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Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
Update of Soybean Life Cycle Analysis Final Report
Prepared for: United Soybean Board
Prepared by: Melissa Zgola, Jürgen Reinhard, Xun Liao, Gregory Simonnin,
Simon Gmuender, and Jon Dettling, Quantis
Catherine Benoit Norris, New Earth
Julie Parent and Jean-Michel Couture, Groupe AGÉCO
August 2016
LAUSANNE – PARIS – BOSTON – ZURICH - BERLIN | www.quantis-intl.com
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page i
Quantis (main contractor) is a leading life cycle assessment (LCA) consulting firm specialized in
supporting companies to measure, understand and manage the environmental impacts of their
products, services and operations. Quantis is a global company of 60 people with offices in the
United States, Canada, Switzerland, France and Germany. Quantis offers cutting-edge services in
environmental footprinting (multiple indicators including carbon and water), eco-design,
sustainable supply chains and environmental communication. Quantis’ team applies its knowledge
and expertise to accompany clients in transforming their sustainability metrics into decisions and
action plans. More information can be found at www.quantis-intl.com. This report has been
prepared by the United States office of Quantis. Please direct all questions regarding this report to
Quantis USA. [email protected]
New Earth (subcontractor) is a non-profit organization with a 12-year history in developing and
delivering software, databases, and assessment methods to improve the social responsibility
impacts of businesses, other organizations, and individuals. They have been at the forefront of
Social LCA methodology development, leading the development process and the publication of the
UNEP SETAC Social LCA Guidelines and Methodological sheets. New Earth offers the first
comprehensive database for Social LCA, the Social Hotspots Database (www.socialhotspot.org). A
database used by companies, universities, NGOs and governments worldwide. Time Magazine
recognized New Earth project “Handprinter” as one of ten projects that can change the World in
2012. New Earth is leading the development of methodologies to quantify the positive impacts that
organizations and individuals can have on people and on the planet. New Earth’s principals teach
Life Cycle Assessment and social responsibility in supply chains at Harvard.
Groupe AGÉCO (subcontractor) was created in 2000 as a spin-off from Université Laval in Québec
(Québec), Canada by a group of researchers with recognized expertise in socioeconomic analysis
applied to the agri-food sector, natural resources and the environment. AGÉCO specializes in agri-
food sector assessment. Its team of professionals has a broad range of expertise in all aspects of the
industry from the economic, political, environmental and social perspectives. AGÉCO provides in-
depth agricultural knowledge, ensuring that results will be contextualized and tailored to each
project. AGÉCO also has unique know-how of corporate social responsibility (CSR) and social LCA
applied to the agri-food sector. Through a strategic partnership with the CIRAIG research centre
and collaborations with international organizations such as the FAO for the development CSR
assessment tools, AGÉCO relies on the most advanced frameworks to assess socioeconomic
performance in the agri-food sector.
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
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PROJECT INFORMATION
Project title Update of Soybean Life Cycle Analysis
Contracting organization
United Soybean Board
Liability statement
Information contained in this report has been compiled from and/or
computed from sources believed to be credible. Application of the data is
strictly at the discretion and the responsibility of the reader. Quantis is not
liable for any loss or damage arising from the use of the information in this
document.
Version Final Report
Project team
Jon Dettling, Quality Control ([email protected])
Melissa Zgola, Project Manager ([email protected])
Jürgen Reinhard ([email protected])
Simon Gmünder ([email protected])
Gregory Simonnin ([email protected])
Catherine Benoit ([email protected])
Jean-Michel Couture ([email protected])
Julie Parent ([email protected])
Client contacts Josiah McClellan ([email protected])
External reviewers Greg Thoma ([email protected])
Associated files
This report is associated with the following electronic files:
Quantis_USB_SoybeanLCA_FinalReport_20160831
Quantis_USB_StateSpecificDatasetGenerationProcedure_final
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page iii
Executive Summary
Context and objectives
United Soybean Board (USB) has commissioned Quantis, New Earth and AGÉCO to produce current
datasets and analyses to support an accurate representation of the environmental and socio-
economic impacts of US soy products. This study has aimed to update and enhance USB’s 2010 life
cycle assessment (LCA) with the most up-to-date farming and production data and impact
assessment methods. The new data include life cycle inventory and environmental impact
assessment results for four products:
• Soybeans, US-average
• Soy meal, US-average
• Crude soybean oil, US-average
• Refined soybean oil, US-average
The environmental life cycle analyses (ELCA) carried out for this project comply with the
International Organization for Standardization (ISO) 14040 and 14044 standards for public
disclosure, including a peer review by an independent panel.
The specific goals of this study are to:
Support communication of sustainability information on soy and soy products to a wide
range of audiences, including to major purchasers of soy, consumer products companies,
biotechnology companies, retailers, governments, NGOs and others.
Provide useful information for a variety of experts in the sustainability fields, including LCA
practitioners and sustainability managers interested in understanding the environmental,
social, and economic impact of soy products.
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Methodology
The functional units for this study are the following:
FUs Environmental
Social (same value of
1kg translated to US
dollars 2002)
a)
1 kg output fresh soybeans, dried to 12% moisture,
ready to be shipped from the farm, unpackaged, at farm
exit gate (average of years 2011-2013)
0.32 USD by kg
b) 1 kg output soymeal, at plant exit gate, 2014 0.17 USD by kg
c) 1 kg output crude soybean oil, at plant exit gate, 2014 0.34 USD by kg
d) 1 kg refined soybean oil, at plant exit gate, 2014 Not included
Three key processes and products were outlined for this study:
1. Soybean agriculture, which yields soybeans (US average)
2. Soybean crushing and degumming, which yields soybean crude oil and soybean meal (US
average)
3. Soybean refinement, which yields soybean refined oil (US average).
Data collection
Activity data to support the development of the soybean agriculture dataset have been sourced
largely in alignment with the publicly available World Food Life Cycle Database Project (WFLDB)
(Nemecek et al. 2015). The development of the soybean crude oil, soy meal, and soybean refined oil
have been compiled using existing soybean LCA data as well as industry expert opinion (OmniTech
2010, SCLCI 2010, NREL 2008, NOPA 2014).
All environmental life cycle inventory data are drawn from the Ecoinvent database v3.1 (SCLCI
2010).
The peer-reviewed impact assessment method IMPACT 2002+ vQ2.21 is used for the
environmental impact assessment phase of the study, evaluating the impact on Human health,
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Ecosystem quality, Resources, Climate change and also reporting Water withdrawal inventory in m3
(Humbert et al. 2012).
Environmental Results
Soybean agriculture
The Human health indicator is dominated by direct combustion emissions due to machine use and
by field emissions caused by fertilizer application. The Ecosystem quality indicator is mainly
affected by land occupation and the related pressure on biodiversity. The Resource depletion
indicator is related to the energy consumption of machinery, irrigation, fertilizer production and
soybean drying. Also the Global warming potential (i.e., Climate change indicator) correlates
with the consumption of fossil energy (machine use, irrigation, fertilizer production and soybean
drying). However, more than half of the impact is caused by dinitrogen monoxide (N2O-) emissions.
Water withdrawal is mainly related to irrigation.
Soybean crude oil and soybean meal
Soybean cultivation is the main contributor to impact (>61%) across all damage categories. In
addition, the heat used for milling is significantly contributing to Human health impacts (12%),
Resource depletion (28%) and Climate change (19%).
Soybean refined oil
Soybean cultivation and oil milling are mainly contributing to the impact of all damage categories
(>96%), while the impacts of oil refining are insignificant.
Conclusions
The environmental impacts of US-average soybean cultivation as defined and scoped in this project
are driven by a handful of activities:
Human health is driven by the dinitrogen monoxide and particulate matter emitted to
the air from farm machinery fuel combustion, as well as heavy metal emissions to soil
from cadmium and zinc and ammonia emissions to air due to field application of
fertilizer.
Ecosystem quality is driven almost entirely by the occupation of arable land due to the
cultivation of soybeans.
Resource depletion is driven largely by the extraction of fuel required to power farm
machinery.
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Climate change is driven heavily by dinitrogen monoxide emissions to air from field
application of fertilizers. Such emissions are however highly site-specific and dependent
on soil type, weather, and timing of fertilizer application. Climate change is driven to a
lesser extent by emissions of carbon dioxide to air from the combustion of fuels used by
farm machinery.
Water withdrawal is driven by irrigation water used to cultivate the soybeans.
The results for the co-products crude oil and soybean meal are highly dependent on the choice of
allocation metric and the use of economic allocation can cause the crude oil results to increase by
293% and those for soybean meal to decrease by 50%.
Due to the high relative contributions of the following soybean cultivation activities, farmers and
their value chains can lighten the environmental footprint of soybeans by doing the following:
More efficiently run farm machine equipment to reduce emissions of NOx and PM to air
More efficiently use irrigation water to reduce demands on water withdrawal
Minimize application of fertilizers to fields to reduce emissions of heavy metals to soil
and water and to reduce N2O emissions to air
It is important to note that, rather than direct measurements of real impacts, the results presented
in this study estimate relative, potential impacts and that results and conclusions should be
considered applicable only within the scope of the study.
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
Contents
Executive Summary ............................................................................................................................................................... iii
Contents ..................................................................................................................................................................................... vii
List of Figures ............................................................................................................................................................................. x
List of Tables ............................................................................................................................................................................. xi
Abbreviations and Acronyms .......................................................................................................................................... xiv
1 Introduction ................................................................................................................................................................ 1
2 Goal of the study ........................................................................................................................................................ 2
2.1 Objectives .................................................................................................................................................................... 2
2.2 Intended audiences ................................................................................................................................................. 3
2.3 Disclosures and declarations .............................................................................................................................. 3
3 Scope of the study ..................................................................................................................................................... 3
3.1 General description of the products studied ................................................................................................ 3
3.1.1 Functions and functional unit ................................................................................................................ 4
3.1.2 Reference Flows .......................................................................................................................................... 4
3.2 System boundaries .................................................................................................................................................. 5
3.2.1 General system description .................................................................................................................... 5
3.2.2 Temporal and geographic boundaries ............................................................................................. 10
3.2.3 Cut-off criteria ............................................................................................................................................ 10
4 Approach .................................................................................................................................................................... 11
4.1 Allocation methodology ...................................................................................................................................... 11
4.1.1 Ecoinvent and USLCI processes with allocation .......................................................................... 11
4.1.2 Recycled content and end-of-life recycling .................................................................................... 12
4.1.3 Transport ..................................................................................................................................................... 12
4.2 Life cycle inventory ............................................................................................................................................... 12
4.2.1 Data sources and assumptions for the E-LCA ............................................................................... 12
4.2.2 Data quality requirements and assessment method ................................................................. 14
4.3 Impact Assessment ................................................................................................................................................ 15
4.3.1 Impact assessment method and indicators ................................................................................... 15
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4.4 Calculation tool ....................................................................................................................................................... 17
4.5 Contribution analysis ........................................................................................................................................... 17
4.6 Sensitivity analyses ............................................................................................................................................... 17
4.7 Uncertainty analysis ............................................................................................................................................. 18
4.8 Critical Review ........................................................................................................................................................ 18
5 Results ......................................................................................................................................................................... 18
5.1 Environmental Life Cycle Impact Assessment ........................................................................................... 18
5.1.1 Soybean cultivation ................................................................................................................................. 18
5.1.2 Crude soybean oil and soybean meal production ....................................................................... 23
5.1.3 Refined soybean oil production .......................................................................................................... 26
5.2 Inventory data quality assessment ................................................................................................................. 31
5.3 Sensitivity Analysis ............................................................................................................................................... 32
5.3.1 Sensitivity of results to impact method ........................................................................................... 32
5.3.2 Sensitivity of results to allocation metric ....................................................................................... 32
5.3.3 Sensitivity of results to indirect land use change of soybean cultivation ......................... 33
5.3.4 Other potential sensitivity tests ......................................................................................................... 34
6 Discussion and implications ............................................................................................................................... 36
6.1 Key findings .............................................................................................................................................................. 36
6.2 Study considerations ............................................................................................................................................ 38
6.3 Recommendations ................................................................................................................................................. 39
6.3.1 Improve and benchmark the environmental and social analysis ......................................... 39
6.3.2 Promote the adoption of best management practices .............................................................. 39
6.4 Conclusion ................................................................................................................................................................. 40
7 References .................................................................................................................................................................. 42
8 Appendix A1. Soybean Agriculture System information, data sources, and assumptions ....... 47
8.1 Introduction ............................................................................................................................................................. 47
8.2 System Characterization ..................................................................................................................................... 47
8.3 Yields ........................................................................................................................................................................... 48
8.4 Inputs from technosphere .................................................................................................................................. 48
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8.4.1 Mineral fertilizer ....................................................................................................................................... 48
8.4.2 Organic fertilizer ....................................................................................................................................... 50
8.4.3 Pesticides ..................................................................................................................................................... 51
8.4.4 Seed ................................................................................................................................................................ 54
8.4.5 Energy use: field operations, irrigation and drying ................................................................... 54
8.4.6 Transportation ........................................................................................................................................... 57
8.5 Inputs from nature ................................................................................................................................................ 57
8.5.1 Land use........................................................................................................................................................ 57
8.5.2 Carbon loss from soil after land transformation ......................................................................... 58
8.5.3 Energy use ................................................................................................................................................... 59
8.5.4 Carbon-dioxide uptake ........................................................................................................................... 59
8.6 Emissions .................................................................................................................................................................. 59
8.6.1 Emissions to air ......................................................................................................................................... 59
8.6.2 Emissions into water ............................................................................................................................... 60
8.6.3 Heavy metal emissions ........................................................................................................................... 61
8.6.4 Pesticide emissions .................................................................................................................................. 62
8.7 Overall soybean inventory ................................................................................................................................. 64
9 Appendix A2. Soybean Crushing and Degumming System information, data sources, and
assumptions ............................................................................................................................................................................. 68
Inputs and Outputs .......................................................................................................................................................... 68
Inventory .............................................................................................................................................................................. 70
10 Appendix A3. Soybean Oil Refinement System information, data sources, and assumptions 73
Inputs and outputs ........................................................................................................................................................... 73
Inventory .............................................................................................................................................................................. 75
11 Appendix B. Description of impact categories ............................................................................................ 77
12 Appendix C. Results of the Data Quality Assessment ............................................................................... 79
13 Appendix D. Results of the LCIA and Contribution analysis.................................................................. 89
14 Appendix E. Results of sensitivity analyses.................................................................................................. 95
15 Appendix F. Critical Review ............................................................................................................................. 107
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List of Figures
Figure 1. Schematic of the systems under evaluation .............................................................................................. 6
Figure 2. System boundary framework for soybean agriculture unit process (adapted from
Nemecek et al. 2015) .............................................................................................................................................................. 7
Figure 3: Proposed process flow chart for the cultivation of soybeans. ........................................................... 8
Figure 4. System boundary framework for food processing systems such as soybean crushing,
degumming and refining (adapted from Nemecek et al. 2015) ........................................................................... 9
Figure 5: IMPACT 2002+ vQ2.2 midpoint and endpoint categories (dashed lines indicate links
between midpoint and endpoint indicators currently not existing, but in development) ..................... 16
Figure 6. Relative contribution of midpoint impacts to damage categories of average soybean
cultivation in US (IMPACT 2002+ v2.21). .................................................................................................................... 20
Figure 7. Relative contribution of midpoints to damage categories of average soy bean milling in US
(IMPACT 2002+ v2.21)........................................................................................................................................................ 25
Figure 8. Relative contribution of midpoints to damage categories of average soybean oil
refinement in US (IMPACT 2002+ v2.21). ................................................................................................................... 28
Figure 9: Relative contribution of the soybean cultivation (green), oil milling (blue) and refining
process (orange) based on the IMPACT 2002+ v2.21 method (I) and the TRACI v2.1 US 2008
method (T). .............................................................................................................................................................................. 32
Figure 10. Summary of hotspots in soybean cultivation, US average (IMPACT 2002+ v2.21).............. 37
Figure 11. Letter of ISO 14044 conformance .......................................................................................................... 126
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August 2016 Page xi
List of Tables
Table 1. Capital equipment assumptions ..................................................................................................................... 10
Table 2. Pedigree matrix used data quality assessment, derived from Weidema and Wesnaes (1996)
....................................................................................................................................................................................................... 14
Table 3: IMPACT 2002+ v2.21 endpoint results of soybean cultivation, average US. .............................. 20
Table 4: IMPACT 2002+ v2.21 midpoint results of soybean cultivation, average US. .............................. 22
Table 5. IMPACT 2002+ v2.21 endpoint results of crude soybean oil and soybean meal, average US.
....................................................................................................................................................................................................... 24
Table 6. IMPACT 2002+ v2.21 midpoint results of soybean crushing and degumming, average US. 26
Table 7. IMPACT 2002+ v2.21 endpoint results of refined soybean oil, average US. ................................ 27
Table 8. IMPACT 2002+ v2.21, midpoint results of soybean oil refinement, average US. ...................... 29
Table 9. Sensitivity of crude oil and soybean meal results to an economic rather than mass
allocation ................................................................................................................................................................................... 33
Table 10: Properties of soybeans used in this study. ............................................................................................. 47
Table 11: Area planted, production volume, and yield related to soybean cultivation in the US. ....... 48
Table 12: Mineral fertilizer application rate in US soybean cultivation for a specific (actually
fertilized) and an average hectare .................................................................................................................................. 49
Table 13: Fertilizer use by fertilizer product in the US.......................................................................................... 49
Table 14: Product specific mineral fertilizer application rate associated with the cultivation of one
ha soybean in the US. ........................................................................................................................................................... 50
Table 15: Manure application rate in US soybean cultivation for a specific (actually fertilized)and
an average hectare. ............................................................................................................................................................... 50
Table 16: Soybean specific manure use by type. ...................................................................................................... 51
Table 17: Pesticides used in soybean cultivation and the dataset (compound class) used for its
representation. ....................................................................................................................................................................... 51
Table 18: Pesticide application associated with the cultivation of one average hectare soybean in
the US in 2012. ........................................................................................................................................................................ 53
Table 19: Energy use data (Duffield 2015). ................................................................................................................ 54
Table 20: Comparison of original (USB 2015) and revised (Duffield 2015) energy use data. All
inventory flows refer to the cultivation of one hectare of soybean, i.e. the production of 2,662 kg
soybean. ..................................................................................................................................................................................... 55
Table 21: Irrigation application rate associated with the cultivation of one hectare soybean in the
US. ................................................................................................................................................................................................ 55
Table 22: Energy used for irrigation (USDA). ............................................................................................................ 56
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Table 23: Amount of water evaporated during the drying process associated with the cultivation of
one hectare soybean US ...................................................................................................................................................... 56
Table 24: Transport service requirements of raw materials and intermediate inputs used in the
cultivation of one hectare soybeans in the US ........................................................................................................... 57
Table 25: Amount of land transformation and land occupation associated with the average hectare
soybean in the US. ................................................................................................................................................................. 57
Table 26: Energy in biomass related to the cultivation of one hectare soybean ........................................ 59
Table 27: Emission to air associated with the cultivation of one ha soybean in the US........................... 59
Table 28: Emissions to water associated with the cultivation of one ha soybean in the US. ................ 60
Table 29: Heavy metal emissions related to the cultivation of one hectare soybean ............................... 61
Table 30: Pesticide emission related to the cultivation of one hectare soybean. ....................................... 62
Table 31: Inventory for soybean production in the US in 2012 ......................................................................... 64
Table 32: Inventory for soybean crude oil and soybean meal in the US......................................................... 70
Table 33: Inventory for soybean refined oil and soybean meal in the US ..................................................... 75
Table 34: Data quality assessment for soybean cultivation, for all relevant data categories ................ 80
Table 35: Data quality assessment for soybean crude oil and soybean meal, for all relevant data
categories .................................................................................................................................................................................. 82
Table 36: Data quality assessment for soybean refined oil, for all relevant data categories ................. 86
Table 37: IMPACT 2002+ v2.21 endpoint results of soybean cultivation, average US ............................. 89
Table 38: Absolute contribution of midpoint impacts to damage categories of average soybean
cultivation in US (IMPACT 2002+ v2.21) ..................................................................................................................... 90
Table 39. IMPACT 2002+ v2.21 midpoint results of soybean cultivation, average US ............................. 91
Table 40. IMPACT 2002+ v2.21 endpoint results of crude soybean oil, soybean meal, and refined
soybean oil, average US ....................................................................................................................................................... 92
Table 41. Relative contribution of midpoints to damage categories of average soybean milling
(including soybean cultivation) in US (IMPACT 2002+ v2.21) ........................................................................... 93
Table 42. IMPACT 2002+ v2.21 midpoint results of crude soybean oil, soybean meal, and refined
soybean oil, average US ....................................................................................................................................................... 94
Table 43. TRACI v2.1 US 2008 midpoint results of soybean cultivation, average US ............................... 95
Table 44. TRACI v2.1 US 2008 midpoint results (absolute values) of soybean cultivation, average US
(per kg soybean). The corresponding Impact 2002+ midpoint results of soybean cultivation are
provided in Table 52. ........................................................................................................................................................... 97
Table 45. TRACI v2.1 US 2008 midpoint results of soybean crushing and degumming, average US . 98
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page xiii
Table 46. TRACI v2.1 US 2008 midpoint results (absolute values) of soybean crude oil production,
average US (per kg crude oil). The corresponding Impact 2002+ midpoint results of soybean crude
oil production are provided in Table 55. .................................................................................................................. 100
Table 47. TRACI v2.1 US 2008 midpoint results of soybean oil refinement, average US ..................... 101
Table 48. TRACI v2.1 US 2008 midpoint results (absolute values) of soybean oil refinement, average
US (per kg soybean). The corresponding Impact 2002+ midpoint results of soybean crude oil
production are provided in Table 55. ........................................................................................................................ 103
Table 49. Consideration of pesticide and heavy metal emissions by Impact 2002+ v2.2 and TRACI
v2.1 US 2008 ......................................................................................................................................................................... 104
Table 50. ISO-14044 compliance checklist .............................................................................................................. 107
Table 51. Review comments and responses............................................................................................................ 111
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
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Abbreviations and Acronyms
BMP Beneficial Management Practices N2O Dinitrogen monoxide,
BOA Bill of Activities NH3 Ammonia
CH4 Methane NOPA National Oilseed Processors Association
CO2 Carbon Dioxide NOx Nitrogen oxides
CSR Corporate Social Responsibility PDF Potentially Disappeared Fraction
CSS Country-Specific Sector PDF*m²*y
Potentially Disappeared Fraction per Square Meter of land per Year
DALY Disability Adjusted Life Years PM Particulate Matter
dLUC Direct Land Use Change PO4 3- Phosphate
eq. Equivalents
FU Functional Unit SCLCI Swiss Center for Life Cycle Inventories
HME Heavy Metal Emissions SETAC Society of Environmental Toxicology and Chemistry
iLUC Indirect Land Use Change SSAP Soybean Sustainability Assurance Protocol
IPCC Intergovernmental Panel on Climate Change
SHDB Social Hotspots Database
ISO International Organization for Standardization
S-LCA Social Life Cycle Assessment
kg Kilogram = 1,000 grams (g) = 2.2 pounds SO2 Sulfur dioxide
kg CO2-eq kilograms of carbon dioxide equivalents SOC Soil Organic Carbon
km Kilometer = 1,000 meters (m) UNEP United Nations Environment Programme
kWh Kilowatt-hour = 3,600,000 joules (j) US USB
United States United Soybean Board
lb. Pound USD US Dollar
LCA Life Cycle Assessment USDA United States Department of Agriculture
LCI Life Cycle Inventory USEPA United States Environmental Protection Agency
LCIA Life Cycle Impact Assessment USLCI United States Life Cycle Inventory Database (SCLCI)
LUC Land Use Change WFLDB World Food Life Cycle Assessment Database
m3 Cubic meter
MJ Megajoule = 1,000,000 joules, (948 Btu)
NA North American
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 1
1 Introduction
Heightened concern around the environmental and social sustainability of society’s consumption
habits has focused attention on understanding and proactively managing the potential
environmental and societal consequences of production and consumption of products and services.
Nearly all major product producers now consider environmental and social impacts as a key
decision point in material selection, and sustainability is a recognized point of competition in many
industries, including agriculture.
A leading tool for assessing environmental performance is life cycle assessment (LCA), a method
defined by the International Organization for Standardization (ISO) 14040-14044 standards (ISO
2006a; ISO 2006b). LCA is an internationally-recognized approach that evaluates the relative
potential environmental and human health impacts of products and services throughout their life
cycle, beginning with raw material extraction and including all aspects of transportation,
manufacturing, use, and end-of-life treatment.
Since environmental performance is only one aspect to consider in regards to sustainability, LCA
can also be used to account for products’ and organizations’ socio-economic performance. Social life
cycle assessment (S-LCA) focuses on organizations’ behavior and on their interactions with their
stakeholders, such as their workers, communities, business partners, etc. S-LCA is a tool based on
the United Nations Environment Program (UNEP)/ Society of Environmental Toxicology and
Chemistry’s (SETAC) Guidelines for social life cycle assessment of products published in 2009
which in turn were based on ISO 14040-14044.
Among other uses, LCA can identify opportunities to improve the environmental and social
performance of products, inform decision-making, and support marketing, communication, and
educational efforts. The importance of the life cycle view in sustainability decision-making is
sufficiently strong that over the past several decades it has become the principal approach to
evaluate a broad view of environmental problems, identify social risks and to help make decisions
within the complex arena of socio-environmental sustainability. Moreover, LCA has become a
standard within the agri-food sector for measuring and communicating on sustainability.
United States (US) soy producers have already taken the opportunity to communicate positive
sustainability and conservation messages regarding their 75 year history of widespread agriculture
conservation programs and captured in their U.S. Soy Sustainability Assurance Protocol.
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 2 August 2016
The United Soybean Board (USB) recognizes the value in taking a life cycle approach to
understanding the environmental, social, and economic impacts and benefits of soybeans and
soybean products.
This research project has produced new updated datasets to support an accurate representation of
the environmental and socio-economic impacts of US soy products for assessments made both by
the soy industry, as well as a variety of external stakeholders.
USB commissioned Quantis to carry out this assessment. Quantis subcontracted New Earth to
perform the Social Hotspot assessment and AGECO to conduct the Specific Analysis, i.e., two
components of the S-LCA methodology. The environmental, economic and social assessments were
integrated together into this report under the appropriate sections (goal and scope, data collection,
LCIA). Direct involvement of New Earth in the project provides the best possible expertise in the
application of the Social Hotspot Database.
It is important to note that, rather than direct measurements of real impacts, LCA estimates
relative, potential impacts. This LCA is intended to conform to the ISO 14040 and 14044 standards
(ISO 2006a; ISO 2006b) for public disclosure of results, including an independent peer review. It is
not intended to be used directly as the primary basis for comparative statements of the
environmental benefits of products.
2 Goal of the study
2.1 Objectives
This study has aimed to update and enhance USB’s 2010 LCA on soybean agriculture and
processing with the most up-to-date farming and production data and impact assessment methods,
providing key supporting data for use by both USB and others. This includes life cycle inventory
(LCI) and environmental impact assessment results for four products:
• Soybeans, US-average
• Soy meal, US-average
• Crude soybean oil, US-average
• Refined soybean oil, US-average
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 3
The specific goals of this study are to:
Support communication of sustainability information on soy and soy products to a wide
range of audiences, including to major purchasers of soy, consumer products companies,
biotechnology companies, retailers, governments, NGOs and others.
Provide useful information for a variety of experts in the sustainability fields, including LCA
practitioners and sustainability managers interested in understanding the environmental,
social, and economic impact of soy products.
2.2 Intended audiences
The project report is intended to support USB’s communication of the social and environmental
performance of these products to internal and external audiences, which could include partners,
suppliers, customers, and the public.
2.3 Disclosures and declarations
USB seeks to quantify and communicate the life cycle impacts of soy and soy products in a US
context, using publicly available data and methodologies. The project is intended to conform to the
ISO 14040 and 14044 standards for public disclosure. The results of this study are not intended to
be the primary basis of comparative claims between products.1
3 Scope of the study
This section describes the scope of the assessment. It includes the methodological framework of the
LCA, a description of the product function and product system, the system boundaries, data sources,
and methodological framework. This section also outlines the requirements for data quality as well
as review of the analysis.
3.1 General description of the products studied
In addition to the descriptions below, additional specific data pertaining to each system can be
found in Appendix A.
1 USB has also requested the development of state-level datasets for internal use. These datasets are not subject to the independent peer-review. Details about the data, assumptions and results of these state-level datasets are provided in Error! Reference source not found..
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3.1.1 Functions and functional unit
Life cycle assessment relies on a “functional unit” (FU) as a reference for evaluating the components
within a single system or among multiple systems on a common basis. It is therefore critical that
this parameter is clearly defined and measurable. Separate FUs are used in this study for each of the
products assessed and are listed below. Any potential secondary functions beyond those listed are
not addressed in this study. The soybean agriculture datasets will be temporally and spatially
explicit and therefore be indicative of a time and place where each “function” is provided.
1 kilogram (kg) output fresh soybeans, dried to 12% moisture, ready to be shipped from the
farm, unpackaged, at farm exit gate (average of years 2011-2013, as described in Appendix A1)
1 kg output soymeal, at plant exit gate, 2014
1 kg output crude soybean oil, at plant exit gate, 2014
1 kg refined soybean oil, at plant exit gate, 2014
3.1.2 Reference Flows
To fulfill the FU, the specific quantities and types of material required must be expressed. These are
known as reference flows. The main reference flows for the systems under study are the following.
A full list of reference flows associated with each system can be found in Appendix A1 through A3.
a) 1 kg output fresh soybeans, unpackaged, at farm exit gate
1 kg/2,662 kg of “soybean production” unit process (based on soybean yield of
2,662 kg soybeans per hectare (Appendix A1)
b) 1 kg output soymeal, at plant exit gate
1.10 kg of soybeans
ancillary materials
electricity and heat
emissions to air and water
wastes to treatment
c) 1 kg output crude soybean oil, at plant exit gate
0.925 kg of soybeans
ancillary materials
electricity and heat
emissions to air and water
wastes to treatment
d) 1 kg refined soybean oil, at plant exit gate
1.035 kg of crude soybean oil
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ancillary materials
electricity and heat
emissions to air and water
wastes to treatment
3.2 System boundaries
For the environmental LCA specifically, this section identifies the life cycle stages, processes, and
flows considered in the LCA and include all activities relevant to attaining the above-mentioned
study objectives. The following sections present a general description of the system as well as
temporal and geographical boundaries of this study.
On the environmental side, this project addresses cradle-to-gate impacts of soybean production
(i.e., cultivation and harvesting) as well as gate-to-gate impacts of soybean meal and soybean oil
production. As in the 2010 study (OmniTech 2010), unit processes will be developed to enable the
impact assessment of four products: soybeans, soy meal, crude soy oil, and refined soy oil.
The resulting inventories could be used for LCAs of downstream products, such as those studied in
USB’s 2010 LCA (e.g., biodiesel, soy-based oil for lubricant), although assessment of those
downstream products is not included in the present scope.
3.2.1 General system description
This study has been grouped into the following principal processes and products:
1. Soybean agriculture which yields soybeans – representing a US average.
2. Soybean crushing and degumming which yields soybean crude oil and soybean meal –
representing a US average.
3. Soybean refinement which yields soybean refined oil – representing a US average.
Within each of these groups, the LCA considers all identifiable “upstream” inputs to provide as
comprehensive a view as is practical of the product system. For example, when considering the
environmental impact of transportation, not only are the emissions of the truck or airplane
considered, but also included are the impact of additional processes and inputs needed to produce
the fuel. In this way, the production chains of all inputs are traced back to the original extraction of
raw materials.
A schematic of the project’s system boundaries is shown in Figure 1. Figure 2 shows a detailed
system boundary framework for soybean cultivation, and Figure 3 shows the proposed process
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flow chart for soybean cultivation. Figure 4 shows the detailed system boundary framework for
soybean processing.
Figure 1. Schematic of the systems under evaluation
The cut-off criteria for defining the system has been set to allow exclusion of processes that can
reasonably be assumed to contribute less than 1% mass to the system and therefore assumed to
contribute to less than 1% of the environmental and social impact when no data are available.
Whenever data are available, they are included. Exclusions for the environmental assessment
include on-farm, post-harvest processes (excluding drying to 12% moisture), production and
storage of animal manure2, packaging of output products, labor and commuting of workers,
administrative work.
2 Where manure is used as a fertilizer, the related field emissions are assigned to soybean cultivation.
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Figure 2. System boundary framework for soybean agriculture unit process (adapted from Nemecek et al. 2015)
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Figure 3: Proposed process flow chart for the cultivation of soybeans.
Grey colored boxes represent inputs from the techno-sphere. Green colored boxes represent inputs from or emissions to nature.
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Figure 4. System boundary framework for food processing systems such as soybean crushing, degumming and refining
(adapted from Nemecek et al. 2015)
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3.2.2 Temporal and geographic boundaries
The environmental LCA is representative of current soybean agriculture, crushing and degumming, and
oil refinement. Data and assumptions are intended to reflect current equipment, processes, and market
conditions. It should be noted, however, that some processes within the system boundaries might take
place anywhere or anytime. For example, the processes associated with the supply chain and with waste
management can take place in Asia, North America or elsewhere in the world. In addition, certain
processes may generate emissions over a longer period of time than the reference year. This applies, for
example, to landfilling, which causes emissions (biogas and leachate) over a period of time whose length
(several decades to over a century) depends on the design and operation parameters of the burial cells.
All emissions are represented as though they take place at the same time.
The intended temporal boundary is to represent as best as possible the soybean system at the current
time. At the time the assessment is being done, the 2014 growing season was the most recent completed
growing season.
It is possible that temporal differences in farming practices or field conditions could have significant
bearing on the results. Temporal differences could reflect changes in key variables such as yield,
herbicide and pesticide application. A multi-year average has been used to model soybean cultivation
under the assumption that smoothing over yearly variability more accurately represents the state of
soybean cultivation impacts.
With regard to the development of the other unit processes for the production of crude soybean oil,
soybean meal, and refined soybean oil, substantial modifications to these processes over time were not
expected, and temporal and technological variability was expected to be low and negligible.
3.2.3 Cut-off criteria
Processes may be excluded if their contributions to the total system’s environmental impact are expected
to be less than 1%. Unless there is a reason to assume otherwise (e.g., use of a highly hazardous
chemical), materials that are less than 1% by mass are assumed to also contribute less than 1% of the
environmental impact. Despite this criterion for allowing components to be excluded, all product
components and production processes have been included when the necessary information was readily
available or a reasonable estimate could be made. The capital equipment and infrastructure processes
from the Ecoinvent database (v. 3.1.) which have been linked directly to the foreground system of this
study are shown in Table 1 (SCLCI 2010).
Table 1. Capital equipment assumptions
Infrastructure process used
In Process Amortization time Construction
time Source
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Oil mill Soybean crushing and degumming
50 years 2 years (Jungbluth et al., 2007)
Vegetable oil refinery
Soy oil refining 50 years 2 years (Jungbluth et al., 2007)
For detailed information with regard to agricultural machinery the interested reader may refer to
(Nemecek et al. 2004).
4 Approach
4.1 Allocation methodology
A common methodological decision point in LCA occurs when the system being studied produces co-
products. When systems are linked in this manner, the boundaries of the system of interest must be
widened to include the system using all co-products, or the impacts of producing the linked product must
be distributed—or allocated—across the systems. While there is no clear scientific consensus regarding
an optimal method for handling this in all cases (Reap et al. 2008), many possible approaches have been
developed, and each may have a greater level of appropriateness in certain circumstances.
ISO 14044 prioritizes the methodologies related to applying allocation to be used to resolve multi-
functionality. It is best to avoid allocation through system subdivision or expansion when possible. If that
is not possible, then one should perform allocation using an underlying physical relationship. If allocation
using a physical relationship is not possible or does not makes sense, then one can use another
relationship.
The production of soymeal, crude soy oil and refined soy oil involve co-product relationships that require
allocation decisions. The mass allocation metric has been updated based on recommendations from the
National Oilseed Processors Association, as discussed in Section 4.2.1.1.2 and Section 4.2.1.1.3 (NOPA
2014).
4.1.1 Ecoinvent and USLCI processes with allocation
Many of the processes in the Ecoinvent and the USCLI databases also provide multiple functions, and
allocation is required to provide inventory data per function (or per process) (SCLCI 2010; NREL 2008).
This study accepts the allocation method used by these databases for those processes. It should be noted
that the background allocation methods used in these databases, such as mass or economic allocation,
may be inconsistent with the approaches used to model the foreground system. Continuation of a single
methodology into the background datasets would add substantial complexity without substantially
improving the quality of the study.
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4.1.2 Recycled content and end-of-life recycling
Recycling systems are a unique example of multi-output processes. At the same time as providing a waste
treatment service, these systems also produce valuable materials or energy for use in other applications.
Often the product systems for which the waste treatment service is provided and the system which uses
the material or energy produced are not the same, requiring an allocation to be made between these
systems. A variety of approaches are available for addressing this unique type of allocation and each
option may be more or less suitable for difference contexts.
Here, in alignment with Ecoinvent methodology, we have applied by default the “cut-off” approach to
allocating recycled content and recycling at end-of-life (Ekvall and Tillman 1997).
4.1.3 Transport
Transport or freight vehicles have both a weight capacity and a volume capacity. These are important
aspects to consider when allocating the impacts of an entire transportation journey to one product.
Vehicles transporting products with a high density (high mass-per-volume ratio) will reach their weight
capacity before reaching their volume capacity. Vehicles transporting products with a low density (low
mass-per-volume ratio) will reach their volume capacity before reaching their weight capacity. Therefore,
the density of the product is critical for determining whether to model transportation as volume-limited
or weight-limited. In this study, all transport is assumed to be weight-limited due to the high density of
soybeans. The Ecoinvent database provides road, rail and sea transportation inventory based on a
weight-limited approach.
4.2 Life cycle inventory
The LCI of the unit processes are produced when combining the bill of activities (BOA) with existing LCI
data representing each of the material and energy input to the system, as well as accounting for direct
raw material use and emissions. The quality of LCA results are dependent on the quality of data used in
the evaluation. Every effort has been made for this investigation to implement the most credible and
representative information available.
4.2.1 Data sources and assumptions for the E-LCA
4.2.1.1 Bill of Activities
4.2.1.1.1 Soybean agriculture in the US
The compilation of LCI for soybean agriculture has been done largely in alignment with the publicly
available World Food Life Cycle Database Project (WFLDB) methodology, whenever possible, and to the
extent possible with other major LCA databases, standards and initiatives globally. Per the WFLDB
guidelines, it was the goal to collect “high detail” data. Primary data were used whenever possible, and
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when unavailable, existing models were applied. The LCI includes inputs from technosphere, inputs from
nature, and direct emissions to air, water and soil. All documentation of specific models and assumptions
for inputs from technosphere, inputs from nature, and emissions to air, water and soil are described in
full detail in Appendix A1.
Data to support the development of the soybean agriculture dataset have come from a variety of sources,
including a literature review of current soybean LCI datasets for comparison and refinement. Ultimately,
the sources chosen for inclusion in the modeling were determined based on temporal and spatial
relevance and level of quality.
4.2.1.1.2 Soybean crushing and degumming in the US
The development of the soybean crude oil and soybean meal datasets highly leveraged existing databases
and literature, in conjunction with new data provided by industry partners. These data sources include
Ecoinvent v3 for soybean oil and meal, the OmniTech (2010) study on soybean processing, Fediol
research on soybean processing related to crushing and degumming, and correspondence with NOPA
with regard to updated electricity demand and allocation of crushing and degumming impacts across the
co-products of soybean crude oil, soybean meal and soy hulls (NOPA 2014). A more detailed description
of how the crude oil and meal datasets were compiled, as well as the full inventory, are available in
Appendix A2.
4.2.1.1.3 Soybean oil refining in the US
As was the case with the soybean crude oil and soybean meal dataset development, the development of
the soybean oil refining dataset highly leveraged existing databases and literature. The inventory is
available in Appendix A3.
4.2.1.2 Unit process inventory data
The LCI data were derived from the Ecoinvent v3.1 database (system model “Allocation, cut-off by
classification”) (SCLCI 2010). It should be noted that much of the data within Ecoinvent is of European
origin and produced to represent European industrial conditions and processes. The use of Ecoinvent
data to represent Asian or North American processes could therefore introduce some error in certain
areas. However, Ecoinvent is recognized as one of the most complete LCI databases available, from a
quantitative (number of included processes) and a qualitative (quality of the validation processes, data
completeness, etc.) perspective. It is believed that the credibility and transparency of this database make
it a preferable option for representing Asian and North American conditions when more specific data
sources are not available. The data’s geographic representativeness is one aspect evaluated as part of the
data quality assessment. A full list of data sources is available in Appendix A.
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4.2.1.3 Key assumptions
All data sources and assumptions are documented in Appendix A1 through A3.
4.2.2 Data quality requirements and assessment method
Foreground processes and data sources are assessed by the practitioner on the basis of time-related
coverage, geographical coverage, technology coverage, precision, completeness, representativeness,
consistency, reproducibility, reliability of data source and uncertainty of the information as prescribed in
ISO 14044. The pedigree matrix for rating inventory data appears below, with a score of one being most
favorable and a score of five being least favorable, and a complete discussion of this topic can be found in
Frischknecht, et al (2007).
Table 2. Pedigree matrix used data quality assessment, derived from Weidema and Wesnaes (1996)
Indicator score
1 2 3 4 5
Reliability
Verified data based on measurements
Verified data partly based on assumptions or non-verified data based on measurements
Non-verified data partly based on assumptions
Qualified estimate (e.g. by industrial expert)
Non-qualified estimate
Completeness
Representative data from a sufficient sample of sites over an adequate period to even out normal fluctuations
Representative data from a smaller number of sites but for adequate periods
Representative data from an adequate number of sites but from shorter periods
Representative data but from a smaller number of sites and shorter periods or incomplete data from an adequate number of sites and periods
Representativeness unknown or incomplete data from a smaller number of sites and/or from shorter periods
Temporal correlation
Less than 3 years of difference to year of study
Less than 6 years difference
Less than 10 years difference
Less than 15 years difference
Age of data unknown or more than 15 years of difference
Geographical correlation
Data from area under study
Average data from larger area in which the area under study is included
Data from area with similar production conditions
Data from area with slightly similar production conditions
Data from unknown area or area with very different production conditions
Further technological correlation
Data from enterprises, processes and materials under study
Data from processes and materials under study but from different enterprises
Data from processes and materials under study but from different technology
Data on related processes or materials but same technology
Data on related processes or materials but different technology
The data quality assessment results are included in Appendix C, which lists all life cycle processes as well
as data quality ratings for those processes that contribute to the top 80% of the four main impact
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indicators focused on in the assessment (excludes water withdrawal inventory). The importance of the
data on the total system results is discussed in Section 5.2 .
Per 14044, additional techniques can help understand the significance, uncertainty and sensitivity of the
life cycle impact assessment (LCIA) results to help distinguish if significantly different results could be
found and to guide the iterative LCIA process. The need for such techniques depends on the accuracy and
detail needed to fulfill the goal and scope of the project. One such technique is sensitivity analysis.
4.3 Impact Assessment
4.3.1 Impact assessment method and indicators
4.3.1.1 Environmental assessment
Impact assessment classifies and combines the flows of materials, energy, and emissions into and out of
each product system by the type of impact their use or release has on the environment. The method used
here to evaluate environmental impact is the peer-reviewed and internationally-recognized LCIA method
IMPACT 2002+ vQ2.21 (Humbert et al. 2012). This method assesses 17 different potential impacts
categories (midpoint)3 and aggregates these into four endpoint (damage) categories. They are presented
along with the inventory indicator for water withdrawal, which is not yet accounted for in any endpoint
category. In total, the five indicators are the following:
Climate change (in kilograms of carbon dioxide equivalents (kg CO2-eq));
Human health (in disability adjusted life-years (DALYs));
Ecosystem quality (in Potentially Disappeared Fraction per Square Meter of land per Year
(PDF*m2*y));
Resources depletion (in megajoules (MJ));
Water withdrawal (in cubic meters (m3)).
3 The Human toxicity midpoint category is divided between carcinogenic and non-carcinogenic effects, hence a total of 17 midpoint indicators.
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Figure 5: IMPACT 2002+ vQ2.2 midpoint and endpoint categories (dashed lines indicate links between midpoint and endpoint indicators currently not existing, but in development)
Detailed information about the IMPACT 2002+ vQ2.21 method and indicators is available at
http://www.quantis-intl.com/en/impact-2002, while a description of the impact categories evaluated
will be provided in the appendices.
No normalization of the results is carried out with the exception of results presented on a relative basis
(%) compared to the reference for each system. No weighting of the damage categories is done; they are
presented individually and not as a single score, as there is no objective method by which to achieve this.
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4.3.1.1.1 Limitations of Environmental LCIA
LCIA results present potential and not actual environmental impacts. They are relative expressions,
which are not intended to predict the final impact or risk or whether standards or safety margins are
exceeded. Additionally, these categories do not cover all the environmental impacts associated with
human activities. For example, impacts such as noise, odors, and electromagnetic fields are not included
in the present assessment, as the methodological developments regarding such impacts are not sufficient
to allow for their consideration within life cycle assessment.
4.4 Calculation tool
SimaPro 8.1.0 software, developed by PRé Consultants (www.pre.nl) was used to assist the LCA
modeling, link the reference flows with the LCI database, and compute the complete LCI of the systems.
The final LCI result was calculated combining foreground data (intermediate products and elementary
flows) with generic datasets providing cradle-to-gate background elementary flows to create a complete
inventory of the two systems. This process was used for the E-LCA.
4.5 Contribution analysis
A contribution analysis was performed to determine the extent to which each process modeled
contributes to the overall environmental impact of the systems under study. Lower quality data may be
suitable in the case of a process whose contribution is minimal. Similarly, processes with a great influence
on the study results should be characterized by high-quality information. In this study, the contribution
analysis is a simple observation of the relative importance of the different processes to the overall
potential impact.
4.6 Sensitivity analyses
The parameters, methodological choices and assumptions used when modeling the systems present a
certain degree of uncertainty and variability. It is important to evaluate whether the choice of
parameters, methods, and assumptions significantly influences the study’s conclusions and to what
extent the findings are dependent upon certain sets of conditions. Following the ISO 14044 standard,
sensitivity analyses are used to study the influence of the uncertainty and variability of modeling
assumptions and data on the results and conclusions, thereby evaluating their robustness and reliability.
Sensitivity analyses help in the interpretation phase to understand the uncertainty of results and identify
limitations. Sensitivity results are presented in Section 5.3.
As a sensitivity test, and to allow for comparison with past work, we provide impact assessment
results using the TRACI 2.1 V1.03 / US 2008 methodology, which has been developed under
support of US EPA to represent US conditions (USEPA 2012).
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Because most databases on crop products apply an economic allocation for oil and seed co-
products, for comparability, the results for crude oil and soybean meal were tested using an
economic allocation metric instead of a mass allocation metric.
Because indirect land use change (iLUC) is known to be a potentially large driver of impact in
agricultural systems, the relevance of iLUC for soybean cultivation was tested.
4.7 Uncertainty analysis
A quantitative uncertainty analysis was not conducted as it is only required for statements of
comparative assertion per ISO 14044. Only the data quality assessment described in Section 4.2.2 to
evaluate the uncertainty in use of inventory data has been carried out. The characterization models used
to calculate midpoint and endpoint results also introduce uncertainty; however, there is currently no way
to quantify this uncertainty in the software tools being used. Therefore, the overall uncertainties will be
necessarily underestimated due to this uncharacterized uncertainty in the characterization models.
4.8 Critical Review
The environmental aspect of the LCA was submitted to an external peer review expert to validate its
conformance with the ISO 14040/14044 standards (ISO 2006a, 2006b). This independent expert is Dr.
Greg Thoma of the University of Arkansas. The external critical review report, as well as Quantis’
comments and responses to the review report, is presented in Appendix F.
5 Results
5.1 Environmental Life Cycle Impact Assessment
5.1.1 Soybean cultivation
The environmental impacts of the average soybean cultivation in the US are provided in Table 3, and
supporting data are provided in Table 37. The impacts are calculated based on the IMPACT 2002+ v2.21
method and are expressed per kg soybean at farm gate. Furthermore, the relative contribution of
different soybean cultivation activities to the overall impact is provided.
The Human health indicator is dominated by direct combustion emissions due to machine use and by
field emissions caused by fertilizer application. The Ecosystem quality indicator is mainly affected by
land occupation and the related pressure on biodiversity. The Resource depletion indicator is related to
the energy consumption of machinery, irrigation, fertilizer production and soybean drying. Also the
Global warming potential (i.e., Climate change indicator) correlates with the consumption of fossil
energy (machine use, irrigation, fertilizer production and soybean drying). However, more than half of
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the impact is caused by dinitrogen monoxide (N2O) emissions related to nitrogen-fertilizer application.
Water withdrawal is mainly related to irrigation.
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Table 3: IMPACT 2002+ v2.21 endpoint results of soybean cultivation, average US.
The absolute impacts are provided for each endpoint category and are expressed per kg soybean at farm gate. On
the right, the relative contribution of different farming activities to each endpoint impact is provided (green: < 5%,
yellow: 5%-20%, orange: 20%-50%, red:>50%).
More details about the impact results of soybean cultivation are provided in Figure 6, where the relative
importance of each midpoint impact category is presented, and Table 4, where the relative and absolute
impacts of each midpoint category are provided. Supporting data are provided in the appendix Table 38
and Table 39.
Figure 6. Relative contribution of midpoint impacts to damage categories of average soybean cultivation in US
(IMPACT 2002+ v2.21).
Respiratory
inorganics,60.9%
Humantoxicity,
non-carcinogens,
35.3%
Humantoxicity,
carcinogens,3.7%Others,0.1%
HumanHealth
Landoccupation,
84.1%
Terrestrial
ecotoxicity,15.2%
Others,0.8%
EcosystemQuality
Non-renewableenergy,99.5%
Mineralextraction,0.5%
Resources
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The Human Health indicator is mainly affected by emissions of inorganic substances causing respiratory
diseases and by the release of toxic substances (non-carcinogenic). As illustrated in Table 4, the nitrogen
oxides (NOx) and particulate matter (PM) emissions from machine use and ammonia emissions due to
fertilizer application cause respiratory diseases. The human toxicological effects are mainly caused by
heavy metal emissions to soil (mainly by zinc and cadmium).
The Ecosystem quality indicator is mainly affected by land occupation (84%) and ecotoxicity (15%).
Occupying arable land in order to cultivate a crop hinders the regrowth of natural vegetation, which
typically shows a higher biodiversity. The eco-toxic effects are due to fertilizer application and the related
heavy metal emissions to soil (81 % of the impact). Acidification and eutrophication seem to be
insignificant.
The Resource depletion indicator is mainly driven by the extraction and combustion of non-renewable
energy sources used by machinery (49%), for fertilizer production (17%) and irrigation (14%).
The Climate change indicator is mainly caused by dinitrogen monoxide (N2O) field emissions and CO2
emissions due to combustion of fossil fuels. Even though soybean production increased over the last
decade, the CO2 emissions related to land transformation are insignificant (see Appendix A1 for details of
the inventory and the sensitivity analysis in chapter 5.5.3 for potential iLUC at international scale).
Overall, the production of 1 kg soybean potentially creates 421 g CO2-eq emissions.
Most Water withdrawal is related to irrigation (92%), while the Water withdrawal related to
background processes is marginal. Overall, 111 liters of water is withdrawn.
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Table 4: IMPACT 2002+ v2.21 midpoint results of soybean cultivation, average US.
The absolute impacts are provided for each midpoint category and are expressed per kg soybean at farm gate. On
the right, the relative contribution of different farming activities to each midpoint impact is provided (green: < 5%,
yellow: 5%-20%, orange: 20%-50%, red:>50%).
As shown and explained in Section 8.6.3, the use of WFLDB models and transfer coefficients to represent
the flow of heavy metal from soil result in the use of negative values for some heavy metals. The influence
of the heavy metals and negative flows on the baseline results is explored below.
Heavy metals released due to soybean cultivation affect Human health (33% of damage) and Ecosystem
quality (15% of damage). Heavy metals are relevant for following three midpoint impact categories of the
IMPACT 2002+ v2.21 indicators:
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Human toxicity (non-carcinogens): The impact of heavy metals is mainly related to zinc (82%)
and cadmium (10%) emissions to soil, while the negative flows are negligible (<0.1%).
Terrestrial ecotoxicity: The impact of heavy metals is mainly related to zinc (104%) emissions to
soil, while the negative chromium emissions (-11%) compensate for some of the overall impacts.
Aquatic ecotoxicity: The impact of heavy metals is mainly related to copper emissions (59%) to
water and zinc emissions (23%) to soil, while negative copper (-3%) and chromium (-2%)
emissions to soil compensate for some of the overall impacts.
5.1.2 Crude soybean oil and soybean meal production
The environmental impacts of crude soybean oil and meal production in the US are provided in Table 5
and supporting data are provided in the appendix.
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Table 40. Soybean cultivation is the main contributor to impact (>61%) across all damage categories. In
addition, the heat used for milling is significantly contributing to Human health impacts (12%), Resource
depletion (28%) and Climate change (19%).
Table 5. IMPACT 2002+ v2.21 endpoint results of crude soybean oil and soybean meal, average US.
The absolute impacts are provided for each endpoint category and are expressed per kg soybean oil and kg soybean
meal at factory gate. On the right, the relative contribution of different oil milling activities to each endpoint impact
is provided, which is the same for both products (crude oil and meal) (green: < 5%, yellow: 5%-20%, orange: 20%-
50%, red:>50%).
More details about the impact results of crude soybean oil production and soybean meal are provided in
Figure 7, where the relative importance of each midpoint impact category is presented, and Table 6,
where the relative and absolute impacts of each midpoint category are provided. Supporting data are
provided in the appendix, Table 41 and Table 42.
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Figure 7. Relative contribution of midpoints to damage categories of average soy
bean milling in US (IMPACT 2002+ v2.21)
The relative contribution pattern of crude soybean oil is similar to the one of soybean cultivation (see
Figure 6) and in the following only the impacts related to soybean crushing and degumming are
explained, while the impacts of soybean cultivation are not repeated (see previous chapter).
For Human health the category “respiratory diseases (inorganics)” is slightly more important compared
to soybean cultivation, due to combustion emissions of natural gas for heat generation (sulfur dioxide
(SO2), NOx and PM emissions). Also the carcinogenic effects are slightly higher due to aromatic
hydrocarbon emissions during heat generation. Even though the impact caused by respiratory organic
substances is relatively low (<0.3%), more than 60% of the emissions occur due to soybean crushing and
degumming (mainly non-methane volatile organic compounds).
More than 99% of the potential impact on Ecosystem quality is related to soybean cultivation (see
previous chapter).
The Resource depletion indicator is mainly driven by the extraction and combustion of non-renewable
energy sources used for soybean cultivation (61%), as well as for heating (28%) and electricity (10%)
used within the oil mill. For heating mainly natural gas is used and for the electricity impacts are
dominated by coal extraction and combustion.
The Global warming potential (Climate change indicator) of 1 kg crude soybean oil is 616 g CO2-eq and
for 1 kg soybean meal it is 519 g CO2-eq. The main impact is related to soybean cultivation, as well as to
heat (19%) and electricity 51%) consumption during oil milling.
Most water withdrawn is related to the irrigation of the soybean fields, while only 5% of the crude
soybean water impacts are related to soybean crushing and degumming (mainly related to seawater used
during fuel production).
Non-renewableenergy,99.6%
Mineralextraction,0.3%
Resources
Landoccupation,
83.8%
Terrestrialecotoxicity,15.4%
Others,0.8%
EcosystemQuality
Respiratory
inorganics,62.9%
Humantoxicity,
non-carcinogens,
30.5%
Humantoxicity,
carcinogens,6.3% Others,0.3%
HumanHealth
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Table 6. IMPACT 2002+ v2.21 midpoint results of soybean crushing and degumming, average US.
The absolute impacts are provided for each midpoint category and are expressed per kg crude soybean oil and kg
soybean meal at factory gate. On the right, the relative contribution of different oil milling activities to each
midpoint impact is provided (green: < 5%, yellow: 5%-20%, orange: 20%-50%, red:>50%).
5.1.3 Refined soybean oil production
The environmental impacts of refined soybean oil are provided in Table 7 and supporting data are
provided in the appendix Table 40.
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Soybean cultivation and oil milling are the main contributors to the impact of all damage categories (>
96%), while the impacts of oil refining are insignificant.
Table 7. IMPACT 2002+ v2.21 endpoint results of refined soybean oil, average US.
The absolute impacts are provided for each endpoint category and are expressed per kg refined soybean oil at
factory gate. On the right, the relative contribution of different refining activities to each endpoint impact is
provided (green: < 5%, yellow: 5%-20%, orange: 20%-50%, red:>50%).
More details about the impact results of refined soybean oil are provided in Figure 8, where the relative
importance of each midpoint impact category is presented, and Table 8 where the relative and absolute
impacts of each midpoint category are provided. Supporting data are provided in the appendix Table 41
and Table 42. However, also on a midpoint level, the relative contribution of the refining process is
marginal (<6%) compared to soybean cultivation and oil processing.
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Figure 8. Relative contribution of midpoints to damage categories of average soybean oil refinement in US (IMPACT
2002+ v2.21).
Non-renewableenergy,99.7%
Mineralextraction,0.3%
Resources
Landoccupation,83.8%
Terrestrial
ecotoxicity,15.4%Others,0.8%
EcosystemQuality
Respiratoryinorganics,63.1%
Humantoxicity,non-carcinogens,
30.1%
Humantoxicity,carcinogens,6.5% Others,0.3%
HumanHealth
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Table 8. IMPACT 2002+ v2.21, midpoint results of soybean oil refinement, average US.
The absolute impacts are provided for each midpoint category and are expressed per kg crude soybean oil and kg
soybean meal at factory gate. On the right, the relative contribution of different refinement activities to each
midpoint impact is provided (green: < 5%, yellow: 5%-20%, orange: 20%-50%, red:>50%).
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5.2 Inventory data quality assessment
Data quality assessment has been performed following the pedigree matrix methodology for all processes
with a contribution to total impacts higher than 1%. These include data used for all four of the developed
datasets.
The analysis shows that the overall data quality is good or adequate. For the great majority of data,
reliability, completeness, geographical and technological correlations are granted scores equal or lower
(lower is good) than 3. All details about the quality assessment of inventory data used in this study are
presented in Appendix C.
Among the key data (those that fall within the top 80% of contribution to Climate change, Human health,
Ecosystem quality, Resources, and Water withdrawal) for each of the developed datasets, three
significant issues were identified, as described here.
For the soybean cultivation dataset, some data received a score of 5 on geographic correlation
due to the use of models that were not intended for application to a US-average context. Among
these data, three points potentially fall within the top 80% of contributions:
1. Ammonia emissions to air, which may contribute up to 19% towards the Human health
indicator
2. Dinitrogen monoxide emissions to air which may contribute 53% to the Climate change
indicator
i. Although half of the impacts related to climate change result from N2O emissions,
the assumptions regarding N2O are rather conservative and the merits of
sensitivity testing are not clear.
3. Zinc and cadmium emissions to soil which may contribute 27% of potential Human health
impacts and 15% of Ecosystem quality
For the soybean meal and soybean crude oil datasets, some data received a score of 5, but none of
these data contribute to the top 80% of indicator scores.
For the soybean refined oil dataset, some data received a score of 5, but none of these “poor” data
contribute to the top 80% of indicator scores, as altogether they make up only 0.5 to 3.5% of
indicator scores.
Beyond these three issues, the key drivers of life cycle impact have data quality scores that are ranked
good or adequate. The objectives of the study are met given the data quality, and useful and meaningful
conclusions can be drawn from the results of this study.
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5.3 Sensitivity Analysis
5.3.1 Sensitivity of results to impact method
In the following the sensitivity of the results on methodological choices, namely the choice of the impact
method, is analysed. We compare the IMPACT 2002+ v2.21 midpoint indicator used in this study with
results calculated based on the TRACI 2.1 v1.03 US 2008 method. The absolute values are provided in the
appendix Table 43 through Table 48 and cannot directly be compared, due to differences in units and
characterization factors (different fate and effect models). Therefore, the relative importance of each life
cycle stage is compared (Figure 9).
Figure 9: Relative contribution of the soybean cultivation (green), oil milling (blue) and refining process (orange) based on the IMPACT 2002+ v2.21 method (I) and the TRACI v2.1 US 2008 method (T).
Only the common midpoint impact categories are compared.
As illustrated in Figure 9, the relative comparison shows that for most indicators the conclusion is
independent of the methodology selection. However, for the “Human toxicity - Carcinogenic” and
“Respiratory organics” indicators, the cultivation phase is relatively more important using the TRACI 2.1
US 2008 method. The higher impact of carcinogenic substances during the cultivation phase is caused by
the higher sensitivity of the TRACI method to heavy metal emissions, while the IMPACT 2002+ v2.21
indicator shows a higher sensitivity to aromatic hydrocarbon emissions, which are mainly released
during heat generation. A list of the pesticide and heavy metal emissions considered by each of these
methods is presented in the appendices in Table 49. The impact caused by respiratory organic substances
is caused by non-methane volatile organic compounds during soybean crushing and degumming
(IMPACT 2002+ v2.21).
5.3.2 Sensitivity of results to allocation metric
Because most databases on crop products apply an economic allocation for oil and seed co-products, for
comparability, the results for crude oil and soybean meal have been tested using an economic allocation
metric instead of a mass allocation metric. In the baseline analysis, a mass allocation metric of 21%
oil/73% meal/6% hulls was applied (NOPA 2014). For this sensitivity, an economic allocation of 61.6%
oil/36.5% meal/1.84% hulls was applied based on approximate prices of 0.34 USD/pound of oil, 330
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
I T I T I T I T I T I T I T I T I T I T
Humantoxicity,
carcinogens
Humantoxicity,
non-carcinogens
Respiratory
inorganics
Ozonelayer
depletion
Respiratory
organics
Terrestrial
ecotoxicity
Aquatic
acidification
Aquatic
eutrophication
Mineral
extraction
Globalwarming
Relativecontribution
Refinery
Oilmill
Cultivation
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USD/short ton of soybean meal, and 120 USD/short ton of soy hulls. The prices for oil and meal were
roughly approximated for the July 2014 to July 2015 timeframe based on NASDAQ commodity charts and
the price for hulls was approximated by USB. The same mass relationship of between the co-products
was applied to calculate the economic allocation factors.
The results of this economic allocation are presented in Table 9. Relative to the mass allocation results,
the economic allocation results for crude oil increase by 293% and those for soybean meal decrease by
50%.
Table 9. Sensitivity of crude oil and soybean meal results to an economic rather than mass allocation
Damage category Unit
Crude soybean oil Soybean meal Baseline (mass
allocation of 21%)
Sensitivity (economic
allocation of 61.6%)
% increase
relative to base
Baseline (mass
allocation of 73%)
Sensitivity (economic allocation of 36.5%)
% decrease (negative
values) relative to base
Human Health DALY 6.7E-07 2.0E-06 293% 5.6E-07 2.8E-07 -50%
Ecosystem Quality PDF.m2.y 5.7E+00 1.7E+01 293% 4.8E+00 2.4E+00 -50%
Resources MJ 5.2E+00 1.5E+01 293% 4.3E+00 2.2E+00 -50%
Climate Change kg CO2 -eq 6.2E-01 1.8E+00 293% 5.2E-01 2.6E-01 -50%
Water withdrawal m3 1.3E-01 3.8E-01 293% 1.1E-01 5.4E-02 -50%
5.3.3 Sensitivity of results to indirect land use change of soybean cultivation
Between 1991 and 2010 in the US, the area cultivated with soybeans increased by 24% from roughly 23
to 30 million hectares. This increase in soybean cultivation area occurred at the expense of perennial land
(FAOSTAT 2013).
Within this study the emissions of changing the land use from perennial crop cultivation to soybean
cultivation are considered at national scale (namely changes in soil carbon content, see Section 8.5).
However, displacing perennial crops might also trigger land use change effects at a global scale. When
soybean cultivation diverts perennial crops from food and feed production, one can expect a mix of three
basic responses (Searchinger et al. 2015). First, farmers in other countries produce the food and feed
function (formerly provided by the diverted perennial crops) by expanding cropland into natural areas.
This land use change causes CO2 emissions. Second, farmers in other countries produce the food and feed
function by intensification (increasing yield) of existing cropland more than they otherwise would. This
can cause a greater use of inputs (e.g., fertilizer, water) and greater output of emissions. Finally, some of
the food and feed function may not be replaced, ‘meaning that someone will eat less or less well’
(Searchinger et al. 2015). Note that most of the uncertainty is about which response dominates, not
whether adverse effects occur (Searchinger et al. 2015).
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In the past decade several models have been developed, aimed at quantifying GHG emissions related to
iLUC effects. Most iLUC models were established in order to account for the indirect consequences of
biofuel promotion and are predominantly based on economic equilibrium models (e.g., GTAP, MIRAGE,
FAPRI-CARD). A review study shows the large variations of iLUC values for soybean biodiesel, ranging
from 5g CO2-eq to 110 g CO2-eq per MJ biodiesel produced, depending on the underlying assumptions of
the equilibrium model (Ahlgren and di Luca 2014). Assuming a yield of 2,300 kg biodiesel per hectare
these results translate to about 430 kg CO2-eq to 9,400 kg CO2 emissions per hectare.
An alternative approach to account for GHG emissions caused by LUC is the “global average approach”
(FAO 2015). “The method is based on the concept that all agricultural production systems are connected.”
Therefore it is the cumulated area of all agricultural production that drives land use change.
Consequently, all land-use change emissions are related to the entire agricultural production meaning
that each hectare cultivated is equally responsible for the cumulated global land use change. Thereby, the
global GHG emissions from land use change (5.77 Gt CO2 y-1) are equally distributed over all crops
produced (4.42 billion hectares). This approach avoids the differentiation between direct and indirect
LUC and results in average annual emissions of 1,305 kg CO2-eq per hectare.
The GHG emissions from soybean cultivation of this study are 1,120 kg CO2-eq/ha, while direct land use
change (dLUC) emissions are responsible for 2.8% or 31 kg CO2-eq/ha. Considering indirect land use
effects can therefore significantly increase (double) the amount of GHG emissions related to soybean
cultivation. However, the indirect effects are complex and in practice one can expect a mix of each basic
response mentioned above. The global average approach on the other hand does distribute the impacts
uniformly and thus does not account for local conditions on where and how crops are cultivated. Bearing
this in mind, the amount of GHG emission can be considered an upper limit because intensification and
decreases in food availability (with further social implications) may buffer some of the effects.
Overall the indirect consequences are not directly observable, follow complex links and are impossible to
attribute to a certain field. Even though the iLUC impacts might be substantial, the lack of conclusiveness
and definitiveness in the knowledge makes it difficult to provide practical measures besides trying to
avoid indirect land use changes (e.g., increasing productivity). While dLUC is, at least potentially, under
control of foreground operators, iLUC may be best addressed in the realm of policy.
5.3.4 Other potential sensitivity tests
Some potentially interesting assumptions to test in future work may be among the following:
The impacts of soybean decrease if the benefits of corn are considered, which is (almost always)
cultivated in rotation. The nitrogen fixation capacity of soybean decreases the actual nitrogen
requirement of corn cultivation. In other words, without the nitrogen fixation capacity of
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soybeans, corn would require more mineral fertilizer. We are not aware of any study which uses
this approach and the question is indeed whether we should be the first to consider such effects
in a sensitivity analysis given that the interrelation between soybean and corn in rotation might
go beyond nitrogen (http://corn.agronomy.wisc.edu/AA/A014.aspx)
The data quality analysis elucidated the following three items as potential candidates for testing.
1. Methane and ammonia emissions to air, which may contribute up to 16% towards the Human
health indicator.
2. Zinc and cadmium emissions to soil which may contribute 27% of potential Human health
impacts and 15% of Ecosystem quality.
Ammonia, zinc and cadmium emissions are dependent on the amount and type of NPK and
organic fertilizers applied. This leaves two options for sensitivity testing:
(i) vary the final emission flow, e.g. ammonia. This emphasizes the possible influence of
model uncertainties related to the WFLDB calculation model.
(ii) vary the input and type of NPK and organic fertilizer, model the corresponding change in
(ammonia and heavy metal) emissions and analyze the overall change in environmental
impacts. This highlights the cumulated effect of uncertainty in the amount and type of NPK
and organic fertilizer.
Since the emissions were kept constant for state-specific datasets both options offer new insights
into the sensitivity of our inventory.
3. Dinitrogen monoxide emissions to air which may contribute 53% to the Climate change indicator.
Although half of the impacts related to climate change result from N2O emissions, the
assumptions regarding N2O are rather conservative and the merits of sensitivity testing are not
clear. However, one could test the sensitivity of N2O with regard to (i), i.e. by assuming (or using
a published) uncertainty range of the EPA RIA model. Note that we cannot test with regard to (ii)
because we are only using the result of the EPA RIA model.
Other data considered for potential sensitivity testing was determined to not be worthwhile:
Machine use is consistently important, and comparison with similar studies shows that the used
inventory is in a reasonable range. Therefore, we do not see the need to apply a sensitivity
analysis.
We do not see a need for sensitivity testing with regard to yield, land occupation (which is
directly related), drying and irrigation because the state specific datasets demonstrate the full
range of regional fluctuations.
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6 Discussion and implications
6.1 Key findings
US-average soybean cultivation as defined and scoped in this project is driven by a handful of activities,
as the cells highlighted in red show in Figure 10. In this figure, each farming activity’s impact is shown as
a relative contribution to Human health, Ecosystem quality, Resources, Climate change, and Water
withdrawal.
o Human health is driven by the dinitrogen monoxide and particulate matter emitted to the air
from farm machinery fuel combustion, as well as heavy metal emissions to soil from cadmium and
zinc due to field application of fertilizer.
o Ecosystem quality is driven almost entirely by the occupation of arable land due to the cultivation
of soybeans.
o Resource depletion is driven largely by the extraction of fuel required to power farm machinery.
o Climate change is driven heavily by dinitrogen monoxide emissions to air from field application of
fertilizers as well as emissions of carbon dioxide to air from the combustion of fuels used by farm
machinery.
o Water withdrawal is driven by irrigation water used to cultivate the soybeans.
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Figure 10. Summary of hotspots in soybean cultivation, US average (IMPACT 2002+ v2.21).
The results are shown as relative contributions of cultivation activities to each endpoint impact category, such as Human health and Ecosystem quality
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US-average crude soybean oil and soybean meal are driven by soybean cultivation rather than the
crushing and degumming activities at the mill. The contribution of soybean cultivation to the total
potential impacts ranges from 61% (Resources) to 99% (Ecosystem quality). Otherwise, impacts due to
the fuels and electricity required at the mill are the key drivers. The results for the co-products crude oil
and soybean meal are highly dependent on the choice of allocation metric, and the use of economic
allocation can cause the crude oil results to increase by 293% and those for soybean meal to decrease by
50%.
US-average refined soybean oil is likewise driven by soybean cultivation (ranging from 59% for
Resources to 99% for Ecosystem quality) rather than activities from crushing and degumming or
refinement.
6.2 Study considerations
The following section serves to present transparency and discussion of several important aspects of the
modeling to assist in the interpretation of results.
Land transformation. In this project, transformation from arable to arable land has zero impacts but the
average land transformation (from perennial to arable land) caused by the expansion of soybean is
considered, i.e., causes some CO2 emissions from soil organic carbon (2.5% contribution to the Climate
Change category). Note that IMPACT 2002+ does not assess land transformations, i.e., the biodiversity
impacts related to the transformation of perennial to arable land are not assessed.
Irrigation energy. Some previously published soybean cultivation datasets do not include the energy
required to support irrigation (eg. Soybean DS in ecoinvent v2.2). In this project, irrigation energy is
based on the most recent energy use data of the USDA (Duffield 2015).
N2O emissions. Modeling of N2O emissions is highly variable across datasets (e.g., Ecoinvent, USLCI) and
for this project the GREET value was used (Regulatory Impact Assessment for the Renewable Fuel
Standard program, RFS2). The AgriFood DB dataset assumes larger inputs of manure which increases
N2O emissions substantially and the EPA values “were close to those for corn on a per acre basis, which
was inconsistent with the nitrogen fertilizer use for soybean, which was about 1/15 of that for corn on a
per acre basis” according to Cai et al. (2015). In general, N2O emissions significantly contribute to some
impact indicators (e.g. climate change) and depend on many parameters. Thus, the emissions can vary
substantially among different sites and even within the same field.
Limitation of IMPACT 2002+ with regard to pesticide emissions. The use of this methodology might
underrepresent the potential impacts of pesticides. A list of the pesticide and heavy metal emissions
considered by this method is presented in the appendices in Table 49.
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Heavy metals uptake by crops. Uptake of heavy metals by soybeans of other crops used in rotation was
out of scope of this project.
Land use change. LUC emissions are modeled at a national scale (low contribution) and do not include
potential indirect effects occurring outside the United States.
Phosphorus and nitrogen emissions. LCIA impacts are relatively unaffected by changes in how we
modeled these emissions, but those used in this study are roughly two to three times lower in
comparison with NRCS model, probably due to the shortcomings in our model to account for the full
spectrum of emissions. In comparison to more recent LCI data, we are within the same range.
Impact Assessment (choice of indicators).
Water impact assessment. Water withdrawal inventory was used instead of water consumption
inventory. This inventory is not regionalized, and therefore other methods may be more
appropriate for future work.
Eutrophication. Phosphorus emissions are veryF low (also low phosphorus application rate),
nitrate emissions are higher, but are not considered in freshwater eutrophication -> marine
eutrophication midpoint indicator in order to value nitrogen emissions.
6.3 Recommendations
6.3.1 Improve and benchmark the environmental and social analysis
It is recommended that the data and results of this project be benchmarked with previous soybean data
in US, to other production regions/systems (e.g., Brazil), to other oils, etc.
6.3.2 Promote the adoption of best management practices
The impact of the US soybean industry is largely due to the on-farm activities of hundreds of soybean
producing farms. Promoting practices that will allow for reduction of the environmental impact of these
farms, while sustaining or improving their productivity, is the most important area of focus to reduce the
environmental impact of the industry as a whole. Because each of these farms is unique in terms of the
landscape, climate, soil conditions and economic constraints it faces, it is important to consider the
diversity of conditions within the industry when identifying and promoting opportunities for
improvement. In promoting best management practices, it is important to enable the farm operators to
identify which set of practices will help them to improve or optimize their activities for environmental
performance and how to include environmental and economic considerations together when making
operational decisions.
The results of the life cycle assessment identify three main areas in which farms can focus their efforts to
reduce environmental impact: farm equipment operation, irrigation water use, and fertilizer application.
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Farm equipment is among the biggest contributors to emissions of several air pollutants, such as NOx and
particulate matter in the soybean production life cycle. In addition to identifying potential options to
avoid machine use and machine operation time, upgrading the fleet of farm equipment to newer and less
emitting technology may be suitable solutions. An opportunity assessment could be conducted at the
industry-wide level to identify the characteristics of the existing equipment fleet and how emissions
compare among this fleet and the latest equipment.
Where soybeans are irrigated, this irrigation water is by far the most significant demand on water
resources for the soybean life cycle. Considering expanding or shifting production to geographies where
less irrigation is needed is one option for reducing the water-related impacts across the industry.
Improving irrigation practices where irrigation is being used is another option. There are a wide variety
of water-saving irrigation technologies and practices being introduced in recent years that are able to
deliver to plants the needed water, with a lesser amount being lost to evaporation.
Like water use, fertilizer use may be an area where opportunities exist to reduce the amounts applied
without negative consequences on farm yield. Better identification of the exact needs of a field for
fertilizer, along with more precise timing of when fertilizer is needed may be opportunities to achieve
reductions in fertilizer use.
The opportunities mentioned above all need to be considered within the economic constraints of the
farm operators and assistance may be needed for operators to identify where the specific opportunities
are for their farm conditions. Other constraints, such as availability of capital to invest in upgrades, can
also pose barriers that may need to be addressed to allow the implementation of best management
practices.
6.4 Conclusion
This study goal was to update, enhance and expand USB’s 2010 LCA with the most up-to-date soybean
farming and production data and impact assessment methods. The new data include life cycle inventory,
environmental impact assessment results for four products: Soybeans, US-average; Soymeal, US-average;
Crude soybean oil, US-average; Refined soybean oil, US-average.
This detailed assessment provides many insights on the sector’s current performance in regard to its
environmental, social and, to some extent, economic impact on society. The updated and enhanced
baseline provides a wealth of information that can be leveraged by the members of the industry to better
understand what are the social and environmental hotspots to address, compare their current
performance to past results or to other sectors’ performances and take action to improve the sector and
products overall social and environmental impact. This project is hence another leading milestone in the
US soybean sustainability continuous journey.
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8 Appendix A1. Soybean Agriculture System information, data sources, and
assumptions
8.1 Introduction
The present report elaborates the inventory dataset for the cultivation of an average hectare of soybean
fresh matter cultivated in 2012 in the US. Important properties of soybeans are given in Table 10.
Table 10: Properties of soybeans used in this study.
Property Value Unit Source
Water content (fresh matter) [prior drying] 0.15 kg/kg soybean fresh matter (Sadaka, 2014)
Water content (fresh matter) [after drying] 0.12
kg/kg 12% moisture fresh soybean
matter (Sadaka, 2014)
Carbon content 0.388 kg C/kg (Nemecek et al., 2004)
Higher heating value (HHV) 20.45
MJ/ kg 12% moisture fresh soybean
matter (Nemecek et al., 2004)
Cadmium 0.053
mg/kg 12% moisture fresh soybean
matter (Nemecek et al., 2004)
Chromium 0.463
mg/kg 12% moisture fresh soybean
matter (Nemecek et al., 2004)
Copper 13.4
mg/kg 12% moisture fresh soybean
matter (Nemecek et al., 2004)
Nickel 4.73
mg/kg 12% moisture fresh soybean
matter (Nemecek et al., 2004)
Lead 0.07
mg/kg 12% moisture fresh soybean
matter (Nemecek et al., 2004)
Zinc 42.45
mg/kg 12% moisture fresh soybean
matter (Nemecek et al., 2004)
8.2 System Characterization
Figure 3 in the body of the report shows the direct inputs and outputs associated with soybean
cultivation. The typical soybean cultivation in the US requires intermediate inputs in the form of
irrigation, seeds, fertilizers, pesticides, field operations, transports and drying as well as direct inputs
from nature in the form of land, sunlight and carbon. The cultivation, i.e. primarily the application of
organic and mineral fertilizers, generates emissions into air (nitrogen oxides, carbon dioxide, dinitrogen
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monoxide and ammonia), into water (heavy metals, nitrate, phosphorus and phosphate) and soil (heavy
metal and pesticides). Heavy metal uptake by soybeans is not considered.
The following sections explain the data sources and computations applied to determine all inputs and
outputs associated with the cultivation of an average hectare of soybean fresh matter in 2012 in the US.
8.3 Yields
Table 11 shows recent data for soybean cultivation in the USA. The data used in this study represents a
three-year average, i.e. from 2011-2013. These aggregated national data provided by NASS were taken as
the basis for defining “average” production.
Table 11: Area planted, production volume, and yield related to soybean cultivation in the US.
Source: (USDA, 2015)
Year Area Planted Unit Production Unit Yield Unit
2013 76,840,000 acres 3,357,984 1000 bushels 44 bushels/acre
2012 77,198,000 acres 3,042,044 1000 bushels 39 bushels/acre
2011 75,046,000 acres 3,097,179 1000 bushels 41 bushels/acre
Average 76,361,333 acres 3,165,736 bushels 41 bushels/acre
2013 31,269,270 ha 91,404,324 tons 2,923 kg/ha
2012 31,414,954 ha 82,804,438 tons 2,636 kg/ha
2011 30,539,219 ha 84,305,212 tons 2,761 kg/ha
Average 31,074,481 ha 86,171,325 tons 2,773 kg/ha
The average yield of 41 bushels/acre or 2,773 kg/ha is reduced by losses during harvest (1%) and by
water losses during drying (3%) (Sadaka, 2014). Therefore, the final soybean yield used in this study
amounts to 2,662 kg/ha.
8.4 Inputs from technosphere
8.4.1 Mineral fertilizer
Soybeans are legumes which form nitrogen-fixing root nodules, so that mineral fertilizer is applied on
less than 40 percent of soybean acreage; a much lower rate than for most row crops (e.g., corn and
cotton) (“USDA ERS - Soybeans & Oil Crops: Background,” n.d.). Table 12 shows the specific and the
average application rate per fertilizer type.
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Table 12: Mineral fertilizer application rate in US soybean cultivation for a specific (actually fertilized) and an average hectare
The application rate refers to nutrient content (expressed as N, K2O or P2O5 equivalents). The data refers to the year 2012. Source: (ARMS 2015).
Fertilizer type % of planted area Unit Application rate
Unit Specific Average
N 27.16 % 15.04 4.08 lb/acre
16.76 4.55 kg/ha
P2O5 36.6 % 46.65 17.07 lb/acre
52.00 19.03 kg/ha
K2O 36.63 % 77.57 28.41 lb/acre
86.46 31.67 kg/ha
Although the quantities of fertilizer types are soybean specific, in order to obtain product specific
application rates, the average application rate per fertilizer type is disaggregated on the basis of the
average fertilizer product consumption in the US (“USDA ERS - Fertilizer Use and Price,” n.d.). The most
recent data point on fertilizer product consumption (USDA 2011) and the nutrient content is used to
determine the application rates of specific fertilizer products. “Nitrogen solutions” and other nitrogen
fertilizer is considered as “urea ammonium nitrate” based on (O’Connor 2016).
Table 13 shows the proportion of each product per fertilizer type.
Table 13: Fertilizer use by fertilizer product in the US. The proportion is calculated using the most recent point data .(Source: USDA 2015)
Type Name Product Proportion Used dataset (EI3.1)
Nitrogen
Ammonia Anhydrous 31.5% ammonia, liquid
Aqua 1.1% ammonia, liquid
Ammonium
Nitrate 2.4% ammonium nitrate, as N
Sulfate 2.5% ammonium sulfate, as N
Nitrogen solutions 30.1% Urea ammonium nitrate
Sodium nitrate 0.00% Sodium nitrate
Urea 23.2% urea, as N
Other 9.2% Urea ammonium nitrate
Total 100.0%
Phosphate
Superphosphates
Grades 22% and under 0.1% phosphate fertilizer, as P2O5 from triple superphosphate
Grades over 22% 1.2% phosphate fertilizer, as P2O5 from triple superphosphate
Other single phosphates 1/
3.6% phosphate fertilizer, as P2O5, from single superphosphate
Other
Diammonium phosphate (18-46-0) 2/
28.9% phosphate fertilizer, as P2O5, RER from di-ammonium
Monoammonium phosphate (11-(51-55)-0)
31.0% phosphate fertilizer, as P2O5, RER from mono-ammonium
Other nitrogen-phosphate grades 3/
35.2% phosphate fertilizer, as P2O5, RER from di-ammonium
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Total 100.0%
Potash Potassium chloride 88.7% potassium chloride, as K2O
Other single nutrient 4/ 11.3% potassium chloride, as K2O
Total 100.0%
The final, product specific fertilizer application rate is calculated by multiplying the average application
rate per fertilizer type with the corresponding product specific proportions (Table 14).
Table 14: Product specific mineral fertilizer application rate associated with the cultivation of one ha soybean in the US.
The amount in brackets indicates the amount of N in ‘Ammonia liquid’; since ‘Ammonia, liquid’ refers to the product and not the nutrient content its amount is calculated by the mole ratio of NH3 to N (17/14).
Used dataset (EI3.1) Amount [kg/ha]
Ammonia, liquid 1.80 (1.48)
Ammonium nitrate, as N 0.11
Ammonium sulfate, as N 0.11
Urea ammonium nitrate 1.79
Sodium nitrate 0.00
Urea, as N 1.06
phosphate fertilizer, as P2O5 from triple superphosphate 0.25
phosphate fertilizer, as P2O5, from single superphosphate 0.68
phosphate fertilizer, as P2O5, RER from diammonium 12.21
phosphate fertilizer, as P2O5, RER from monoammonium 5.89
potassium chloride, as K2O 31.67
8.4.2 Organic fertilizer
Table 15 shows the specific and average application rate of manure according to ARMS (ARMS 2015). The
data refers to 2012.
Table 15: Manure application rate in US soybean cultivation for a specific (actually fertilized)and an average hectare.
Source: (ARMS, 2015).
Type % of planted
area Unit
Application rate Unit Specific Average
Manure 3.2 % 7.68 0.25 tons/acre
18.87 0.60 ton/ha
The average amount of manure applied is disaggregated into manure types on the basis of data from
(MacDonald, Ribaudo, Livingston, Beckman, & Huang 2009), which provides specific rates of manure
type on soybean plantation areas for the year 2006.
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Table 16 shows the soybean specific proportion of manure use by type.
Table 16: Soybean specific manure use by type. Source: (MacDonald et al. 2009), Table 2.
Manure type Proportion
Dairy cows 36.05 %
Beef cattle 33.30 %
Swine 14.15 %
Poultry 13.44 %
Other 3.05 %
The application of manure is not recorded as an input in the inventory since it is free of any
environmental burden due to its waste character. However, the type and amount of manure applied to
the average hectare is an important input factor for the calculation of field emissions as the nitrogen or
heavy metal content varies depending on the manure type (ammonia, dinitrogen monoxide, etc.).
8.4.3 Pesticides
USDA (USDA 2015) reports amounts of pesticides used in the soybean cultivation in the USA for 2012.
Table 17 shows the application rate per average planted acre per pesticide according to USDA and the
dataset (or compound class) used for the representation of each pesticide in the framework of Ecoinvent
EI3.1.
Table 17: Pesticides used in soybean cultivation and the dataset (compound class) used for its representation.
Source: (USDA 2015).
Pesticide Application rate
[kg/ha] Used datasets (EI3.1)
CHEMICAL, FUNGICIDE: (AZOXYSTROBIN = 128810) 5.43E-03 dinitroaniline-compound
CHEMICAL, FUNGICIDE: (PROPICONAZOLE = 122101) 1.82E-03 cyclic-N compounds
CHEMICAL, FUNGICIDE: (PYRACLOSTROBIN = 99100) 5.79E-03 dinitroaniline-compound
CHEMICAL, FUNGICIDE: (TETRACONAZOLE = 120603) 2.48E-04 cyclic-N compounds
CHEMICAL, FUNGICIDE: (TRIFLOXYSTROBIN = 129112) 1.07E-03 dinitroaniline-compound
CHEMICAL, HERBICIDE: (2,4-D = 30001) 3.65E-04 2,4-dichlorophenol
CHEMICAL, HERBICIDE: (2,4-D, 2-EHE = 30063) 5.98E-02 2,4-dichlorophenol
CHEMICAL, HERBICIDE: (2,4-D, BEE = 30053) 9.93E-04 2,4-dichlorophenol
CHEMICAL, HERBICIDE: (2,4-D, DIMETH. SALT = 30019) 2.67E-02 2,4-dichlorophenol
CHEMICAL, HERBICIDE: (ACETOCHLOR = 121601) 9.27E-03 pesticide, unspecified
CHEMICAL, HERBICIDE: (ACIFLUORFEN, SODIUM = 114402)
3.07E-03 pesticide, unspecified
CHEMICAL, HERBICIDE: (CARFENTRAZONE-ETHYL = 128712)
1.46E-05 cyclic-N compounds
CHEMICAL, HERBICIDE: (CHLORIMURON-ETHYL = 128901)
2.73E-03 pesticide, unspecified
CHEMICAL, HERBICIDE: (CLETHODIM = 121011) 7.65E-03 pesticide, unspecified
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Pesticide Application rate
[kg/ha] Used datasets (EI3.1)
CHEMICAL, HERBICIDE: (CLORANSULAM-METHYL = 129116)
1.21E-03 pesticide, unspecified
CHEMICAL, HERBICIDE: (DICAMBA, DIGLY. SALT = 128931)
2.63E-04 pesticide, unspecified
CHEMICAL, HERBICIDE: (DICAMBA, DIMET. SALT = 29802)
1.01E-03 pesticide, unspecified
CHEMICAL, HERBICIDE: (DIMETHENAMID-P = 120051) 3.43E-03 dimethenamide
CHEMICAL, HERBICIDE: (FENOXAPROP-P-ETHYL = 129092)
1.02E-04 phenoxy-compounds
CHEMICAL, HERBICIDE: (FLUAZIFOP-P-BUTYL = 122809)
2.85E-03 phenoxy-compounds
CHEMICAL, HERBICIDE: (FLUMETSULAM = 129016) 2.04E-04 pesticide, unspecified
CHEMICAL, HERBICIDE: (FLUMICLORAC-PENTYL = 128724)
5.11E-04 pesticide, unspecified
CHEMICAL, HERBICIDE: (FLUMIOXAZIN = 129034) 8.79E-03 pesticide, unspecified
CHEMICAL, HERBICIDE: (FLUTHIACET-METHYL = 108803)
1.46E-04 acetamide-anilide-compounds
CHEMICAL, HERBICIDE: (FOMESAFEN = 123802) 1.97E-02 pesticide, unspecified
CHEMICAL, HERBICIDE: (GLUFOSINATE-AMMONIUM = 128850)
1.83E-02 organo-phosphorous compounds
CHEMICAL, HERBICIDE: (GLYPHOSATE = 417300) 9.54E-02 glyphosate
CHEMICAL, HERBICIDE: (GLYPHOSATE DIM. SALT = 103608)
3.53E-02 glyphosate
CHEMICAL, HERBICIDE: (GLYPHOSATE ISO. SALT = 103601)
4.31E-01 glyphosate
CHEMICAL, HERBICIDE: (GLYPHOSATE POT. SALT = 103613)
1.03E+00 glyphosate
CHEMICAL, HERBICIDE: (IMAZAMOX = 129171) 8.76E-05 diazoles
CHEMICAL, HERBICIDE: (IMAZAQUIN = 128848) 3.36E-04 pesticide, unspecified
CHEMICAL, HERBICIDE: (IMAZAQUIN, MON. SALT = 128840)
1.61E-04 pesticide, unspecified
CHEMICAL, HERBICIDE: (IMAZETHAPYR = 128922) 2.99E-03 pesticide, unspecified
CHEMICAL, HERBICIDE: (IMAZETHAPYR, AMMON. = 128923)
2.34E-04 pesticide, unspecified
CHEMICAL, HERBICIDE: (LACTOFEN = 128888) 2.80E-03 pesticide, unspecified
CHEMICAL, HERBICIDE: (METOLACHLOR = 108801) 4.26E-03 metolachlor
CHEMICAL, HERBICIDE: (METRIBUZIN = 101101) 9.85E-03 triazines
CHEMICAL, HERBICIDE: (PARAQUAT = 61601) 1.19E-02 pyridines
CHEMICAL, HERBICIDE: (PENDIMETHALIN = 108501) 2.28E-02 pendimethanlin
CHEMICAL, HERBICIDE: (QUIZALOFOP-P-ETHYL = 128709)
1.72E-03 pesticide, unspecified
CHEMICAL, HERBICIDE: (RIMSULFURON = 129009) 5.84E-05 [sulfony]ureas
CHEMICAL, HERBICIDE: (S-METOLACHLOR = 108800) 7.87E-02 metolachlor
CHEMICAL, HERBICIDE: (SAFLUFENACIL = 118203) 1.17E-03 pesticide, unspecified
CHEMICAL, HERBICIDE: (SETHOXYDIM = 121001) 9.20E-04 pesticide, unspecified
CHEMICAL, HERBICIDE: (SULFENTRAZONE = 129081) 1.57E-02 pesticide, unspecified
CHEMICAL, HERBICIDE: (THIFENSULFURON = 128845) 4.53E-04 [sulfony]ureas
CHEMICAL, HERBICIDE: (TRIBENURON-METHYL = 128887)
1.46E-04 [sulfony]ureas
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Pesticide Application rate
[kg/ha] Used datasets (EI3.1)
CHEMICAL, HERBICIDE: (TRIFLURALIN = 36101) 1.91E-02 dinitroanilines
CHEMICAL, INSECTICIDE: (ACEPHATE = 103301) 1.44E-02 organo-phosphorous compounds
CHEMICAL, INSECTICIDE: (BETA-CYFLUTHRIN = 118831)
5.84E-05 pyrethroids
CHEMICAL, INSECTICIDE: (BIFENTHRIN = 128825) 2.23E-03 pesticide, unspecified
CHEMICAL, INSECTICIDE: (CHLORPYRIFOS = 59101) 3.05E-02 organo-phosphorous compounds
CHEMICAL, INSECTICIDE: (CYFLUTHRIN = 128831) 6.42E-04 pyrethroids
CHEMICAL, INSECTICIDE: (CYPERMETHRIN = 109702) 1.46E-04 pyrethroids
CHEMICAL, INSECTICIDE: (DIFLUBENZURON = 108201) 8.76E-05 [sulfony]ureas
CHEMICAL, INSECTICIDE: (DIMETHOATE = 35001) 4.03E-03 organo-phosphorous compounds
CHEMICAL, INSECTICIDE: (ESFENVALERATE = 109303) 1.46E-04 pyrethroids
CHEMICAL, INSECTICIDE: (FLUBENDIAMIDE = 27602) 3.07E-04 pesticide, unspecified
CHEMICAL, INSECTICIDE: (GAMMA-CYHALOTHRIN = 128807)
8.76E-05 pyrethroids
CHEMICAL, INSECTICIDE: (IMIDACLOPRID = 129099) 1.90E-04 benzimidazole-compound
CHEMICAL, INSECTICIDE: (LAMBDA-CYHALOTHRIN = 128897)
2.06E-03 pyrethroids
CHEMICAL, INSECTICIDE: (METHOXYFENOZIDE = 121027)
1.90E-03 pesticide, unspecified
CHEMICAL, INSECTICIDE: (THIAMETHOXAM = 60109) 2.77E-04 pesticide, unspecified
CHEMICAL, INSECTICIDE: (ZETA-CYPERMETHRIN = 129064)
5.84E-05 pyrethroids
Table 18 summarizes the average application rate and proportion of the consolidated pesticides
compounds.
Table 18: Pesticide application associated with the cultivation of one average hectare soybean in the US in 2012.
Compound-class (Ei3.1) Application rate [in kg/ha] Proportion
dinitroaniline-compound 1.23E-02 0.61%
cyclic-N compounds 2.09E-03 0.10%
2,4-dichlorophenol 8.79E-02 4.38%
pesticide, unspecified 8.51E-02 4.24%
dimethenamide 3.43E-03 0.17%
phenoxy-compounds 2.95E-03 0.15%
acetamide-anilide-compounds 1.46E-04 0.01%
organo-phosphorous compounds 6.73E-02 3.35%
glyphosate 1.60E+00 79.49%
diazoles 8.76E-05 0.00%
metolachlor 8.30E-02 4.13%
triazines 9.85E-03 0.49%
pyridines 1.19E-02 0.59%
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Compound-class (Ei3.1) Application rate [in kg/ha] Proportion
pendimethanlin 2.28E-02 1.13%
[sulfony]ureas 7.44E-04 0.04%
dinitroanilines 1.91E-02 0.95%
pyrethroids 3.20E-03 0.16%
benzimidazole-compound 1.90E-04 0.01%
Total 2.01E+00 100.00%
Glyphosate accounts for roughly 80% of the overall pesticides compounds applied. Other important
pesticides compounds are 2.4-dichlorphenol, metolachlor and organo-phosphorous compounds.
8.4.4 Seed
The value of 65.24 kg seeds per ha is taken from the most recent USDA data (USDA 2015) and
approximated by the exchange flow “pea seed IP, at regional storehouse”.
8.4.5 Energy use: field operations, irrigation and drying
The energy use related to field operation, irrigation and drying are based on soybean specific energy use
data from the USDA (Duffield 2015).
Table 19: Energy use data (Duffield 2015).
Energy carrier Used for Unit Amount (total)
Amount (field
operation)
Amount (irrigation)
Ammount (drying)
Diesel field operations MJ/ha 1,436 1,436 - -
Gasoline field operations MJ/ha 321 321 - -
Sum
MJ/ha 1,757 1,757 - -
LPG drying MJ/ha 79 - 18 61
Electricity drying & irrigation MJ/ha 97 - 22 75
Natural gas drying MJ/ha 105 - 24 81
Sum
MJ/ha 281 - 64 217
Total energy use
MJ/ha 2,038 1,757 64 217
In order to allocate energy to processes we assumed the most common energy use of the energy carriers
(column “Used for”). The amount of LPG, electricity and natural gas used for irrigation and drying is split
according to direct energy demand of the ecoinvent processes “irrigation US” and “drying US”. This
results in an allocation key of 23% to irrigation and 77% to drying.
The provision of the energy carriers was modeled with corresponding products from the ecoinvent
database (see Table 32). For each fuel, we modeled provision and emissions (associated with its
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
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combustion) in two separate datasets. The fuel provision is modeled with existing datasets. Fuel emission
profiles are modeled by using default datasets from version 3.1 of the ecoinvent database—natural gas
and LPG combustion—or by removing everything but the emissions from a combine harvesting dataset
(for diesel) and a transport, passenger car dataset (for gasoline). The required electricity is modeled with
the US average electricity mix.
8.4.5.1 Field operations
The values on direct energy use were complemented with infrastructure requirements. The
infrastructure associated with the field operations were modeled by deleting all direct energy
requirements (i.e. input of diesel) and corresponding emissions from the field operation datasets so that
the adapted datasets only represent the intervention associated with infrastructure, i.e., the provision
and maintenance of required machineries and buildings.
Table 20: Comparison of original (USB 2015) and revised (Duffield 2015) energy use data. All inventory flows refer to the cultivation of one hectare of soybean, i.e. the production of 2,662 kg soybean.
Inventory flow Includes Unit Amount
Tillage, harrowing by rotary harrow
Infrastructure only
ha 1
Application of plant protection products
ha 1
Fertilizing by broadcaster ha 1
Sowing ha 1
Combine harvesting ha 1
Diesel Provision kg 33.56
Diesel Emissions kg 33.56
Gasoline Provision kg 7.55
Gasoline Emissions kg 7.55
8.4.5.2 Irrigation
According to ARMS (ARMS 2015) irrigation was used on 7.3 million acres of soybeans, or 9.95 percent of
US soybean acreage in 2012. Table 21 shows the amount of water irrigated, specifically for the irrigated
hectare and for average hectare.
Table 21: Irrigation application rate associated with the cultivation of one hectare soybean in the US.
Type % of planted area Unit Application rate
Unit Used dataset (EI3.1) Specific Average
Irrigation 9.95% % 1,106 110 m3/acre Irrigation {US}| processing | Alloc
Rec, U 2,718 270 m3/ha
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The energy used for irrigation is specified in Table 22 and includes the provision and use of LPG, natural
gas and electricity.
Table 22: Energy used for irrigation (USDA).
Intermediate input Amount Unit Used dataset (created for this project)
LPG combustion 18.23 MJ Propane, burned in building machine {GLO}| market for | Alloc Rec, U
LPG provision 0.39 Kg Liquefied petroleum gas {RoW}| market for | Alloc Rec, U
Natural gas combustion 24.14 MJ Natural gas, burned in gas motor, for storage {RoW}| processing | Alloc Rec, U
Natural gas provision 0.64 m3 Natural gas, high pressure {US}| market for | Alloc Rec, U
Electricity 6.19 kWh electricity, low voltage, at grid {US}|| Alloc Rec, U
8.4.5.3 Drying
Drying of soybean grains consists of exposing the beans to forced ventilation of air that is heated to
certain degree in special equipment called "dryers" (Islas-Rubio & Higuera-Ciapara 2002). This study
assumes artificial drying of soybeans from a moisture content of 15% to 12% (Sadaka 2014).
The drying process is modeled with the adapted EI3.1 dataset “Drying of bread grain, seed and legume /
USSB”. The reference flow of the dataset is the amount of water which needs to be evaporated. The
amount of water to evaporate per kg of grain fresh matter, Qr, is calculated by equation 1 with the initial
moisture Wi(%) to be set at a final moisture Wf (%) (Islas-Rubio and Higuera-Ciapara 2002).
(1)
With Wi(15%) and Wf(12%) the amount of water to evaporate per kg of grain can be calculated with
0.031 kg / kg of soybean fresh matter. Table 23 shows the total amount of water evaporated per hectare
soybeans. The energy used for drying is specified in Table 23 and includes the provision and use of LPG,
natural gas and electricity.
Table 23: Amount of water evaporated during the drying process associated with the cultivation of one hectare soybean US
Source: (Nemecek et al., 2004). And the energy used for drying (USDA).
Intermediate input Amount Unit Used dataset (created for this project)
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Drying of soybean grain 85.87 kg Drying of bread grain, seed and legumes {US}| /USB
LPG combustion 61.05 MJ Propane, burned in building machine {GLO}| market for | Alloc Rec, U
LPG provision 1.31 Kg Liquefied petroleum gas {RoW}| market for | Alloc Rec, U
Natural gas combustion 80.83 MJ Natural gas, burned in gas motor, for storage {RoW}| processing | Alloc Rec, U
Natural gas provision 2.14 m3 Natural gas, high pressure {US}| market for | Alloc Rec, U
Electricity 20.72 kWh electricity, low voltage, at grid {US}|| Alloc Rec, U
8.4.6 Transportation
Due to the lack of specific data, default transport distances given by the WFLDB database guidelines
(Nemecek et al. 2015) were used to calculate the transport service requirements of raw materials and
intermediate inputs. Table 24 shows the final service requirements per hectare soybean. All intermediate
inputs not mentioned explicitly here already consider average transport services by default.
Table 24: Transport service requirements of raw materials and intermediate inputs used in the cultivation of one hectare soybeans in the US
Source: WFLDB, default data (Nemecek et al. 2015).
Raw material / intermediate input
Mode Amount Unit Used dataset (EI3.1)
Transport, manure lorry 30.2 tkm Transport, freight, lorry 16-32 metric ton, EURO4 {GLO}| market for | Alloc Rec, U
Transport, P fertilizer barge 94.18 tkm Transport, freight, sea, transoceanic ship {GLO}| market for | Alloc Rec, U
Transport, P fertilizer rail 9.42 tkm Transport, freight train {US}| market for | Alloc Rec, U
Transport, P fertilizer lorry 5.65 tkm Transport, freight, lorry 16-32 metric ton, EURO4 {GLO}| market for | Alloc Rec, U
8.5 Inputs from nature
8.5.1 Land use
Land use and related emissions are calculated according to the WFLDB guidelines (Nemecek et al. 2015).
Land use in LCA is assessed with land occupation and land transformation. Table 25 shows the land use
associated with the cultivation of one hectare soybean in the US.
Table 25: Amount of land transformation and land occupation associated with the average hectare soybean in the US.
Land use type / Elementary flow Amount Unit
Occupation, arable, non-irrigated 9,005 m2a
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Occupation, arable, irrigated 995 m2a
Transformation, from arable land, 9,881 m2 /ha
Transformation, from perennial land 119 m2/ha
Transformation to arable, irrigated 995 m2/ha
Transformation, to arable, non-irrigated 9,005 m2/ha
Land occupation is calculated by multiplying the occupied area by time taking the period from harvesting
of the previous crop until harvesting of soybeans into account (Nemecek et al. 2015). Typically, soybean
cultivation in the US covers one entire cropping season per year with planting around April and harvest
in November/December (USDA 2010). Consequently, and in accordance with the WFLDB guidelines
(Nemecek et al. 2015), the occupation duration of soybean cultivation is calculated with 12 months.
Land transformation is calculated on the basis of the average area expansion of soybean cultivation in the
US from 1991-2010. During this time period, the area cultivated with soybeans increased by 23.8% from
roughly 23 to 30 million hectares. The increase in soybean area occurred at the expense of perennial land
(FAOSTAT 2013). In order to reflect the average annual land transformation, we allocate 1.19% (23.8% /
20 years) of the land requirements related to soybean cultivation to perennial land. That is annually,
1.19% of the average hectare soybean comes from the transformation of perennial land. The related
emissions of carbon dioxide (from the soil and the vegetation) is explained and reported in section 8.6.
The differentiation between irrigated and non-irrigated land use is considered via both occupation and
transformation. According to (ARMS 2015) 9.95 percent of US soybean acreage was irrigated in 2012.
Because a large fraction of soy is produced in soy-corn rotation and because soy agriculture results in N-
fixation, the question arises as to how to account for any credit and burden associated with use of this
nutrient by corn or soy planted on that land. In this project, no specific land use changes were considered,
for example, soy to corn. With regard to accounting for any benefits associated with such fixed N, no
‘credit’ beyond the natural reduction in N input is accounted for. With regard to accounting for any
‘burden’ we referred to the N2O production and emissions from the fixed N as determined by a 2013 EPA
RIA analysis (Life Cycle Associates 2015). All burdens associated with emissions of N2O from fixed N and
plant residue have been assigned to the soy crop system.
8.5.2 Carbon loss from soil after land transformation
According to IPCC 2006 the average soil organic carbon content (SOC) is lower in annual than in
perennial cropland. Therefore, the prior mentioned transformation from perennial to annual cropland
causes a loss in soil organic carbon (SOC). According to the WFLDB guidelines (Nemecek et al. 2015) this
loss is recorded as a resource consumption of “carbon, organic, in soil or biomass stock”. The amount
recorded is 2.69 kg C/yr/ha per annum and ha of soybean cultivated.
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8.5.3 Energy use
Energy in biomass is calculated with the cross calorific value of 20.45 MJ/kg soybean fresh matter
(Jungbluth et al. 2007). Table 26 shows the energy in biomass per hectare of soybean cultivated.
Table 26: Energy in biomass related to the cultivation of one hectare soybean Source: (Jungbluth et al. 2007)
Elementary flow Amount Unit Elementary flow (EI3.1)
Energy, in biomass 54,432 MJ/ha Energy, gross calorific value, in biomass
8.5.4 Carbon-dioxide uptake
With regard to carbon sequestration, the uptake of CO2 by biota in the inventory was included, but
including no impact assessment value to this uptake when applying the impact assessment method for
reporting results, resulting in no net impact or benefit due to fluxes of carbon into or out of the bio-based
systems being modelled. The reason for not including an impact assessment value to this CO2 is that it
will very likely be emitted again to the atmosphere in the “gate to grave” half of the product life cycle into
which the soy mass is incorporated.
8.6 Emissions
Direct field emissions are substances emitted from an agricultural area and depend strongly on the site
characteristics and the management practices. The calculation of direct field emissions requires the
application of specific models (Nemecek et al. 2015).
8.6.1 Emissions to air
Emissions to air are calculated according to the WFLDB guidelines (Nemecek et al. 2015). Cai et al. (2015,
p. 4) shows that the "direct N2O emissions from nitrogen fertilizers modeled by the EPA were close to
those for corn on a per acre basis, which was inconsistent with the nitrogen fertilizer use for soybean,
which was about 1/15 of that for corn on a per acre basis as shown in the same 2010 Regulatory Impact
Assessment for the Renewable Fuel Standard program (RFS2)”. Considering this inconsistency, we have
applied the GREET value for N2O emissions, i.e. 1.85 kg N2O per hectare (RFS2).
Table 27 shows the emissions associated with the cultivation of one hectare soybean and provides a brief
explanation of its origin.
Table 27: Emission to air associated with the cultivation of one ha soybean in the US.
Emission / Elementary flow
(EI3.1) Amount Unit Explanation Model
Ammonia 3.04 kg/ha
NH3 emissions caused by the application of organic and mineral fertilizer.
Default WFLDB methodology - EMEP
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Emission / Elementary flow
(EI3.1) Amount Unit Explanation Model
Carbon dioxide, fossil 3.06 kg/ha
CO2 emissions caused by the application of lime and urea
Default WFLDB methodology
Nitrogen oxides 2.69E-1 kg/ha
NOx emissions caused by the application of organic and mineral fertilizer,
Default WFLDB methodology - IPCC 2006
Dinitrogen monoxide 1.48E-1 kg/ha
N2O emissions caused by NH3 and NOx losses
Default WFLDB methodology
Dinitrogen monoxide 1.85 kg/ha
N2O emission caused by nitrate leaching RFS2 value. and GWP of N2O = 298.
Carbon dioxide, land transformation 9.86 kg/ha
CO2 emission caused by the losses in soil organic carbon
Direct Land Use Change Assessment tool
Carbon dioxide, land transformation 20.99 kg/ha
CO2 emission resulting from the transformation of perennial land into arable land for soybean cultivation
Direct Land Use Change Assessment tool
Water, to air 170.42 m3/ha
Irrigation water emitted to air Default WFLDB methodology
8.6.2 Emissions into water
The emission into water are calculated according to the WFLDB guidelines (Nemecek et al. 2015). Table
28 shows the emissions associated with the cultivation of one hectare soybean and provides a brief
explanation of its origin as well as the model used for its calculation.
Table 28: Emissions to water associated with the cultivation of one ha soybean in the US.
Emission / Elementary flow
(EI3.1) Amount Unit Explanation Model
Phosphate, to river 5.73E-1 kg/ha Run-off of soluble phosphate (PO4) to surface water caused by fertilizer application.
Default WFLDB methodology
Phosphorus, to river 3.68E-1 kg/ha Water erosion of soil particles containing phosphorus (P)
Default WFLDB methodology
Phosphate, to groundwater
2.15E-1 kg/ha Leaching of soluble phosphate (PO4) to ground water caused by fertilizer application.
Default WFLDB methodology
Nitrate 89.66 kg/ha Nitrate leaching to groundwater caused by fertilizer application.
Default WFLDB methodology - SQCB
Water, to surface water 80.08 m3/ha Irrigation water emitted to rivers Default WFLDB methodology
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Emission / Elementary flow
(EI3.1) Amount Unit Explanation Model
Water, to ground water 20.02 m3/ha Irrigation water emitted to groundwater Default WFLDB methodology
8.6.3 Heavy metal emissions
Heavy metal emissions (HME) are related to the application of organic and mineral fertilizer and are
emitted into agricultural soil, groundwater and surface water. HME are calculated according to the
WFLDB guidelines (Nemecek et al. 2015) based on the SALCA model. The model operates on the basis of
the amount and heavy metal content of the used organic and mineral fertilizers. Table 29 shows the HME
associated with the cultivation of one hectare soybean.
Table 29. Heavy metal emissions related to the cultivation of one hectare soybean Emissions are calculated with the WFLDB model “SALCA-SM_Kultur_V3.5.
Emission Unit Compartment
Soil Groundwater Surface water
Cadmium kg/ha 9.29E-04 2.98E-05 6.72E-05
Copper kg/ha -4.87E-04 2.92E-03 7.65E-03
Zinc kg/ha 4.60E-02 1.45E-02 1.03E-02
Lead kg/ha 1.01E-03 5.62E-05 8.58E-04
Nickel kg/ha -8.50E-04 0.00E+00 3.77E-03
Chromium kg/ha -1.31E-02 1.62E-02 8.68E-03
Mercury kg/ha 5.29E-05 7.79E-07 2.06E-05
The heavy metal uptake of soybeans is not considered as the release of heavy metals (during the use
stage of the soybean products) is outside the scope of this study. Heavy metal uptake by soybean seeds as
modeled in the Ecoinvent inventory datasets were removed for this project, and heavy metal uptake is
likewise not included in the impact assessment methodology. The negative flows presented in Table 29
are the results of transfer coefficients used in the WFLDB heavy metal modeling. A portion of the heavy
metal inputs are emitted to and remain in the soil. Yet, a large portion of the heavy metals is relocated to
other compartments, e.g., soil erosion or surface wash transfer to surface and ground water. If the
emission to soil is negative, the emissions to other compartments are larger than the input. This is
possible because surface wash and erosion associated with the cultivation can cause the emission of
heavy metals which stem from the base concentration of heavy metals in the soil. That is, the negative
flows in the soil compartment are a consequence of the relocation of the heavy metals.
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8.6.4 Pesticide emissions
Pesticides emissions are calculated on the basis of the pesticide inputs listed in Table 17. Table 30 shows
the emissions and the elementary flow used for its representation in EI3.1. All pesticide emissions are
recorded as an emission to agricultural soil.
Table 30: Pesticide emission related to the cultivation of one hectare soybean.
Pesticide emission Amount [kg/ha]
Elementary flow used (EI3.1)
CHEMICAL, FUNGICIDE: (AZOXYSTROBIN = 128810) 5.43E-03 Azoxystrobin
CHEMICAL, FUNGICIDE: (PROPICONAZOLE = 122101) 1.82E-03 Propiconazole
CHEMICAL, FUNGICIDE: (PYRACLOSTROBIN = 99100) 5.79E-03 Pyraclostrobin
CHEMICAL, FUNGICIDE: (TETRACONAZOLE = 120603) 2.48E-04 Fungizides, unspecified
CHEMICAL, FUNGICIDE: (TRIFLOXYSTROBIN = 129112) 1.07E-03 Trifloxystrobin
CHEMICAL, HERBICIDE: (2,4-D = 30001) 8.79E-02 2,4-D
CHEMICAL, HERBICIDE: (2,4-D, 2-EHE = 30063) 9.27E-03 2,4-D
CHEMICAL, HERBICIDE: (2,4-D, BEE = 30053) 3.07E-03 2,4-D
CHEMICAL, HERBICIDE: (2,4-D, DIMETH. SALT = 30019) 1.46E-05 2,4-D
CHEMICAL, HERBICIDE: (ACETOCHLOR = 121601) 2.73E-03 Acetochlor
CHEMICAL, HERBICIDE: (ACIFLUORFEN, SODIUM = 114402) 7.65E-03 Acifluorfen
CHEMICAL, HERBICIDE: (CARFENTRAZONE-ETHYL = 128712) 1.21E-03 Carfentrazone-ethyl
CHEMICAL, HERBICIDE: (CHLORIMURON-ETHYL = 128901) 1.27E-03 Chlorimuron-ethyl
CHEMICAL, HERBICIDE: (CLETHODIM = 121011) 3.43E-03 Clethodim
CHEMICAL, HERBICIDE: (CLORANSULAM-METHYL = 129116) 1.02E-04 Cloransulam-methyl
CHEMICAL, HERBICIDE: (DICAMBA, DIGLY. SALT = 128931) 2.85E-03 Dicamba
CHEMICAL, HERBICIDE: (DICAMBA, DIMET. SALT = 29802) 2.04E-04 Dicamba
CHEMICAL, HERBICIDE: (DIMETHENAMID-P = 120051) 5.11E-04 Dimethenamid
CHEMICAL, HERBICIDE: (FENOXAPROP-P-ETHYL = 129092) 8.79E-03 Fenoxaprop-Pethyl ester
CHEMICAL, HERBICIDE: (FLUAZIFOP-P-BUTYL = 122809) 1.31E-03 Flluazifop-p-butyl
CHEMICAL, HERBICIDE: (FLUMETSULAM = 129016) 1.97E-02 Flumetsulam
CHEMICAL, HERBICIDE: (FLUMICLORAC-PENTYL = 128724) 1.83E-02 Flumiclorac-pentyl
CHEMICAL, HERBICIDE: (FLUMIOXAZIN = 129034) 1.60E+00 Flumioxazin
CHEMICAL, HERBICIDE: (FLUTHIACET-METHYL = 108803) 8.76E-05 Herbizides, unspecific
CHEMICAL, HERBICIDE: (FOMESAFEN = 123802) 4.96E-04 Fomesafen
CHEMICAL, HERBICIDE: (GLUFOSINATE-AMMONIUM = 128850) 3.23E-03 Glufosinat ammonium
CHEMICAL, HERBICIDE: (GLYPHOSATE = 417300) 2.80E-03 Glyphosate
CHEMICAL, HERBICIDE: (GLYPHOSATE DIM. SALT = 103608) 8.30E-02 Glyphosate
CHEMICAL, HERBICIDE: (GLYPHOSATE ISO. SALT = 103601) 9.85E-03 Glyphosate
CHEMICAL, HERBICIDE: (GLYPHOSATE POT. SALT = 103613) 1.19E-02 Glyphosate
CHEMICAL, HERBICIDE: (IMAZAMOX = 129171) 2.28E-02 Imazamox
CHEMICAL, HERBICIDE: (IMAZAQUIN = 128848) 1.72E-03 Imazaquin
CHEMICAL, HERBICIDE: (IMAZAQUIN, MON. SALT = 128840) 5.84E-05 Imazaquin
CHEMICAL, HERBICIDE: (IMAZETHAPYR = 128922) 9.20E-04 Imazethapyr
CHEMICAL, HERBICIDE: (IMAZETHAPYR, AMMON. = 128923) 1.57E-02 Imazethapyr
CHEMICAL, HERBICIDE: (LACTOFEN = 128888) 4.53E-04 Lactofen
CHEMICAL, HERBICIDE: (METOLACHLOR = 108801) 1.46E-04 Metolachlor
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Pesticide emission Amount [kg/ha]
Elementary flow used (EI3.1)
CHEMICAL, HERBICIDE: (METRIBUZIN = 101101) 1.91E-02 Metribuzin
CHEMICAL, HERBICIDE: (PARAQUAT = 61601) 1.44E-02 Paraquat
CHEMICAL, HERBICIDE: (PENDIMETHALIN = 108501) 7.01E-04 Pendimethalin
CHEMICAL, HERBICIDE: (QUIZALOFOP-P-ETHYL = 128709) 2.23E-03 Quizalofop-p-ethyl
CHEMICAL, HERBICIDE: (RIMSULFURON = 129009) 3.05E-02 rimsulfuron
CHEMICAL, HERBICIDE: (S-METOLACHLOR = 108800) 1.46E-04 Metolachlor
CHEMICAL, HERBICIDE: (SAFLUFENACIL = 118203) 8.76E-05 Herbizides, unspecific
CHEMICAL, HERBICIDE: (SETHOXYDIM = 121001) 4.03E-03 Sethoxydim
CHEMICAL, HERBICIDE: (SULFENTRAZONE = 129081) 1.46E-04 Sulfentrazone
CHEMICAL, HERBICIDE: (THIFENSULFURON = 128845) 3.07E-04 Thifensulfuron
CHEMICAL, HERBICIDE: (TRIBENURON-METHYL = 128887) 8.76E-05 Tribenuron-methyl
CHEMICAL, HERBICIDE: (TRIFLURALIN = 36101) 1.90E-04 Trifluralin
CHEMICAL, INSECTICIDE: (ACEPHATE = 103301) 2.06E-03 Acephate
CHEMICAL, INSECTICIDE: (BETA-CYFLUTHRIN = 118831) 1.90E-03 Cyfluthrin
CHEMICAL, INSECTICIDE: (BIFENTHRIN = 128825) 2.77E-04 Bifenthrin
CHEMICAL, INSECTICIDE: (CHLORPYRIFOS = 59101) 5.84E-05 Chlorpyrifos
CHEMICAL, INSECTICIDE: (CYFLUTHRIN = 128831) 5.43E-03 Cyfluthrin
CHEMICAL, INSECTICIDE: (CYPERMETHRIN = 109702) 1.82E-03 Cypermethrin
CHEMICAL, INSECTICIDE: (DIFLUBENZURON = 108201) 5.79E-03 Diflubenzuron
CHEMICAL, INSECTICIDE: (DIMETHOATE = 35001) 2.48E-04 Dimethoate
CHEMICAL, INSECTICIDE: (ESFENVALERATE = 109303) 1.07E-03 Esfenvalerate
CHEMICAL, INSECTICIDE: (FLUBENDIAMIDE = 27602) 8.79E-02 Insecticide, unspecified
CHEMICAL, INSECTICIDE: (GAMMA-CYHALOTHRIN = 128807) 9.27E-03 Cyhalothrin, gamma-
CHEMICAL, INSECTICIDE: (IMIDACLOPRID = 129099) 3.07E-03 Imidacloprid
CHEMICAL, INSECTICIDE: (LAMBDA-CYHALOTHRIN = 128897) 1.46E-05 Lambda-cyhalothrin
CHEMICAL, INSECTICIDE: (METHOXYFENOZIDE = 121027) 2.73E-03 Methoxyfenozide
CHEMICAL, INSECTICIDE: (THIAMETHOXAM = 60109) 7.65E-03 Thiamethoxam
CHEMICAL, INSECTICIDE: (ZETA-CYPERMETHRIN = 129064) 1.21E-03 Zeta-cypermethrin
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8.7 Overall soybean inventory
The following table shows all input and output flows related to the cultivation of an average hectare
soybean in the USA in 2012 as well as the corresponding uncertainty information.
Table 31: Inventory for soybean production in the US in 2012
Exchange Uncertainty Information
Type Name Amount Unit Compart. Subcompart. Dist. StDev95
% Indicator score
Ref
eren
ce f
low
soybean, at farm /US
2.66E+03
kg n.a. n.a. n.a.
Fro
m e
nvi
ron
men
t occupation, arable, non-irrigated 9.00E+03 m2a lognormal 1.12 (2,2,2,1,1,na)
occupation, arable, irrigated 9.95E+02 m2a lognormal 1.12 (2,2,2,1,1,na)
transformation, from arable land, 9.88E+03 m2 lognormal 1.21 (2,2,2,1,1,na)
transformation, from perennial land 1.19E+02 m2 lognormal 1.21 (2,2,2,1,1,na)
transformation to arable, irrigated 9.95E+02 m2 lognormal 1.21 (2,2,2,1,1,na)
transformation, to arable, non-irrigated 9.00E+03 m2 lognormal 1.21 (2,2,2,1,1,na)
carbon, organic, in soil or biomass stock 2.69E+00 kg lognormal 1.08 (2,2,2,1,1,na)
energy, gross calorific value, in biomass 5.44E+04 MJ lognormal 1.08 (2,2,2,1,1,na)
Fro
m t
ech
no
sph
ere
harrowing, rotary (infrastructure) 1.00E+00 ha lognormal 1.26 (3,2,3,3,3,na)
application plant protection (infrastructure) 1.00E+00 ha lognormal 1.26 (3,2,3,3,3,na)
fertilizing (infrastructure) 1.00E+00 ha lognormal 1.26 (3,2,3,3,3,na)
sowing (infrastructure) 1.00E+00 ha lognormal 1.26 (3,2,3,3,3,na)
combine harvesting (infrastructure) 1.00E+00 ha lognormal 1.26 (3,2,3,3,3,na)
irrigation (infrastructure / resource flows) 2.70E+02 m3 lognormal 1.08 (2,2,2,1,1,na)
Diesel (provision) 3.36E+01 kg lognormal 1.26 (3,2,3,3,3,na)
Diesel (emissions) 3.36E+01 kg lognormal 1.26 (3,2,3,3,3,na)
Gasoline (provision) 7.55E+00 kg lognormal 1.26 (3,2,3,3,3,na)
Gasoline (emission) 7.55E+00 kg lognormal 1.26 (3,2,3,3,3,na)
LPG (provision) 1.70E+00 kg lognormal 1.26 (3,2,3,3,3,na)
LPG (emission) 7.92E+01 MJ lognormal 1.26 (3,2,3,3,3,na)
Natural gas (provision) 2.78E+00 m3 lognormal 1.26 (3,2,3,3,3,na)
Natural gas (emission) 1.05E+02 MJ lognormal 1.26 (3,2,3,3,3,na)
Electricity, low voltage/ US 2.69E+01 kWh lognormal 1.26 (3,2,3,3,3,na)
Ammonia, liquid 1.80E+00 kg lognormal 1.08 (2,1,2,1,1,na)
Ammonium nitrate, as N 1.09E-01 kg lognormal 1.08 (2,1,2,1,1,na)
Ammonium sulfate, as N 1.14E-01 kg lognormal 1.08 (2,1,2,1,1,na)
Sodium nitrate 3.90E-03 kg lognormal 1.08 (2,1,2,1,1,na)
Urea, as N 1.06E+00 kg lognormal 1.08 (2,1,2,1,1,na)
Urea ammonium nitrate 1.78E+00 kg lognormal 1.08 (2,1,2,1,1,na)
phosphate fertilizer, as P2O5 from triple
superpshospate 2.50E-01
kg lognormal 1.08 (2,1,2,1,1,na)
phosphate fertilizer, as P2O5, from single
supersphospate 6.80E-01
kg lognormal 1.08 (2,1,2,1,1,na)
phosphate fertilizer, as P2O5, RER from
diammonium 1.22E+01
kg lognormal 1.08 (2,1,2,1,1,na)
phosphate fertilizer, as P2O5, RER from
monoammonium 5.89E+00
kg lognormal 1.08 (2,1,2,1,1,na)
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 65
Exchange Uncertainty Information
Type Name Amount Unit Compart. Subcompart. Dist. StDev95
% Indicator score
potassium chloride, as K2O 3.17E+01
kg lognormal 1.08 (2,1,2,1,1,na)
drying of bread grain, seed and legume
(infrastructure) 8.59E+0
kg lognormal 1.26 (3,2,3,3,3,na)
transport, lorry >16t, fleet average, RER 3.02E+01 tkm lognormal 2.05 (4,2,2,1,1,na)
transport, transoceanic freight ship, OCE 0.00E+00 tkm lognormal 2.05 (4,2,2,1,1,na)
transport, freight rail, diesel, US 0.00E+00 tkm lognormal 2.05 (4,2,2,1,1,na)
transport, lorry >16t, fleet average, RER 0.00E+00 tkm lognormal 2.05 (4,2,2,1,1,na)
transport, transoceanic freight ship, OCE 9.42E+01 tkm lognormal 2.05 (4,2,2,1,1,na)
transport, freight rail, diesel, US 9.42E+00 tkm lognormal 2.05 (4,2,2,1,1,na)
transport, lorry >16t, fleet average, RER 5.65E+00 tkm lognormal 2.05 (4,2,2,1,1,na)
transport, transoceanic freight ship, OCE 0.00E+00 tkm lognormal 2.05 (4,2,2,1,1,na)
transport, freight rail, diesel, US 0.00E+00 tkm lognormal 2.05 (4,2,2,1,1,na)
transport, lorry >16t, fleet average, RER 0.00E+00 tkm lognormal 2.05 (4,2,2,1,1,na)
pea seed, ip, at regional storehouse 6.52E+01 kg lognormal 1.27 (3,3,3,3,3,na)
transport, lorry >16t, fleet average, RER 2.10E+01 tkm lognormal 2.05 (4,2,2,1,1,na)
dinitroaniline-compound 1.23E-02 kg lognormal 1.08 (2,2,2,1,1,na)
cyclic-N compounds 2.09E-03 kg lognormal 1.08 (2,2,2,1,1,na)
2,4-dichlorophenol 8.79E-02 kg lognormal 1.08 (2,2,2,1,1,na)
pesticide, unspecified 8.51E-02 kg lognormal 1.08 (2,2,2,1,1,na)
dimethenamide 3.43E-03 kg lognormal 1.08 (2,2,2,1,1,na)
phenoxy-compounds 2.95E-03 kg lognormal 1.08 (2,2,2,1,1,na)
acetamide-anilide-compounds 1.46E-04 kg lognormal 1.08 (2,2,2,1,1,na)
organo-phosphorous compounds 6.73E-02 kg lognormal 1.08 (2,2,2,1,1,na)
glyphosate 1.60E+00 kg lognormal 1.08 (2,2,2,1,1,na)
diazoles 8.76E-05 kg lognormal 1.08 (2,2,2,1,1,na)
metolachlor 8.30E-02 kg lognormal 1.08 (2,2,2,1,1,na)
triazines 9.85E-03 kg lognormal 1.08 (2,2,2,1,1,na)
pyridines 1.19E-02 kg lognormal 1.08 (2,2,2,1,1,na)
pendimethanlin 2.28E-02 kg lognormal 1.08 (2,2,2,1,1,na)
[sulfony]ureas 7.44E-04 kg lognormal 1.08 (2,2,2,1,1,na)
dinitroanilines 1.91E-02 kg lognormal 1.08 (2,2,2,1,1,na)
pyrethroids 3.20E-03 kg lognormal 1.08 (2,2,2,1,1,na)
benzimidazole-compound 1.90E-04 kg lognormal 1.08 (2,2,2,1,1,na)
To
en
viro
nm
ent ammonia 3.04E+00 kg to air unspecified lognormal 1.34 (2,2,3,5,3,na)
carbon dioxide, fossil 3.06E+00 kg to air unspecified lognormal 1.27 (2,2,3,5,3,na)
nitrogen oxides 2.68E-01 kg to air unspecified lognormal 1.51 (2,2,3,5,3,na)
dinitrogen monoxide 1.48E-01 kg to air unspecified lognormal 1.51 (2,2,3,5,3,na)
dinitrogen monoxide 1.85E+00 kg to air unspecified lognormal 1.51 (2,2,3,5,3,na)
Carbon dioxide, land transformation 9.86E+00 kg to air unspecified lognormal 1.27 (2,2,3,5,3,na)
Carbon dioxide, land transformation 2.10E+01 kg to air unspecified lognormal 1.27 (2,2,3,5,3,na)
Water 2.70E+02 m3 to air unspecified lognormal 1.08 (2,2,2,1,1,na)
Phosphate, to river 5.73E-01 kg to water river lognormal 1.60 (2,2,3,5,3,na)
Phosphorus, to river 3.68E-01 kg to water river lognormal 1.60 (2,2,3,5,3,na)
Phosphate, to groundwater 2.15E-01 kg to water groundwater lognormal 1.60 (2,2,3,5,3,na)
Nitrate 8.97E+01 kg to water river lognormal 1.60 (2,2,3,5,3,na)
Water 8.01E+01 m3 to water river lognormal 1.08 (2,2,2,1,1,na)
Water 2.00E+01 m3 to water groundwater lognormal 1.08 (2,2,2,1,1,na)
Cadmium 9.29E-04 kg to soil agricultural lognormal 1.60 (2,2,3,5,3,na)
Copper -4.87E-04 kg to soil agricultural lognormal 1.60 (2,2,3,5,3,na)
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 66 August 2016
Exchange Uncertainty Information
Type Name Amount Unit Compart. Subcompart. Dist. StDev95
% Indicator score
Zinc 4.60E-02 kg to soil agricultural lognormal 1.60 (2,2,3,5,3,na)
Lead 1.01E-03 kg to soil agricultural lognormal 1.60 (2,2,3,5,3,na)
Nickel -8.50E-04 kg to soil agricultural lognormal 1.60 (2,2,3,5,3,na)
Chromium -1.31E-02 kg to soil agricultural lognormal 1.60 (2,2,3,5,3,na)
Mercury 5.29E-05 kg to soil agricultural lognormal 1.60 (2,2,3,5,3,na)
Cadmium 2.98E-05 kg to water groundwater lognormal 1.88 (2,2,3,5,3,na)
Copper 2.92E-03 kg to water groundwater lognormal 1.88 (2,2,3,5,3,na)
Zinc 1.45E-02 kg to water groundwater lognormal 1.88 (2,2,3,5,3,na)
Lead 5.62E-05 kg to water groundwater lognormal 1.88 (2,2,3,5,3,na)
Nickel 0.00E+00 kg to water groundwater lognormal 1.88 (2,2,3,5,3,na)
Chromium 1.62E-02 kg to water groundwater lognormal 1.88 (2,2,3,5,3,na)
Mercury 7.79E-07 kg to water groundwater lognormal 1.88 (2,2,3,5,3,na)
Cadmium 6.72E-05 kg to water river lognormal 1.88 (2,2,3,5,3,na)
Copper 7.65E-03 kg to water river lognormal 1.88 (2,2,3,5,3,na)
Zinc 1.03E-02 kg to water river lognormal 1.88 (2,2,3,5,3,na)
Lead 8.58E-04 kg to water river lognormal 1.88 (2,2,3,5,3,na)
Nickel 3.77E-03 kg to water river lognormal 1.88 (2,2,3,5,3,na)
Chromium 8.68E-03 kg to water river lognormal 1.88 (2,2,3,5,3,na)
Mercury 2.06E-05 kg to water river lognormal 1.88 (2,2,3,5,3,na)
Azoxystrobin 5.43E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Propiconazole 1.82E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Pyraclostrobin 5.79E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Fungizides, unspecified 2.48E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Trifloxystrobin 1.07E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
2,4-D 8.79E-02 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Acetochlor 9.27E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Acifluorfen 3.07E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Carfentrazone-ethyl 1.46E-05 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Chlorimuron-ethyl 2.73E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Clethodim 7.65E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Cloransulam-methyl 1.21E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Dicamba 1.27E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Dimethenamid 3.43E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Fenoxaprop-Pethyl ester 1.02E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Flluazifop-p-butyl 2.85E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Flumetsulam 2.04E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Flumiclorac-pentyl 5.11E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Flumioxazin 8.79E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Herbizides, unspecific 1.31E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Fomesafen 1.97E-02 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Glufosinat ammonium 1.83E-02 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Glyphosate 1.60E+00 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Imazamox 8.76E-05 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Imazaquin 4.96E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Imazethapyr 3.23E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Lactofen 2.80E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Metolachlor 8.30E-02 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Metribuzin 9.85E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Paraquat 1.19E-02 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 67
Exchange Uncertainty Information
Type Name Amount Unit Compart. Subcompart. Dist. StDev95
% Indicator score
Pendimethalin 2.28E-02 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Quizalofop-p-ethyl 1.72E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
rimsulfuron 5.84E-05 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Sethoxydim 9.20E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Sulfentrazone 1.57E-02 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Thifensulfuron 4.53E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Tribenuron-methyl 1.46E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Trifluralin 1.91E-02 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Acephate 1.44E-02 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Cyfluthrin 7.01E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Bifenthrin 2.23E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Chlorpyrifos 3.05E-02 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Cypermethrin 1.46E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Diflubenzuron 8.76E-05 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Dimethoate 4.03E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Esfenvalerate 1.46E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Insecticide, unspecified 3.07E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Cyhalothrin, gamma- 8.76E-05 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Imidacloprid 1.90E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Lambda-cyhalothrin 2.06E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Methoxyfenozide 1.90E-03 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Thiamethoxam 2.77E-04 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Zeta-cypermethrin 5.84E-05 kg to soil agricultural lognormal 1.21 (2,2,2,1,1,na)
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 68 August 2016
9 Appendix A2. Soybean Crushing and Degumming System information, data sources, and assumptions
Inputs and Outputs
As described in Section 5.2.1.1.2, the development of the soybean crude oil and soybean meal datasets
highly leveraged existing databases and publically-available information, in conjunction with new data
provided by industry partners. These data sources include Ecoinvent v3 for soybean oil and meal, the
OmniTech 2010 study on soybean processing, Fediol research on soybean processing related to crushing
and degumming, and correspondence with NOPA with regard to updated electricity demand and
allocation of crushing and degumming impacts across the co-products of soybean crude oil, soybean meal
and soy hulls. In general, data from OmniTech (2010) was used by default and supplemented with other
sources containing newer information, more thorough information, or information recommended by
industry experts. The OmniTech 2010 dataset represents the combined output of 1000 kg of soybean
crude oil and 4131 kg soybean meal, and so modifications were made to develop two distinct datasets for
each of soybean crude oil and soybean meal, as well as to scale down the functional unit to 1 kg soybean
crude oil and 1 kg soybean meal. These modifications are described below.
Allocation of materials, energy and emissions across co-products
The allocation metric between oil and meal was updated from that used in the prior study, which was a
mass allocation of 80.5% to oil and 19.5% to meal (OmniTech 2010). NOPA crush reports substantiate an
oil yield of 19% (11.4 lb. per bushel long term average); dehulled meal yield runs about 44 lb. per bushel
(~73%); hull yield is 3.6 lb. per bushel (6%) which implies a shrink of 1 lb. per bushel. This shrink is
moisture loss (beans at 13%, meal at 12%, hulls at 12%, and oil at 0.5%) plus normal processing loss.
Prorating the moisture loss based on the moisture content of each of these co-products, the new mass
allocation will be 73.0 percent to soy meal, 21.0% to crude oil, and 6.0% to soybean hulls (Thompson
Reuters 2014).
Materials
Data from the OmniTech report (2010) were leveraged to represent the quantities of soybeans, hexane,
and tap water required for crushing and degumming. These quantities were scaled to the relevant
functional unit.
Electricity and heat
NOPA (2014) provided updated electricity demand data of 202 kWh per 1000 kg crude soybean oil,
relative to 289 kWh used in USB’s past report (OmniTech 2010). Data to permit modeling regional
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 69
electricity demand based on facility location was not available and so the Ecoinvent v3 US average
regional electricity grid mix was applied4.
For heat/fuels, data from the Omnitech (2010) report was used, including the types of fuels and their
relative proportions. These quantities were scaled to the relevant functional unit and to MJ instead of
kCal.
Emissions to air and water
Hexane emissions to air are based on OmniTech data (2010). Water emissions to air were calculated
based on a water balance of water input and wastewater outputs. Water emissions to water (i.e.,
wastewater) are based on based on OmniTech data (2010) and by applying an estimated density of one
kg/l.
Waste outputs
Inert waste quantity is based on OmniTech data (2010).
4http://www.nopa.org/content/oilseed/NOPA%20Plants%20-%20Location%20by%20State%20_%20June%202013.pdf
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 70 August 2016
Inventory
The following table shows all input and output flows related to the crushing and degumming process which produces soybean crude oil and
soybean meal, as well as the corresponding uncertainty information.
Table 32. Inventory for soybean crude oil and soybean meal in the US
Exchange Uncertainty Information
Amount
Type Name Soybean
Crude Oil Soybean meal Unit Ref. flow choice Dataset choice Dist. StDev95%
Indicator
score
Ref
eren
ce
flo
w soybean crude oil 1.00E+00 kg n.a. n.a. n.a.
soybean meal 1.00E+00 kg n.a. n.a. n.a.
Mat
eria
ls/f
uel
s
Soybean production /US [kg] 1.10E+00 9.25E-01 kg OmniTech 2010 USB 2014 lognormal 1.14 (2, 2, 3, 1, 1,
3, 1.05)
Hexane {GLO}| market for | Alloc Rec,
U
6.22E-04 5.23E-04 kg OmniTech 2010 ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.51 (2, 2, 5, 2, 1,
3, 1.05)
Tap water {RoW}| market for | Alloc
Rec, U
5.35E-01 4.50E-01 kg OmniTech 2010 ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.53 (2, 4, 5, 2, 1,
3, 1.05)
Oil mill {GLO}| market for | Alloc Rec,
U
6.95E-11 2.42E-10 p ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 4.04 (4, 5, 5, 3, 5,
5, 3)
Ele
ctri
city
/hea
t
Heat, district or industrial, natural gas
{RoW}| market for heat, district or
industrial, natural gas | Alloc Rec, U
9.29E-01 7.82E-01 MJ OmniTech 2010, 238 kCal per
MJ, 80% boiler efficiency to
convert heat to steam
modeler choice (USB 2015) lognormal 1.14 (2, 2, 3, 1, 1,
3, 1.05)
Heat, district or industrial, other than
natural gas {RoW}| heat production,
light fuel oil, at industrial furnace
1MW | Alloc Rec, U
7.15E-03 6.01E-03 MJ OmniTech 2010, 238 kCal per
MJ, 80% boiler efficiency to
convert heat to steam
modeler choice (USB 2015);
interpreted FO #2 = light fuel
oil
lognormal 1.14 (2, 2, 3, 1, 1,
3, 1.05)
Heat, district or industrial, other than
natural gas {RoW}| heat production,
heavy fuel oil, at industrial furnace
1MW | Alloc Rec, U
1.43E-02 1.20E-02 MJ OmniTech 2010, 238 kCal per
MJ, 80% boiler efficiency to
convert heat to steam
modeler choice (USB 2015);
interpreted FO#6 as "heavy
fuel oil"
lognormal 1.14 (2, 2, 3, 1, 1,
3, 1.05)
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 71
Exchange Uncertainty Information
Amount
Type Name Soybean
Crude Oil Soybean meal Unit Ref. flow choice Dataset choice Dist. StDev95%
Indicator
score
Heat, district or industrial, other than
natural gas {RoW}| heat production,
at hard coal industrial furnace 1-
10MW | Alloc Rec, U
4.57E-01 3.85E-01 MJ OmniTech 2010, 238 kCal per
MJ, 80% boiler efficiency to
convert heat to steam
modeler choice (USB 2015) lognormal 1.14 (2, 2, 3, 1, 1,
3, 1.05)
Heat, central or small-scale, other
than natural gas {CH}| treatment of
biogas, burned in micro gas turbine
100kWe | Alloc Rec, U
1.43E-02 1.20E-02 MJ OmniTech 2010, 238 kCal per
MJ, 80% boiler efficiency to
convert heat to steam
modeler choice (USB 2015) lognormal 1.14 (2, 2, 3, 1, 1,
3, 1.05)
Heat, central or small-scale, other
than natural gas {CH}| treatment of
biogas, burned in micro gas turbine
100kWe | Alloc Rec, U
7.15E-03 6.01E-03 MJ OmniTech 2010, 238 kCal per
MJ, 80% boiler efficiency to
convert heat to steam
modeler choice (USB 2015) lognormal 1.25 (2, 2, 3, 1, 3,
3, 1.05)
Electricity, medium voltage {HICC}|
market for | Alloc Rec, U
1.15E-04 9.64E-05 kWh NOPA 2014 electricity,
regionalized using ecoinvent
ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.09 (2, 2, 1, 2, 1,
3, 1.05)
Electricity, medium voltage {NPCC, US
only}| market for | Alloc Rec, U
2.77E-03 2.33E-03 kWh NOPA 2014 electricity,
regionalized using ecoinvent
ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.09 (2, 2, 1, 2, 1,
3, 1.05)
Electricity, medium voltage {WECC,
US only}| market for | Alloc Rec, U
7.86E-03 6.61E-03 kWh NOPA 2014 electricity,
regionalized using ecoinvent
ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.09 (2, 2, 1, 2, 1,
3, 1.05)
Electricity, medium voltage {FRCC}|
market for | Alloc Rec, U
2.21E-03 1.86E-03 kWh NOPA 2014 electricity,
regionalized using ecoinvent
ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.09 (2, 2, 1, 2, 1,
3, 1.05)
Electricity, medium voltage {RFC}|
market for | Alloc Rec, U
9.77E-03 8.22E-03 kWh NOPA 2014 electricity,
regionalized using ecoinvent
ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.09 (2, 2, 1, 2, 1,
3, 1.05)
Electricity, medium voltage {MRO, US
only}| market for | Alloc Rec, U
2.37E-03 1.99E-03 kWh NOPA 2014 electricity,
regionalized using ecoinvent
ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
lognormal 1.09 (2, 2, 1, 2, 1,
3, 1.05)
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 72 August 2016
Exchange Uncertainty Information
Amount
Type Name Soybean
Crude Oil Soybean meal Unit Ref. flow choice Dataset choice Dist. StDev95%
Indicator
score
Alloc Recy, U>
Electricity, medium voltage {ASCC}|
market for | Alloc Rec, U
7.23E-05 6.08E-05 kWh NOPA 2014 electricity,
regionalized using ecoinvent
ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.09 (2, 2, 1, 2, 1,
3, 1.05)
Electricity, medium voltage {TRE}|
market for | Alloc Rec, U
3.63E-03 3.05E-03 kWh NOPA 2014 electricity,
regionalized using ecoinvent
ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.09 (2, 2, 1, 2, 1,
3, 1.05)
Electricity, medium voltage {SERC}|
market for | Alloc Rec, U
1.14E-02 9.61E-03 kWh NOPA 2014 electricity,
regionalized using ecoinvent
ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.09 (2, 2, 1, 2, 1,
3, 1.05)
Electricity, medium voltage {SPP}|
market for | Alloc Rec, U
2.21E-03 1.86E-03 kWh NOPA 2014 electricity,
regionalized using ecoinvent
ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.09 (2, 2, 1, 2, 1,
3, 1.05)
Em
issi
on
s to
air
Water/m3 2.44E-04 2.06E-04 m3 OmniTech 2010 ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.93 (4, 5, 5, 2, 4,
5, 1.05)
Hexane 6.22E-04 5.23E-04 kg OmniTech 2010 ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 2.03 (3, 2, 3, 1, 1,
3, 2)
Was
te t
o t
reat
men
t
Wastewater from vegetable oil
refinery {GLO}| treatment of | Alloc
Rec, U
2.90E-04 2.44E-04 m3 Assume average density of oily
water is 1
lognormal 1.93 (4, 5, 5, 2, 4,
5, 1.05)
Inert waste, for final disposal {GLO}|
market for | Alloc Rec, U
1.83E-03 1.54E-03 kg OmniTech 2010 ecoinvent 3 dataset, <Soybean
oil, crude {US}| soybean meal
and crude oil production |
Alloc Recy, U>
lognormal 1.14 (2, 2, 3, 1, 1,
3, 1.05)
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 73
10 Appendix A3. Soybean Oil Refinement System information, data sources, and assumptions
Inputs and outputs
As described in Section 5.2.1.1.3, the development of the soybean refined oil dataset highly leveraged
existing databases and publically-available information. These data sources include Ecoinvent v3 data for
soybean oil and meal, and the OmniTech 2010 study on soybean processing. In general, data from
OmniTech (2010) was used by default and supplemented with other sources as needed. The OmniTech
2010 dataset represents the combined output of 1000 kg of soybean refined oil and 7.4 kg soap, and so
modifications were made to develop a single dataset devoted to the refined oil, as well as to scale down
the functional unit to 1 kg of refined oil. These modifications are described below.
Allocation of materials, energy and emissions across co-products
The OmniTech (2010) dataset for refinement included co-products of refined oil and soap. To create a
dedicated refined oil dataset, a mass allocation metric was used based on the masses of refined oil and
soap output from the OmniTech study, resulting in 99.3% to refined oil and 0.7% to soap (OmniTech
2010). It is expected that an economic allocation metric would be quite similar or perhaps allocating
nearly 100% of flows to the refined oil.
Materials
Data from the Omnitech report (2010) were leveraged to represent the quantities of soybean crude oil,
sodium hydroxide, and tap water required for refinement. These quantities were scaled to the relevant
functional unit. A portion of vegetable oil refinery was assigned to the refined oil as well as was done in
the Ecoinvent v3 process for soybean oil refinery operation.
Electricity and heat
Total electricity demand was based on OmniTech (2010) data. Data to permit modeling regional
electricity demand based on facility location was not available and so the Ecoinvent v3 US average
regional electricity grid mix was applied.5.
Natural gas heat quantity was calculated based on OmniTech (2010) data using an estimated boiler
efficiency of 80%6 and a presumed lower heating value of 38.2 MJ/m3 natural gas 7.
5http://www.nopa.org/content/oilseed/NOPA%20Plants%20-%20Location%20by%20State%20_%20June%202013.pdf 6 http://www.ncsu.edu/project/feedmill/pdf/E_Reducing%20Energy%20Cost%20Through%20Boiler%20Efficiency.pdf 7 MIT Units and Conversion Fact Sheet: http://ecreee.wikischolars.columbia.edu/file/view/MIT+-+Units+and+Conversion+Factors+Fact+Sheet.pdf
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 74 August 2016
Emissions to air
Water emissions to air were calculated based on a water balance of water input and wastewater outputs
from OmniTech (2010).
Waste outputs
Wastewater quantity is based on quantities of water, unsaponifiable materials, and saponifiable materials
identified by OmniTech (2010), using the following densities, respectively: 1 kg/l, 1.1261 kg/l, and 0.926
kg/l8.
8 http://www.simetric.co.uk/si_water.htm
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 75
Inventory
The following table shows all input and output flows related to the soybean oil refinement process which produces refined soybean oil, as
well as the corresponding uncertainty information.
Table 33. Inventory for soybean refined oil and soybean meal in the US
Exchange Uncertainty Information
Amount
Type Name Soybean
Crude Oil Unit Ref. flow choice Dataset choice Dist. StDev95%
Indicator
score
Ref
eren
c
e fl
ow
soybean refined oil 1.00E+00 kg n.a. n.a. n.a.
Mat
eria
ls/f
uel
s
Vegetable oil refinery {GLO}| market for | Alloc
Rec, U 1.07E-10 p
Based on ecoinvent v3.1:
Soybean oil, refined {US}|
soybean oil refinery operation |
Alloc Rec, U
EI 3.1 lognormal 3.96 (4, 3, 5, 3, 5,
4, 3)
Tap water {RoW}| market for | Alloc Rec, U 1.55E-01 kg Based on OmniTech 2010 EI 3.1 lognormal 2.32 (3, 5, 5, 5, 5,
4, 1.05)
Sodium hydroxide, without water, in 50%
solution state {GLO}| market for | Alloc Rec, U 2.28E-03 kg Based on OmniTech 2010 lognormal 1.61
(3, 5, 5, 5, 1,
4, 1.05)
Crude soybean oil production, US, cutoff
allocation 1.03E+00 kg Based on OmniTech 2010 USB 2014 dataset lognormal 1.16
(2, 2, 3, 1, 1,
4, 1.05)
Ele
ctri
city
/hea
t
Heat, district or industrial, natural gas {RoW}|
market for heat, district or industrial, natural
gas | Alloc Rec, U
7.47E-02 MJ
Based on Omnitech 2010: steam
energy 56,644 Btu, boiler
efficiency of 80%
EI 3.1 lognormal 1.22 (2, 4, 3, 5, 1,
4, 1.05)
Electricity, medium voltage {HICC}| market for
| Alloc Rec, U 1.20E-05 kWh
OmniTech 2010 electricity,
regionalized using ecoinvent EI 3.1 regionalization lognormal 1.22
(2, 4, 3, 5, 1,
4, 1.05)
Electricity, medium voltage {NPCC, US only}|
market for | Alloc Rec, U 2.89E-04 kWh
OmniTech 2010 electricity,
regionalized using ecoinvent EI 3.1 regionalization lognormal 1.22
(2, 4, 3, 5, 1,
4, 1.05)
Electricity, medium voltage {WECC, US only}|
market for | Alloc Rec, U 8.21E-04 kWh
OmniTech 2010 electricity,
regionalized using ecoinvent EI 3.1 regionalization lognormal 1.22
(2, 4, 3, 5, 1,
4, 1.05)
Electricity, medium voltage {FRCC}| market for
| Alloc Rec, U 2.31E-04 kWh
OmniTech 2010 electricity,
regionalized using ecoinvent EI 3.1 regionalization lognormal 1.22
(2, 4, 3, 5, 1,
4, 1.05)
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 76 August 2016
Exchange Uncertainty Information
Amount
Type Name Soybean
Crude Oil Unit Ref. flow choice Dataset choice Dist. StDev95%
Indicator
score
Electricity, medium voltage {RFC}| market for |
Alloc Rec, U 1.02E-03 kWh
OmniTech 2010 electricity,
regionalized using ecoinvent EI 3.1 regionalization lognormal 1.22
(2, 4, 3, 5, 1,
4, 1.05)
Electricity, medium voltage {MRO, US only}|
market for | Alloc Rec, U 2.47E-04 kWh
OmniTech 2010 electricity,
regionalized using ecoinvent EI 3.1 regionalization lognormal 1.22
(2, 4, 3, 5, 1,
4, 1.05)
Electricity, medium voltage {ASCC}| market for
| Alloc Rec, U 7.55E-06 kWh
OmniTech 2010 electricity,
regionalized using ecoinvent EI 3.1 regionalization lognormal 1.22
(2, 4, 3, 5, 1,
4, 1.05)
Electricity, medium voltage {TRE}| market for
| Alloc Rec, U 3.79E-04 kWh
OmniTech 2010 electricity,
regionalized using ecoinvent EI 3.1 regionalization lognormal 1.22
(2, 4, 3, 5, 1,
4, 1.05)
Electricity, medium voltage {SERC}| market for
| Alloc Rec, U 1.19E-03 kWh
OmniTech 2010 electricity,
regionalized using ecoinvent EI 3.1 regionalization lognormal 1.22
(2, 4, 3, 5, 1,
4, 1.05)
Electricity, medium voltage {SPP}| market for |
Alloc Rec, U 2.31E-04 kWh
OmniTech 2010 electricity,
regionalized using ecoinvent EI 3.1 regionalization lognormal 1.22
(2, 4, 3, 5, 1,
4, 1.05)
Em
issi
on
s to
air
Water/m3 3.30E-05 m3 water balance calculation, idea
from eiv3 0 lognormal 1.91
(4, 5, 5, 5, 4,
4, 1.05)
Was
te t
o t
reat
men
t
Wastewater from vegetable oil refinery {GLO}|
market for | Alloc Rec, U 1.22E-04 m3
Based on OmniTech 2010 waste
outputs (kg) and density
assumptions
EI 3.1 lognormal 1.87 (3, 5, 5, 5, 4,
4, 1.05)
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 77
11 Appendix B. Description of impact categories
Human health
Impact that can be caused by the release of substances that affect humans through acute toxicity, cancer-
based toxicity, respiratory effects, increases in UV radiation, and other causes; an evaluation of the
overall impact of a system on human health has been made following the human health end-point in the
IMPACT 2002+ methodology, in which substances are weighted based on their abilities to cause each of a
variety of damages to human health. These impacts are measured in units of disability-adjusted life years
(DALY), which combine estimations of morbidity and mortality from a variety of causes.
Ecosystem quality
Impairment from the release of substances that cause acidification, eutrophication, toxicity to wildlife,
land occupation, and a variety of other types of impact; an evaluation of the overall impact of a system on
ecosystem quality has been made following the Ecosystem quality endpoint IMPACT 2002+ methodology,
in which substances are weighted based on their ability to cause each of a variety of damages to wildlife
species. These impacts are measured in units of potentially disappearing fractions (PDF), which relate to
the likelihood of species loss.
Resources depletion
Depletion caused when nonrenewable resources are used or when renewable resources are used at a
rate greater than they can be renewed; various materials can be weighted more heavily based on their
abundance and difficulty to obtain. An evaluation of the overall impact of a system on resource depletion
has been made following the resources end-point in the IMPACT 2002+ methodology, which combines
nonrenewable energy use with an estimate of the increased amount of energy that will be required to
obtain an additional incremental amount of that substance from the earth based on the Ecoindicator 99
method (Goedkoop and Spriensma 2000).
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 78 August 2016
Climate change
Alterations in the statistical distribution of weather patterns of the planet over time that last for decades
or longer9; Climate change is represented based on the International Panel on Climate Change’s 100-year
weightings of the global warming potential of various substances (IPCC 2007)10. Substances known to
contribute to global warming are weighted based on an identified global warming potential expressed in
grams of CO2 equivalents. Because the uptake and emission of CO2 from biological sources can often lead
to misinterpretations of results, it is not unusual to omit this biogenic CO2 from consideration when
evaluating global warming potentials. Here, the recommendation of the PAS 2050 product carbon
footprinting guidance is followed in not considering either the uptake or emission of CO2 from biological
systems and correcting biogenic emissions of other gasses accordingly by subtracting the equivalent
value for CO2 based on the carbon content of the gas (BSI 2008).
Water withdrawal
Sum of all volumes of water used in the life cycle of the product, with the exception of water used in
turbines (for hydropower production). This includes the water use (m3 of water needed) whether it is
evaporated, consumed or released again downstream. Drinking water, irrigation water and water for and
in industrialized processes (including cooling water) are all taken into account. It considers freshwater
and sea water.
9 Quantis definition 10 IPCC published updated CFs in 2013; however, IPCC 2007 CFs were used in this study in alignment with the use of the IMPACT 2002+ vQ2.2 methodology (Humbert et al. 2012) and to permit comparison of results to those of the former USB soybean analysis (OmniTech 2010).
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 79
12 Appendix C. Results of the Data Quality Assessment
Data sources are assessed on the basis of time-related coverage, geographical coverage, technology coverage, precision, completeness,
representativeness, consistency, reproducibility, reliability of data source and uncertainty of the information as prescribed in ISO 14044.
The pedigree matrix for rating inventory data appears below, with a score of one being most favorable and a score of five being least
favorable, and a complete discussion of this topic can be found in Frischknecht, et al (2007).
The data quality assessment results are applied on the basis of data categories. Table 34 through Table 36 list all relevant data categories
and their corresponding quality assessment scores.
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 80 August 2016
Table 34: Data quality assessment for soybean cultivation, for all relevant data categories The basic uncertainty and the pedigree scores are considered in the calculation of the standard deviation.
Data category
Re
lia
bil
ity
Co
mp
lete
ne
ss
Te
mp
ora
l co
rre
lati
on
Ge
og
rap
hic
al
corr
ela
tio
n
Fu
rth
er
tech
no
log
ica
l co
rre
lati
on
Sa
mp
le s
ize
Ba
sic
Un
cert
ain
ty
Ind
ica
tor
sco
re
Sta
nd
ard
De
via
tio
n9
5%
Da
ta q
ua
lity
va
lue
To
p 8
0%
co
ntr
ibu
tor?
Seed quantity 3 3 3 3 3 9 1.05 (3,3,3,3,3,na) 1.27 Adequate
Fertiliser quantity and fertiliser application 2 1 2 1 1 9 1.05 (2,1,2,1,1,na) 1.08 Good
Pesticide manufacturing (input) 2 2 2 1 1 9 1.05 (2,2,2,1,1,na) 1.08 Good
Pesticide active ingredient emission to the soil 2 2 2 1 1 9 1.2 (2,2,2,1,1,na) 1.21 Good
Land occupation 2 2 2 1 1 9 1.1 (2,2,2,1,1,na) 1.12 Good
Land transformation 2 2 2 1 1 9 1.2 (2,2,2,1,1,na) 1.21 Good
Energy carriers, fuel, work processes 3 2 3 3 3 9 1.05 (3,2,3,3,3,na) 1.26 Adequate
Electricity 3 2 3 1 1 9 1.05 (3,2,3,1,1,na) 1.16 Adequate
Transports 4 2 2 1 1 9 2 (4,2,2,1,1,na) 2.05 Adequate
Irrigation water 2 2 2 1 1 9 1.05 (2,2,2,1,1,na) 1.08 Good
CO2 and energy uptake in biomass 2 2 2 1 1 9 1.05 (2,2,2,1,1,na) 1.08 Good
CO2 emissions 2 2 3 5 3 9 1.05 (2,2,3,5,3,na) 1.27 Poor No, <2.4%
From agriculture: CH4, NH3 to air 2 2 3 5 3 9 1.2 (2,2,3,5,3,na) 1.34
Poor Perhaps – NH3 contributes 19% to Human health
From agriculture: N2O, NOx to air 2 2 3 5 3 9 1.4 (2,2,3,5,3,na) 1.51
Poor Yes – N2O contributes 53% to Climate
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 81
Data category
Re
lia
bil
ity
Co
mp
lete
ne
ss
Te
mp
ora
l co
rre
lati
on
Ge
og
rap
hic
al
corr
ela
tio
n
Fu
rth
er
tech
no
log
ica
l co
rre
lati
on
Sa
mp
le s
ize
Ba
sic
Un
cert
ain
ty
Ind
ica
tor
sco
re
Sta
nd
ard
De
via
tio
n9
5%
Da
ta q
ua
lity
va
lue
To
p 8
0%
co
ntr
ibu
tor?
change
From agriculture: NO3, PO4 to water 2 2 3 5 3 9 1.5 (2,2,3,5,3,na) 1.60 Poor No, <0.2%
From agriculture: heavy metals to water 2 2 3 5 3 9 1.8 (2,2,3,5,3,na) 1.88 Poor No, <1%
From agriculture: heavy metals to soil 2 2 3 5 3 9 1.5 (2,2,3,5,3,na) 1.60
Poor Perhaps – zinc and cadmium contribute 27% to Human health
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 82 August 2016
Table 35. Data quality assessment for soybean crude oil and soybean meal, for all relevant data categories The basic uncertainty and the pedigree scores are considered in the calculation of the standard deviation.
Unit process
Re
lia
bil
ity
Co
mp
lete
ne
ss
Te
mp
ora
l co
rre
lati
on
Ge
og
rap
hic
al
corr
ela
tio
n
Fu
rth
er
tech
no
log
ica
l co
rre
lati
on
Sa
mp
le s
ize
Ba
sic
Un
cert
ain
ty
Ind
ica
tor
sco
re
Sta
nd
ard
De
via
tio
n9
5%
Da
ta q
ua
lity
va
lue
To
p 8
0%
co
ntr
ibu
tor?
Materials/fuels
Soybean production /US [kg] 2 2 3 1 1 3 1.05 (2, 2, 3, 1, 1,
3, 1.05) 1.14 Adequate
Hexane {GLO}| market for | Alloc Rec, U 2 2 5 2 1 3 1.05 (2, 2, 5, 2, 1,
3, 1.05) 1.51 Poor No, <0.5%
Tap water {RoW}| market for | Alloc Rec, U 2 4 5 2 1 3 1.05 (2, 4, 5, 2, 1,
3, 1.05) 1.53 Poor No, <0.5%
Oil mill {GLO}| market for | Alloc Rec, U 4 5 5 3 5 5 3 (4, 5, 5, 3, 5,
5, 3) 4.04 Poor No, <0.2%
Electricity/heat
Heat, district or industrial, natural gas {RoW}| market for heat, district or industrial, natural gas | Alloc Rec, U
2 2 3 1 1 3 1.05 (2, 2, 3, 1, 1,
3, 1.05) 1.14 Adequate
Heat, district or industrial, other than natural gas {RoW}| heat production, light fuel oil, at industrial furnace 1MW | Alloc Rec, U
2 2 3 1 1 3 1.05 (2, 2, 3, 1, 1,
3, 1.05) 1.14 Adequate
Heat, district or industrial, other than natural gas {RoW}| heat production, heavy fuel oil, at industrial furnace 1MW | Alloc
2 2 3 1 1 3 1.05 (2, 2, 3, 1, 1,
3, 1.05) 1.14 Adequate
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 83
Unit process
Re
lia
bil
ity
Co
mp
lete
ne
ss
Te
mp
ora
l co
rre
lati
on
Ge
og
rap
hic
al
corr
ela
tio
n
Fu
rth
er
tech
no
log
ica
l co
rre
lati
on
Sa
mp
le s
ize
Ba
sic
Un
cert
ain
ty
Ind
ica
tor
sco
re
Sta
nd
ard
De
via
tio
n9
5%
Da
ta q
ua
lity
va
lue
To
p 8
0%
co
ntr
ibu
tor?
Rec, U
Heat, district or industrial, other than natural gas {RoW}| heat production, at hard coal industrial furnace 1-10MW | Alloc Rec, U
2 2 3 1 1 3 1.05 (2, 2, 3, 1, 1,
3, 1.05) 1.14 Adequate
Heat, central or small-scale, other than natural gas {CH}| treatment of biogas, burned in micro gas turbine 100kWe | Alloc Rec, U
2 2 3 1 1 3 1.05 (2, 2, 3, 1, 1,
3, 1.05) 1.14 Adequate
Heat, central or small-scale, other than natural gas {CH}| treatment of biogas, burned in micro gas turbine 100kWe | Alloc Rec, U
2 2 3 1 3 3 1.05 (2, 2, 3, 1, 3,
3, 1.05) 1.25 Adequate
Electricity, medium voltage {HICC}| market for | Alloc Rec, U
2 2 1 2 1 3 1.05 (2, 2, 1, 2, 1,
3, 1.05) 1.09 Adequate
Electricity, medium voltage {NPCC, US only}| market for | Alloc Rec, U
2 2 1 2 1 3 1.05 (2, 2, 1, 2, 1,
3, 1.05) 1.09 Adequate
Electricity, medium voltage {FRCC}| market for | Alloc Rec, U
2 2 1 2 1 3 1.05 (2, 2, 1, 2, 1,
3, 1.05) 1.09 Adequate
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 84 August 2016
Unit process
Re
lia
bil
ity
Co
mp
lete
ne
ss
Te
mp
ora
l co
rre
lati
on
Ge
og
rap
hic
al
corr
ela
tio
n
Fu
rth
er
tech
no
log
ica
l co
rre
lati
on
Sa
mp
le s
ize
Ba
sic
Un
cert
ain
ty
Ind
ica
tor
sco
re
Sta
nd
ard
De
via
tio
n9
5%
Da
ta q
ua
lity
va
lue
To
p 8
0%
co
ntr
ibu
tor?
Electricity, medium voltage {RFC}| market for | Alloc Rec, U
2 2 1 2 1 3 1.05 (2, 2, 1, 2, 1,
3, 1.05) 1.09 Adequate
Electricity, medium voltage {MRO, US only}| market for | Alloc Rec, U
2 2 1 2 1 3 1.05 (2, 2, 1, 2, 1,
3, 1.05) 1.09 Adequate
Electricity, medium voltage {ASCC}| market for | Alloc Rec, U
2 2 1 2 1 3 1.05 (2, 2, 1, 2, 1,
3, 1.05) 1.09 Adequate
Electricity, medium voltage {TRE}| market for | Alloc Rec, U
2 2 1 2 1 3 1.05 (2, 2, 1, 2, 1,
3, 1.05) 1.09 Adequate
Electricity, medium voltage {SERC}| market for | Alloc Rec, U
2 2 1 2 1 3 1.05 (2, 2, 1, 2, 1,
3, 1.05) 1.09 Adequate
Electricity, medium voltage {SPP}| market for | Alloc Rec, U
2 2 1 2 1 3 1.05 (2, 2, 1, 2, 1,
3, 1.05) 1.09 Adequate
Emissions to air
Water/m3 4 5 5 2 4 5 1.05 (4, 5, 5, 2, 4,
5, 1.05) 1.93 Poor No, <0.1%
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 85
Unit process
Re
lia
bil
ity
Co
mp
lete
ne
ss
Te
mp
ora
l co
rre
lati
on
Ge
og
rap
hic
al
corr
ela
tio
n
Fu
rth
er
tech
no
log
ica
l co
rre
lati
on
Sa
mp
le s
ize
Ba
sic
Un
cert
ain
ty
Ind
ica
tor
sco
re
Sta
nd
ard
De
via
tio
n9
5%
Da
ta q
ua
lity
va
lue
To
p 8
0%
co
ntr
ibu
tor?
Hexane 3 2 3 1 1 3 2 (3, 2, 3, 1, 1,
3, 2) 2.03 Adequate
Waste to treatment
Wastewater from vegetable oil refinery {GLO}| treatment of | Alloc Rec, U
4 5 5 2 4 5 1.05 (4, 5, 5, 2, 4,
5, 1.05) 1.93 Poor No, <0.5%
Inert waste, for final disposal {GLO}| market for | Alloc Rec, U
2 2 3 1 1 3 1.05 (2, 2, 3, 1, 1,
3, 1.05) 1.14 Adequate
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 86 August 2016
Table 36. Data quality assessment for soybean refined oil, for all relevant data categories The basic uncertainty and the pedigree scores are considered in the calculation of the standard deviation.
Unit process
Re
lia
bil
ity
Co
mp
lete
ne
ss
Te
mp
ora
l co
rre
lati
on
Ge
og
rap
hic
al
corr
ela
tio
n
Fu
rth
er
tech
no
log
ica
l co
rre
lati
on
Sa
mp
le s
ize
Ba
sic
Un
cert
ain
ty
Ind
ica
tor
sco
re
Sta
nd
ard
De
via
tio
n9
5%
Da
ta q
ua
lity
va
lue
To
p 8
0%
co
ntr
ibu
tor?
Materials/fuels
Vegetable oil refinery {GLO}| market for | Alloc Rec, U
4 3 5 3 5 4 3 (4, 3, 5, 3, 5,
4, 3) 3.96 Poor No, <0.5%
Tap water {RoW}| market for | Alloc Rec, U 3 5 5 5 5 4 1.05 (3, 5, 5, 5, 5,
4, 1.05) 2.32 Poor No, <0.2%
Sodium hydroxide, without water, in 50% solution state {GLO}| market for | Alloc Rec, U
3 5 5 5 1 4 1.05 (3, 5, 5, 5, 1,
4, 1.05) 1.61 Poor No, <1%
Crude soybean oil production, US, cutoff allocation
2 2 3 1 1 4 1.05 (2, 2, 3, 1, 1,
4, 1.05) 1.16 Adequate
Electricity/heat
Heat, district or industrial, natural gas {RoW}| market for heat, district or industrial, natural gas | Alloc Rec, U
2 4 3 5 1 4 1.05 (2, 4, 3, 5, 1,
4, 1.05) 1.22 Poor No, <1.5%
Electricity, medium voltage {HICC}| market for | Alloc Rec, U
2 4 3 5 1 4 1.05 (2, 4, 3, 5, 1,
4, 1.05) 1.22
Poor <0.1% Electricity, medium voltage {NPCC, US only}| market for | Alloc Rec, U
2 4 3 5 1 4 1.05 (2, 4, 3, 5, 1,
4, 1.05) 1.22
Electricity, medium voltage {WECC, US only}| market for | Alloc Rec, U
2 4 3 5 1 4 1.05 (2, 4, 3, 5, 1,
4, 1.05) 1.22
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 87
Unit process
Re
lia
bil
ity
Co
mp
lete
ne
ss
Te
mp
ora
l co
rre
lati
on
Ge
og
rap
hic
al
corr
ela
tio
n
Fu
rth
er
tech
no
log
ica
l co
rre
lati
on
Sa
mp
le s
ize
Ba
sic
Un
cert
ain
ty
Ind
ica
tor
sco
re
Sta
nd
ard
De
via
tio
n9
5%
Da
ta q
ua
lity
va
lue
To
p 8
0%
co
ntr
ibu
tor?
Electricity, medium voltage {FRCC}| market for | Alloc Rec, U
2 4 3 5 1 4 1.05 (2, 4, 3, 5, 1,
4, 1.05) 1.22
Electricity, medium voltage {RFC}| market for | Alloc Rec, U
2 4 3 5 1 4 1.05 (2, 4, 3, 5, 1,
4, 1.05) 1.22
Electricity, medium voltage {MRO, US only}| market for | Alloc Rec, U
2 4 3 5 1 4 1.05 (2, 4, 3, 5, 1,
4, 1.05) 1.22
Electricity, medium voltage {ASCC}| market for | Alloc Rec, U
2 4 3 5 1 4 1.05 (2, 4, 3, 5, 1,
4, 1.05) 1.22
Electricity, medium voltage {TRE}| market for | Alloc Rec, U
2 4 3 5 1 4 1.05 (2, 4, 3, 5, 1,
4, 1.05) 1.22
Electricity, medium voltage {SERC}| market for | Alloc Rec, U
2 4 3 5 1 4 1.05 (2, 4, 3, 5, 1,
4, 1.05) 1.22
Electricity, medium voltage {SPP}| market for | Alloc Rec, U
2 4 3 5 1 4 1.05 (2, 4, 3, 5, 1,
4, 1.05) 1.22
Emissions to air
Water/m3 4 5 5 5 4 4 1.05 (4, 5, 5, 5, 4,
4, 1.05) 1.91 Poor No, <1%
Waste to treatment
Wastewater from vegetable oil refinery {GLO}| market for | Alloc Rec, U
3 5 5 5 4 4 1.05 (3, 5, 5, 5, 4,
4, 1.05) 1.87 Poor No, <0.1%
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 88 August 2016
Although every effort is made to establish the best available information, and to consider key influential factors, such as geography,
temporal relevance, scientific credibility, and internal study consistency, life cycle assessment is a complex task and relies on numerous data
sources and assumptions. While the results presented by this study are intended to be considered reliable, they should be used only within
the context of the boundaries and limitations discussed in this report. In cases where important information is unknown, uncertain, or
highly variable, sensitivity analyses are performed to evaluate the potential importance of the data gap.
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 89
13 Appendix D. Results of the LCIA and Contribution analysis
Table 37. IMPACT 2002+ v2.21 endpoint results of soybean cultivation, average US The absolute impacts are provided for each endpoint category and are expressed per kg soybean at farm gate.
Human Health
Ecosystem Quality
Resources Climate Change Water withdrawal
DALY PDF.m2.y MJ kg CO2 -eq m3
Land use occupation 0.00E+00 4.32E+00 0.00E+00 0.00E+00 0.00E+00
Irrigation 2.21E-08 5.53E-03 4.06E-01 2.23E-02 1.03E-01
Machine use 1.55E-07 2.26E-02 1.39E+00 9.80E-02 1.85E-03
Transport 3.28E-09 2.53E-03 4.63E-02 2.91E-03 3.76E-05
Pesticide production 9.06E-09 1.76E-03 1.44E-01 8.62E-03 8.70E-04
N - Fertilizer 6.14E-09 9.97E-04 1.07E-01 7.94E-03 4.99E-04
P - Fertilizer 2.04E-08 3.83E-03 2.56E-01 1.26E-02 2.15E-03
K - Fertilizer 6.09E-09 1.89E-03 1.13E-01 6.76E-03 4.18E-04
Seeds 1.60E-08 8.80E-03 1.38E-01 1.25E-02 2.43E-03
Drying of beans 1.02E-08 1.91E-02 2.55E-01 1.26E-02 4.79E-04
Heavy metal - field emission (soil) 1.68E-07 7.27E-01 0.00E+00 0.00E+00 0.00E+00
N/P - field emission (water) 0.00E+00 8.20E-03 0.00E+00 0.00E+00 0.00E+00
Ammonia - field emission (air) 9.71E-08 1.79E-02 0.00E+00 0.00E+00 0.00E+00
CO2 - field emission (air) 0.00E+00 0.00E+00 0.00E+00 1.27E-02 0.00E+00
N2O - field emission (air) 0.00E+00 0.00E+00 0.00E+00 2.24E-01 0.00E+00
Pesticide - field emission (soil) 1.75E-09 2.42E-02 0.00E+00 0.00E+00 0.00E+00
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 90 August 2016
Table 38. Absolute contribution of midpoint impacts to damage categories of average soybean cultivation in US (IMPACT 2002+ v2.21) The absolute results are expressed per kg soybean at farm gate.
Human Health
Ecosystem Quality
Resources Climate Change
Water withdrawal
Human toxicity, carcinogens 1.91E-08
Human toxicity, non-carcinogens 1.82E-07
Respiratory inorganics 3.14E-07
Ionizing radiation 3.80E-10
Ozone layer depletion 2.65E-11
Respiratory organics 3.11E-10
Aquatic ecotoxicity 5.28E-03 Terrestrial ecotoxicity 7.83E-01 Terrestrial acid/nutri 2.43E-02 Land occupation 4.34E+00 Aquatic acidification 3.10E-05 Aquatic eutrophication 9.24E-03 Water turbined 1.36E-03 Non-renewable energy
2.84E+00 Mineral extraction
1.38E-02
Global warming
4.21E-01
Water withdrawal
1.11E-01
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 91
Table 39. IMPACT 2002+ v2.21 midpoint results of soybean cultivation, average US The absolute impacts are provided for each midpoint category and are expressed per kg soybean at farm gate.
Hu
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ific
atio
n
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ter
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Min
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Wa
ter
with
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l
Unit
kg
C2H3Cl-
eq
kg
C2H3Cl-
eq
kg PM2.5-
eq
Bq C-14-
eq
kg CFC-
11-eq
kg C2H4-
eq
kg TEG
water
kg TEG
soil
kg SO2-
eq
m2 org
ara.y
kg SO2-
eq
kg PO4-
eq m3
MJ
primary
MJ
surplus
kg CO2-
eq m3
Total 6.81E-03 6.48E-02 4.48E-04 1.81E+00 2.52E-08 1.46E-04 1.05E+02 9.90E+01 2.33E-02 3.98E+00 3.52E-03 8.11E-04 3.39E-01 2.84E+00 1.38E-02 4.21E-01 1.11E-01Soybean production_blueprint for state
level_modular /US 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00
Land use - occupation /USSB 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.96E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00
Land use - transformation /USSB 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00
Irrigation {US}| processing | Alloc Rec, U 1.53E-03 5.14E-04 2.33E-05 2.16E-01 1.41E-09 2.35E-05 1.48E+00 4.55E-01 3.37E-04 1.14E-03 1.11E-04 3.92E-06 5.55E-02 4.04E-01 2.29E-03 2.23E-02 1.03E-01
Machine use /USSB 2.74E-03 1.63E-03 2.04E-04 7.57E-01 1.31E-08 9.86E-05 5.84E+00 1.84E+00 4.45E-03 2.07E-03 7.86E-04 1.97E-05 1.45E-01 1.38E+00 8.31E-03 9.80E-02 1.85E-03
Nitrogen fertilizer/USSB 3.04E-04 7.99E-05 7.20E-06 4.73E-02 8.67E-10 1.76E-06 3.52E-01 9.57E-02 1.49E-04 3.02E-05 4.27E-05 1.02E-06 5.84E-03 1.06E-01 2.27E-04 7.94E-03 4.99E-04
Phosphates fertilizer /USSB 2.94E-04 5.22E-04 2.58E-05 2.00E-01 2.37E-09 4.64E-06 3.77E+00 3.39E-01 3.62E-04 1.39E-04 1.60E-04 2.72E-05 2.92E-02 2.55E-01 1.09E-03 1.26E-02 2.15E-03
Potash fertilizer/USSB 3.14E-04 1.52E-04 6.80E-06 5.39E-02 7.44E-10 2.69E-06 5.44E-01 1.74E-01 1.26E-04 2.19E-04 3.71E-05 2.48E-06 2.36E-02 1.12E-01 7.32E-04 6.76E-03 4.18E-04
Transport services /USSB 2.46E-05 7.18E-05 4.29E-06 2.47E-02 5.13E-10 1.54E-06 3.24E-01 2.89E-01 1.06E-04 1.02E-04 2.14E-05 3.16E-07 1.97E-03 4.63E-02 3.84E-05 2.91E-03 3.76E-05
Pesticide application /USSB 3.36E-04 1.27E-04 1.10E-05 1.48E-01 2.36E-09 3.72E-06 1.13E+00 1.35E-01 1.60E-04 7.11E-05 6.27E-05 2.40E-05 2.96E-02 1.44E-01 2.92E-04 8.62E-03 8.70E-04
Pesticide emissions /USSB 6.82E-06 6.16E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.99E+00 3.04E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00Pea seed, for sowing {GLO}| market for | Alloc Rec,
U w/o HM 2.17E-04 1.00E-03 1.79E-05 1.20E-01 2.02E-09 5.76E-06 1.44E+00 9.92E-01 3.68E-04 2.55E-04 8.23E-05 1.15E-05 2.22E-02 1.38E-01 6.01E-04 1.25E-02 2.43E-03
Drying of bread grain, seed and legumes /USSB 1.05E-03 2.76E-04 9.19E-06 2.44E-01 1.83E-09 4.03E-06 1.01E+00 2.08E-01 2.06E-04 1.56E-02 7.30E-05 1.65E-06 2.57E-02 2.55E-01 2.10E-04 1.26E-02 4.79E-04
Heavy metal emissions per hectare /USSB 1.76E-21 5.98E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 8.72E+01 9.14E+01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00Emissions into water - nitrate and phosphate per ha
/USSB 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 7.19E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00
Emission into air - ammonia per ha /USSB 0.00E+00 5.82E-05 1.38E-04 0.00E+00 0.00E+00 0.00E+00 4.45E-03 1.12E-02 1.71E-02 0.00E+00 2.14E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00
Emission into air - carbon dioxide per ha /USSB 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.27E-02 0.00E+00Emission into air - (di)nitrogen (m)onoxide per
ha/USSB 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.24E-01 0.00E+00
Water emissions /USSB 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 92 August 2016
Table 40. IMPACT 2002+ v2.21 endpoint results of crude soybean oil, soybean meal, and refined soybean oil, average US The absolute impacts are provided for each endpoint category and are expressed per kg soybean oil,
kg soybean meal and kg refined soybean oil at factory gate.
Crude soybean oil production, US,
cutoff allocation
Soybean meal production, US,
cutoff allocation
Refined soybean oil production
/US
Human Health DALY 6.69E-07 5.64E-07 7.02E-07
Ecosystem Quality PDF.m2.y 5.70E+00 4.79E+00 5.90E+00
Resources MJ 5.14E+00 4.33E+00 5.50E+00
Climate Change kg CO2 -eq 6.16E-01 5.19E-01 6.49E-01
Water withdrawal m3 1.29E-01 1.08E-01 1.34E-01
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 93
Table 41. Relative contribution of midpoints to damage categories of average soybean milling (including soybean cultivation) in US (IMPACT 2002+ v2.21)
Human Health
Ecosystem Quality
Resources Climate Change
Water withdrawal
Human toxicity, carcinogens 4% 0% 0% 0% 0%
Human toxicity, non-carcinogens 35% 0% 0% 0% 0%
Respiratory inorganics 61% 0% 0% 0% 0%
Ionizing radiation 0% 0% 0% 0% 0%
Ozone layer depletion 0% 0% 0% 0% 0%
Respiratory organics 0% 0% 0% 0% 0%
Aquatic ecotoxicity 0% 0% 0% 0% 0%
Terrestrial ecotoxicity 0% 15% 0% 0% 0%
Terrestrial acid/nutri 0% 0% 0% 0% 0%
Land occupation 0% 84% 0% 0% 0%
Aquatic acidification 0% 0% 0% 0% 0%
Aquatic eutrophication 0% 0% 0% 0% 0%
Water turbined 0% 0% 0% 0% 0%
Non-renewable energy 0% 0% 100% 0% 0%
Mineral extraction 0% 0% 0% 0% 0%
Global warming 0% 0% 0% 100% 0%
Water withdrawal 0% 0% 0% 0% 100%
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 94 August 2016
Table 42. IMPACT 2002+ v2.21 midpoint results of crude soybean oil, soybean meal, and refined soybean oil, average US The absolute impacts are provided for each midpoint category and are expressed per kg soybean oil,
kg soybean meal and kg refined soybean oil at factory gate.
Crude soybean oil production,
US, cutoff allocation
Soybean meal
production, US, cutoff allocation
Refined soybean oil production
/US
Human toxicity, carcinogens kg C2H3Cl-eq 1.51E-02 1.27E-02 1.63E-02
Human toxicity, non-carcinogens kg C2H3Cl-eq 7.27E-02 6.12E-02 7.55E-02
Respiratory inorganics kg PM2.5-eq 6.01E-04 5.07E-04 6.33E-04
Ionizing radiation Bq C-14-eq 3.22E+00 2.71E+00 3.53E+00
Ozone layer depletion kg CFC-11-eq 3.40E-08 2.86E-08 3.75E-08
Respiratory organics kg C2H4-eq 4.84E-04 4.08E-04 5.03E-04
Aquatic ecotoxicity kg TEG water 1.25E+02 1.05E+02 1.30E+02
Terrestrial ecotoxicity kg TEG soil 1.11E+02 9.32E+01 1.15E+02
Terrestrial acid/nutri kg SO2-eq 2.77E-02 2.33E-02 2.88E-02
Land occupation m2 org ara.y 4.38E+00 3.69E+00 4.53E+00
Aquatic acidification kg SO2-eq 4.72E-03 3.98E-03 4.96E-03
Aquatic eutrophication kg PO4-eq 9.08E-04 7.64E-04 9.43E-04
Water turbined m3 5.13E-01 4.35E-01 5.64E-01
Non-renewable energy MJ primary 5.12E+00 4.32E+00 5.48E+00
Mineral extraction MJ surplus 1.57E-02 1.34E-02 1.67E-02
Global warming kg CO2-eq 6.16E-01 5.19E-01 6.49E-01
Water withdrawal m3 1.29E-01 1.08E-01 1.34E-01
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 95
14 Appendix E. Results of sensitivity analyses
Table 43. TRACI v2.1 US 2008 midpoint results of soybean cultivation, average US The absolute impacts are provided for each endpoint category and are expressed per kg soybean at farm gate. On the right, the relative contribution of different farming activities to each endpoint impact is provided (green: < 5%, yellow: 5%-20%, orange: 20%-50%, red:>50%). The corresponding Impact
2002+ contribution analysis of soybean cultivation is provided in Table 4 and in chapter 5.3.1 the sensitivity of results on impact method are discussed.
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 96 August 2016
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 97
Table 44. TRACI v2.1 US 2008 midpoint results (absolute values) of soybean cultivation, average US (per kg soybean). The corresponding Impact 2002+ midpoint results of soybean cultivation are provided in Table 39.
Ozone depletion
Global warming
Smog Acidifi-cation
Eutro-phication
Carcino-genics
Non carcino-genics
Respiratory effects
Ecotoxicity Fossil fuel depletion
kg CFC-11 eq
kg CO2 eq kg O3 eq kg SO2 eq kg N eq CTUh CTUh kg PM2.5 eq CTUe MJ surplus
Total 3.23E-08 4.17E-01 2.47E-02 3.52E-03 1.07E-02 5.22E-08 9.33E-07 2.68E-04 5.39E+00 3.18E-01
Land use occupation 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00
Irrigation 1.81E-09 2.16E-02 1.15E-03 1.10E-04 6.39E-05 3.49E-09 7.12E-09 1.92E-05 2.28E-01 3.95E-02
Machine use 1.73E-08 9.59E-02 1.89E-02 7.85E-04 2.26E-04 6.78E-09 2.80E-08 1.14E-04 7.93E-01 1.61E-01
Transport 6.81E-10 2.89E-03 4.33E-04 2.14E-05 3.43E-06 9.50E-11 7.44E-10 2.33E-06 2.18E-02 6.12E-03
Pesticide production 2.54E-09 8.45E-03 5.29E-04 6.27E-05 8.01E-05 4.25E-10 2.60E-09 8.35E-06 1.07E-01 1.25E-02
N - Fertilizer 1.14E-09 7.89E-03 2.95E-04 4.26E-05 9.78E-06 1.74E-10 1.41E-09 5.34E-06 4.82E-02 1.41E-02
P - Fertilizer 3.10E-09 1.25E-02 9.11E-04 1.60E-04 9.60E-05 1.26E-09 8.15E-09 2.12E-05 2.29E-01 3.08E-02
K - Fertilizer 9.43E-10 6.68E-03 4.50E-04 3.70E-05 2.26E-05 5.40E-10 3.92E-09 4.52E-06 1.35E-01 1.34E-02
Seeds 2.35E-09 1.24E-02 1.47E-03 8.22E-05 3.50E-04 1.38E-09 1.06E-08 1.09E-05 4.36E-01 1.44E-02
Drying of beans 2.44E-09 1.25E-02 5.94E-04 7.28E-05 2.63E-05 1.02E-09 2.53E-09 5.75E-06 1.27E-01 2.60E-02 Heavy metal - field emission (soil) 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.70E-08 8.66E-07 0.00E+00 1.32E+00 0.00E+00 N/P - field emission (water) 0.00E+00 0.00E+00 0.00E+00 0.00E+00 9.68E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Ammonia - field emission (air) 0.00E+00 0.00E+00 0.00E+00 2.14E-03 1.35E-04 0.00E+00 0.00E+00 7.60E-05 0.00E+00 0.00E+00 CO2 - field emission (air) 0.00E+00 1.27E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 N2O - field emission (air) 0.00E+00 2.24E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Pesticide - field emission (soil) 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 9.44E-12 1.69E-09 0.00E+00 1.94E+00 0.00E+00
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 98 August 2016
Table 45. TRACI v2.1 US 2008 midpoint results of soybean crushing and degumming, average US The absolute impacts are provided for each midpoint category and are expressed per kg crude soybean oil and kg soybean meal at factory gate.
On the right, the relative contribution of different oil milling activities to each midpoint impact is provided (green: < 5%, yellow: 5%-20%, orange: 20%-50%, red:>50%). The corresponding Impact 2002+ contribution analysis of soybean crushing and
degumming is provided in Table 6 and in chapter 5.3.1 the sensitivity of results on impact method are discussed.
Quantis - New Earth – AGÉCO Update of Soybean Life Cycle Analysis
August 2016 Page 99
Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO
Page 100 August 2016
Table 46. TRACI v2.1 US 2008 midpoint results (absolute values) of soybean crude oil production, average US (per kg crude oil). The corresponding Impact 2002+ midpoint results of soybean crude oil production are provided in Table 42.
Process
emissions Soybean
cultivation Hexane Tap Water
Infrastru-cture
Heat Electricity Waste Water
Treatment
Solid Waste
Treatment
Ozone depletion 0.00E+00 3.56E-08 4.18E-10 1.01E-10 2.73E-11 4.39E-09 3.32E-09 1.87E-11 5.69E-12
Global warming 0.00E+00 4.59E-01 4.25E-04 3.69E-04 3.14E-04 1.18E-01 3.23E-02 2.94E-04 1.58E-05
Smog 7.73E-04 2.72E-02 3.17E-05 2.40E-05 2.37E-05 4.55E-03 1.51E-03 1.74E-05 3.01E-06
Acidification 0.00E+00 3.87E-03 3.41E-06 2.46E-06 2.48E-06 6.53E-04 1.88E-04 1.56E-06 1.26E-07
Eutrophication 0.00E+00 1.18E-02 1.81E-06 1.06E-06 1.27E-06 1.62E-04 9.75E-05 9.63E-06 2.56E-08
Carcinogenics 1.20E-13 5.74E-08 1.88E-11 9.65E-11 1.45E-10 2.10E-09 1.35E-09 2.35E-11 6.21E-13
Non carcinogenics 1.63E-11 1.03E-06 1.20E-10 1.39E-10 2.97E-10 9.33E-09 4.93E-09 7.13E-11 2.68E-12
Respiratory effects 0.00E+00 2.95E-04 3.29E-07 4.19E-07 4.25E-07 5.88E-05 1.10E-05 2.47E-07 1.55E-08
Ecotoxicity 3.58E-08 5.93E+00 3.71E-03 1.23E-02 1.00E-02 2.05E-01 1.75E-01 4.59E-03 8.13E-05
Fossil fuel depletion 0.00E+00 3.50E-01 3.81E-03 3.23E-04 3.10E-04 1.38E-01 2.63E-02 2.12E-04 5.27E-05
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Table 47. TRACI v2.1 US 2008 midpoint results of soybean oil refinement, average US The absolute impacts are provided for each midpoint category and are expressed per kg refined soybean oil at factory gate. On the right, the relative
contribution of different refinement activities to each midpoint impact is provided (green: < 5%, yellow: 5%-20%, orange: 20%-50%, red:>50%). The
corresponding Impact 2002+ contribution analysis of soybean oil refinement is provided in Table 8 and in chapter 5.3.1 the sensitivity of results on impact method are discussed.
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Table 48. TRACI v2.1 US 2008 midpoint results (absolute values) of soybean oil refinement, average US (per kg soybean). The corresponding Impact 2002+ midpoint results of soybean crude oil production are provided in Table 42.
Soybean
cultivation Oil mill
Tap Water
Sodium Hydroxide
Infra-structure
Heat Electricity Waste Water
Treatment Ozone depletion kg CFC-11
eq 3.68E-08 8.58E-09 2.92E-11 1.82E-09 6.65E-11 2.87E-10 3.47E-10 7.86E-12 Global warming kg CO2 eq 4.75E-01 1.57E-01 1.07E-04 3.07E-03 7.45E-04 3.90E-03 3.37E-03 1.23E-04 Smog kg O3 eq 2.81E-02 7.17E-03 6.95E-06 2.05E-04 6.35E-05 9.01E-05 1.58E-04 7.30E-06 Acidification kg SO2 eq 4.00E-03 8.81E-04 7.12E-07 2.07E-05 1.64E-05 1.40E-05 1.96E-05 6.54E-07 Eutrophication kg N eq 1.22E-02 2.83E-04 3.08E-07 1.12E-05 5.32E-06 7.14E-07 1.02E-05 4.04E-06 Carcinogenics CTUh 5.94E-08 3.86E-09 2.79E-11 1.67E-10 1.82E-10 2.23E-11 1.41E-10 9.89E-12 Non carcinogenics CTUh 1.06E-06 1.54E-08 4.04E-11 1.22E-09 1.56E-09 1.27E-10 5.15E-10 2.99E-11 Respiratory effects kg PM2.5 eq 3.05E-04 7.38E-05 1.21E-07 3.12E-06 1.77E-06 8.76E-07 1.15E-06 1.04E-07 Ecotoxicity CTUe 6.13E+00 4.25E-01 3.55E-03 3.15E-02 2.82E-02 3.23E-03 1.83E-02 1.93E-03 Fossil fuel depletion MJ surplus 3.62E-01 1.74E-01 9.36E-05 2.69E-03 6.53E-04 1.00E-02 2.74E-03 8.88E-05
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Table 49. Consideration of pesticide and heavy metal emissions by Impact 2002+ v2.2 and TRACI v2.1 US 2008
Substances with corresponding characterization factors are marked with a cross.
Substance Compartment Sub-
compartment Impact
2002+ v2.2 Traci v2.1 US 2008
Total of all compartments 0 NA x x
2,4-D Soil NA x x
Acephate Soil NA x x
Acetochlor Soil NA x x
Acifluorfen Soil NA x x
Azoxystrobin Soil NA x x
Bifenthrin Soil NA x x
Cadmium Water groundwater
x
Cadmium Water river x x
Cadmium Soil agricultural x x
Carfentrazone-ethyl Soil NA
x
Chlorimuron-ethyl Soil NA x
Chlorpyrifos Soil NA x x
Chromium Water groundwater
x
Chromium Water river x x
Chromium Soil agricultural x x
Clethodim Soil NA
x
Cloransulam-methyl Soil NA
Copper Water groundwater
x
Copper Water river x x
Copper Soil agricultural x x
Cyfluthrin Soil NA x x
Cyhalothrin, gamma- Soil NA x
Cypermethrin Soil NA x x
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Substance Compartment Sub-
compartment Impact
2002+ v2.2 Traci v2.1 US 2008
Dicamba Soil NA x x
Diflubenzuron Soil NA
x
Dimethenamid Soil NA x x
Dimethoate Soil NA x x
Esfenvalerate Soil NA
x
Fenoxaprop-P ethyl ester Soil NA
x
Fluazifop-P-butyl Soil NA
x
Flumetsulam Soil NA x x
Flumiclorac-pentyl Soil NA x x
Flumioxazin Soil NA
x
Fomesafen Soil NA
x
Fungicides, unspecified Soil NA
Glufosinate-ammonium Soil NA
Glyphosate Soil NA x x
Herbicides, unspecified Soil NA
Imazamox Soil NA x x
Imazaquin Soil NA x
Imazethapyr Soil NA x x
Imidacloprid Soil NA
x
Insecticides, unspecified Soil NA
Lactofen Soil NA x
Lambda-cyhalothrin Soil NA x x
Lead Water groundwater
x
Lead Water river x x
Lead Soil agricultural x x
Mercury Water groundwater
x
Mercury Water river x x
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Substance Compartment Sub-
compartment Impact
2002+ v2.2 Traci v2.1 US 2008
Mercury Soil agricultural x x
Methoxyfenozide Soil NA x x
Metolachlor Soil NA x x
Metribuzin Soil NA x x
Nickel Water river
x
Nickel Soil agricultural x x
Paraquat Soil NA x x
Pendimethalin Soil NA x x
Propiconazole Soil NA x x
Pyraclostrobin (prop) Soil NA
Quizalofop-p-ethyl Soil NA
Rimsulfuron Soil NA x x
Sethoxydim Soil NA x x
Sulfentrazone Soil NA x x
Thiamethoxam Soil NA
Thifensulfuron Soil NA
Tribenuron-methyl Soil NA x x
Trifloxystrobin Soil NA
Trifluralin Soil NA x x
Zeta-cypermethrin Soil NA
Zinc Water groundwater
x
Zinc Water river x x
Zinc Soil agricultural x x
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15 Appendix F. Critical Review
This appendix contains the ISO-compliance checklist, a table of review comments and feedback, and the ISO 14044 conformance letter.
Table 50. ISO-14044 compliance checklist
Are the methods used to carry out the study consistent with the ISO 14040/14044 standards?
1 Page ii ISO Requirement: General Aspects - LCA Commissioner, practitioner of LCA (internal or external)
Requirement met.
2 ISO Requirement: General Aspects - date of the report Requirement met.
3 §2
ISO Requirement: General Aspects - statement that the report has been conducted according to the requirements of ISO applicable standards (14040/14044)
Requirement met.
§3.1 ISO Requirement: Goal of the study – reasons for carrying out the study.
Requirement met.
§3.1 ISO Requirement: Goal of the study – its intended applications
Requirement met.
§3.2 ISO Requirement: Goal of the study – its target audience
Requirement met.
§3.3 ISO Requirement: Goal of the study – statement of intent to support comparative assertion to be disclosed to the public
Requirement met.
§4.1 ISO Requirement: Scope of the study – function, including performance characteristics and any omission of additional functions in comparisons.
Requirement met.
§4.1 ISO Requirement: Scope of the study – functional unit, including consistency with goal and scope, definition, result of performance measurement
Requirement met.
§4.2 Figures 1-4. ISO Requirement: Scope of the study – system boundary including omissions of life cycle stages,
Requirement met.
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processes or data needs, quantification of energy and material inputs and outputs, assumptions about electricity production.
§4.2.3
ISO Requirement: Scope of the study – cut off criteria for initial inclusion of inputs and outputs, including description of cut-off criteria and assumptions, effect of selection on results, inclusion of mass, energy and environmental cut-off criteria
Requirement met.
Appendix A ISO Requirement: Life Cycle Inventory Analysis – data collection procedures
Requirement met.
Appendix A ISO Requirement: Life Cycle Inventory Analysis – qualitative and quantitative description of unit processes
Requirement met.
§5.2 ISO Requirement: Life Cycle Inventory Analysis – sources of published literature
Requirement met.
§5.4 ISO Requirement: Life Cycle Inventory Analysis – calculation procedures for relating data to unit process and functional unit
Requirement met.
§5.2.1.3 §5.2.2
Appendix B
ISO Requirement: Life Cycle Inventory Analysis – validation of data including data quality assessment and treatment of missing data.
Requirement met.
§5.6 ISO Requirement: Life Cycle Inventory Analysis – sensitivity analysis for refining the system boundary
Requirement met.
§5.1
§5.6
ISO Requirement: Life Cycle Inventory Analysis – allocation principles and procedures, including documentation and justification of allocation procedures and uniform application of allocation procedures
Requirement met.
§5.3.1 ISO Requirement: Life Cycle Impact Assessment - the LCIA procedures, calculations and results of the study
Requirement met. Standard LCIA framework(s) to be adopted. Impact 2002+ modified by Quantis and TRACI
§7.2 ISO Requirement: Life Cycle Impact Assessment - limitations of the LCIA results to the defined goal and scope
Requirement met.
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§7.4 ISO Requirement: Life Cycle Impact Assessment - relationship of LCIA results to the defined goal and scope
Requirement met. .
ISO Requirement: Life Cycle Impact Assessment - relationship of the LCIA results to the LCI results
Requirement met. This is not explicitly discussed; however, the use of publicly available, transparent LCIA methods satisfies this requirement.
ISO Requirement: Life Cycle Impact Assessment - impact categories and category indicators considered, including a rationale for their selection and a reference to their source.
Requirement met.
Standard LCIA framework(s) to be adopted. Impact 2002+ modified by Quantis and TRACI. A broad range of mil-point and end-point categories were considered
§5.3
ISO Requirement: Life Cycle Impact Assessment - descriptions/reference to all characterization models, characterization factors and methods used including assumptions and limitations
Requirement met.
ISO Requirement: Life Cycle Impact Assessment - descriptions of or reference to all value-choices
Requirement met. None used in the study
§5.3.1.1.1
ISO Requirement: Life Cycle Impact Assessment – a statement that the LCIA results are relative expressions and do not predict impacts on category endpoints, the exceeding of thresholds, safety margins or risks.
Requirement met.
§7 ISO Requirement: Life Cycle Interpretation – summary of the results
Requirement met.
§ 5.3.1.1
§ 5.6
§ 7.2
ISO Requirement: Life Cycle Interpretation – assumptions and limitations associated with the interpretations of results, both methodology and data related
Requirement met.
Appendix C. ISO Requirement: Life Cycle Interpretation – data quality assessment
Requirement met. Appendix C
ISO Requirement: Life Cycle Interpretation – full transparency in terms of value-choices, rationales and expert judgments
Requirement met.
§ 5.8 ISO Requirement: Critical Review – name and affiliation of reviewers
Requirement met.
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Are the methods used to carry out the study scientifically and technically valid?
Yes.
Are the data used appropriate and reasonable in relation of the goal of the study?
Yes.
Do the interpretations reflect the limitations identified and the goal of the study
Yes.
Is the report transparent and consistent? Yes. Inclusion of detailed LCI in the Appendix very good
General Comments
See separate section with comments and responses.
Editorial Comments
See separate section with comments and responses.
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Table 51. Review comments and responses
Page Line Context Comment Reply
5 23
All environmental life cycle
inventory data are drawn from the
ecoinvent database v3 (SCLCI
2010).
Greg Thoma 07/26/2015:
Elsewhere, throughout the report, you say data come from both v3 and v2.2;
please update (here or multiple other locations) to reflect what you did
It is true that in the end, we succeeded in using ecoinvent v3 throughout.
The report has been updated to remove any suggestion of using
ecoinvent v2.2.
8 5
Human health is driven by the
dinitrogen monoxide and
particulate matter emitted to the
air from farm machinery fuel
combustion, as well as heavy metal
emissions to soil from cadmium
and zinc due to field application of
fertilizer.
Greg Thoma 07/26/2015:
As noted later, please check as there is a negative value for emission of Cr, Ni, and
Cu in Table 41.
Yes, these are the result of transfer coefficients used in the WFLDB model
(see explanation for the comment on page 121 below).
8 11
Climate change is driven heavily by
dinitrogen monoxide emissions to
air from field application of
fertilizers as well as emissions of
carbon dioxide to air from the
combustion of fuels used by farm
machinery.
Greg Thoma 07/26/2015:
Given the data quality assessment, it may be worth noting here that this
inventory is highly site specific and dependent on soil type, weather and timing
of fertilizer application.
This note has been added to the report.
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25 7
1 kg/2,662 kg of “soybean
agriculture” unit process (based on
soybean yield of 2,662 kg soybeans
per hectare (Appendix A1)
Greg Thoma 04/24/2015:
Yield will vary with location; how will this be handled? It is not possible to
specify a single reference flow for all the analyses.
The main analysis in this study centers around the US average soybean
production (and not the state-level). An add-on piece of the scope of
work is the development of some state-specific datasets for some of the
major producing states to use internally within their agencies and by
USB, but those will not be addressed in any great detail in this report. So
with regard to some of your comments – whether a single average yield
is warranted, and whether there is a US average – indeed, I think our
statements are valid – and perhaps we need to change the language in
the report to be clearer that temporal/spatial variability was something
we’d hoped to incorporate into the regional datasets before we were told
that state boundaries were necessary. And we didn’t wind up using a
bottom-up aggregation to develop the US average (and instead relied on
US-average data for the most part) so I hope to make that clearer in the
report.
26 17
Soybean agriculture which yields
soybeans – representing a US
average
Greg Thoma 03/14/2015:
Above you stated as temporally and spatially explicit – is this a contradiction, or
will average be constructed from a weighted average of spatially explicit
datasets?
There is no aggregation or bottom-up generation of a national average on
the basis of state specific data. In fact, the state-specific datasets (of the
most important states) is analyzed to highlight the spatial sensitivity (or
variability) of environmental impacts and to facilitate the development of
spatial explicit improvement strategies within the USB.
Greg Thoma 04/29/2015:
This relates to my question about ‘average’ in an earlier section- how do you use
average if you are using the actual sourcing of soybeans?
This is actually no longer the case – no production information available
to introduce geographic weighting in to the processing activities. This
text will be revised.
With regard to our use of the word “average”, it is true that we are not
taking an average of any set of data
Greg Thoma: Here I think that it is sufficient, in §7.3, to state that the
aggregated dataset provided by NASS was taken as the basis for defining
‘average’ production.
26 17
Soybean agriculture which yields
soybeans – representing a US
average
Greg Thoma: 4/24/2015:
In this case the meaning of average in this section is not clear. How do you use
yields ‘representing a US average’ in a state-specific analysis?
Not a state-specific analysis. The data in used (outlined in Section 7.3)
are aggregated at the national level by NASS Quickstats.
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27 6
The cut-off criteria for defining the
system has been set to allow
exclusion of processes that can
reasonably be assumed to
contribute less than 1% mass to
the system and therefore assumed
to contribute to less than 1% of the
environmental and social impact
when no data are available.
Greg Thoma 03/14/2015:
Good to state that if data are available, these processes are included. (I see it is,
below).
Also, good to give some indication of the criteria needed to “reasonably assume”
…
Updated to clarify that mass will be a predictor of environmental impact
27 8
Exclusions for the environmental
assessment include on-farm, post-
harvest processes (excluding
drying to 12% moisture),
production and storage of animal
manure, packaging of output
products, labor and commuting of
workers, administrative work.
Greg Thoma 03/14/2015:
Specifically on-farm, as the meal and oil production could be considered post-
harvest too.
True, added.
27 8
Exclusions for the environmental
assessment include on-farm, post-
harvest processes (excluding
drying to 12% moisture),
production and storage of animal
manure, packaging of output
products, labor and commuting of
workers, administrative work.
Greg Thoma 04/24/2015:
This phrase is still unclear: what on-farm processes are excluded? What post-
harvest processes are excluded? That is in addition to the list provided?
For clarification, where manure is used as a fertilizer, the field emissions are
assigned to the soybeans, correct?
We consider the drying of soybean, which is the only on-farm post-
harvest process that is included. This doesn’t conflict with the functional
unit of fresh soybeans because the soybeans still include 12% moisture
after drying.
Yes, the field emissions (related to manure) are accounted for.
29 3
Green colored boxes represent
inputs from or emissions to nature.
Greg Thoma 04/24/2015:
Why is drying of beans an input? The FU is fresh, unpackaged soybeans – are
they dried in the field prior to shipping to the next stage?
Yes, dried to 12% moisture content. This has now been emphasized in
the FU description.
Greg Thoma: Agree: simply need to state the FU is 12% moisture beans
ready to be shipped from the farm.
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31 9
This applies, for example, to
landfilling, which causes emissions
(biogas and leachate) over a period
of time whose length (several
decades to over a century) depends
on the design and operation
parameters of the burial cells.
Greg Thoma 04/24/2015:
How are delayed emissions going to be modeled?
All emissions are represented as though they take place at the same time
(this text has been added to the report).
31 16
The potential significance of such
temporal variability has been
considered.
Greg Thoma 04/24/2015:
What are the proposed methods for assessing this potential?
We used a three year average for the calculation of the yield. Calculation
of moving averages was not done for pesticide/herbicide and fertilizer
application since there were no data points for the years around 2012
(the next data point was 2006). We were not able to evaluate temporal
variation in the use of GMO, climate change and tillage practice due to the
lack of data.
We propose revising the text so that everything after “pesticide
application” is removed.
31 16
The potential significance of such
temporal variability has been
considered.
Greg Thoma: 7/25/2015:
It seems that the impact of temporal variation is actually not included at all, but
that a smoothing of temporal effects was achieved through 3 year averaging of
yield –Table 24; please include a statement to this effect if I understand correctly.
Something to the effect that conditions may change inputs and the goal is to
assess both a spatial and temporal average US production.
Greg Thoma:
NASS does report bio-tech acres planted; I do not know if you want to include
this as a management scenario. That is assuming that sufficient data regarding
yield, pesticide application and other inputs for the GMO variety exists.
Thank you for the hint. This would be interesting indeed but it seems
that the available data does not support the deduction of a GMO
management scenario.
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31 29
, use of a highly hazardous
chemical), materials that are less
than 1% by mass are assumed to
also contribute less than 1% of the
environmental impact.
Greg Thoma 04/24/2015:
And capital equipment in the ag production and processing foreground
processes?
Yes, in the soybean processing datasets (crude, meal and refined), a
portion of mill/plant is included in the foreground processes.
Yes, in the soybean production dataset, field operations include capital
equipment (tractor, till equipment, etc.)
From my perspective this already justifies the consideration of capital
equipment in the background system. I would recommend to focus only
on the capital equipment used directly in the foreground system, i.e.
which is a direct input to a foreground process. Otherwise, we have to
add and explain many infrastructure datasets. According to this
definition agricultural machinery is already in the background system.
Greg Thoma: Please specify the assumed amortization lifetime of the mill and
other capital equipment for transparency.
Ecoinvent assumes a construction time of 2 and a lifetime of 50 years for
the oil mill. The details can be found in ecoinvent report 17. Such
lifetimes will be added to the report.
37 3
Ultimately, the sources chosen for
inclusion in the modeling were
determined based on temporal and
spatial relevance and level of
quality.
Greg Thoma 03/14/2015:
Some, at least qualitative, discussion of the criteria to be employed in defining
‘relevance’ and ‘quality’ would be beneficial here.
07/11/2015:
Greg, are relevance and quality adequately addressed in Appendix
section 7?
Greg Thoma: Appendix C for data quality and table 45 for sources are
sufficient.
37 19
The majority of LCI data were
derived from the Ecoinvent v3.1
database (system model
“Allocation, cut-off by
classification”) and supported with
the Ecoinvent database v2.2 only
when needed (SCLCI 2010).
Greg Thoma 03/14/2015:
Which allocation version?
system model “Allocation, cut-off by classification
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Page Line Context Comment Reply
37 20
The majority of LCI data were
derived from the Ecoinvent v3.1
database (system model
“Allocation, cut-off by
classification”) and supported with
the Ecoinvent database v2.2 only
when needed (SCLCI 2010).
Greg Thoma 03/14/2015:
Do you use the Earthshift product with US electricity for EI 2.2? If not, there will
be some processes that with NERC-specific electricity and others with a to-be-
specified EU fuel mix. The potential effect on study conclusions of mixing
databases should be included in the analysis – perhaps as part of the geographic
representativeness as mentioned – although mixing of databases has other
potential consequences (e.g., USLCI often excludes infrastructure).
eiv2.2 will be used only minimally (if at all) and this “weakness” will be
included in DQI estimates.
Greg Thoma:
OK.
38 5
The pedigree matrix for rating
inventory data appears below, with
a score of one being most favorable
and a score of five being least
favorable, and a complete
discussion of this topic can be
found in Frischknecht, et al (2007).
Greg Thoma 03/14/2015:
Presumably, though not stated, this evaluation will be performed by the
practitioner (or other expert opinion – and documented as to source?) for the
foreground processes. The pedigree variability will be additional uncertainty
above the basic uncertainty/variability found from the data collected or
simulated from various production/processing unit processes? Will Monte Carlo
simulations be used for uncertainty propagation?
Reviewer follow up:
Then how will the pedigree matrix information be used in evaluating the study
conclusions? If MCS is not included, it seems that a more qualitative DQI is all
that you would need.
Monte Carlo will not be performed because it is only required for
comparative assertion
The pedigree matrix is done primarily as a requirement for submitting
datasets to a database. Its purpose for this study is minimal, since as you
suggested, this study will use a more qualitative DQI evaluation.
38 26
The data quality assessment
results are included in Appendix C,
which lists all life cycle processes
as well as data quality ratings for
those processes that contribute to
the top 80% of the four main
impact indicators focused on in the
assessment (excludes water
withdrawal inventory).
Greg Thoma 04/24/2015:
the pedigree ratings? I don’t think that will be useful to any one not deeply
familiar with the table above. I am not familiar with the way in which this
information informs a sensitivity assessment. Will it be identification of sensitive
parameters linked with a qualitative description of the degree of uncertainty?
Frankly, an MCS seems a little easier to explain.
No, not the pedigree ratings, but rather the qualitative data quality
ratings which will document the quality of key data choices (e.g.,
good/adequate/poor).
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Page Line Context Comment Reply
41 4
Figure 5: IMPACT 2002+ vQ2.2
midpoint and endpoint categories
(dashed lines indicate links
between midpoint and endpoint
indicators currently not existing,
but in development)
Greg Thoma 03/14/2015:
Will the ‘missing’ endpoint impacts (dashed lines) be discussed in the
interpretation stage?
Greg Thoma: “OK. Presumably the omission results in a lower than actual impact
in the category.”
We weren’t planning to discuss them, apart from mentioning their
omission. We do have midpoint assessments we can talk about. These
impacts are included and disclosed at the midpoint level.
57 4
The human toxicological effects are
mainly caused by heavy metal
emissions to soil (96%, mainly zinc
and cadmium).
Greg Thoma 07/25/2015:
Cu, Ni, Cr have negative inventory flows – why?
Please see response to comment for page 121.
57 7
Occupying arable land in order to
cultivate a monocrop hinders the
regrowth of natural vegetation,
which typically shows a higher
biodiversity.
Greg Thoma 07/25/2015:
isn’t most soy in a corn-soy rotation, so monocrop may not be best descriptor.
“Monocrop” changed to “crop”.
57 9
The eco-toxic effects are due to
fertilizer application and the
related heavy metal emissions to
soil (81.1 % of the impact).
Greg Thoma 07/25/2015:
same question on negative heavy metal flows.
Please see response to comment for page 121.
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94 22
Zinc and cadmium emissions to soil
which may contribute 25% of
potential Human health impacts
and 12% of Ecosystem quality
Greg Thoma 07/25/2015:
How do negative inventories of some metals affect this?
Effect of negative HM flows on the results: HMs released due to
soybean cultivation affect Human Health (28% of damage) and the
Ecosystem (14% of damage). HM are relevant for following three
midpoint impact categories of the Impact 2002v2.2 indicator:
Human toxicity (non-carcinogens): The impact of HMs is mainly related
to Zinc (89%) and Cadmium (10%) emissions to soil, while the negative
flows are negligible (<0.1%).
Terestrial Ecotoxicity: The impact of HMs is mainly related to Zinc
(111%) emissions to soil, while the negative Chromium emissions (-
12%) compensate for some of the overall impacts.
Aquatic ecotoxicity: The impact of HMs is mainly related to
Zinc emissions (112%), while negative Copper (-14%) and Chromium (-
9%) impacts are compensating for some of the overall impacts.
There are some beneficial effects of washing HM from soil to water;
however, the effects are not dominating the results and thus do not
influence the overall conclusion. Zinc emissions to soil are the
dominating emissions for all categories and it has to be noted that also a
part of the Zinc contained in the soil is also washed to the river (35% of
reduced Zinc emissions to soil due to erosion and leaching).
Note: not all exchanges are linked to CFs (e.g. copper, nickel and
cadmium emissions to water are not considered). This means there is
room for improvement in future work.
Question to Greg: would you like such elaboration/explanation added to
the report? Or was this more of a personal curiosity?
Greg Thoma: please include in report, at least the discussion of why flows
are negative.
Quantis: Ok; the explanation of negative flows was added to Section 9.6.3,
Heavy metal emissions
96 4
To quickly test the iLUC relevance
for soybean cultivation US one can
check if soybean notably benefited
Greg Thoma 07/25/2015:
LEAP guidelines suggest 1305 kg CO2e/ha as a global average for iLUC (Feed
guidelines, page 58) http://www.fao.org/3/a-mj751e.pdf
Thanks; we have developed a brief sensitivity analysis based on this and
other LUC data to Section 6.5.
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from a strong contraction in the
cultivation area of other crops.
101 10
LCIA impacts are relatively
unaffected by changes in how we
modeled these emissions, but those
used in this study are roughly 2 to
3 times lower in comparison with
NRCS model, probably due to the
shortcomings in our model to
account for the full spectrum of
emissions.
Greg Thoma 07/25/2015:
in this case, what is the justification for not adopting NRCS model emissions?
That is, if you know your model is incomplete, why continue to use it?
Because of the low relevance of N/P emissions for the environmental
impacts of soybean cultivation (see figure 30) we decided against an
adoption of the NRCS model, which is a model we are not necessarily
very familiar with. However, the ‘blank spots’ in the applied model are
recognized and are the subject of further inquiries.
101 13
Not regionalized, other methods
more appropriate
Greg Thoma 07/25/2015:
can this choice be justified on the goal and scope definition?
We believe this limitation can be justified with respect to the goal and
scope definition. Within this study, the impacts related to water
degradation are captured via different pollution indicators (e.g.
eutrophication) and the impacts on water availability are approximated
with the indicator “water withdrawal”. For agricultural products, the
amount of water withdrawn correlates with the amount of water
consumed (since a fraction of it is “lost" due to evapotranspiration,
depending on the irrigation efficiency). Thus, the method used allows the
identification of water consumption hotspots (which are clearly linked to
irrigation). In order to assess the risks and impacts related to water
availability in more detail (in future work), the water consumption
should be assessed and also the local water stress should be considered.
A detailed water footprint study should be conducted according to ISO
14046 and using the methodologies suggested and provided by WULCA
(http://wulca-waterlca.org/). However, this goes clearly beyond the
scope of the study.
102 1
More efficiently run farm machine
equipment to reduce emissions of
NOx and PM to air
Greg Thoma 07/26/2015:
Are there some more specific recommendations you could provide here (e.g.,
matching tractor power more closely to implement requirements to ensure
operation at most efficient engine load)
Apart from the measures described by the reviewer (match between the
intended use and choice of machine to operate near ideal loads), we
propose to renew the fleet with low emission profiles.
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102 3
Minimize application of fertilizers
to fields to reduce emissions of
heavy metals to soil and water and
to reduce N2O emissions to air
Greg Thoma 07/26/2015:
Again, more specificity in recommendations would be helpful here.
Implementation of precision farming to match nutrient application to plant
needs or timing of fertilization to match plant growth needs – I have heard this is
done in Europe to some extent, that is application of fertilizer to growing plants
rather than before planting; sourcing fertilizers with lower HM content? Possible,
but may come at a cost
The reviewer’s suggestion to implement precision farming to match
nutrient application and timing of fertilization to plant needs sounds like
a good one. We also recommend to focus on the right amount of fertilizer
(precision farming), the right schedule and maybe to change the types of
fertilizer.
107 5
MJ/ kg soybean fresh matter
Greg Thoma 04/29/2015:
Pre or post drying? How are these data used in the LCA? Do they represent
transfers from the soil to the plant? Does fresh matter include only the beans
(here I am not sure exactly what is shipped from the farm – hulls are obviously
still attached, but I don’t know how the combine(?) separates all the other stuff
The energy content refers to post-drying. In the inventory, it is reported
as “energy, cross calorific value, in biomass”. The energy content is rather
calculated for completeness (it’s an ecoinvent requirement to do so in
order to allow the application of the cumulated energy demand proxy
indicator) but not used by any of the impact assessment methods we
apply in the USB project. Yes, it includes only the beans.
Greg Thoma: For clarity of presentation, please be sure to specify that the
3rd – 10th lines in the table refer to 12% moisture fresh beans.
108 25
primarily the application of organic
and mineral fertilizers, generates
emissions into air (nitrogen oxides,
carbon dioxide, dinitrogen
monoxide and ammonia), into
water (heavy metals, nitrate,
phosphorus and phosphate) and
soil (heavy metal and pesticides).
Greg Thoma 04/24/2015:
And heavy metals also apparently into the soybeans themselves. Will any kind of
mass balance on metal flows be included – it would be a great step forward, but
hard to execute without good knowledge of fertilizer sources and metal content.
We actually establish a mass balance for soybean cultivation with all
related uncertainties. We calculate the input of heavy metals via organic
and mineral fertilizers (we know the heavy metal content of the
ecoinvent fertilizers and of different manure types), the heavy metal
uptake of soybean and calculate the release of heavy metals to the soil as
the difference between the input and the uptake.
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108 25
Primarily the application of organic
and mineral fertilizers, generates
emissions into air (nitrogen oxides,
carbon dioxide, dinitrogen
monoxide and ammonia), into
water (heavy metals, nitrate,
phosphorus and phosphate) and
soil (heavy metal and pesticides).
Greg Thoma:
There is no accounting of later effects associated with heavy metals in the
soybeans, correct? I am not aware of studies that do this accounting. In my
experience, depending on the impact assessment methodology (for sure using
Imapct 2002+), the uptake for some crops resulted in a net removal of heavy
metals from the soil and a beneficial effect in some categories – one of the ecotox
categories as I recall. Some mention that the metals taken up by the soybeans are
not accounted in downstream impact assessment may be important to mention
since the scope of the study currently mentions heavy metal uptake by the plant
(certainly these impacts would occur beyond the boundaries of the current
system under study). This may be important in assessing Human Health and
Ecosystem Quality impact categories – in our work we noticed that when we
excluded metal uptake by plants, there was a noticeable change in the impact; in
addition, we noticed that when using ReCiPe that the impacts to ecosystems and
human health (I think) were driven by pesticides rather than driven by metals
when we used Impact 2002+. I have not used the specific version you have at
Quantis, so do not know if these effects remain.
The description above represents the typical approach. The uptake of
heavy metals is actually NOT considered in our study. As you point out
correctly, without the consistent consideration of the corresponding
impact (the release of heavy metals which happens outside of our system
boundary) the consideration of the uptake would cause unjustified
distortions in environmental impacts.
We will revise the report text to clarify that heavy metal update is not
considered.
109 25
The application rate refers to
nutrient content (expressed as N,
K2O or P2O5 equivalents).
Greg Thoma 04/24/2015:
So, as-P and as-K? the EI data I am familiar with reports as-P2O5, has this
changed in EI3? Seems, not as shown in table below. Please clarify the
application rate information.
Greg Thoma: It is correct, as I understand, to say that N, P, and K are the
nutrients, and they are delivered by fertilizers of various composition. The only
trouble with stating ‘fertilizer type’ would be that there are several fertilizers
that deliver N (ammonium nitrate, urea, …) or phosphorus (single
superphosphate and triple superphosphate) each with their own upstream
impacts. Can you just add parenthetically after ‘nutrient content’ ( expressed as
N, K2O, or P2O5 equivalents)? Thus if specific fertilizer type is known, it can be
expressed as an equivalency (and should be easy to connect to ecoinvent as I
believe they use that convention)
If I understand correctly, then the issue is that “nutrient content”
exclusively refers to N, P and K and not P2O5 and K2O – I had a different
conception. If that’s the case I would propose to change nutrient content
into “fertilizer type”. ARMS 2015 provide the amount as N, K2O and
P2O5. In most cases, the fertilizer products in ecoinvent use the same
units (if not, we did unit transformations). That is, there is no error
associated with this misnomer.
Quantis: ok.
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110 5
Although the quantities of fertilizer
types are soybean specific, in order
to obtain product specific
application rates, the average
application rate per fertilizer type
is disaggregated on the basis of the
average fertilizer product
consumption in the US (“USDA
ERS - Fertilizer Use and Price,”
n.d.).
Greg Thoma 04/24/2015:
Does USB have information on differential use by soy farmers? That is, do soy
farmers have any bias in preference for fertilizer type?
The amount of fertilizers groups applied are soybean specific but the
specific fertilizer types represent the US average mix. (language added to
reflect this)
Greg Thoma: 7/25/2015
OK
112 3
The application of manure is not
recorded as an input in the
inventory since it is free of any
environmental burden due to its
waste character.
Greg Thoma 04/24/2015:
The NAL database has information on manure application for specific crops
derived from ARMS reports; If manure is a sensitive input, this may be a source
for better data.
Thanks a lot for this information.
112 3
The application of manure is not
recorded as an input in the
inventory since it is free of any
environmental burden due to its
waste character
Greg Thoma 04/29/2015:
It is cut off from the animal system at the point of application, so all the
transport/application is assigned to the animal system, right?
This becomes awkward if manure is purchased. I do not know if this is common
yet in US, but the EU is starting to see this and manure as a co-product is
challenging.
We followed the ecoinvent approach
(http://www.ecoinvent.org/database/ecoinvent-version-3/system-
models-in-ecoinvent-3/cut-off-system-model/allocation-cut-off-by-
classification.html ). That is, manure is available burden-free. Only the
interventions associated with its use as a fertilizer are accounted for.
That is, transport to the field and application as a fertilizer are attributed
to soybean production. With the allocation at the point of substitution
system model provided by ecoinvent
(http://www.ecoinvent.org/database/ecoinvent-version-3/system-
models-in-ecoinvent-3/apos-system-model/allocation-ecoinvent-
default.html) we can evaluate the cut-off assumption for manure.
112 9
Table 30 shows the application
rate per average planted acre per
pesticide according to USDA and
the dataset (or compound class)
used for the representation of each
pesticide in the framework of
Ecoinvent EI3.1.
Greg Thoma 04/24/2015:
Presumably per planted acre rather than harvested acre? Is it accurate to assume
that all of these are applied to every acre? Also, are these data available at state
level? I recall that they were for the dairy sector
Yes, the amounts are given per planted acre. Yes, because we are
interested in the representation and assessment of an average hectare. If
the pesticide application rate turns out to be of central importance in the
LCIA, we will explore its possible variability on the basis of sensitivity
analysis. Yes, these data are available at the state level, as well.
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114 23
, 2004) and approximated by the
exchange flow “pea seed IP, at
regional storehouse”.
Greg Thoma 04/24/2015:
Why not have a loop from farm output to farm input?
The cultivation of soybeans for seed includes some extra infrastructure
processes which are accounted for when using pea seed.
Greg Thoma: 7/25/2015
OK
115 16
MJ/ha Greg Thoma 04/24/2015:
How will state level variation in yield be accounted? It seems that drying would
be more related to the quantity of beans dried than the land area.
The drying needs were indeed calculated for each state. With regard to
the state level datasets, we adapted the soybean output per hectare and
the corresponding drying interventions (measured in the amount of
water which needs to be evaporated to arrive at the moisture content of
12%).
115 20
Table 33 shows the field
operations considered in the
inventory of soybean cultivation in
the US and their associated diesel
requirements according to
corresponding datasets in EI3.1.
Greg Thoma 04/24/2015:
I think some US specific data are in NAL from Joyce Cooper’s work.
Thanks a lot for the information. I looked into NAL database. However,
due to the quite different technosphere processes or “nomenclature”, the
data is difficult to operationalize within the time budget of the project.
Greg Thoma: I understand.
116 25
Table 35 shows the total amount of
water evaporated per hectare
soybeans.
Greg Thoma 04/24/2015:
Again, my question of yield variation is relevant.
I think this is resolved – only evaluating US national yield in this study.
Greg Thoma: Agreed
( With regard to the issue of
modeling conversion of corn to
soybean, which we did not do)
Greg Thoma 04/29/2015:
I see. There is still, at least potentially, a question arising from corn- soy rotation.
I understand that a large fraction (80%?) of soy is produced in corn-soy rotation;
which crop gets the benefit of the N fixation? And of the N2O that may result
from this N input to the coupled system
Soybean receives both, the benefit and the burden.
Greg Thoma: Is the benefit accounted as an avoided production and use
of inorganic nitrogen fertilizer?
Greg Thoma: explanation in the final report has been clarified
117 25
Transformation, to arable, non-
irrigated
Greg Thoma 04/24/2015:
This doesn’t make sense: how can there be 1 ha transformed for every acre
under cultivation?
Greg Thoma:
I see. So only the transformation from perennial land has a non-zero
characterization factor. Some additional explanation will be beneficial in the
interpretation phase.
The transformation from and to arable land has zero impacts. It is
included to highlight (the maybe counterintuitive fact) that “this share of
the occupied land already is arable land and not subject to any land
transformation”.
Quantis: okay.
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118 14
With regard to accounting for any
‘burden’ we referred to the N2O
production and emissions from the
fixed N as determined by a 2013
EPA RIA analysis (Life Cycle
Associates 2015).
Greg Thoma 07/25/2015:
For clarity: N2O from fixed N and plant residue is assigned to the soy crop in the
rotation, correct?
Yes; note added to text for clarity.
118 22
The amount recorded is 2.69 kg/C
per annum and ha of soybean
cultivated.
Greg Thoma 07/25/2015:
typo: 2.69 kg C/yr/ha
Fixed.
118 29 4'503
Greg Thoma 07/25/2015:
superscript as decimal?
Fixed.
121 10
Heavy metal uptake by soybean
seeds as modeled in the Ecoinvent
inventory datasets were removed
for this project, and heavy metal
uptake is likewise not included in
the impact assessment
methodology.
Greg Thoma 07/25/2015:
what is the mechanism for removal of the HM with negative values in the table
above if not uptake by the plant?
It’s the result of transfer coefficients used in the heavy metal modeling.
Parts of the heavy metal inputs are emitted to and remain in the soil. Yet,
a large proportion of heavy metals is relocated to other compartments,
e.g. soil erosion or surface wash transfer a large proportion to surface
and ground water. If the emission to soil is negative, the emissions to
other compartments are larger than the input. This is possible because
surface wash and erosion associated with the cultivation can cause the
emission of heavy metals which stem from the base concentration of
heavy metals in the soil. That is, the negative flows in the soil
compartment are a consequence of the relocation of the heavy metals.
124 4
5.3800E+01
Greg Thoma 07/25/2015:
Table 35 has 84.87 kg on ha basis (equivalent to 2662 kg)
The difference results from an updating error in the .doc. The value in
Table 35 is correct, and it is aligned with the value in the SimaPro
inventory and the background excel. Table 43 has been updated
accordingly.
124 44
dinitrogen monoxide
Greg Thoma: 7/25/2015
In table above the value is 0.2486 vs. 0.23862 in this one – typo someplace?
The cultivation inventory table has been updated to reflect the correct
N2O value (0.2486 kg/kg soybeans). The value per ha soybeans was used
in the inventory tables.
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128 2
The following table shows all input
and output flows related to the
crushing and degumming process
which produces soybean crude oil
and soybean meal, as well as the
corresponding uncertainty
information.
Greg Thoma 07/25/2015:
Given the second bullet on page 22: Some practitioners may be also interested in
the inventory for the hulls.
Agreed. If we had any remaining budget, this would have been a nice
additional product from this work effort.
136 4
Alterations in the statistical
distribution of weather patterns of
the planet over time that last for
decades or longer; Climate change
is represented based on the
International Panel on Climate
Change’s 100-year weightings of
the global warming potential of
various substances (IPCC 2007).
Greg Thoma 07/25/2015:
It may be relevant to mention new CFs in 2013, but continued use of 2007 CFs
for comparison to previous study.
Thanks—footnote added:
IPCC published updated CFs in 2013; however, IPCC 2007 CFs were used
in this study in alignment with the use of the IMPACT 2002+ vQ2.2
methodology (Humbert et al. 2012) and to permit comparison of results
to those of the former USB soybean analysis (OmniTech 2010).
146 21
1.68E-07
Greg Thoma 07/25/2015:
I am interested in the effect that negative inventory flows shown in LCI table
above affect this result.
Greg, please see the response to your comment on page 94.
149 11
Relative contribution of midpoints
to damage categories of average
soybean milling in US (IMPACT
2002+ v2.21)
Greg Thoma 07/25/2015:
please clarify if these are gate-to-gate contributions or if cultivation is included
too.
Good suggestion – caption updated to reflect inclusion of soybean
cultivation in the results.
Sensitivity on the mass allocation modeling choice. Most databases on crop
products use economic allocation for oil and seed co products. So for
comparability purposes it would be nice, if it's possible, to also include an
economic allocation as a sensitivity analysis. I think that roughly speaking, and
economic allocation would The closer to 60:40 or 65:35 than the mass allocation
which is 73:21. of course, the end result will be a slightly higher footprint for the
oil and a slightly lower footprint for the soymeal in the economic allocation
scenario.
A sensitivity test has been performed and is included in the sensitivity
chapter. Based on the economic allocation we agreed upon with you
(61.6% oil/36.5% meal/1.84% hulls based on approximate prices of 0.34
USD/pound of oil, 330 USB/short ton of soybean meal, and 120
USD/short ton of soy hulls), the economic allocation results for crude oil
increase by 157% and those for soybean meal decrease by 48%.
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Figure 11. Letter of ISO 14044 conformance
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-End of report-