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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


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.

http://www.quantis-intl.com/

mailto:[email protected]

Update of Soybean Life Cycle Analysis Quantis - New Earth – AGÉCO

Page ii August 2016

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







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.


Page iv August 2016

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,


August 2016 Page v

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.


Page vi August 2016

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.


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


Page viii August 2016

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


August 2016 Page ix

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


Page x August 2016

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


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


Page xii August 2016

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


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


Page xiv August 2016

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


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.


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


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..


Page 4 August 2016

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



wastes to treatment

d) 1 kg refined soybean oil, at plant exit gate

1.035 kg of crude soybean oil


August 2016 Page 5

ancillary materials



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


Page 6 August 2016

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.


August 2016 Page 7

Figure 2. System boundary framework for soybean agriculture unit process (adapted from Nemecek et al. 2015)


Page 8 August 2016

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.


August 2016 Page 9

Figure 4. System boundary framework for food processing systems such as soybean crushing, degumming and refining

(adapted from Nemecek et al. 2015)


Page 10 August 2016

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


August 2016 Page 11

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.


Page 12 August 2016

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


August 2016 Page 13

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.


Page 14 August 2016

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



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


August 2016 Page 15

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.


Page 16 August 2016

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.

http://www.quantis-intl.com/en/impact-2002


August 2016 Page 17

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).


Page 18 August 2016

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


August 2016 Page 19

the impact is caused by dinitrogen monoxide (N2O) emissions related to nitrogen-fertilizer application.

Water withdrawal is mainly related to irrigation.


Page 20 August 2016

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


August 2016 Page 21

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.


Page 22 August 2016

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:


August 2016 Page 23

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.


Page 24 August 2016

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.


August 2016 Page 25

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).



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


Page 26 August 2016

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.


August 2016 Page 27

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.


Page 28 August 2016

Figure 8. Relative contribution of midpoints to damage categories of average soybean oil refinement in US (IMPACT

2002+ v2.21).



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


August 2016 Page 29

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%).


Page 30 August 2016


August 2016 Page 31

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.


Page 32 August 2016

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


August 2016 Page 33

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).


Page 34 August 2016

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


August 2016 Page 35

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.

http://corn.agronomy.wisc.edu/AA/A014.aspx


Page 36 August 2016

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.


August 2016 37

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


Page 38 August 2016

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.


August 2016 Page 39

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.


Page 40 August 2016

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.


August 2016 Page 41


Page 42 August 2016

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August 2016 Page 47

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


Chromium 0.463



Copper 13.4



Nickel 4.73



Lead 0.07



Zinc 42.45



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


Page 48 August 2016

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



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.


August 2016 Page 49

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


Page 50 August 2016

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.


August 2016 Page 51

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)


CHEMICAL, HERBICIDE: (CLETHODIM = 121011) 7.65E-03 pesticide, unspecified


Page 52 August 2016



CHEMICAL, HERBICIDE: (CLORANSULAM-METHYL = 129116)


CHEMICAL, HERBICIDE: (DICAMBA, DIGLY. SALT = 128931)


CHEMICAL, HERBICIDE: (DICAMBA, DIMET. SALT = 29802)


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)


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)


CHEMICAL, HERBICIDE: (IMAZETHAPYR = 128922) 2.99E-03 pesticide, unspecified

CHEMICAL, HERBICIDE: (IMAZETHAPYR, AMMON. = 128923)


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)


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


August 2016 Page 53



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)


CHEMICAL, INSECTICIDE: (IMIDACLOPRID = 129099) 1.90E-04 benzimidazole-compound

CHEMICAL, INSECTICIDE: (LAMBDA-CYHALOTHRIN = 128897)


CHEMICAL, INSECTICIDE: (METHOXYFENOZIDE = 121027)


CHEMICAL, INSECTICIDE: (THIAMETHOXAM = 60109) 2.77E-04 pesticide, unspecified

CHEMICAL, INSECTICIDE: (ZETA-CYPERMETHRIN = 129064)


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%


Page 54 August 2016

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


August 2016 Page 55

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


Page 56 August 2016

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)


August 2016 Page 57

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


Page 58 August 2016

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.


August 2016 Page 59

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


Page 60 August 2016



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


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.



Phosphate, to river 5.73E-1 kg/ha Run-off of soluble phosphate (PO4) to surface water caused by fertilizer application.


Phosphorus, to river 3.68E-1 kg/ha Water erosion of soil particles containing phosphorus (P)


Phosphate, to groundwater

2.15E-1 kg/ha Leaching of soluble phosphate (PO4) to ground water caused by fertilizer application.


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


August 2016 Page 61



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.


Page 62 August 2016

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


August 2016 Page 63

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


Page 64 August 2016

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


phosphate fertilizer, as P2O5, RER from

diammonium 1.22E+01


phosphate fertilizer, as P2O5, RER from

monoammonium 5.89E+00



August 2016 Page 65



% Indicator score

potassium chloride, as K2O 3.17E+01


drying of bread grain, seed and legume

(infrastructure) 8.59E+0


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)








pea seed, ip, at regional storehouse 6.52E+01 kg lognormal 1.27 (3,3,3,3,3,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)


Page 66 August 2016



% 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)


August 2016 Page 67



% 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)


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


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


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


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




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



Alloc Recy, U>

ecoinvent 3 dataset, <Soybean



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




modeler choice (USB 2015);

interpreted FO #2 = light fuel

oil

lognormal 1.14 (2, 2, 3, 1, 1,

3, 1.05)



heavy fuel oil, at industrial furnace

1MW | Alloc Rec, U




modeler choice (USB 2015);

interpreted FO#6 as "heavy

fuel oil"

lognormal 1.14 (2, 2, 3, 1, 1,

3, 1.05)


August 2016 Page 71


Amount

Type Name Soybean


Indicator

score



at hard coal industrial furnace 1-

10MW | Alloc Rec, U





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





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





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




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






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






Alloc Recy, U>

lognormal 1.09 (2, 2, 1, 2, 1,

3, 1.05)

Electricity, medium voltage {FRCC}|







Alloc Recy, U>

lognormal 1.09 (2, 2, 1, 2, 1,

3, 1.05)

Electricity, medium voltage {RFC}|







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






lognormal 1.09 (2, 2, 1, 2, 1,

3, 1.05)


Page 72 August 2016


Amount

Type Name Soybean


Indicator

score

Alloc Recy, U>

Electricity, medium voltage {ASCC}|







Alloc Recy, U>

lognormal 1.09 (2, 2, 1, 2, 1,

3, 1.05)

Electricity, medium voltage {TRE}|







Alloc Recy, U>

lognormal 1.09 (2, 2, 1, 2, 1,

3, 1.05)

Electricity, medium voltage {SERC}|







Alloc Recy, U>

lognormal 1.09 (2, 2, 1, 2, 1,

3, 1.05)

Electricity, medium voltage {SPP}|







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



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



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}|





Alloc Recy, U>

lognormal 1.14 (2, 2, 3, 1, 1,

3, 1.05)


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


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


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


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



(2, 4, 3, 5, 1,

4, 1.05)

Electricity, medium voltage {WECC, US only}|




(2, 4, 3, 5, 1,

4, 1.05)

Electricity, medium voltage {FRCC}| market for




(2, 4, 3, 5, 1,

4, 1.05)


Page 76 August 2016


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



(2, 4, 3, 5, 1,

4, 1.05)

Electricity, medium voltage {MRO, US only}|




(2, 4, 3, 5, 1,

4, 1.05)

Electricity, medium voltage {ASCC}| market for




(2, 4, 3, 5, 1,

4, 1.05)

Electricity, medium voltage {TRE}| market for




(2, 4, 3, 5, 1,

4, 1.05)

Electricity, medium voltage {SERC}| market for




(2, 4, 3, 5, 1,

4, 1.05)

Electricity, medium voltage {SPP}| market for |

Alloc Rec, U 2.31E-04 kWh



(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)


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).


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).


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.


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


August 2016 Page 81

Data category

Re

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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


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


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


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%


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


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


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%


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.


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


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


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

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ter

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Min

era

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Glo

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ing

Wa

ter

with

dra

wa

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


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


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%


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


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.


Page 96 August 2016


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


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.


August 2016 Page 99


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


August 2016 Page 101

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.



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



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




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




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



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.



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.



§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.



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.



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.


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.


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.




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)


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


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.


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.




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.


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.


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.


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.


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.




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.


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.


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.




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.


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.


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).


Which allocation version?

system model “Allocation, cut-off by classification




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).


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).


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).


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).




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)


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).


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.


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).


same question on negative heavy metal flows.

Please see response to comment for page 121.




94 22

Zinc and cadmium emissions to soil

which may contribute 25% of

potential Human health impacts

and 12% of Ecosystem quality


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


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.




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.


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


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


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.

http://wulca-waterlca.org/




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


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


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).


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.




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).


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.




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.).


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.


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


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.


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.

http://www.ecoinvent.org/database/ecoinvent-version-3/system-models-in-ecoinvent-3/cut-off-system-model/allocation-cut-off-by-classification.html



http://www.ecoinvent.org/database/ecoinvent-version-3/system-models-in-ecoinvent-3/apos-system-model/allocation-ecoinvent-default.html






114 23

, 2004) and approximated by the

exchange flow “pea seed IP, at

regional storehouse”.


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.


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.


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.


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)


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


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.




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).


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.


typo: 2.69 kg C/yr/ha

Fixed.

118 29 4'503


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.


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


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


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.




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.


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).


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


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)


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%.



Figure 11. Letter of ISO 14044 conformance


August 2016 127


August 2016 129

-End of report-

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