from 30-year averages) 2 – 1 3 – 1Legend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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0.5 (0.8) (0.9) (10.5) (1.2) (2.5) (2.6) (62.7) (70.0) (60.0) (50.0) (40.0) (30.0) (20.0) (10.0) 10.0 PotatoCarrotOnionTomatokgC/ha/yrCropIndirect Land Use Change -Methane (CH4) Flux from Soil (all values derived from 30-year averages) 2 – 1 3 – 1Legend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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(4.09) (7.73) (9.45) (219.27) (4.10) (7.76) (9.52) (220.83) (250.00) (200.00) (150.00) (100.00) (50.00) PotatoCarrotOnionTomatokgN/ha/yrCropIndirect Land Use Change -Nitrous Oxide (N2O) Flux from Soil (all values derived from 30-year averages) 2 – 1 3 – 1Legend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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(643) (717) (502) (28,090) (1,497) (1,580) (1,425) (54,208) (60,000) (50,000) (40,000) (30,000) (20,000) (10,000) PotatoCarrotOnionTomatomm/yrCropIndirect Land Use Change -Applie d Irrigation (excluding precipitation) (all values derived from 30-year averages) 2 – 1 3 – 1Legend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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APPENDIX I IMPACTS BY FUNCTIONAL UNIT

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VEGETABLE PRODUCTION DATA COLLECTION FOR LIFE CYCLE ASSESSMENT (LCA) USE PER FUNCTIONAL UNIT = 1 LB 5col DNDC4754674567col DNDC8910111213141516171819 electricity1012676483295 water473739424443414540384648 Location Brett Denver Brett Denver Brett Denver Farmyard Denver Farmyard Denver Farmyard Denver Farmyard Denver Denver CODenver CODenver CODenver COTerreton IDAlamosa COBakersfield CAGreeley COMission TXGreeley CODeming NMOntario ORCuliac n SINBakersfield CAPunta Gorda FLDenver CO Crop carrotpotatotomatocarrotpotatooniontomatopotatocarrotoniontomatopotatopotatocarrotcarrotoniononiononiononiontomatotomatotomatot urf FactorLCI row ItemDescriptionQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuan tityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUni tQuantityUnitQuantityUnitQuantityUnit PRODUCTION DIRECT row dndccolumn lci 6Energy(all non-renewable) 0.222 MJ/lb0.049 MJ/lb7.010 MJ/lb0.056 MJ/lb0.252 MJ/lb0.072 MJ/lb2.210 MJ/lb0.049 MJ/lb0.031 MJ/lb0.040 MJ/lb2.228 MJ/lb0.013 MJ/lb0.013 MJ/lb0.010 MJ/lb0.010 MJ/lb0.026 MJ/lb0.026 MJ/lb0.026 MJ/lb0.027 MJ/lb0.019 MJ/lb0.018 MJ/lb0.026 MJ/lb0.080 MJ/sqft 10Land Use(arable, non-irrigated)1.373 sqft/lb 3.245 sqft/lb 1.693 sqft/lb 1.069 sqft/lb 5.600 sqft/lb 1.355 sqft/lb 1.134 sqft/lb 3.240 sqft/lb 1.126 sqft/lb 1.456 sqft/lb 1.416 sqft/lb 0.871 sqft/lb 0.871 sqft/lb 0.495 sqft/lb 0.495 sqft/lb 1.675 sqft/lb 1.675 sqft/lb 1.675 sqft/lb 1.675 sqft/lb 1.675 sqft/lb 1.675 sqft/lb 1.675 sqft/lb 1.000 sqft/sqft 14Water(all fresh water sources)31.899 gal/lb 18.526 gal/lb 39.325 gal/lb 35.069 gal/lb 40.143 gal/lb 44.452 gal/lb 37.453 gal/lb 18.620 gal/lb 25.512 gal/lb 32.957 gal/lb 28.942 gal/lb 10.883 gal/lb 11.404 gal/lb 9.257 gal/lb 7.405 gal/lb 35.089 gal/lb 43.862 gal/lb 50.128 gal/lb 41.794 gal/lb 32.608 gal/lb 31.330 gal/lb 32.608 gal/lb 24.972 gal/sqft 216Total Carbon Dioxide eq 0.054 kgCO2e/lb 0.080 kgCO2e/lb 0.073 kgCO2e/lb 0.042 kgCO2e/lb 0.222 kgCO2e/lb 0.052 kgCO2e/lb 0.049 kgCO2e/lb 0.081 kgCO2e/lb 0.043 kgCO2e/lb 0.051 kgCO2e/lb 0.059 kgCO2e/lb 0.067 kgCO2e/lb 0.047 kgCO2e/lb 0.053 kgCO2e/lb 0.119 kgCO2e/lb 0.277 kgCO2e/lb 0.485 kgCO2e/lb 0.315 kgCO2e/lb 0.516 kgCO2e/lb 0.492 kgCO2e/lb 0.790 kgCO2e/lb 0.370 kgCO2e/lb 0.053 kgCO2e/s 318Carbon Dioxide 0.054 kgCO2e/lb 0.080 kgCO2e/lb 0.073 kgCO2e/lb 0.042 kgCO2e/lb 0.221 kgCO2e/lb 0.053 kgCO2e/lb 0.049 kgCO2e/lb 0.080 kgCO2e/lb 0.043 kgCO2e/lb 0.051 kgCO2e/lb 0.059 kgCO2e/lb 0.040 kgCO2e/lb 0.040 kgCO2e/lb 0.041 kgCO2e/lb 0.075 kgCO2e/lb 0.209 kgCO2e/lb 0.400 kgCO2e/lb 0.198 kgCO2e/lb 0.451 kgCO2e/lb 0.336 kgCO2e/lb 0.738 kgCO2e/lb 0.339 kgCO2e/lb 0.053 kgCO2e/s 520Nitrous Oxide 0.001 kgCO2e/lb 0.002 kgCO2e/lb 0.002 kgCO2e/lb 0.001 kgCO2e/lb 0.006 kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.002 kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.028 kgCO2e/lb 0.008 kgCO2e/lb 0.013 kgCO2e/lb 0.044 kgCO2e/lb 0.075 kgCO2e/lb 0.087 kgCO2e/lb 0.120 kgCO2e/lb 0.067 kgCO2e/lb 0.158 kgCO2e/lb 0.058 kgCO2e/lb 0.034 kgCO2e/lb 0.001 kgCO2e/s422Methane (0.001) kgCO2e/lb (0.002) kgCO2e/lb (0.002) kgCO2e/lb (0.001) kgCO2e/lb (0.005) kgCO2e/lb (0.001) kgCO2e/lb (0.001) kgCO2e/lb (0.002) kgCO2e/lb (0.001) kgCO2e/lb (0.001) kgCO2e/lb (0.001) kgCO2e/lb (0.001) kgCO2e/lb (0.001) kgCO2e/lb (0.001) kgCO2e/lb (0.000) kgCO2e/lb (0.006) kgCO2e/lb (0.002) kgCO2e/lb (0.003) kgCO2e/lb (0.002) kgCO2e/lb (0.002) kgCO2e/lb (0.006) kgCO2e/lb (0.003) kgCO2e/lb (0.001) kgCO2e/s 24TRACI Carcinogens 26TRACI Non-carcinogens 28TRACI Air compartment 30TRACI Water compartment 32TRACI Soil compartment 1Soil organic carbon (0.000) kgC/kg/year kgC/kg/ye 0.000 kgC/kg/ye (0.000) kgC/kg/ye kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye kgC/kg/ye (0.000) kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye 0.000 kgC/kg/ye Employment hours 0.133 hr/lb 0.161 hr/lb 0.106 hr/lb 0.034 hr/lb 0.183 hr/lb 0.048 hr/lb 0.035 hr/lb 0.161 hr/lb 0.055 hr/lb 0.048 hr/lb 0.052 hr/lb 0.001 hr/lb 0.001 hr/lb 0.000 hr/lb 0.000 hr/lb 0 .002 hr/lb 0.002 hr/lb 0.002 hr/lb 0.002 hr/lb 0.000 hr/lb 0.000 hr/lb 0.000 hr/lb 0.006 hr/sqft Laborer rate vegetable $ 8.00 $/hr $ 8.00 $/hr $ 8.00 $/hr $ 8.00 $/hr $ 8.00 $/hr $ 8.00 $/hr $ 8.00 $/hr $ 8.00 $/hr $ 8.00 $/hr $ 8.00 $/hr $ 8.00 $ /hr $ 14.00 $/hr $ 14.00 $/hr $ 14.00 $/hr $ 14.00 $/hr $ 14.00 $/hr $ 14.00 $/hr $ 14.00 $/hr $ 14.00 $/hr $ 14.00 $/hr $ 14.00 $/hr $ 14.00 $/hr $ 40.00 $/hr Laborer pay vegetable $ 1.06 $/lb $ 1.29 $/lb $ 0.85 $/lb $ 0.28 $/lb $ 1.47 $/lb $ 0.39 $/lb $ 0.28 $/lb $ 1.29 $/lb $ 0.44 $/lb $ 0.39 $/lb $ 0.42 $ /lb $ 0.01 $/lb $ 0.01 $/lb $ 0.00 $/lb $ 0.00 $/lb $ 0.03 $/lb $ 0.03 $/lb $ 0.03 $/lb $ 0.03 $/lb $ 0.01 $/lb $ 0.01 $/lb $ 0.01 $/lb 0.240 $/sqft Laborer pay turf eq. $ 0.33 $/lb $ 4.19 $/lb $ 0.41 $/lb $ 0.26 $/lb $ 1.34 $/lb $ 0.32 $/lb $ 0.27 $/lb $ 4.18 $/lb $ 0.50 $/lb $ 0.56 $/lb $ 0.59 $ /lb $ 0.21 $/lb $ 0.21 $/lb $ 0.12 $/lb $ 0.12 $/lb $ 0.40 $/lb $ 0.40 $/lb $ 0.40 $/lb $ 0.40 $/lb $ 0.40 $/lb $ 0.40 $/lb $ 0.40 $/lb PRODUCTION INDIRECT row dndccolumn lci 6Energy(all non-renewable) 0.888 MJ/lb1.086 MJ/lb26.652 MJ/lb1.364 MJ/lb6.286 MJ/lb1.758 MJ/lb9.526 MJ/lb1.089 MJ/lb0.801 MJ/lb1.030 MJ/lb9.137 MJ/lb0.889 MJ/lb0.793 MJ/lb1.290 MJ/lb1.214 MJ/lb1.049 MJ/lb1.051 MJ/lb2.055 MJ/lb1.066 MJ/lb0.541 MJ/lb0.674 MJ/lb0.761 MJ/lb1.441 MJ/sqft 10Land Use(arable, non-irrigated)0.000 sqft/lb 0.009 sqft/lb 0.000 sqft/lb 0.010 sqft/lb 0.045 sqft/lb 0.013 sqft/lb 0.009 sqft/lb 0.009 sqft/lb 0.006 sqft/lb 0.007 sqft/lb 0.006 sqft/lb 0.002 sqft/lb 0.002 sqft/lb 0.002 sqft/lb 0.002 sqft/lb 0.004 sqft/lb 0.004 sqft/lb 0.004 sqft/lb 0.004 sqft/lb 0.003 sqft/lb 0.003 sqft/lb 0.006 sqft/lb 0.014 sqft/sqft 14Water(all fresh water sources) 0.054 gal/lb 0.115 gal/lb 1.217 gal/lb 0.139 gal/lb 0.570 gal/lb 0.179 gal/lb 0.502 gal/lb 0.116 gal/lb 0.081 gal/lb 0.105 gal/lb 0.458 gal/lb 0.117 gal/lb 0.102 gal/lb 0.163 gal/lb 0.152 gal/lb 0.179 gal/lb 0.179 gal/lb 0.460 gal/lb 0.184 gal/lb 0.100 gal/lb 0.119 gal/lb 0.125 gal/lb 0.191 gal/sqft 216Total Carbon Dioxide eq 0.087 kgCO2e/lb 0.104 kgCO2e/lb 1.891 kgCO2e/lb 0.131 kgCO2e/lb 0.508 kgCO2e/lb 0.169 kgCO2e/lb 0.665 kgCO2e/lb 0.104 kgCO2e/lb 0.079 kgCO2e/lb 0.102 kgCO2e/lb 0.595 kgCO2e/lb 0.065 kgCO2e/lb 0.053 kgCO2e/lb 0.088 kgCO2e/lb 0.078 kgCO2e/lb 0.083 kgCO2e/lb 0.083 kgCO2e/lb 0.220 kgCO2e/lb 0.084 kgCO2e/lb 0.050 kgCO2e/lb 0.065 kgCO2e/lb 0.054 kgCO2e/lb 0.166 kgCO2e/s 318Carbon Dioxide 0.083 kgCO2e/lb 0.073 kgCO2e/lb 1.887 kgCO2e/lb 0.096 kgCO2e/lb 0.349 kgCO2e/lb 0.124 kgCO2e/lb 0.667 kgCO2e/lb 0.074 kgCO2e/lb 0.059 kgCO2e/lb 0.076 kgCO2e/lb 0.643 kgCO2e/lb 0.058 kgCO2e/lb 0.046 kgCO2e/lb 0.071 kgCO2e/lb 0.063 kgCO2e/lb 0.055 kgCO2e/lb 0.055 kgCO2e/lb 0.180 kgCO2e/lb 0.055 kgCO2e/lb 0.040 kgCO2e/lb 0.052 kgCO2e/lb 0.043 kgCO2e/lb 0.111 kgCO2e/s 520Nitrous Oxide 0.000 kgCO2e/lb 0.000 kgCO2e/lb (0.100) kgCO2e/lb 0.000 kgCO2e/lb 0.002 kgCO2e/lb 0.001 kgCO2e/lb (0.067) kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb (0.101) kgCO2e/lb 0.003 kgCO2e/lb 0.003 kgCO2e/lb 0.010 kgCO2e/lb 0.010 kgCO2e/lb 0.022 kgCO2e/lb 0.022 kgCO2e/lb 0.023 kgCO2e/lb 0.022 kgCO2e/lb 0.009 kgCO2e/lb 0.009 kgCO2e/lb 0.009 kgCO2e/lb 0.007 kgCO2e/s 422Methane 0.003 kgCO2e/lb 0.005 kgCO2e/lb 0.103 kgCO2e/lb 0.006 kgCO2e/lb 0.029 kgCO2e/lb 0.008 kgCO2e/lb 0.038 kgCO2e/lb 0.005 kgCO2e/lb 0.004 kgCO2e/lb 0.005 kgCO2e/lb 0.036 kgCO2e/lb 0.004 kgCO2e/lb 0.003 kgCO2e/lb 0.006 kgCO2e/lb 0.005 kgCO2e/lb 0.004 kgCO2e/lb 0.004 kgCO2e/lb 0.016 kgCO2e/lb 0.004 kgCO2e/lb 0.002 kgCO2e/lb 0.004 kgCO2e/lb 0.002 kgCO2e/lb 0.006 kgCO2e/s 24TRACI Carcinogens 7.55E-11 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 5.75E-09 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 8.90E-09 CTUh/sqft 26TRACI Non-carcinogens 1.61E-09 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 2.42E-08 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 3.99E-08 CTUh/sqft 28TRACI Air compartment 2.61E-04 CTUe/lb 0.021 CTUe/lb 0.008 CTUe/lb 0.034 CTUe/lb 0.179 CTUe/lb 0.043 CTUe/lb 0.039 CTUe/lb 2.15E-02 CTUe/lb 0.021 CTUe/lb 0.027 CTUe/lb 0.024 CTUe/lb 0.017 CTUe/lb 0.017 CTUe/lb 0.075 CTUe/lb 0.075 CTUe/lb 0.035 CTUe/lb 0.035 CTUe/lb 0.035 CTUe/lb 0.035 CTUe/lb 0.005 CTUe/lb 0.005 CTUe/lb 0.005 CTUe/lb 2.95E-03 CTUe/sqft 30TRACI Water compartment 1.50E-02 CTUe/lb 0.181 CTUe/lb 0.473 CTUe/lb 0.205 CTUe/lb 0.926 CTUe/lb 0.267 CTUe/lb 0.340 CTUe/lb 1.82E-01 CTUe/lb 0.116 CTUe/lb 0.149 CTUe/lb 0.264 CTUe/lb 0.184 CTUe/lb 0.180 CTUe/lb 0.286 CTUe/lb 0.274 CTUe/lb 0.092 CTUe/lb 0.091 CTUe/lb 0.209 CTUe/lb 0.093 CTUe/lb 0.093 CTUe/lb 0.113 CTUe/lb 0.112 CTUe/lb 3.02E-01 CTUe/sqft 32TRACI Soil compartment 0.00E+00 CTUe/lb 0.000 CTUe/lb (0.092) CTUe/lb 0.001 CTUe/lb 0.002 CTUe/lb 0.001 CTUe/lb (0.061) CTUe/lb 4.68E-04 CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb (0.093) CTUe/lb 0.001 CTUe/lb 0.001 CTUe/lb 0.001 CTUe/lb 0.001 CTUe/lb 0.001 CTUe/lb 0.001 CTUe/lb 0.001 CTUe/lb 0.001 CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 8.16E-04 CTUe/sqft 1Soil organic carbon Employment hours Laborer rate Laborer pay vegetable Laborer pay turf eq. POST-PRODUCTION column 6Energy(all non-renewable) MJ/lb0.011 MJ/lbMJ/lb0.018 MJ/lb0.018 MJ/lb0.018 MJ/lb2.406 MJ/lb0.011 MJ/lb0.011 MJ/lb0.011 MJ/lb1.807 MJ/lb0.918 MJ/lb0.529 MJ/lb0.739 MJ/lb0.197 MJ/lb2.844 MJ/lb0.665 MJ/lb1.877 MJ/lb2.242 MJ/lb3.529 MJ/lb2.547 MJ/lb4.385 MJ/lbMJ/sqft 10Land Use(arable, non-irrigated)sqft/lb 0.130 sqft/lb sqft/lb 0.036 sqft/lb 0.166 sqft/lb 0.046 sqft/lb 0.034 sqft/lb 0.130 sqft/lb 0.029 sqft/lb 0.046 sqft/lb 0.026 sqft/lb 0.013 sqft/lb 0.012 sqft/lb 0.007 sqft/lb 0.007 sqft/lb 0.026 sqft/lb 0.023 sqft/lb 0.024 sqft/lb 0.025 sqft/lb 0.026 sqft/lb 0.025 sqft/lb 0.028 sqft/lb sqft/sqft 14Water(all fresh water sources) gal/lb 0.042 gal/lb gal/lb 0.013 gal/lb 0.054 gal/lb 0.016 gal/lb 0.049 gal/lb 0.042 gal/lb 0.010 gal/lb 0.016 gal/lb 0.036 gal/lb 0.209 gal/lb 0.128 gal/lb 0.164 gal/lb 0.051 gal/lb 0.627 gal/lb 0.173 gal/lb 0.426 gal/lb 0.502 gal/lb 0.759 gal/lb 0.555 gal/lb 0.938 gal/lb gal/sqft 16Total Carbon Dioxide eq kgCO2e/lb 0.001 kgCO2e/lb kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.104 kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.078 kgCO2e/lb 0.063 kgCO2e/lb 0.034 kgCO2e/lb 0.051 kgCO2e/lb 0.012 kgCO2e/lb 0.199 kgCO2e/lb 0.040 kgCO2e/lb 0.129 kgCO2e/lb 0.155 kgCO2e/lb 0.247 kgCO2e/lb 0.176 kgCO2e/lb 0.310 kgCO2e/lb kgCO2e/s 18Carbon Dioxide kgCO2e/lb 0.001 kgCO2e/lb kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.090 kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.067 kgCO2e/lb 0.060 kgCO2e/lb 0.032 kgCO2e/lb 0.049 kgCO2e/lb 0.011 kgCO2e/lb 0.191 kgCO2e/lb 0.037 kgCO2e/lb 0.123 kgCO2e/lb 0.148 kgCO2e/lb 0.236 kgCO2e/lb 0.167 kgCO2e/lb 0.297 kgCO2e/lb kgCO2e/s 20Nitrous Oxide kgCO2e/lb 0.000 kgCO2e/lb kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.001 kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.002 kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb 0.002 kgCO2e/lb 0.001 kgCO2e/lb 0.002 kgCO2e/lb kgCO2e/s 22Methane kgCO2e/lb 0.000 kgCO2e/lb kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.014 kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.000 kgCO2e/lb 0.011 kgCO2e/lb 0.002 kgCO2e/lb 0.001 kgCO2e/lb 0.002 kgCO2e/lb 0.001 kgCO2e/lb 0.007 kgCO2e/lb 0.002 kgCO2e/lb 0.005 kgCO2e/lb 0.006 kgCO2e/lb 0.009 kgCO2e/lb 0.007 kgCO2e/lb 0.011 kgCO2e/lb kgCO2e/s 24TRACI Carcinogens CTUh/lb 0.000 CTUh/lb CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb CTUh/sqft 26TRACI Non-carcinogens CTUh/lb 0.000 CTUh/lb CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb 0.000 CTUh/lb CTUh/sqft 28TRACI Air compartment CTUe/lb 0.000 CTUe/lb CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 0.004 CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 0.003 CTUe/lb 0.002 CTUe/lb 0.002 CTUe/lb 0.001 CTUe/lb 0.001 CTUe/lb 0.005 CTUe/lb 0.005 CTUe/lb 0.005 CTUe/lb 0.005 CTUe/lb 0.005 CTUe/lb 0.005 CTUe/lb 0.005 CTUe/lb CTUe/sqft 30TRACI Water compartment CTUe/lb 0.002 CTUe/lb CTUe/lb 0.003 CTUe/lb 0.003 CTUe/lb 0.003 CTUe/lb 0.003 CTUe/lb 0.002 CTUe/lb 0.002 CTUe/lb 0.002 CTUe/lb 0.002 CTUe/lb 0.118 CTUe/lb 0.046 CTUe/lb 0.107 CTUe/lb 0.007 CTUe/lb 0.426 CTUe/lb 0.024 CTUe/lb 0.248 CTUe/lb 0.315 CTUe/lb 0.553 CTUe/lb 0.372 CTUe/lb 0.711 CTUe/lb CTUe/sqft 32TRACI Soil compartment CTUe/lb 0.000 CTUe/lb CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 0.000 CTUe/lb 0.021 CTUe/lb 0.021 CTUe/lb 0.012 CTUe/lb 0.012 CTUe/lb 0.039 CTUe/lb 0.039 CTUe/lb 0.039 CTUe/lb 0.039 CTUe/lb 0.039 CTUe/lb 0.039 CTUe/lb 0.039 CTUe/lb CTUe/sqft Soil organic carbon Employment hours Laborer rate Laborer pay vegetable Laborer pay turf eq. TOTAL Energy(all non-renewable) 1.111 MJ/lb1.146 MJ/lb33.662 MJ/lb1.438 MJ/lb6.556 MJ/lb1.849 MJ/lb14.142 MJ/lb1.149 MJ/lb0.843 MJ/lb1.081 MJ/lb13.171 MJ/lb1.821 MJ/lb1.335 MJ/lb2.038 MJ/lb1.421 MJ/lb3.918 MJ/lb1.742 MJ/lb3.958 MJ/lb3.336 MJ/lb4.089 MJ/lb3.238 MJ/lb5.172 MJ/lb1.521 MJ/sqft Land Use(arable, non-irrigated)1.373 sqft/lb 3.384 sqft/lb 1.693 sqft/lb 1.115 sqft/lb 5.811 sqft/lb 1.413 sqft/lb 1.177 sqft/lb 3.379 sqft/lb 1.160 sqft/lb 1.508 sqft/lb 1.447 sqft/lb 0.886 sqft/lb 0.886 sqft/lb 0.504 sqft/lb 0.503 sqft/lb 1.705 sqft/lb 1.702 sqft/lb 1.703 sqft/lb 1.704 sqft/lb 1.705 sqft/lb 1.704 sqft/lb 1.709 sqft/lb 1.014 sqft/sqft Water(all fresh water sources)31.952 gal/lb 18.683 gal/lb 40.541 gal/lb 35.221 gal/lb 40.768 gal/lb 44.647 gal/lb 38.004 gal/lb 18.777 gal/lb 25.603 gal/lb 33.077 gal/lb 29.436 gal/lb 11.209 gal/lb 11.634 gal/lb 9.584 gal/lb 7.609 gal/lb 35.895 gal/lb 44.214 gal/lb 51.013 gal/lb 42.480 gal/lb 33.468 gal/lb 32.004 gal/lb 33.671 gal/lb 25.163 gal/sqft Total Carbon Dioxide eq 0.141 kgCO2e/lb 0.185 kgCO2e/lb 1.964 kgCO2e/lb 0.174 kgCO2e/lb 0.732 kgCO2e/lb 0.223 kgCO2e/lb 0.818 kgCO2e/lb 0.186 kgCO2e/lb 0.122 kgCO2e/lb 0.154 kgCO2e/lb 0.733 kgCO2e/lb 0.195 kgCO2e/lb 0.133 kgCO2e/lb 0.192 kgCO2e/lb 0.209 kgCO2e/lb 0.559 kgCO2e/lb 0.608 kgCO2e/lb 0.664 kgCO2e/lb 0.755 kgCO2e/lb 0.789 kgCO2e/lb 1.031 kgCO2e/lb 0.734 kgCO2e/lb 0.219 kgCO2e/s Carbon Dioxide 0.137 kgCO2e/lb 0.154 kgCO2e/lb 1.960 kgCO2e/lb 0.139 kgCO2e/lb 0.572 kgCO2e/lb 0.178 kgCO2e/lb 0.805 kgCO2e/lb 0.155 kgCO2e/lb 0.102 kgCO2e/lb 0.128 kgCO2e/lb 0.770 kgCO2e/lb 0.157 kgCO2e/lb 0.118 kgCO2e/lb 0.162 kgCO2e/lb 0.150 kgCO2e/lb 0.454 kgCO2e/lb 0.492 kgCO2e/lb 0.500 kgCO2e/lb 0.654 kgCO2e/lb 0.612 kgCO2e/lb 0.958 kgCO2e/lb 0.680 kgCO2e/lb 0.164 kgCO2e/s Nitrous Oxide 0.001 kgCO2e/lb 0.003 kgCO2e/lb (0.099) kgCO2e/lb 0.001 kgCO2e/lb 0.008 kgCO2e/lb 0.002 kgCO2e/lb (0.066) kgCO2e/lb 0.003 kgCO2e/lb 0.001 kgCO2e/lb 0.001 kgCO2e/lb (0.099) kgCO2e/lb 0.032 kgCO2e/lb 0.011 kgCO2e/lb 0.024 kgCO2e/lb 0.054 kgCO2e/lb 0.099 kgCO2e/lb 0.110 kgCO2e/lb 0.144 kgCO2e/lb 0.091 kgCO2e/lb 0.169 kgCO2e/lb 0.068 kgCO2e/lb 0.045 kgCO2e/lb 0.008 kgCO2e/s Methane 0.002 kgCO2e/lb 0.003 kgCO2e/lb 0.101 kgCO2e/lb 0.005 kgCO2e/lb 0.025 kgCO2e/lb 0.007 kgCO2e/lb 0.051 kgCO2e/lb 0.003 kgCO2e/lb 0.003 kgCO2e/lb 0.003 kgCO2e/lb 0.045 kgCO2e/lb 0.006 kgCO2e/lb 0.004 kgCO2e/lb 0.007 kgCO2e/lb 0.005 kgCO2e/lb 0.004 kgCO2e/lb 0.004 kgCO2e/lb 0.018 kgCO2e/lb 0.008 kgCO2e/lb 0.008 kgCO2e/lb 0.005 kgCO2e/lb 0.010 kgCO2e/lb 0.006 kgCO2e/s TRACI Carcinogens 7.55E-11 CTUh/lb 5.74E-09 CTUh/lb 2.38E-09 CTUh/lb 6.65E-09 CTUh/lb 3.03E-08 CTUh/lb 8.62E-09 CTUh/lb 7.35E-09 CTUh/lb 5.76E-09 CTUh/lb 3.78E-09 CTUh/lb 4.86E-09 CTUh/lb 4.72E-09 CTUh/lb 1.63E-09 CTUh/lb 1.23E-09 CTUh/lb 1.77E-09 CTUh/lb 1.19E-09CTUh/lb 4.59E-09 CTUh/lb 2.41E-09 CTUh/lb 4.02E-09 CTUh/lb 4.00E-09 CTUh/lb 4.13E-09 CTUh/lb 3.20E-09 CTUh/lb 5.11E-09 CTUh/lb 8.90E-09 CTUh/sqft TRACI Non-carcinogens 1.61E-09 CTUh/lb 2.42E-08 CTUh/lb 5.06E-08 CTUh/lb 2.75E-08 CTUh/lb 1.23E-07 CTUh/lb 3.56E-08 CTUh/lb 4.16E-08 CTUh/lb 2.43E-08 CTUh/lb 1.55E-08 CTUh/lb 1.99E-08 CTUh/lb 3.14E-08 CTUh/lb 2.71E-08 CTUh/lb 2.30E-08 CTUh/lb 7.44E-08 CTUh/lb 6.87E-08 CTUh/lb 3.91E-08 CTUh/lb 1.82E-08 CTUh/lb 3.56E-08 CTUh/lb 3.35E-08 CTUh/lb 4.30E-08 CTUh/lb 3.42E-08 CTUh/lb 5.29E-08 CTUh/lb 3.99E-08 CTUh/sqft TRACI Air compartment 2.61E-04 CTUe/lb 2.14E-02 CTUe/lb 8.06E-03 CTUe/lb 3.41E-02 CTUe/lb 1.79E-01 CTUe/lb 4.32E-02 CTUe/lb 4.21E-02 CTUe/lb 2.15E-02 CTUe/lb 2.13E-02 CTUe/lb 2.73E-02 CTUe/lb 2.63E-02 CTUe/lb 1.98E-02 CTUe/lb 1.98E-02 CTUe/lb 7.64E-02 CTUe/lb 7.64E-02 CTUe/lb 3.96E-02 CTUe/lb 3.96E-02 CTUe/lb 3.96E-02 CTUe/lb3.96E-02 CTUe/lb 9.66E-03 CTUe/lb 9.64E-03 CTUe/lb 9.70E-03 CTUe/lb 2.95E-03 CTUe/sqft TRACI Water compartment 1.50E-02 CTUe/lb 1.83E-01 CTUe/lb 4.73E-01 CTUe/lb 2.09E-01 CTUe/lb 9.29E-01 CTUe/lb 2.70E-01 CTUe/lb 3.43E-01 CTUe/lb 1.84E-01 CTUe/lb 1.17E-01 CTUe/lb 1.51E-01 CTUe/lb 2.66E-01 CTUe/lb 3.02E-01 CTUe/lb 2.26E-01 CTUe/lb 3.94E-01 CTUe/lb 2.82E-01 CTUe/lb 5.18E-01 CTUe/lb 1.15E-01 CTUe/lb 4.57E-01 CTUe/lb 4.09E-01 CTUe/lb 6.46E-01 CTUe/lb 4.85E-01 CTUe/lb 8.23E-01 CTUe/lb 3.02E-01 CTUe/sqft TRACI Soil compartment 0.00E+00 CTUe/lb 4.66E-04 CTUe/lb -9.24E-02 CTUe/lb 5.27E-04 CTUe/lb 2.38E-03 CTUe/lb 6.85E-04 CTUe/lb -6.13E-02 CTUe/lb 4.68E-04 CTUe/lb 2.97E-04 CTUe/lb 3.82E-04 CTUe/lb -9.27E-02 CTUe/lb 2.10E-02 CTUe/lb 2.10E-02 CTUe/lb 1.25E-02 CTUe/lb 1.25E-02 CTUe/lb 4.00E-02 CTUe/lb 4.00E-02 CTUe/lb 4.00E-02 CTUe/lb 4.00E-02 CTUe/lb 3.98E-02 CTUe/lb 3.97E-02 CTUe/lb 3.98E-02CTUe/lb 8.16E-04 CTUe/sqft Soil organic carbon -3.33E-06kgC/kg/year0.00E+00kgC/kg/ye a 2.00E-05kgC/kg/ye a -3.33E-06kgC/kg/ye a 0.00E+00kgC/kg/ye a 6.67E-06kgC/kg/ye a 2.00E-05kgC/kg/ye a 0.00E+00kgC/kg/ye a -3.33E-06kgC/kg/ye a 6.67E-06kgC/kg/ye a 2.00E-05kgC/kg/ye a 1.20E-04kgC/kg/ye a 9.00E-05kgC/kg/ye a 4.33E-05kgC/kg/ye a 2.07E-04kgC/kg/ye a 8.00E-05kgC/kg/ye a 3.77E-04kgC/kg/ye a 1.33E-04kgC/kg/ye a 4.53E-04kgC/kg/ye a 7.67E-05kgC/kg/ye a 4.80E-04kgC/kg/ye a 7.33E-05kgC/kg/ye a 1.10E-04kgC/kg/year Employment hours 0.133 hr/lb 0.161 hr/lb 0.106 hr/lb 0.034 hr/lb 0.183 hr/lb 0.048 hr/lb 0.035 hr/lb 0.161 hr/lb 0.055 hr/lb 0.048 hr/lb 0.052 hr/lb 0.001 hr/lb 0.001 hr/lb 0.000 hr/lb 0.000 hr/lb 0.002 hr/lb 0.002 hr/lb 0.002 hr/lb 0.002 hr/lb 0.000 hr/lb 0.000 hr/lb 0.000 hr/lb 0.006 hr/sqft Laborer rate vegetable 8.00 $/hr 8.00 $/hr 8.00 $/hr 8.00 $/hr 8.00 $/hr 8.00 $/hr 8.00 $/hr 8.00 $/hr 8.00 $/hr 8.00 $/hr 8.00 $/hr 14.00 $/hr 14.00 $/hr 14.00 $/hr 14.00 $/hr 14.00 $/hr 14.00 $/hr 14.00 $/hr 14.00 $/hr 14.00 $/hr 14.00 $/hr 14.00 $/hr 40.00 $/hr Laborer pay vegetable 1.06 $/lb 1.29 $/lb 0.85 $/lb 0.28 $/lb 1.47 $/lb 0.39 $/lb 0.28 $/lb 1.29 $/lb 0.44 $/lb 0.39 $/lb 0.42 $/lb 0.01 $/lb 0.01 $/lb 0.00 $/lb 0.00 $/lb 0.03 $/lb 0.03 $/lb 0.03 $/lb 0.03 $/lb 0.01 $/lb 0.01 $/lb 0.01 $/lb 0.24 $/sqft Laborer pay turf eq. 0.33 $/lb 4.19 $/lb 0.41 $/lb 0.26 $/lb 1.34 $/lb 0.32 $/lb 0.27 $/lb 4.18 $/lb 0.50 $/lb 0.56 $/lb 0.59 $/lb 0.21 $/lb 0.21 $/lb 0.12 $/lb 0.12 $/lb 0.40 $/lb 0.40 $/lb 0.40 $/lb 0.40 $/lb 0.40 $/lb 0.40 $/lb 0.40 $/lb $/lb column12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061ANNUAL S HARE Denver CODenver CODenver CODenver CODenver COCommercialCommercialCommercialCommercialFallowFallowFallowFallowEquiv FU-potatoEqu iv FU-carrotEquiv FU-onionEquiv FU-tomatoEquiv FUEquiv FUEquiv FUEquiv FU potatocarrotoniontomatoturfpotatocarrotoniontomatopotatocarrotoniontomatoDegraded urbanDegraded urbanDegraded urbanDegraded urb anturf-potatoturf-carrotturf-onionturf-tomatopotatocarrotoniontomatopotatocarrotoniontomatoPotatoTerreton, I D 50% SUMDIRECT QuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantit yUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnitQuantityUnit 2 – 1Unit 2 – 1Unit 2 – 1Unit 2 – 1Unit 3 – 1Unit 3 – 1Unit 3 – 1Unit 3 – 1Unit A lamosa, C 50% 1Energy(all non-renewable) 0.05 MJ/lb0.03 MJ/lb0.04 MJ/lb2.23 MJ/lb0.08 MJ/sqft0.01 MJ/lb0.01 MJ/lb0.03 MJ/lb0.02 MJ/lb 0.04 MJ/lb0.02 MJ/lb0.01 MJ/lb2.21 MJ/lb(0.04) MJ/lb(0.06) MJ/lb(0.07) MJ/lb2.13 MJ/lb 2Land Use(arable, non-irrigated)3.24 sqft/lb 1.13 sqft/lb 1.46 sqft/lb 1.42 sqft/lb 1.00 sqft/sqft 0.87 sqft/lb 0.50 sqft/lb 1.68 sqft/lb 1.68 sqft/lb 2.37 sqft/lb 0.63 sqft/lb (0.22) sqft/lb (0.26) sqft/lb 1.37 sqft/lb (0.37) sqft/lb (1.22) sqft/lb (1.26) sqft/lb CarrotBakersfiel d 83% 3Water(all fresh water sources) 18.62 gal/lb 25.51 gal/lb 32.96 gal/lb 28.94 gal/lb 24.97 gal/sqft 11.14 gal/lb 8.94 gal/lb 43.01 gal/lb 31.97 gal/lb 7.48 gal/lb 16.57 gal/lb (10.05) gal/lb (3.03) gal/lb (17.50) gal/lb (8.40) gal/lb (35.02) gal/lb (28.00) gal/lb Greeley, C O 17% 4Total Carbon Dioxide eq 0.08 kgCO2e/lb 0.04 kgCO2e/lb 0.05 kgCO2e/lb 0.06 kgCO2e/lb 0.05 kgCO2e/s 0.06 kgCO2e/lb 0.06 kgCO2e/lb 0.43 kgCO2e/lb 0.61 kgCO2e/lb 0.00 kgCO2e/lb 0.01 kgCO2e/lb 0.05 kgCO2e/lb 0.05 kgCO2e/lb 0.03 kgCO2e/lb 0.01 kgCO2e/lb 0.01 kgCO2e/lb 0.01 kgCO2e/lb 0.24 kgCO2e/lb 0.06 kgCO2e/lb 0.07 kgCO2e/lb 0.07 kgCO2e/lb (0.00) kgCO2e/lb (0.02) kgCO2e/lb (0.34) kgCO2e/lb (0.51) kgCO2e/lb (0.03) kgCO2e/lb (0.06) kgCO2e/lb (0.39) kgCO2e/lb (0.55) kgCO2e/lb 5Carbon Dioxide 0.08 kgCO2e/lb 0.04 kgCO2e/lb 0.05 kgCO2e/lb 0.06 kgCO2e/lb 0.05 kgCO2e/s 0.04 kgCO2e/lb 0.05 kgCO2e/lb 0.35 kgCO2e/lb 0.54 kgCO2e/lb 0.00 kgCO2e/lb 0.01 kgCO2e/lb 0.05 kgCO2e/lb 0.06 kgCO2e/lb 0 kgCO2e/lb 0 kgCO2e/lb 0 kgCO2e/lb 0 kgCO2e/lb 0.24 kgCO2e/lb 0.06 kgCO2e/lb 0.07 kgCO2e/lb 0.07 kgCO2e/lb 0.01 kgCO2e/lb 0.00 kgCO2e/lb (0.26) kgCO2e/lb (0.43) kgCO2e/lb (0.01) kgCO2e/lb (0.04) kgCO2e/lb (0.30) kgCO2e/lb (0.48) kgCO2e/lb OnionMission, T X 16% 6Nitrous Oxide 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/s 0.02 kgCO2e/lb 0.02 kgCO2e/lb 0.09 kgCO2e/lb 0.08 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb (0.02) kgCO2e/lb (0.02) kgCO2e/lb (0.08) kgCO2e/lb (0.08) kgCO2e/lb (0.02) kgCO2e/lb (0.02) kgCO2e/lb (0.08) kgCO2e/lb (0.08) kgCO2e/lb Greeley, C O 42% 7Methane (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/s (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.01) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb 0.01 kgCO2e/lb 0.00 kgCO2e/lb (0.00) kgCO2e/lb 0.00 kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb Deming, N M 17% 8TRACI Carcinogens Ontario, O R 25% 9TRACI Non-carcinogens 10TRACI Air compartment TomatoCuliacn, S 25% 11TRACI Water compartment Bakersfiel d 50% 12TRACI Soil compartment Punta Gor d 25% 13Soil organic carbon kgC/kg/year(0.00) kgC/kg/ye a 0.00 kgC/kg/ye a 0.00 kgC/kg/ye a 0.00 kgC/kg/ye a 0.00 kgC/kg/ye a 0.00 kgC/kg/ye a 0.00 kgC/kg/ye a 0.00 kgC/kg/ye a (0.00) kgC/kg/ye a 0.00 kgC/kg/ye a (0.00) kgC/kg/ye a (0.00) kgC/kg/ye a (0.00) kgC/kg/ye a (0.00) kgC/kg/ye a (0.00) kgC/kg/ye a (0.00) kgC/kg/ye a 0.00 kgC/kg/ye a 0.00 kgC/kg/ye a 0.00 kgC/kg/ye a 0.00 kgC/kg/ye a (0.00) kgC/kg/ye a (0.00) kgC/kg/ye a (0.00) kgC/kg/ye a (0.00) kgC/kg/ye a (0.00) kgC/kg/ye a (0.00) kgC/kg/ye a (0.00) kgC/kg/ye a (0.00) kgC/kg/year 14Employment hours 0.16 hr/lb 0.06 hr/lb 0.05 hr/lb 0.05 hr/lb 0.01 hr/sqft 0.00 hr/lb 0.00 hr/lb 0.00 hr/lb 0.00 hr/lb 0.16 hr/lb 0.06 hr/lb 0.05 hr/lb 0.05 hr/lb 0.15 hr/lb 0.05 hr/lb 0.04 hr/lb 0.05 hr/lb 15Laborer rate vegetable 8.00 $/hr 8.00 $/hr 8.00 $/hr 8.00 $/hr 40.00 $/hr 14.00 $/hr 14.00 $/hr 14.00 $/hr 14.00 $/hr (6.00) $/hr (6.00) $/hr (6.00) $/hr (6.00) $/hr (46.00) $/hr (46.00) $/hr (46.00) $/hr (46.00) $/hr 16Laborer pay vegetable 1.29 $/lb 0.44 $/lb 0.39 $/lb 0.42 $/lb 0.24 $/sqft 0.01 $/lb 0.00 $/lb 0.03 $/lb 0.01 $/lb 1.28 $/lb 0.44 $/lb 0.36 $/lb 0.41 $/lb 1.04 $/lb 0.20 $/lb 0.12 $/lb 0.17 $/lb 17Laborer pay turf eq. 4.18 $/lb 0.50 $/lb 0.56 $/lb 0.59 $/lb $/lb 0.21 $/lb 0.12 $/lb 0.40 $/lb 0.40 $/lb 3.98 $/lb 0.38 $/lb 0.16 $/lb 0.19 $/lb 3.98 $/lb 0.38 $/lb 0.16 $/lb 0.19 $/lb SUMINDIRECT 1Energy(all non-renewable) 1.09 MJ/lb0.80 MJ/lb1.03 MJ/lb9.14 MJ/lb1.44 MJ/sqft0.84 MJ/lb1.28 MJ/lb1.23 MJ/lb0.66 MJ/lb 0.25 MJ/lb(0.48) MJ/lb(0.20) MJ/lb8.47 MJ/lb(1.19) MJ/lb(1.92) MJ/lb(1.64) MJ/lb7.03 MJ/lb 2Land Use(arable, non-irrigated)0.01 sqft/lb 0.01 sqft/lb 0.01 sqft/lb 0.01 sqft/lb 0.01 sqft/sqft 0.00 sqft/lb 0.00 sqft/lb 0.00 sqft/lb 0.00 sqft/lb 0.01 sqft/lb 0.00 sqft/lb 0.00 sqft/lb 0.00 sqft/lb (0.01) sqft/lb (0.01) sqft/lb (0.01) sqft/lb (0.01) sqft/lb 3Water(all fresh water sources) 0.12 gal/lb 0.08 gal/lb 0.10 gal/lb 0.46 gal/lb 0.19 gal/sqft 0.11 gal/lb 0.16 gal/lb 0.23 gal/lb 0.12 gal/lb 0.01 gal/lb (0.08) gal/lb (0.12) gal/lb 0.34 gal/lb (0.18) gal/lb (0.27) gal/lb (0.31) gal/lb 0.15 gal/lb 4Total Carbon Dioxide eq 0.10 kgCO2e/lb 0.08 kgCO2e/lb 0.10 kgCO2e/lb 0.59 kgCO2e/lb 0.17 kgCO2e/s 0.06 kgCO2e/lb 0.09 kgCO2e/lb 0.11 kgCO2e/lb 0.06 kgCO2e/lb 0.05 kgCO2e/lb (0.01) kgCO2e/lb (0.00) kgCO2e/lb 0.54 kgCO2e/lb (0.12) kgCO2e/lb (0.17) kgCO2e/lb (0.17) kgCO2e/lb 0.37 kgCO2e/lb 5Carbon Dioxide 0.07 kgCO2e/lb 0.06 kgCO2e/lb 0.08 kgCO2e/lb 0.64 kgCO2e/lb 0.11 kgCO2e/s 0.05 kgCO2e/lb 0.07 kgCO2e/lb 0.08 kgCO2e/lb 0.05 kgCO2e/lb 0.02 kgCO2e/lb (0.01) kgCO2e/lb (0.00) kgCO2e/lb 0.60 kgCO2e/lb (0.09) kgCO2e/lb (0.12) kgCO2e/lb (0.11) kgCO2e/lb 0.49 kgCO2e/lb 6Nitrous Oxide 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb (0.10) kgCO2e/lb 0.01 kgCO2e/s 0.00 kgCO2e/lb 0.01 kgCO2e/lb 0.02 kgCO2e/lb 0.01 kgCO2e/lb (0.00) kgCO2e/lb (0.01) kgCO2e/lb (0.02) kgCO2e/lb (0.11) kgCO2e/lb (0.01) kgCO2e/lb (0.02) kgCO2e/lb (0.03) kgCO2e/lb (0.12) kgCO2e/lb 7Methane 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.04 kgCO2e/lb 0.01 kgCO2e/s 0.00 kgCO2e/lb 0.01 kgCO2e/lb 0.01 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb 0.03 kgCO2e/lb (0.01) kgCO2e/lb (0.01) kgCO2e/lb (0.01) kgCO2e/lb 0.03 kgCO2e/lb 8TRACI Carcinogens 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/sqft 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb 9TRACI Non-carcinogens 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/sqft 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb (0.00) CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb 10TRACI Air compartment 0.02 CTUe/lb 0.02 CTUe/lb 0.03 CTUe/lb 0.02 CTUe/lb 0.00 CTUe/sqft 0.02 CTUe/lb 0.07 CTUe/lb 0.03 CTUe/lb 0.00 CTUe/lb 0.00 CTUe/lb (0.05) CTUe/lb (0.01) CTUe/lb 0.02 CTUe/lb 0.00 CTUe/lb (0.06) CTUe/lb (0.01) CTUe/lb 0.02 CTUe/lb 11TRACI Water compartment 0.18 CTUe/lb 0.12 CTUe/lb 0.15 CTUe/lb 0.26 CTUe/lb 0.30 CTUe/sqft 0.18 CTUe/lb 0.28 CTUe/lb 0.11 CTUe/lb 0.11 CTUe/lb 0.00 CTUe/lb (0.17) CTUe/lb 0.04 CTUe/lb 0.16 CTUe/lb (0.30) CTUe/lb (0.47) CTUe/lb (0.27) CTUe/lb (0.15) CTUe/lb 12TRACI Soil compartment 0.00 CTUe/lb 0.00 CTUe/lb 0.00 CTUe/lb (0.09) CTUe/lb 0.00 CTUe/sqft 0.00 CTUe/lb 0.00 CTUe/lb 0.00 CTUe/lb 0.00 CTUe/lb (0.00) CTUe/lb (0.00) CTUe/lb (0.00) CTUe/lb (0.09) CTUe/lb (0.00) CTUe/lb (0.00) CTUe/lb (0.00) CTUe/lb (0.09) CTUe/lb 13Soil organic carbon kgC/kg/yearkgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/year kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/year 14Employment hours hr/lb hr/lb hr/lb hr/lb hr/sqft hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb 15Laborer rate vegetable $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr 16Laborer pay vegetable $/lb $/lb $/lb $/lb $/sqft $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb 17Laborer pay turf eq. $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb SUMPOST 1Energy(all non-renewable) 0.01 MJ/lb0.01 MJ/lb0.01 MJ/lb1.81 MJ/lbMJ/sqft0.72 MJ/lb0.65 MJ/lb1.61 MJ/lb3.25 MJ/lb (0.71) MJ/lb(0.64) MJ/lb(1.60) MJ/lb(1.45) MJ/lb(0.71) MJ/lb(0.64) MJ/lb(1.60) MJ/lb(1.45) MJ/lb 2Land Use(arable, non-irrigated)0.13 sqft/lb 0.03 sqft/lb 0.05 sqft/lb 0.03 sqft/lb sqft/sqft 0.01 sqft/lb 0.01 sqft/lb 0.02 sqft/lb 0.03 sqft/lb 0.12 sqft/lb 0.02 sqft/lb 0.02 sqft/lb (0.00) sqft/lb 0.12 sqft/lb 0.02 sqft/lb 0.02 sqft/lb (0.00) sqft/lb 3Water(all fresh water sources) 0.04 gal/lb 0.01 gal/lb 0.02 gal/lb 0.04 gal/lb gal/sqft 0.17 gal/lb 0.14 gal/lb 0.37 gal/lb 0.70 gal/lb (0.13) gal/lb (0.13) gal/lb (0.36) gal/lb (0.67) gal/lb (0.13) gal/lb (0.13) gal/lb (0.36) gal/lb (0.67) gal/lb 4Total Carbon Dioxide eq 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.08 kgCO2e/lb kgCO2e/s 0.05 kgCO2e/lb 0.04 kgCO2e/lb 0.11 kgCO2e/lb 0.23 kgCO2e/lb (0.05) kgCO2e/lb (0.04) kgCO2e/lb (0.11) kgCO2e/lb (0.15) kgCO2e/lb (0.05) kgCO2e/lb (0.04) kgCO2e/lb (0.11) kgCO2e/lb (0.15) kgCO2e/lb 5Carbon Dioxide 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.07 kgCO2e/lb kgCO2e/s 0.05 kgCO2e/lb 0.04 kgCO2e/lb 0.10 kgCO2e/lb 0.22 kgCO2e/lb (0.05) kgCO2e/lb (0.04) kgCO2e/lb (0.10) kgCO2e/lb (0.15) kgCO2e/lb (0.05) kgCO2e/lb (0.04) kgCO2e/lb (0.10) kgCO2e/lb (0.15) kgCO2e/lb 6Nitrous Oxide 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb kgCO2e/s 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb 7Methane 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.01 kgCO2e/lb kgCO2e/s 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.00 kgCO2e/lb 0.01 kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb 0.00 kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb (0.00) kgCO2e/lb 0.00 kgCO2e/lb 8TRACI Carcinogens 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb CTUh/sqft 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb 9TRACI Non-carcinogens 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb CTUh/sqft 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb 0.00 CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb (0.00) CTUh/lb 10TRACI Air compartment 0.00 CTUe/lb 0.00 CTUe/lb 0.00 CTUe/lb 0.00 CTUe/lb CTUe/sqft 0.00 CTUe/lb 0.00 CTUe/lb 0.00 CTUe/lb 0.00 CTUe/lb (0.00) CTUe/lb (0.00) CTUe/lb (0.00) CTUe/lb (0.00) CTUe/lb (0.00) CTUe/lb (0.00) CTUe/lb (0.00) CTUe/lb (0.00) CTUe/lb 11TRACI Water compartment 0.00 CTUe/lb 0.00 CTUe/lb 0.00 CTUe/lb 0.00 CTUe/lb CTUe/sqft 0.08 CTUe/lb 0.09 CTUe/lb 0.20 CTUe/lb 0.50 CTUe/lb (0.08) CTUe/lb (0.09) CTUe/lb (0.20) CTUe/lb (0.50) CTUe/lb (0.08) CTUe/lb (0.09) CTUe/lb (0.20) CTUe/lb (0.50) CTUe/lb 12TRACI Soil compartment 0.00 CTUe/lb 0.00 CTUe/lb 0.00 CTUe/lb 0.00 CTUe/lb CTUe/sqft 0.02 CTUe/lb 0.01 CTUe/lb 0.04 CTUe/lb 0.04 CTUe/lb (0.02) CTUe/lb (0.01) CTUe/lb (0.04) CTUe/lb (0.04) CTUe/lb (0.02) CTUe/lb (0.01) CTUe/lb (0.04) CTUe/lb (0.04) CTUe/lb 13Soil organic carbon kgC/kg/yearkgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/year kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/ye a kgC/kg/year 14Employment hours hr/lb hr/lb hr/lb hr/lb hr/sqft hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb hr/lb 15Laborer rate vegetable $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr $/hr 16Laborer pay vegetable $/lb $/lb $/lb $/lb $/sqft $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb 17Laborer pay turf eq. $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb $/lb NOTES Turf pesticide and fertilizer applications taken from: Pesticide Usage on Turf of West Virginia Golf Courses and Lawn Care Busi nesses. 1995. West Virginia University Extension Service. Turf water use 2 waterings per week for 9.8 mm/ea in May, Jun, Sep, Oct; 39 mm/ea Jul, Aug. Adapted from: Denver Water. 2011 Sustainable Landscape Conversion Design and irrigation recommendations for converting bluegrass turf to sustainable low wa ter usage landscapes. August 31. Value of wages determined by WWOOF guidelines and 48 hours/wk. WWOOF experience discounts value of labor to account for the le arning experience. Value of room and board will be less than value of labor if labor is above minimum wage. Therefore, minimum wage (Denver, CO $8.00/hr) was used. Adapted from: World Wide Opportunities on Organic Farms (WWOOF). 2013. Host Guidelines. November 6. Accessed online at: www.wwoof.ca Loss of edible harvest from farm gate to consumer taken into account for commercial farm produce. No losses reported for urban production. Farmyard potato water use was an outlier. GardenWeb was used to get per plant water use. This manipulated value resulted in a value close to Brett potato use. Adapted from: http://www.backyardgardener.com/veg/VEGETABLE/growingpotatoes.htm Conversion from N2O reported as N multiplied by 477 to get CO2e Conversion from CO2 reported as C multiplied by 3.66 to get CO2e Conversion from CH4 reported as C multiplied by 45.33 to get CO2e Emission factors provided by PR Consultants. 2013. SimaPro Life Cycle Analysis version 7.3 (software).

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APPENDIX J IMPACTS GRAPHS

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EnergyFunctional Unit 1 lb vegetable to consumer0 2 4 6 8 10 12 14U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oMJ/lb-6 -4 -2 0 2 4 6 8 10 12MJ/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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Land UseFunctional Unit 1 lb vegetable to consumer0 0.5 1 1.5 2 2.5 3 3.5 4U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oft2/lb-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3ft2/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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WaterFunctional Unit 1 lb vegetable to consumer0 5 10 15 20 25 30 35 40 45 50U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t ogallons/lb-40 -30 -20 -10 0 10 20gallons/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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Global Warming PotentialFunctional Unit 1 lb vegetable to consumer0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oCO2e/lb-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6CO2e/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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Carbon DioxideFunctional Unit 1 lb vegetable to consumer0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oCO2/lb-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8CO2/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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Nitrous OxideFunctional Unit 1 lb vegetable to consumer-0.15 -0.1 -0.05 0 0.05 0.1 0.15U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oN2O as CO2e/lb-0.25 -0.2 -0.15 -0.1 -0.05 0N2O as CO2e/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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MethaneFunctional Unit 1 lb vegetable to consumer-0.01 0 0.01 0.02 0.03 0.04 0.05U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oCH4 as CO2e/lb-0.02 -0.01 0 0.01 0.02 0.03 0.04CH4 as CO2e/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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TRACI Human Health CarcinogensFunctional Unit 1 lb vegetable to consumer0.0E+00 1.0E-09 2.0E-09 3.0E-09 4.0E-09 5.0E-09 6.0E-09 7.0E-09 8.0E-09 9.0E-09 1.0E-08U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oCTUh/lb-1.0E-08 -8.0E-09 -6.0E-09 -4.0E-09 -2.0E-09 0.0E+00 2.0E-09 4.0E-09 6.0E-09CTUh/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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TRACI Human Health Non-carcinogensFunctional Unit 1 lb vegetable to consumer0.0E+00 1.0E-08 2.0E-08 3.0E-08 4.0E-08 5.0E-08 6.0E-08 7.0E-08 8.0E-08U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oCTUh/lb-1.2E-07 -1.0E-07 -8.0E-08 -6.0E-08 -4.0E-08 -2.0E-08 0.0E+00 2.0E-08 4.0E-08CTUh/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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TRACI Ecological Releases to AirFunctional Unit 1 lb vegetable to consumer0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oCTUe/lb-0.07 -0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03CTUe/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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TRACI Ecological Releases to WaterFunctional Unit 1 lb vegetable to consumer0 0.1 0.2 0.3 0.4 0.5 0.6 0.7U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oCTUe/lb-0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2CTUe/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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TRACI Ecological Releases to SoilFunctional Unit 1 lb vegetable to consumer-0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oCTUe/lb-0.14 -0.12 -0.1 -0.08 -0.06 -0.04 -0.02 0CTUe/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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Soil Organic CarbonFunctional Unit 1 lb vegetable to consumer-5.0E-05 0.0E+00 5.0E-05 1.0E-04 1.5E-04 2.0E-04 2.5E-04 3.0E-04 3.5E-04U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oChange in kg C / kg soil per year-6.0E-04 -5.0E-04 -4.0E-04 -3.0E-04 -2.0E-04 -1.0E-04 0.0E+00Change in kg C / kg soil per year Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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Labor HoursFunctional Unit 1 lb vegetable to consumer0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t ohours/lb0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18hours/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).

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Labor PayFunctional Unit 1 lb vegetable to consumer$0.00 $0.20 $0.40 $0.60 $0.80 $1.00 $1.20 $1.40U r b a n P o t a t o U r b a n C a r r o t U r b a n O n i o n U r b a n T o m a t o U r b a n T u r f ( p e r e q s q f t ) C o m m e r c i a l P o t a t o C o m m e r c i a l Ca r r o t C o m m e r c i a l O n i o n C o m m e r c i a l T o m a t o 2 – 1 P o t a t o 2 – 1 C a r r o t 2 – 1 O n i o n 2 – 1 T o m a t o 3 – 1 P o t a t o 3 – 1 C a r r o t 3 – 1 O n i o n 3 – 1 T o m a t oWages $/lb$0.00 $0.20 $0.40 $0.60 $0.80 $1.00 $1.20 $1.40Wages $/lb Production (Direct) Production (Indirect) Post-Production Production (Direct) Production (Indirect) Post-production use y-axis to right use y-axis to leftLegend Explanation 2 – 1 Conversions from degraded urban land to urban garden ( 2) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1). 3 – 1 Conversions from urban turf to urban garden ( 3) displace demand on large commercial farms. The displacement results in commercial farmland to be left fallow ( 1).