more sustainable beef optimization project phase 1 final ......phase 1 final report june 2013...

More Sustainable Beef Optimization Project

Phase 1 Final Report

June 2013

Submitted by:

BASF Corporation 100 Park Avenue, Florham Park, NJ, 07932

Prepared by:

Thomas Battagliese, Metrics Manager, SET – applied sustainability

Juliana Andrade, Sustainability Specialist

Isabel Schulze, Sustainability Specialist

Bruce Uhlman, Senior Sustainability Specialist

Cristian Barcan, Regional Head, SET – applied sustainability

Copyright © 2013 BASF Corporation

1

Contents

I. EXECUTIVE SUMMARY ................................................................................ 4

II. HOT SPOT ANALYSIS ................................................................................... 9

1. Desktop Research ....................................................................................... 9

2. Stakeholder Survey ................................................................................... 10

3. Summary of Results and Implications of the HSA .................................... 11

III. ECO-EFFICIENCY ANALYSIS: REPORT AS VERIFIED BY NSF INTERNATIONAL ……………………………………………………………………………………………..13

1. Purpose and Intent of this Submission .................................................... 13

2. Content of this Submission ....................................................................... 13

3. BASF’s EEA Methodology .......................................................................... 13 3.1 Overview ....................................................................................................... 13 3.2 Preconditions ................................................................................................. 13 3.2.1 Environmental Burden Metrics ..................................................................... 14 3.2.2 Economic Metrics ........................................................................................ 15 3.3 Work Flow ..................................................................................................... 15

4. Study Goals, Decision Criteria and Target Audience ................................ 16 4.1 Study Goals ................................................................................................... 16 4.2 Context & Decision Criteria ............................................................................. 16 4.2.1 Study Drivers .............................................................................................. 16 4.2.2 Geography ................................................................................................. 16 4.2.3 Scenario and Horizon .................................................................................. 17 4.2.4 Engagement ............................................................................................... 17 4.2.5 Life Cycle ................................................................................................... 17 4.2.6 Product and Market ..................................................................................... 17 4.2.7 Economy .................................................................................................... 18 4.2.8 Innovation .................................................................................................. 18

5. Customer Benefit, Alternatives and System Boundaries .......................... 18 5.1. Customer Benefit (CB) .................................................................................... 18 5.2. Alternatives ................................................................................................... 18 5.3. System Boundaries ......................................................................................... 19 5.4 Scenario Analyses .......................................................................................... 20

6. Input Parameters and Assumptions ......................................................... 21 6.1. Input Parameters ........................................................................................... 21 6.1.1 Overall Study Assumptions .......................................................................... 21 6.1.2 USMARC Feed Production and Pasture ......................................................... 23 6.1.3 USMARC Cattle Production .......................................................................... 30 6.1.4 Harvesting .................................................................................................. 32 6.1.5 Case-Ready ................................................................................................ 34 6.1.6 Retail ......................................................................................................... 35 6.1.7 Consumer ................................................................................................... 35


2

7. Data Sources ............................................................................................. 36

8. Eco-efficiency Analysis Results and Discussion ....................................... 38 8.1. Environmental Impact Results ......................................................................... 38 8.1.1. Cumulative Energy Demand......................................................................... 39 8.1.2. Abiotic Depletion Potential (ADP) ................................................................. 40 8.1.3 Consumptive Water Use .............................................................................. 42 8.1.4 Air Emissions .............................................................................................. 43 8.1.4.1 Global Warming Potential (GWP) .............................................................. 43 8.1.4.2 Photochemical Ozone Creation Potential (POCP) ........................................ 44 8.1.4.3 Ozone Depletion Potential (ODP) .............................................................. 45 8.1.4.4 Acidification Potential (AP) ....................................................................... 46 8.1.5 Water Emissions ......................................................................................... 46 8.1.6 Solid Waste Generation ............................................................................... 47 8.1.7 Land Use .................................................................................................... 48 8.1.8 Toxicity Potential ........................................................................................ 49 8.1.9 Risk (Occupational Illnesses and Accidents potential) .................................... 50 8.1.10 Environmental Fingerprint ........................................................................... 51 8.2. Economic Cost Results .................................................................................... 52 8.3 Eco-efficiency Analysis Portfolio....................................................................... 53

9. Scenario #1: 1970 On-Farm Scenario ...................................................... 54 9.1 General Discussion ......................................................................................... 54 9.2 Cumulative Energy Demand ............................................................................ 55 9.3 Consumptive Water Use ................................................................................. 55 9.4 Global Warming Potential ............................................................................... 56 9.5 Acidification Potential ..................................................................................... 57 9.6 Water Emissions............................................................................................. 58 9.7 Land Use ....................................................................................................... 59 9.8 Environmental Fingerprint ............................................................................... 59 9.9 Eco-Efficiency Analysis Portfolio ...................................................................... 60

10. Data Quality Assessment .......................................................................... 61 10.1 Data Quality Statement ............................................................................... 61

11. Sensitivity and Uncertainty Analysis ........................................................ 62 11.1 Sensitivity and Uncertainty Considerations .................................................... 62 11.2 Critical Uncertainties ................................................................................... 63 11.3 Sensitivity Analyses .................................................................................... 63 11.3.1 Scenario #1: Distiller’s Grains Mass Allocation ........................................... 63 11.3.2 Scenario #2: Distiller’s Grains Energy Content Allocation ............................ 65 11.3.3 Scenario #3: Economic Allocation for Retail and Consumer Refrigeration

and Retail Refrigerant Leakage ............................................. 66

12. Conclusions ............................................................................................... 68

13. Limitations of EEA Study Results ............................................................. 69

IV. SEEBALANCE® REPORT ............................................................................. 70

1. Introduction .............................................................................................. 70

2. Methodology ............................................................................................. 70


3

3. SEEbalance® Analysis Results and Discussion .......................................... 71

3.1 Employees ....................................................................................................... 72 3.1.1 Working Accidents, Fatal Working Accidents, and Occupational Diseases ........ 72 3.1.2 Toxicity Potential ........................................................................................ 73 3.1.3 Wages and Salaries ..................................................................................... 75 3.1.4 Material and External Costs for Further Professional Training ......................... 75 3.1.5 Lost Working Hours Due to Strikes and Lockouts .......................................... 76 3.2 Local and National Community ........................................................................ 77 3.2.1 Employees .................................................................................................. 77 3.2.2 Qualified Employees .................................................................................... 78 3.2.3 Gender Equality (as represented by Female Managers) ................................. 79 3.2.4 Integration of Disabled Employees ............................................................... 80 3.2.5 Part-Time Workers ...................................................................................... 80 3.2.6 Family Support ........................................................................................... 81 3.3 Future Generations ......................................................................................... 82 3.3.1 Trainees ..................................................................................................... 82 3.3.2 R&D Expenditures ....................................................................................... 82 3.3.3 Capital Investments .................................................................................... 83 3.3.4 Social Security ............................................................................................ 84 3.4 International Community ................................................................................ 84 3.4.1 Developing Country Foreign Direct Investment ............................................. 84 3.4.2 Imports from Developing Countries .............................................................. 85 3.5 Social Fingerprint ........................................................................................... 86 3.6 SEEbalance® Fingerprint and Portfolio ............................................................. 87

4. 1970 On-Farm Scenario ............................................................................ 89 4.1 Wages and Salaries ........................................................................................ 89 4.2 Lost Working Hours Due to Strikes and Lockouts .............................................. 89 4.3 Employees ..................................................................................................... 90 4.4 Female Managers ........................................................................................... 91 4.5 Trainees ........................................................................................................ 91 4.6 Imports from Developing Countries ................................................................. 92 4.7 Social Fingerprint ........................................................................................... 93 4.8 SEEbalance® Fingerprint and Portfolio ............................................................. 93

5. Conclusions ............................................................................................... 95

V. ECO-EFFICIENCY MANAGER ..................................................................... 95

VI. CONCLUSIONS FOR PHASE 1 OF THE MORE SUSTAINABLE BEEF OPTIMIZATION PROJECT ……………………………………………………………………………………..98

VII. References ............................................................................. ……………99


4

I. EXECUTIVE SUMMARY Title: Phase 1 More Sustainable Beef Optimization Project

Authors: Tom Battagliese (BASF), Juliana Andrade (BASF), Isabel Schulze (BASF), Bruce Uhlman (BASF), Cristian Barcan (BASF)

Date of Submission: June 26, 2013

Background

A sustainable beef industry is critically important as we work toward the goal of feeding more than 9 billion people by the year 2050. Experts estimate that this future global population will require 70 percent more food with fewer available resources. The goals of this study were to benchmark the eco-efficiency of the U.S. beef industry and to analyze the positive and negative trends associated with changes in practices over time. This provides a starting point for ongoing analysis and a journey of continuous improvement within the industry. Any established trends will be used to set the U.S. beef industry on a more sustainable pathway through various opportunities, which may include sharing and communicating best practices, embedding improvement opportunities throughout the industry, prioritizing solution-oriented research on sustainability criteria that are determined to be critical, and empowering the industry through ongoing education.

This Eco-Efficiency Analysis (EEA) submission is the first phase (Phase 1) of an ongoing study of the U.S. beef industry funded by the Beef Checkoff Program. Phase 1 is intended to provide specific on-farm data from the largest research farm in the U.S. combined with post-farm data that is representative of the entire U.S. beef industry. Phase 2 of the life cycle assessment will require additional on-farm data to be collected at a regional level to provide complete value chain data that is representative of the whole U.S. beef industry.

Methods

Phase 1 of this program consisted of multiple projects as described below:

Hot Spot Analysis (HSA): The HSA was intended to provide a qualitative perception analysis of the sustainability attributes of the U.S. beef industry. The HSA consisted of an analysis of more than 150 literature sources and a survey reaching out to 39 stakeholders by means of an online questionnaire, telephone, or face-to-face interviews. The survey covered six different groups that included industry, retailers/restaurants, NGOs/NPOs, government, capital markets, and academia.

Eco-Efficiency Analysis (EEA): The EEA was intended to provide a quantitative assessment of the environmental and economic sustainability attributes of the U.S. beef industry using life cycle analysis. The EEA was originally intended to review the eco-efficiency attributes of the beef value chain over time by evaluating data for the years 1970, 2005, and 2011. The 1970s were chosen to represent the industry’s transition to boxed beef. The second point in time, 2005, was selected to represent the feeding of


5

significant volumes of distiller’s grains and the 2011 calculations represent present day. Unfortunately, insufficient data was available for the 1970 analysis, with the exception of data for the on-farm phases of the value chain. Therefore, the final EEA contained an on-farm scenario that included the 1970 data while the base case EEA considered only 2005 and 2011. Additionally, while the post-farm phases of the EEA are representative of the entire U.S. beef industry, the on-farm phases of the EEA represent the U.S. Department of Agriculture’s Roman L. Hruska Meat Animal Research Center (USMARC) located in Clay Center, Nebraska.

SEEbalance® Analysis: The SEEbalance® analysis was intended to expand upon the EEA and integrate the social impacts of the U.S. beef industry in order to assess a more complete sustainability framework that considers environmental, social, and economic issues.

Eco-Efficiency Manager (EEM): The EEM was intended to provide the U.S. beef industry with an on-line, web-based interface that allows users to create scenarios of the EEA results in order to see how changes in their individual operations could positively or negatively impact the sustainability attributes of the industry.

Important Findings

Hot Spot Analysis

As a result of the desktop research and survey, 21 hot spots were identified for the U.S. beef industry. These include 7 environmental, 7 social, and 7 economic hot spots of either high or medium relevance. Figure 1 summarizes these hot spots that were identified.


6

Figure 1: HSA Results

Eco-Efficiency Analysis

As presented in the EEA portfolio analysis in Figure 3, there has been a 5% improvement in the eco-efficiency of the U.S. beef industry between 2005 and 2011 as represented by the system boundary of this study. This correlates to a 6% increase in cost (based on consumer retail price) and a 7% decrease in environmental impact (as represented by the environmental fingerprint analysis in Figure 2) over that same timeframe.

While environmental impacts stem from all phases of the beef value chain as represented throughout the study analysis, the majority of the impacts are attributed to on-farm processes.

Figure 2: Environmental Fingerprint - U.S. Beef Phase 1 Figure 3: EEA Portfolio - U.S. Beef Phase 1


7

Likewise, many of the impact reductions have been made on-farm. These reductions relate directly to improvements in on-farm efficiencies. For example, improvements in yield of feed crops and animal performance result in less system inputs being required per unit of land in order to achieve the same desired output of edible beef.

In general, it appears that the overall eco-efficiency of the beef value chain is improved, at least to a small extent, with the use of the distiller’s grains. Additionally, using distiller’s grains as a feed source provides a beneficial use of a by-product of bioethanol processing, thus providing additional environmental benefit outside of the beef value chain.

The processess associated with the post-farm phases, while generally contributing less overall value-chain impacts, present significant opportunities for improvement. Additionally, these opportunities generally may be more straightforward in terms of implementation with examples such as biogas capture and recovery at the harvesting facilities, packaging optimizations, and energy efficiency opportunities throughout.

SEEbalance® Analysis

While the SEEbalance® analysis shows some overall improvement in the socio-eco-efficiency attributes of the U.S. beef industry from 2005 and 2011, the overall analysis is deemed to be inconclusive due to the state of U.S. industry sector data quality. Due to extreme trending that was noted throughout several of the social impacts analyzed, the significant lack of quality for the available data is evident.

The main reasons for the lack of data quality include changes over time in the way that data is aggregated for industry sectors as well as the reliance on both national economic data and per-unit product pricing in order to derive social impacts at a product level. As a result of these issues, it is extremely difficult to pinpoint specific and reliable trends at this time that relate to the social attributes of the U.S. beef industry. Social life cycle analysis is in its infancy and this was further proven with this study. It is recommended that the U.S. beef industry revisit a social analysis once national data is more readily available in outputs that allow correlation at the product level.

Eco-Efficiency Manager

The Eco-Efficiency Manager (EEM) for the harvesting and case-ready phases of the beef value chain was designed and is being tested by beef industry partners. This web-based optimization tool provides the opportunity for sector members to analyze how changes in specific inputs to their production systems can positively or negatively impact the eco-efficiency of their operations as well as the overall beef value chain. Further research is necessary for design of the EEM for the remainder of the value chain. This is particularly the case for the on-farm phases of the value chain, which consider not only the anthropogenic inputs to the system, but the complex biological interactions related to agricultural and livestock systems. Additionally, expanded analysis with the retail and restaurant sectors in Phase 2 of this project will provide primary data for the post-harvesting phases that will be used to expand upon this initial EEM and provide improved insight for the entire beef value chain.


8

Implications

The work completed in Phase 1 of the More Sustainable Beef Optimization Project provides the U.S. beef industry with qualitative information from the HSA as well as quantitative data and results from the EEA that is necessary to understand perceptions, trends, and opportunities for improving the sustainability attributes of the beef industry. The EEA results provide the roadmap to identify and prioritize those opportunities and to allow better understanding of the specific practices that can be used to further reduce the environmental impacts of the beef value chain, while maintaining the overall economic value proposition for the U.S. beef industry.

Improvements to data quality on the post-harvesting phases as well as analysis of regionalized data for the on-farm phases in Phase 2 will provide even more clarity to these trends and opportunities. However, with the results from Phase 1, the industry can already begin to enhance the current pathway of action for making a more sustainable beef value chain.


9

II. HOT SPOT ANALYSIS The Hot Spot Analysis (HSA) was intended to provide the U.S. beef industry an understanding of the perceptions held by stakeholders. The analysis is intended to be a qualitative review and therefore, stakeholder perceptions may or may not be based upon scientifically founded research. The HSA was completed using a two-pronged approach which included a desktop literature review as well as a survey of stakeholders of the U.S. beef industry. While the beef checkoff received extensive supporting details on the HSA in 2012, the following provides a summary of the overall analysis so that the reader may understand how perceptions may or may not compare with the quantitative sustainability analyses which are described in more detail after this summary.

1. Desktop Research The desktop literature review included an analysis of more than 150 publicly available sources from organizations across different stakeholder groups including academia, government, NGOs/NPOs, industry, and general media. The objective of the desktop research was to identify perceived sustainability issues along the beef value chain based on information from reliable and varied literature sources. The analysis was not intended to be an academic review of purely peer-reviewed literature but instead was supposed to demonstrate the perception of the U.S. beef industry held by a spectrum of stakeholders. Figures 4 and 5 below demonstrate the scope of sources that were considered.

As a result of the desktop literature research, 18 key sustainability issues were identified as shown in Figure 6.

Figure 4: Types of sources used in the HSA desktop literature review.

Figure 5: Geographical scope of sources used in the HSA desktop literature review.


10

Figure 6: Hot spots identified from the desktop literature research.

2. Stakeholder Survey The stakeholder survey covered six different stakeholder groups, which included industry, retailer/restaurants, NGOs/NPOs, government, capital markets, and science. Of the 90 stakeholders targeted for the survey, 39 stakeholders participated, which represents a respectable response rate of 43%. A breakout of the types of stakeholders surveyed is shown below in Figure 7.

Figure 7: Survey stakeholder participation


11

Stakeholders were surveyed both by an on-line survey as well as by phone or an in-person interview. The relevance of hot spots as a result of the stakeholder survey are shown below in Figure 8.

Figure 8: Hot spots identified from the stakeholder survey.

3. Summary of Results and Implications of the HSA

Results from both the desktop literature research as well as the stakeholder survey were compiled, evaluated, and prioritized as shown in Figure 9. This analysis resulted in 7 environmental, 7 social, and 7 economic hot spots of either high or medium relevance.


12

Figure 9: Hot spots identified from the HSA.

Following completion of the HSA, a materiality analysis of the results was completed by the beef checkoff project managers to gauge priorities for improvement opportunities regarding the perceptions of the sustainability of beef. The materiality analysis showed agreement by the beef checkoff with most of the results of the HSA, with a few exceptions including the following points:

“Waste and By-products” was viewed as a high priority hot spot as opposed to the medium rating that was a result of the HSA.

“Market Concentration and Pricing” was viewed as a low priority hot spot as opposed to the medium rating that was a result of the HSA.

“Product Quality” and “Trade” were viewed as high priority hot spots as opposed to the medium ratings that were a result of the HSA.

“Competition” was viewed with other proteins and markets as a medium priority hot spot as opposed to the low rating that was a result of the HSA.

The combination of the beef checkoff internal materiality analysis with the HSA results provides the beef industry with a roadmap for prioritizing and addressing the perceptions of stakeholders of the U.S. beef industry. This direction is intended to be overlaid with the quantitative EEA and SEEbalance® results of the U.S. beef industry in order to substantiate direction for developing strategies to mitigate the sustainability impacts of the industry through optimization programs that are coordinated with associated marketing and communications messages to stakeholders.


13

III. ECO-EFFICIENCY ANALYSIS: REPORT AS VERIFIED BY NSF INTERNATIONAL

1. Purpose and Intent of this Submission

The purpose of this submission is to provide a written report of the methods and findings of BASF Corporation’s “U.S. Beef Eco-efficiency Analysis,” with the intent of having it verified under the requirements of NSF Protocol P352, Part B: Verification of Eco-efficiency Analysis Studies.

The U.S. Beef Eco-efficiency Analysis was performed by BASF according to the methodology validated by NSF International under the requirements of Protocol P352. More information on BASF’s methodology and the NSF validation can be obtained at http://www.nsf.org/business/eco_efficiency/models.asp?program=EcoEff or http://www.basf.com/group/corporate/en/sustainability/eco-efficiency-analysis/index

2. Content of this Submission

This submission outlines the study goals, procedures, and results for the U.S. Beef Eco-efficiency Analysis (EEA) study, which was conducted in accordance with BASF Corporation’s EEA methodology. This submission will provide a discussion of the basis of the eco-analysis preparation and verification work.

As required under NSF P352 Part A, along with this document, BASF is submitting the final computerized model programmed in Microsoft® Excel. The computerized model, together with this document, will aid in the final review and ensure that the data and critical review findings have been satisfactorily addressed.

3. BASF’s EEA Methodology

3.1 Overview

BASF EEA involves measuring the life cycle environmental impacts and life cycle costs for product alternatives for a defined level of output. At a minimum, BASF EEA evaluates the environmental impact of the production, use, and disposal of a product or process in the areas of energy and resource consumption, water consumption, emissions, toxicity, risk potential, and land use. The EEA also evaluates the life cycle costs associated with the product or process by calculating the costs including materials, labor, manufacturing, waste disposal, and energy.

3.2 Preconditions

The basic preconditions of this eco-efficiency analysis are that all the alternatives being evaluated are compared against a common functional unit or customer benefit. This allows for an objective comparison between the various alternatives.


14

The scoping and definition of the customer benefit are aligned with the goals and objectives of the study. Data gathering and constructing the system boundaries are consistent with the functional unit and consider both the environmental and economic impacts of each alternative over their life cycle in order to achieve the specified customer benefit. An overview of the scope of the environmental and economic assessment carried out is defined below.

3.2.1 Environmental Burden Metrics

For BASF EEA, environmental burden is characterized using twelve categories including: primary energy consumption (expressed as cumulative energy demand), non-renewable (or abiotic) raw material consumption (expressed as abiotic depletion potential), global warming potential (GWP), ozone depletion potential (ODP), acidification potential (AP), photochemical ozone creation potential (POCP), water emissions, solid waste emissions, toxicity potential, risk potential (expressed as occupational illnesses and accidents), consumptive water use, and land use. These are shown below in Figure 10. Metrics shown in blue represent the seven main categories of environmental burden that are used to construct the environmental fingerprint; burdens in green represent all elements of the emissions category; and burdens in pink represent specific air emissions impact categories considered.

Figure 10: Environmental Burden Metrics for BASF Eco-efficiency Methodology


15

3.2.2 Economic Metrics

It is the intent of the BASF EEA methodology to assess the economics of products or processes over their life cycle and to determine an overall total cost of ownership for the defined customer benefit ($/CB). The approach for calculating costs varies from study to study. When chemical products of manufacturing are being compared, the sale price paid by the customer is predominately used followed by any subsequent costs incurred by its use and disposal. When different production methods are compared, the relevant costs include the purchase and installation of capital equipment, depreciation, and operating costs. The costs incurred are summed and combined without additional weighting of individual financial amounts. The BASF EEA methodology incorporates:

the real costs that occur in the process of creating and delivering the product to the consumer;

the subsequent costs which may occur in the future (due to tax policy changes, for example) with appropriate consideration for the time value of money; and

costs having an ecological aspect, such as the costs involved to treat wastewater generated during the manufacturing process.

3.3 Work Flow

A representative flowchart of the overall process steps and calculations conducted for this eco-efficiency analysis is summarized in Figure 11 below.

Figure 11: Overall Process Flow for U.S. Beef EEA Study


16

4. Study Goals, Decision Criteria and Target Audience

4.1 Study Goals

A sustainable beef industry is critically important as we work toward the goal of feeding more than 9 billion people by the year 20501. Experts estimate that this future global population will require 70 percent more food with fewer available resources. The goals of this study were to benchmark the eco-efficiency of the U.S. beef industry and to analyze the positive and negative trends associated with changes in practices over time. This provides a starting point for ongoing analysis and a journey of continuous improvement within the industry. Any established trends will be used to set the U.S. beef industry on a more sustainable path through various opportunities, which may include sharing and communicating best practices, embedding improvement opportunities throughout the industry, prioritizing solution-oriented research on sustainability criteria that are determined to be critical, and empowering the industry through ongoing education.

This EEA submission is the first phase (Phase 1) of an ongoing study of the U.S. beef industry. Phase 1 is intended to provide specific on-farm data from the largest research farm in the U.S. combined with post-farm data that is representative of the entire US beef industry. Phase 2 of the life cycle assessment will require additional on-farm data to be collected at a regional level to provide complete value chain data that is representative of the whole U.S. beef industry.

4.2 Context & Decision Criteria

The study goals, target audience, and context for decision criteria used in this study are displayed in Figure 12.

4.2.1 Study Drivers

The purpose of the study was to quantify changes in the sustainability attributes of beef over time in order to gauge, plan for, and implement improvements for the U.S. beef industry as discussed above in Section 4.1.

4.2.2 Geography

The study considered beef produced by the U.S. beef industry and did not include beef exported from or imported to the U.S. As mentioned in Section 4.1, it is not possible to have a dataset for the full value chain that is representative of the U.S. beef industry without aggregating regionalized on-farm data. For Phase 1 of the U.S. Beef EEA, the post-farm data is representative of the U.S. beef industry. However, the on-farm data are representative of the U.S. Department of Agriculture’s Roman L. Hruska Meat Animal Research Center (USMARC) located in Clay Center, Nebraska. USMARC was selected for Phase 1 of the U.S. Beef EEA because as a research center USMARC has extensive data, including some back to 1970, which would be very difficult to find in a centralized manner elsewhere in the industry.


17

The USMARC is a research facility so its production practices do not fully represent the beef industry as a whole. Really, no single specific beef producing facility can represent the industry due to the considerable variation in management practices that occurs among regions and producers. The crop, feed, and animal management practices used at USMARC are typical of the practices used in this region of the U.S. except for the amount of irrigation used. This operation uses more irrigation than the overall industry and this use has increased over the years with more corn production and some irrigation of pasture. Greater use of irrigation results in increased non-precipitation water use, energy use, and carbon emissions. A major environmental benefit for the beef industry as a whole has been an increased use of dairy calves. When dairy calves are grown for beef, the environmental impact of maintaining their breeding stock is primarily allocated to the dairy industry. This allocation of resources and emissions greatly reduces the environmental footprint of cattle raised from dairy calves. Because dairy cattle are not part of the USMARC system, the analysis of their system does not receive this benefit. Other minor differences in labor and resource use will exist for this government facility, but these differences will have little effect on the environmental impact of the cattle produced.

Representative regionalized data will be collected, aggregated, and analyzed in future phases of the U.S. Beef EEA.

4.2.3 Scenario and Horizon

The study considered the eco-efficiency attributes of the total beef value chain for beef that was produced by the U.S. beef industry (according to the geographical scope defined in Section 4.2.2) in 2005 and 2011 and for the on-farm phases in 1970, 2005, and 2011.

4.2.4 Engagement

The study is intended to be used by the entire value chain of the U.S. beef industry and shared with the stakeholders and any other interested external parties of the industry.

4.2.5 Life Cycle

The study reviewed the entire life cycle of the beef consumed at home that is produced by the U.S. beef industry according to the geographical scope defined in Section 4.2.2. (cradle-to-grave).

4.2.6 Product and Market

The study considered beef produced by the U.S. beef industry (per Section 4.2.2) and consumed at home. Future updates to the study will include regionalized on-farm data, cattle management techniques, and other out-of-home dining venues such as restaurants.


18

4.2.7 Economy

The economy considered the U.S. market a developed economy.

4.2.8 Innovation

The study is intended to lead mainly to incremental innovation within the U.S. beef industry.

Figure 12: Context of U.S. Beef EEA Study

5. Customer Benefit, Alternatives and System Boundaries

5.1. Customer Benefit (CB)

The Customer Benefit (identified also as CB), Functional Unit (FU) or User Benefit (UB) applied to all alternatives for the base case analysis is one pound of consumed, boneless, edible beef. This CB was selected in order to capture a relative average of the beef industry. Because there are so many different types of beef cuts and further-processed beef products, it is not feasible to analyze all types of beef produced in the U.S. Additionally, in order to understand the impacts specific to beef, boneless beef was assumed. Finally, in order to evaluate the entire beef life cycle, the CB considers beef consumed.

5.2. Alternatives

This is a study over time to demonstrate changes in the eco-efficiency attributes of the U.S. beef industry associated with the CB defined above in Section 5.1. The study considered the periods 2005 and 2011 for the entire life cycle of beef and the periods 1970, 2005, and 2011 for the on-farm phases of the U.S. beef industry (feed

10 years

3 years

emerging

survival

global regional

local

technology

competitve regulatory

gap closure step change

NGO/ external

supplier/customer internal

1 product 1 market consumer

post-consumer

full life cycle

all products/markets

Scenario and Horizon

Geography

Drivers

Innovation Economy Product / Material

Engagement

Value Chain supply chain

incremental

consumer

40 years


19

and cattle). Reliable data could not be constructed at this time for the 1970 timeline for post-farm phases of the beef life cycle.

Reasoning for selection of the three alternative periods is as follows:

1970 represents the largest herd of cattle in the U.S. Additionally, during 1970, swinging sides were used as the main mode of transport to the point of retail sale. After this, the industry moved to boxed beef, which represents a significant change in operations for the beef industry.

2005 represents the onset of distiller’s grains used as feed (so for the data set in this study, this was the last year before mainstream use of distiller’s grains in the industry).

2011 is present-day analysis and considers the use of distiller’s grains.

Note that because the 1970 alternative could not be evaluated for the entire value chain, full study results and analysis in Section 8 do not include the 1970 alternative. Instead, the 1970 on-farm (feed and cattle phase) analysis is overviewed in a scenario in Section 9.

5.3. System Boundaries

The system boundary for this study is presented in Figure 13 below. Dairy cattle were not included in the scope of this study because they are not included in the beef production system at USMARC. Additionally, as is common practice in life cycle analysis, capital equipment, buildings, and infrastructure and repair and maintenance material, parts, and supplies were excluded. Office & administrative impacts, employee commutes, seeds for feed, cattle veterinary medicines, and cleaning chemicals used at the retail sector were excluded according to the cut-off criteria defined in the BASF EEA Methodology. Individually, these impacts have less than a 1% contribution or collectively less than a 3% contribution to the overall value chain impacts in this study.


20

Figure 13: System boundary for U.S. Beef – Phase 1

5.4 Scenario Analyses In addition to the base case analysis, the following scenario analyses were

considered:

Scenario #1: Analysis of distiller’s grains using a mass allocation.

Scenario #2: Analysis of distiller’s grains using an energy content allocation.

Scenario #3: Retail and consumer phase refrigeration and retail refrigerant leakage using an economic allocation.

Note that the practice of allocation is applied in life cycle analysis when impacts associated with the study boundary cannot be easily separated from impacts of products or by-products that are part of the same system. ISO defines allocation as “partitioning the input or output flows of a process or product system between the product system under study and one or more other product systems.”2 Through allocation, a percentage of impacts are assigned to the scope product system and the other integrated product system or systems through an appropriate allocation approach that can include weighting by physical attributes (mass, volume, energy content, etc.), economics, or other methods. Within this study, allocation was avoided wherever possible, but was necessary for:

consideration of the animal by-products, which are processed in the same facility as the beef itself;


21

analysis of distiller’s grains, which are a by-product of the bioethanol distillation process; and

analysis of retail and consumer refrigeration and refrigerant leakage from retail refrigeration because refrigeration for beef in these two phases is integrated with numerous other refrigerated foods.

6. Input Parameters and Assumptions

6.1. Input Parameters

Given the size and scope of this study, numerous sources were utilized for input parameters. Specifics on applicable parameters and associated assumptions for each phase of the scope of the study are included below.

6.1.1 Overall Study Assumptions

The following assumptions were used:

1. Table 1 presents the dressing percentage (yield of carcass from live animal) and value chain loss values that were applied in order to obtain the CB of weight of consumed, boneless, edible beef. The dressing percentage value was based upon an industry average of 62% with a 3% reduction to account for cull cows and bulls. Loss values used were from the USDA Economic Research Service.3 Note that the total loss is not a simple sum of each individual phase loss, but instead, each loss is calculated from the previous phase.

Dressing percentage 59% Losses at harvesting & case-ready phase (fat, bone, and shrink)

33%

Loss at retail phase (fat, bone, shrink) 4% Loss as consumer phase (cooking losses, spoilage, plate waste)

20%

Total losses from live animal weight sent from cattle phase

70%

Table 1: Dressing Weight and Value Chain Losses

2. Consumptive water values were taken from coefficients that are defined in the last published USGS water report that contained ranges for consumptive water for high-level sectors.4 Mid-point values of these ranges were assumed for this study as follows:

a. Industrial use: 25% b. Agriculture: 70% c. Livestock: 55% d. Thermoelectric Utilities: 50%


22

3. Packaging used directly or indirectly for the beef product was assumed to be 100% completed in the case-ready phase (i.e., packaged into a retail-ready output). While this assumption was not fully representative of the reality of the beef industry (as there are other packaging modalities including the butcher shop), it provided an initial outlook on the packaging phase due to lack of primary data. The goal is to amend the study with some additional primary data in Phase 2 of the study.

4. For the waste considered in this study, which is not being recycled or reused, it was assumed that 82% of the waste is disposed of in a landfill and 18% is incinerated with energy recovery. This assumption was based on 2010 EPA national waste data.5

5. In order to avoid allocation and the potential for double-counting credits and impacts for energy recovery outside of the study boundary, the cut-off method was applied to the 18% of the waste that is incinerated with energy recovery. Therefore, it was assumed that the impacts of the incineration process were considered to be the burden of the purchaser of the electricity that is generated from energy recovery.

6. For post-farm packaging that was used as direct inputs to the beef system, the following approach was taken regarding waste disposal and recycling:

a. 100% of corrugated cardboard is recycled. In order to avoid allocation and potential for double-counting credits and impacts of the recycling system, a closed-loop recycling process was assumed and the cut-off method was applied. Therefore, the impacts of the recycling process were considered to be the burden of the purchaser of the recycled material. For this study specifically for example, the harvesting facilities surveyed purchased corrugated cardboard that contained 30% recycled fiber content. Therefore, to be consistent, the burden of the recycling process for producing that recycled content was included in the total impacts of this study.

b. All post-farm packaging other than cardboard was assumed to be disposed of according to the above 82%:18%, landfill:incineration ratio.

c. A modified ecoinvent profile was applied for municipal solid waste landfilling for packaging waste.

7. For cost analysis, the present value (2011 dollars) consumer price of the beef was utilized and assumed to reflect the full cost of the value chain up to the point of sale at the retailer. These values were not associated with the operational costs of USMARC. However, using the consumer price was determined to be the best possible approach to achieve a total cost that was representative of the entire U.S. beef industry in order to align representative impacts of the post-farm value chain as discussed in Section 4. Costs were utilized from USDA Economic Research Service data.6


23

6.1.2 USMARC Feed Production and Pasture

The feed production phase accounted for the life cycle of the feed (i.e., the agricultural crops and pastureland) that was consumed by the animals raised in the beef system. Input parameters for the feed phase were considered mainly based on modeling data produced by the U.S. Department of Agriculture’s (USDA) Integrated Farm Systems Model (IFSM). This approach was utilized as some primary data availability for on-farm production is limited, particularly from past years.

The IFSM is a research tool used to assess and compare the environmental and economic sustainability of farming systems. Crop production, feed use, and the return of manure nutrients back to the land are simulated for many years of weather on a crop, beef, or dairy farm.7 Growth and development of crops are predicted for each day based upon soil, water, and nitrogen availability, ambient temperature, and solar radiation. Simulated tillage, planting, harvest, storage, and feeding operations predict resource use, timeliness of operations, crop losses, and nutritive quality of feeds. Feed allocation and animal responses are related to the nutrient contents of available feeds and the nutrient requirements of the animal groups making up the herd. For beef operations, the animal groups can include cows, calves, replacement animals, stockers, and finishing cattle.8 The quantity and nutrient contents of the manure produced are a function of the feeds consumed and herd characteristics.

Nutrient flows are tracked through the farm to predict losses to the environment and potential accumulation in the soil.9 Environmental losses include ammonia emission, denitrification and leaching losses of nitrogen, erosion of sediment across the farm boundaries, and the runoff of sediment-bound and dissolved phosphorus. The sum of the various forms of nitrogen loss provides a total reactive nitrogen loss. Carbon dioxide, methane, and nitrous oxide emissions are tracked from crop, animal, and manure sources and sinks to predict net greenhouse gas emission. Whole-farm mass balances of nitrogen, phosphorus, potassium, and carbon are determined as the sum of nutrient imports in feed, fertilizer, deposition, and legume fixation minus the nutrient exports in milk, excess feed, animals, manure, and losses leaving the farm.

The IFSM boundaries are depicted below in Figure 14.


24

Figure 14: Boundaries, major components and nutrient flows

simulated with the Integrated Farm System Model10

Note that while soil quality and biodiversity are important issues to agricultural sustainability, further research is necessary for quantification of these aspects. As improved data are discovered that are pertinent to this study, an expanded analysis may be performed to include these issues in the future.

The IFSM was used to model the USMARC facility, feed production, feed use and animal production. Simulation of this production system provided system inputs as well as certain emissions and outputs. IFSM data, while providing simulated process-level results, has been extensively demonstrated in this and numerous other projects to provide accurate outputs, representative of actual production systems. An example of the accuracy of the IFSM simulation capability is shown below in Table 2 with USMARC simulated data compared to actual reported feed use, which represents some of the IFSM data directly used in this study.


25

Feed Type Actual tons Dry Matter

Simulated tons Dry Matter

% Difference

Alfalfa / grass hay silage 6,096 6,102 0.0

Corn silage 5,444 5,422 0.4

High moisture corn 3,092 3,109 0.5

Corn grain 1,834 1,820 0.8

Distiller’s grain 1,841 1,837 0.2

Total 18,307 18,290 0.0 Table 2: Actual reported vs. IFSM Simulated feed production at USMARC for 2011 11

All IFSM data related to feed that was used for this analysis are on a dry matter (DM) basis. Where necessary, the DM values were converted to wet matter based on moisture content. IFSM data were used for all direct system inputs and direct emissions where the IFSM provided data necessary to fulfill the BASF EEA methodology. Other sources of data, as discussed further in Section 7, were used for all pre-chain emission eco-portfolios as well as for some additional direct emissions.

The USMARC facility included about 5,000 acres of irrigated farmland used for feed production. Note that in 1970, irrigation was less common. Only about 1,000 acres were irrigated for corn and irrigation was not used at all on pasture at USMARC. This has changed significantly to the current day as can be seen in the consumptive water values in Section 8 of this report.

Feed production at USMARC included alfalfa/grass (and associated silage), corn silage, corn grain (high moisture corn grain, dry grain), and soybeans. A strip tillage system was used for corn and soybean production within the USMARC facility. However, the soybeans were not fed to the cattle but were sold for use outside of the beef system. Any aspects of USMARC such as soybeans as well as other animals that were not part of the beef cattle system boundaries were removed from the boundary conditions so that only the beef system and the associated feed production required were considered.

For the data utilized for the 1970 scenario in this study, adjustments were made from 2011 data by the USDA research team based on available knowledge of the historical USMARC system and overall feed production, including the following:

Some of the corn currently used was replaced with bromegrass hay;

Alfalfa yield was decreased 12% and corn yield was decreased by 40% (to represent genetic improvements that have occurred since 1970);

No irrigation of pasture was used; and


26

Smaller equipment and additional tillage operations were used to reflect the technology of 1970.

Additionally, for the 2005 data, adjustments were made from 2011 data by the USDA research team based on available knowledge of the USMARC system and overall feed production, including the following:

Reduced corn yield by 6% (to represent genetic improvement); and

Replaced distiller’s grains with corn and a small amount of urea to meet energy and protein requirements of the animals.

While most feed used at USMARC was produced directly on-site, some feed was purchased from off-site sources and was also considered in this study, as shown below in Table 3.

Purchased Feed Type ton DM 1970 2005 2011

Corn 4,528 1,411 0 Wet Distiller’s Grains with Solubles (WDGS) 0 0 1,790

Table 3: Purchased Feed considered in MARC system

The following is a list of additional assumptions for the feed phase that were necessary to complete this study:

1. The profile used for purchased corn and distiller’s grains is representative of the corn-belt area in Iowa and is non-irrigated. The Iowa profile represents the region from which USMARC purchases corn (as opposed to the corn grown by USMARC and included in the system considered, which is irrigated). Note that the profile for the USMARC corn was established by primary data from the IFSM based on specific USMARC system simulations.

2. Yields of purchased corn were assumed to be as follows in Table 4 as simulated from the IFSM.

1970 2005 2011

bu/acre 101 158 168

Table 4: Purchased Corn Yields

3. Historical records show little change in the application rates of fertilizer in this region. Essentially improvements in fertilizer use efficiency (requiring less fertilizer) have been offset by increased yields requiring more fertilizer per acre. So although application rates have not changed, nutrient use efficiency has improved substantially. USMARC now harvests about 40%


27

more corn using the same fertilizer application rate as in 1970.12 Based upon this historical data for the region, fertilizer application rates for purchased corn were held constant across all three periods (1970, 2005, and 2011) as follows:

a. 160 lb N/acre b. 90 lb P2O5/acre c. 90 lb K2O/acre d. 920 lb lime/acre

An eco-profile for urea was used to represent the applied N and an eco-profile for single superphosphate was used to represent the applied P2O5 in order to complete a full assessment of pre-chain impacts of the appropriate nutrients.

4. Insufficient information on the type and amount of pesticides was available in a specific and quantifiable manner for 1970. For purposes of this study, the type and amount of pesticides applied to the feed crops in 1970 and 2005 were considered to be equivalent to that used in 2011.13

5. USMARC raises other species of animals for which some of the feed is used. Resource use and emissions from feed crop production were allocated among the animal species at USMARC using mass allocation. The ratio of the mass of feed dry matter fed to cattle over the total feed dry matter produced provides the allocation factor. Through simulation of the various production systems with the IFSM, the portion of the total feed used by cattle within the USMARC system and assigned as the associated allocation factor was found to be:

a. 1970: 85.8% b. 2005: 85.3% c. 2011: 82.5%

6. Manure was considered in this study, including that from the cow-calf operation on pastureland. Manure from within USMARC was used as fertilizer within USMARC. Therefore, no pre-chain impacts were considered for these plant nutrients. Emissions from the manure were considered.

7. Primary data from the IFSM simulations was used to obtain the following emission factors for corn production:

1970 2005 2011

Runoff loss lb P/ton P applied 0.6 0.32 0.3 lb N/ton N applied 2.4 1.28 1.2

Air emissions (direct + crop residue)

lb N2O/ton N applied 0.68 0.43 0.41

Table 5: Corn Direct Emissions


28

Table 6 presents emission factors used to calculate additional emissions from USMARC not included in the IFSM simulations. Note that for N2O emissions, direct emissions were analyzed with IFSM in the above point. Indirect N2O emissions related to leaching and volatilization are shown below as N direct conversion and volatilization to NH3-N and conversion to N2O.

Emission Factors14 N fertilizer leaching 30% Leached N to N2O-N 0.75% (0.00225 kg N2O-N / kg

fertilizer-N) CO2 from urea 0.20 kg CO2-C / kg (NH2)2CO CO2 from limestone 0.12 kg CO2-C / kg CaCO3 Volatilization of NH3 from fertilizer-N 10%

Table 6: Additional Field Emission Factors

Note that direct N2O background emissions from soil were not considered in this study. Chemical Oxygen Demand (COD) for pesticides was calculated based on the chemical formula of a substance (i.e., C, O, N and H stoichiometry) while COD for other inputs was considered directly from the eco-profiles used.

8. For heavy metal water emissions associated with fertilizers, the Swiss Agricultural Life Cycle Assessment (SALCA) calulator was used. All heavy metals considered in the BASF methodology were analyzed with the SALCA tool. While soil type and characteristics specific to the USMARC region were used to determine most aspects of feed production, the SALCA tool does not include U.S. soil physics values. German values for soil heavy metal dynamics values such as heavy metal percolation, deposition, and leaching rates were assumed as representative values and this asssumption would not have a significant impact on the overall results. The analysis includes both runoff and leaching of heavy metals.15

9. With the exception of enteric methane, biogenic carbon was not modeled in this study as it was assumed that for the full life cycle of the beef, any carbon that is taken into the animal (through feed) is again emitted to the atmosphere at some point along the chain. However, because enteric methane is modeled in the cattle phase, a 1 CO2-eq credit was applied to the global warming potential (GWP) factor of methane (thus utilizing a GWP of 24 CO2-eq for methane as opposed to the standard factor of 25 CO2-eq). While all other biogenic carbon within the beef system is assumed to have a net-neutral impact on GWP, this reduction considered that the enteric methane is simply the conversion of the feed to methane and is being released with the higher GWP factor of methane as opposed to carbon dioxide.


29

10. The only impacts associated with irrigation within the USMARC system were the consumptive water value itself (since the water was well water from within the USMARC facility) and the energy required for pumping the water. Power for the pumps used for the pivot irrigation systems require electric or natural gas.

11. Transport distance was assumed to be an average of 20 miles from the distillery to the feedlot for the distiller’s grains and 500 miles roundtrip for the purchased corn (250 miles for corn to distillery for the distiller’s grains).

12. Distiller’s grains is a by-product of the bioethanol distillation process (from corn). In order to derive an appropriate impact analysis of just the distiller’s grains, since the impacts of the distiller’s grains alone are not easily separated from the full bioethanol distillation process, an economic allocation was performed as follows:

a. Utilizing the ecoinvent corn ethanol profile, the distillation process results in the production of 1 kg of ethanol and 1 kg of Dried Distiller’s Grains with Solubles (DDGS). The drying energy was then deducted from the DDGS profile (according to the distillation ecoinvent profile) to derive an appropriate profile for distiller’s grains.

b. Additionally, the corn profile in ecoinvent associated with the bioethanol profile was replaced with the corn profile from Iowa that was assumed in the rest of this study for purchased corn (in order to maintain a consistent profile). Yield of the corn was adjusted to 2011 yield values shown above of 168 bu/acre.

c. An adjustment factor was then applied to the profile of 1.55 to account for the fact that 1.55 times the weight of distiller’s grains is produced compared to DDGS from the distillation process.16

d. The final profile of distiller’s grains was then created by assuming an economic allocation associated with the current pricing of ethanol and distiller’s grains, which resulted in 21% of the burden of the distillation process (and pre-chain impacts) being allocated to distiller’s grains.

13. Gross bioenergy, or the energy released if the feed biomass were combusted, was accounted for in all crops used for feed. While the amount of feed was based on IFSM simulated outputs and includes losses from production to consumption, the gross bioenergy content was based upon ecoinvent profiles with values shown below. Note that the ecoinvent biomass content in the original profile was conveyed on a wet matter basis and therefore was converted to a dry matter basis as shown in Table 7 to correspond with feed inputs already on a DM basis.


30

Table 7: Gross Bioenergy of Crops

14. For the Occupational Illnesses and Accidents risk values, in addition to the standard pre-chain impacts, direct impacts for the feed category were assessed for change over time according to the three period alternatives using the U.S. Bureau of Labor Statistics (BLS) data for the industry category of “Oilseed and Grain Farming”.17 Also, for the 1970’s scenario, illness and accident values were extrapolated based on 2005 and 2011 data using general OSHA total industry trends since 1970. This was necessary because the industry categories in 1970 and their associated data were segregated in a much broader classification system and as a result, direct data for the same industry codes used in 2005 and 2011 were not available.

6.1.3 USMARC Cattle Production

The cattle phase considered the life cycle of the living animal from birth until it leaves the feedlot and is transported to the beef harvesting facility. Input parameters for the cattle phase were considered mainly based on modeling data extracted from the USDA’s Integrated Farm Systems Model (IFSM). The IFSM was used to model the USMARC facility and provide system inputs as well as certain emission outputs.

The USMARC data included inputs and outputs for the cow-calf and feedlot operations. Note that pasture inputs were included in the feed phase. The cow-calf operation is used to describe the portion of the cattle phase in which a herd of cows is maintained for the specific purpose of producing calves. The calves remain at the cow-calf operation until they are weaned and are then sent to the backgrounding program on the feedlot. The USMARC cow-calf operations handled about 6,600 cows on 24,000 acres of grazing pasture, some of which was irrigated. The animals were fed hay and silage during the winter months.

The USMARC facility also included a 3,700 head feedlot operation. Cattle were backgrounded (i.e., taken from weaned calves to yearlings) for 3 months on a high forage diet (hay silage and distiller’s grain) and finished in the feedlot (confined drylot) for 7 months on a high grain diet (corn silage, corn, and distiller’s grain). The cattle were finished at 16 months of age with an average weight of 1,280 pounds. All manure from the feedlot was returned to the USMARC cropland as a fertilizer input.

Crop ecoinvent Profile Gross Bioenergy

(MJ/kg DM) Silage maize IP, at farm/CH S 18.6 Corn, at farm/US U 18.5 hay intensive IP, at farm 17.8 Grass from natural meadow extensive IP, at field/CH S 18.5


31

For the data utilized for 1970 in this study, adjustments were made from 2011 data by the USDA research team based on available knowledge and publications of the historical USMARC system and overall cattle production, including:

Finishing weight was decreased by 19% (to represent genetic improvements) and correspondingly, animal numbers were increased by 19% to produce the same finished weight; and

Finishing was completed with a corn grain and corn silage diet.

Additionally, for the data utilized for 2005 in this study, the following adjustment was made from 2011 data by the USDA research team based on available knowledge of the USMARC system and overall cattle production:

Finishing weight was decreased by 3% and correspondingly, animal numbers were increased by 3%.

The following is a list of additional assumptions that were applied for the cattle phase of this study:

1. The impacts of all calves, heifers, cows, bulls, and beef cattle were included in the study.

2. The mass value of body weight of the cattle sent to harvesting and included in the cattle phase analysis was held constant over the three periods analyzed. This total weight included finished cattle, cull cows, and cull bulls used for harvest.

3. There was a reduction in the animal numbers over time within the system to offset increases in overall body size over time (higher yields per head).

4. All harvested and grazed forage and grains used as feed were included in the feed phase of the study. Only supplementary feeds were included in the cattle phase.

5. As with the feed phase, IFSM data were used wherever available. This included inputs associated with supplementary feed as well as the following emissions:

a. P water emission from pastureland, which was different from the

cropland analyzed in the feed phase. b. CH4 emissions – enteric and manure emissions. The manure

emissions were different in the feed phase where manure was applied to the cropland, while the cattle phase included manure deposits on the pastureland.

c. N2O emission – pastureland and manure emission. d. NH3 emission – urine and manure emission on pastureland.


32

Note that these emissions were predicted through simulation of the biological and physical processes modeled within the IFSM.

6. Drinking water for livestock was included. Since this water came directly from the USMARC wells, the only impact was the energy for pumping. As shown in the general assumptions, a value of 55% was applied for consumptive water related to this drinking water.

7. Transport within USMARC was included with an average distance of 5 miles for cows and 6 miles for calves. Transport of the cattle to the harvesting plant was included within the harvesting phase.

8. For Occupational Illnesses and Accidents risk values, in addition to the standard pre-chain impacts, direct impacts for the cattle category were assessed for change over time according to the three period alternatives using the U.S. Bureau of Labor Statistics (BLS) data for the industry category of “Cattle Ranching and Farming.” Also, for the 1970’s scenario, illness and accident values were extrapolated based on 2005 and 2011 data using general OSHA total industry trends since 1970. This was necessary because the industry categories in 1970 and their associated data were segregated in a much broader classification system and as a result, direct data for the same industry codes used in 2005 and 2011 were not available.

9. Standard BASF risk analysis methodology considers occupational accidents and illnesses and allows for customized risks to be considered as appropriate. There was one additional risk (beyond the occupational illnesses and accidents) considered for the cattle phase, which was animal welfare. Expert opinion, supported by the national Beef Quality Assurance (BQA) program18 evaluated this additional risk category on a scale of 1:10 with 1 representing the most risk and 10 the least risk. 19 The expert opinion scale score applied to the animal welfare category was 2.5, 5.5, and 7.5 for 1970, 2005, and 2011 respectively. The total risk weighting for animal welfare was considered to be 8.5% and this weighting was split between the cattle and harvesting phases at 4.25% in each phase.

6.1.4 Harvesting

The harvesting phase considered the input of the live animal through the output of edible beef ready to be packaged for consumption, so it is essentially where the beef that consumers purchase is processed.

Primary data was collected for the harvesting phase from three beef producers, whose operations represented approximately 60% of the U.S. beef industry for the harvesting phase. These data were collected through on-site facility visits and follow-up discussion and were based on measured data for primary inputs as well as measured or calculated data for operational emissions and waste. The producers selected represented both large and small operations so that the full scale of operations was properly considered. Data were then aggregated in a


33

weighted-average manner. Beef requiring further-processing (smoked, cured, or seasoned) was not included in this study.

Transportation data for all raw material and supply inputs were included in the scope of the study for the harvesting phase. Primary data associated with the transportation of cattle, waste, paper, plastics (packaging), and liquid carbon dioxide were used. For all other raw material and supply inputs, an average transport value of 1,263 miles was assigned based on the average of these 5 categories of primary transportation data.

The following is a list of additional assumptions for the harvesting phase that were necessary to complete this study:

1. An economic allocation that credits the final beef produced for the by-products of the harvesting process was applied to the study. By-products of the animal included hides, offal, blood, tallow, bones, and bonemeal. The economic allocation was based upon primary sales data for both the by-products and edible beef received from the packing sector collaborators. The allocation credits to the beef value chain for 2005 and 2011 were 9.6% and 11.7%, respectively (i.e. 9.6% and 11.7% of the harvesting impacts were allocated to the beef system by-products).

2. Corrugated cardboard used for packaging had a recycled fiber content of 30%.

3. Of the packaging used as inputs to the product system (corrugated cardboard and plastics), 96% went directly to either the case-ready or retail phase. Therefore, end-of-life impacts for this 96% were included at the respective phase. The remaining 4% of packaging plastic consumed in the harvesting plant was included as part of the total facility waste profile for end-of-life impact analysis. For the 4% corrugated cardboard intended for recycling, it was assumed there was no impact from recycling within the scope boundary as discussed in the overall study assumptions in Section 6.1.1.

4. For the Occupational Illnesses and Accidents risk values, in addition to the standard pre-chain impacts, direct impacts for the harvesting category were assessed for change over time according to the two period alternatives (2005 and 2011) using the U.S. Bureau of Labor Statistics (BLS) data for the industry category of “Animal Slaughtering and Processing.”

5. Three additional risks (beyond the occupational illnesses and accidents) were considered for the harvesting phase. Expert opinion evaluated each risk category on a scale of 1:10 with 1 representing the most risk and 10 representing the least risk. Standard BASF risk analysis methodology considers occupational accidents and illnesses and allows for customized risks to be considered as appropriate. This study considered the expert opinion weightings and scoring scales to be a total of 20.75% of the harvesting risk analysis.


34

a. Food Safety: Food safety was measured as contamination from pathogens as well as recalls. Based on data from the Centers for Disease Control20 and expert opinion, the scale scoring applied to the food safety category was 1, 6, and 8 for 1970, 2005, and 2011, respectively.21 The risk weighting for food safety was considered to be 14% of the total harvesting risk.

b. Animal Welfare: Treatment of animals was considered through various auditing programs.22 The expert opinion scale scoring applied to the animal welfare category was 2.5, 5.5, and 7.5 for 1970, 2005, and 2011 respectively. The total risk weighting for animal welfare was considered to be 8.5% and this weighting was split between the cattle and the harvesting phases at 4.25% in each phase.

c. Community Nuisance Dust and Odors: Impact of non-regulated dust and odors from the harvesting plants themselves was considered through trends observed as voluntary best practices to mitigate these community impacts in the industry over time. The expert opinion scale scoring applied to the community nuisance dust and odors was 1, 5, and 7 for 1970, 2005, and 2011 respectively.23 The risk weighting for community nuisance dust and odors was considered to be 2.5% of the total harvesting risk.

6.1.5 Case-Ready

The case-ready phase is where the beef produced in the harvesting phase is packaged into a retail-ready output. As mentioned earlier, for purposes of this study, 100% of the U.S. beef was assumed to be packaged in a case-ready system.

Primary data were collected for the case-ready phase of the study from one of the harvesting partners (the other two did not have case-ready operations). This primary data included inputs for energy, packaging, waste, and consumable items. Based on industry expert opinion and direct operations knowledge from the case-ready data providers, all other data values, such as water, cleaning chemicals, and waste, were assumed to be 10% of the average of the harvesting facility data from the three producers surveyed.

It was also assumed that for packaging used as inputs to the case-ready system, 96.5% of this packaging went on to the retailer or end-consumer. As with the harvesting phase, the remaining 3.5% was included in the case-ready facility waste profile. For the 3.5% corrugated cardboard intended for recycling, it was assumed there was no impact from recycling in scope boundary as discussed in the overall study assumptions in Section 6.1.1.

For the Occupational Illnesses and Accidents risk values, in addition to the standard pre-chain impacts, direct impacts for the case-ready category were assessed for change over time according to the two period alternatives (2005


35

and 2011) using the U.S. Bureau of Labor Statistics (BLS) data for the industry category of “Animal Slaughtering and Processing.”

6.1.6 Retail

The retail phase considers the operations where the packaged beef from the case-ready phase is sold. For this study, no primary data were obtained from retailers, but this will be included in a future second phase of the study. For this submission, literature and other publicly-available sources of information, including from the EPA,24,25 USDA,26 and the Food Marketing Institute27 were used to construct average retail eco-efficiency profiles that included retail electricity consumption, refrigerant leakage, natural gas consumption, and beef waste.

Because average data were only published periodically or in some instances only analyzed for one point in time, the same data were assumed for 2005 and 2011 for all inputs, with the exception of refrigeration energy and refrigerant leakage. For these impacts, a volumetric allocation based on the average U.S. diet was applied (which is also applied to consumer refrigeration). The volumetric allocation was derived from an analysis of USDA Economic Research Service data on U.S. food consumption at home and associated densities.28

For the Occupational Illnesses and Accidents risk values, in addition to the standard pre-chain impacts, direct impacts for the retail category were assessed for change over time according to the two period alternatives (2005 and 2011) using the U.S. Bureau of Labor Statistics (BLS) data for the industry category of “Grocery Stores.”

6.1.7 Consumer

The consumer phase considers the impacts by the consumer from transportation to the retail store through consumption of the beef at the consumer’s home. As with the retail phase, no primary data were used since a targeted consumer survey and study were not conducted. Literature and other publicly-available sources of information were used to construct average consumer eco-efficiency profiles that included transportation,29 electricity consumption associated with refrigeration,30 repackaging of beef by the consumer,31 cooking energy,32 and consumer beef waste33. Data for repackaging and cooking energy were assumed to be equivalent for both 2005 and 2011 as data for these inputs was not published regularly.

As with the retail phase, a volumetric allocation based on the average U.S. diet was applied in order to determine an appropriate allocation for consumer refrigeration associated with beef. The volumetric allocation was derived from an analysis of USDA Economic Research Service data on U.S. food consumption at home and associated densities.34


36

7. Data Sources

The environmental impacts for the production, use, and disposal of the various alternatives were calculated from eco-profiles (i.e. life cycle inventories) for the individual components and for fuel usage and material disposal. Life cycle inventory data for these eco-profiles were from several data sources, including BASF specific manufacturing data, Boustead35, and ecoinvent36. Overall, the quality of the data was considered medium-high to high. None of the eco-profiles data were considered to be of low data quality. A summary of the eco-profiles used by phase is provided below in Table 8.

Eco-Profile Source, Year Comments

General Utility & Waste Profiles Water from well BASF, 2010

Electricity use BASF, 2011 Profile based on 2011 U.S. Energy Information Administration electricity grid profile data.

Natural gas use BASF, 1999 Diesel use BASF, 1999 Gasoline use BASF, 1999 Lubricating oils BASF, 1999 Transportation (diesel; long-haul) US LCI, 201137

Municipal wastewater treatment Ecoinvent 2.2, 2010 Ecoinvent profile: Treatment, sewage, to wastewater treatment, class 3/CH U

Municipal solid waste landfill Ecoinvent 2.2, 2010 Ecoinvent profile: municipal solid waste, 22.9% water, to sanitary landfill/CH U

Feed Phase Urea fertilizer BASF, 2005 Glyphosphate BASF, 1997 Dicamba BASF, 1999

Dimethenamide pesticide Ecoinvent 2.2, 2010 Ecoinvent profile: Pesticide unspecified, at regional storehouse/RER U

Atrazine Ecoinvent 2.2, 2010 Ecoinvent profile: Atrazine, at regional storehouse/RER U

Metolachlor BASF, 1997

Acetochlor BASF, 1997 BASF, 2011

Pyraclostrobin BASF, 2006 Single superphosphate fertilizer BASF, 1997 Potassium fertilizer BASF, 1997

Fludioxinol fungicide Ecoinvent 2.2, 2010 Ecoinvent profile: Nitrile compounds, at regional storehouse/RER U

Mefanoxam fungicide Ecoinvent 2.2, 2010 Ecoinvent profile: Pyretroid compounds, at regional storehouse/RER U

Clothianidin insecticide Ecoinvent 2.2, 2010 Ecoinvent profile: Organophosphorus compounds, at regional storehouse/RER U

2,4-Dichlorophenoxyacetic acid Ecoinvent 2.2, 2010 Ecoinvent profile: 2,4-D, at regional storehouse/RER U

Chlorpyrifos insecticide Ecoinvent 2.2, 2010 Ecoinvent profile: Organophosphorus compounds, at regional storehouse/RER U

Paraquat dichloride Ecoinvent 2.2, 2010 Ecoinvent profile: Pesticide unspecified, at regional storehouse/RER U

Clopyralid herbicide Ecoinvent 2.2, 2010 Ecoinvent profile: Pesticide unspecified, at regional storehouse/RER U

Picloram herbicide Ecoinvent 2.2, 2010 Ecoinvent profile: Pesticide unspecified, at regional storehouse/RER U


37


Carbaryl insecticide BASF, 2002 Ammonium sulfate BASF, 1996 Calcium oxide BASF, 1997

Bioethanol from corn Ecoinvent 2.2, 2010 Ecoinvent profile: Ethanol, 95% in H2O, from corn, at distillery/US

Corn BASF, 2011 Cattle Phase Urea fertilizer BASF, 2005 Calcium oxide BASF, 1997 Magnesium oxide Boustead, 1996 Sodium chloride Boustead, 1996 Copper chloride BASF, 1998 Sodium selenite BASF, 2003 Zinc sulfate BASF, 2003 Thiamine mononitrate BASF, 2003 Molasses BASF, 2000 Corn BASF, 2011 Dicalcium phosphate BASF, 2003 Potassium fertilizer BASF, 1997 Iodine BASF, 2006 Harvesting Phase Propane Boustead, 1996

Biogas Ecoinvent 2.2, 2010 Ecoinvent profile: Biogas, from slurry, at agricultural co-fermentation, covered/CH U

Tallow Food LCA db, 2008 Phosphoric acid Boustead, 1996 Acetic acid Boustead, 1996 Lactic acid BASF, 2003 Nitric acid Boustead, 1996 Sulfamic acid Boustead, 1996 Chlorine Boustead, 1990 Detergent BASF, 1996 Sodium hypochlorite BASF, 2002 Sodium chlorite Boustead, 1996 Sodium hydroxide BASF, 2003 Antifoam BASF, 2002 Silica Boustead, 2000 Citric acid BASF, 1998 Calcium hypochlorite BASF, 2013 Hydrogen peroxide Boustead, 1996 Carbon dioxide BASF, 1996 Sodium chloride Boustead, 2000 Anhydrous ammonia Boustead, 1996 Sodium bicarbonate BASF, 1999 Triazine pesticide Ecoinvent, 1996 HDPE BASF, 2007 Steel BASF, 2010 PVC BASF, 1996 Cotton BASF, 2003 Nylon BASF, 2002 Iron BASF, 1999 Laundering BASF, 2005 LDPE BASF, 2005 Aluminum alloy BASF, 1996 Cardboard, virgin Ecoinvent 2.2, 2010 Ecoinvent profile: Corrugated board, fresh fibre,


38


single wall, at plant/RER U

Cardboard, recycled Ecoinvent 2.2, 2010 Ecoinvent profile: Corrugated board, recycling fibre, double wall, at plant/RER U

Paper Ecoinvent 2.2, 2010 Ecoinvent profile: Paper, woodfree, uncoated, at non-integrated mill/RER U

Polypropylene BASF, 1996

Wood pallets Ecoinvent 2.2, 2010

Ecoinvent profile: Wood container and pallet manufacturing (of project USA Input Output Database)

Case-Ready Phase Nitric acid Boustead, 1996 Sodium hydroxide BASF, 2003 Antifoam BASF, 2002 Silica Boustead, 2000 Steel BASF, 2010 Cotton BASF, 2003 Nylon BASF, 2002 Laundering BASF, 2005 LDPE BASF, 2005 Aluminum alloy BASF, 1996

Cardboard, virgin Ecoinvent 2.2, 2010 Ecoinvent profile: Corrugated board, fresh fibre, single wall, at plant/RER U

Cardboard, recycled Ecoinvent 2.2, 2010 Ecoinvent profile: Corrugated board, recycling fibre, double wall, at plant/RER U

Paper Ecoinvent 2.2, 2010 Ecoinvent profile: Paper, woodfree, uncoated, at non-integrated mill/RER U

Polypropylene BASF, 1996

Wood pallets Ecoinvent 2.2, 2010

Ecoinvent profile: Wood container and pallet manufacturing (of project USA Input Output Database)

Retail Phase R-143a Refrigerant BASF, 2002

R-134a Refrigerant Ecoinvent 2.2, 2010 Ecoinvent profile: Refrigerant R134a, at plant/RER S

Consumer Phase LDPE BASF, 2005 BASF data sources are internal data, while the others are external to BASF. Internal data is confidential to BASF; however, full disclosure was provided to NSF International for verification purposes.

Table 8: Eco-profile Data Sources

8. Eco-efficiency Analysis Results and Discussion

8.1. Environmental Impact Results

The environmental impact results for this U.S. Beef EEA are generated as defined in Section 6 of the BASF EEA methodology and are presented below in Sections 8.1.1 through 8.1.10. Note that the results presented in Section 8 are for the alternatives 2005 and 2011, while the 1970 on-farm scenario is discussed in some detail in Section 9.


39

8.1.1. Cumulative Energy Demand

The bulk of the energy consumed by the beef system was the gross bioenergy contained within the feed used for the animals, which represented nearly 80% of the total Cumulative Energy Demand (CED). There were some minor added energy requirements associated with the use of distiller’s grains in 2011. Additionally, while all phases of the beef value chain contributed to CED through fossil energy consumed for utilities and transportation, the retail and consumer energy requirements were clearly higher as a result of more energy required per pound of beef due to scale (associated with refrigeration, cooking, and transport).

CED declined slightly from 521 MJ/CB to 511 MJ/CB between 2005 and 2011 as shown below in Figure 15. Figure 16 demonstrates the impact of the gross bioenergy from the feed. Since this energy is a biological requirement for the animals and cannot be changed, it is important to recognize that the main opportunities for energy reduction are found with the remaining energy (most of which is currently non-renewable as is associated with the current U.S. energy grid and transportation system). To this point, the main drivers of the slight reduction in CED between the two periods included energy efficiency and conservation improvements related to the following:

utilities and transportation energy consumed per CB throughout the value chain;

increased crop yields and thus less fossil energy consumed per unit of feed produced;

increased use of biogas generated and captured from on-site wastewater lagoons at the harvesting facilities (which allowed a decreased purchase of off-site fossil energy);

fuel switching for boilers at the harvesting facilities from diesel to natural gas; and

packaging consumption reduction due to optimizations in the harvesting and case-ready phases that resulted in decreased transportation of packaging supplied as well as reduced pre-chain energy impacts.


40

Figure 15: Cumulative Energy Demand

Figure 16: Renewable and Non-Renewable Energy Breakout

8.1.2. Abiotic Depletion Potential (ADP)

Zinc in the cattle phase (used as an essential mineral supplement) was the most dominant abiotic depletion factor on a weighted basis in the entire beef value chain. While the amount of zinc/CB was very small (<1 gram as zinc sulfate/CB), the global reserves that are currently economically available coupled with the current rates of extraction cause zinc to be weighted with high significance. The bulk of the remaining ADP was a result of fossil energy (natural gas, oil, and coal) that was used for fertilizers in the feed phase and throughout the entire beef value chain for utilities and transportation fuels.


41

While use of distiller’s grains caused a slight increase in ADP, as can be seen below in Figure 17, there was a small total beef value chain decline from 5.05 to 4.96 mg Ag-eq/CB between 2005 and 2011. The main reduction factors included increased yields of feed crops, an increased use of recovered biogas from wastewater lagoons at the harvesting facilities (resulting in decreased need for diesel purchases for boilers), and energy efficiency improvements throughout the value chain.

Figure 17 represents the ADP by phase while Figure 18 represents the ADP by resource.

Figure 17: Abiotic Depletion Potential by Phase

Figure 18: Abiotic Depletion Potential by Resource


42

8.1.3 Consumptive Water Use

Nearly 95% of the consumptive water was consumed in the feed phase and this was associated mainly with the irrigation of crops. Utility and pre-chain water consumption (especially from pre-chain impacts from materials such as corrugated cardboard) had a significant contribution on consumptive water as well as direct water consumption within the harvesting process.

The total consumptive water use declined 3% from 82,103 to 79,251 L-eq/CB (2,418 to 2,336 L absolute consumptive water) between 2005 and 2011 and this is mainly on account of a reduction in USMARC irrigation water per unit of feed due to increased efficiencies as well as a reduction that can be attributed to use of the distiller’s grains. Some more minor additional points of reduction include those related to harvesting plant water efficiency improvements as well as optimizations in the case-ready phase that lead to packaging reductions. Energy efficiency improvements throughout the value chain also resulted in some additional consumptive water use reductions from reduced pre-chain impacts.

The consumptive water use is shown both at an assessed value as well as an absolute value in Figures 19 and 20 below. Consistent with the BASF EEA methodology, a damage factor was applied to the absolute consumptive water use in order to determine the assessed consumptive water. The damage factor applied to the direct consumptive water and pre-chain electricity consumptive water used for the feed and cattle phases represents the region in which USMARC is located (Nebraska) and is 33.4. The factor applied to the rest of the study, including all other outside inputs to the USMARC modeled in the feed and cattle phases, was 68.7, which is representative of the entire U.S.38

Figure 19: Assessed Consumptive Water Use


43

Figure 20: Absolute Consumptive Water Use

8.1.4 Air Emissions

8.1.4.1 Global Warming Potential (GWP)

Enteric methane emissions in the cattle phase were the largest contributor to GWP in the beef value chain, representing 42% of total GWP. N2O from manure on the feedlots and pastureland was the second largest contributor, with 20% of the total value chain emissions. Other significant contributors included field emissions from fertilizers on the feed phase, refrigerant leakage on the retail phase, and cooking on the consumer phase. Less significant GWP contributors included corrugated cardboard and LDPE packaging pre-chain emissions.

As shown below in Figure 21, GWP had little change with values of 23.7 kg CO2-eq/CB in 2005 and 23.6 kg CO2-eq/CB in 2011. Most of this reduction was a result of energy efficiency improvements throughout the value chain as well as increased use of recovered biogas in the harvesting phase and packaging optimizations (reduced pre-chain emissions reductions). There was some reduction realized from increased crop yields over time, which resulted in greater efficiency per hectare of feed as well as reduced fertilizer use with this increased efficiency. Some increases in GWP on the feed side cancelled out and caused a very slight increase over any gains in the feed phase from crop yield improvements as a result of using distiller’s grains, which has a higher GWP footprint from the associated bioethanol distillation process.


44

Figure 21: Global Warming Potential (GWP)

8.1.4.2 Photochemical ozone creation potential (POCP) The main contributors to POCP included volatile organic compounds (VOCs) from feed silage (as well as some contribution from high moisture corn and distiller’s grains), enteric methane, fossil energy emissions (especially diesel), and packaging pre-chain emissions from corrugated cardboard and LDPE. As shown below in Figure 22, there was a negligible reduction in POCP with values nearly constant at 0.026 kg C2H4-eq/CB for 2005 and 2011. There was a reduction in POCP from increased use of recovered biogas in the harvesting phase as well as other less significant reduction contributions from plant utilization optimizations and packaging optimizations on the case-ready phase. However, these reductions were mainly offset by increased use of high moisture corn and silage. The overall change in POCP due to distiller’s grains use in 2011 was negligible.


45

Figure 22: Photochemical Ozone Creation Potential (POCP)

8.1.4.3 Ozone depletion potential (ODP)

The most significant contributors to ODP were halogenated hydrocarbons in the retail refrigerant and low density polyethylene (LDPE) pre-chain emissions.

As can be seen in Figure 23 below, ODP values were essentially constant between 2005 and 2011 with values in both years of 0.013 g CFC11-eq/CB. There was a small reduction realized from LDPE packaging optimizations. However, the total value chain emissions remained stable due to some increased emissions associated with the distiller’s grains and the associated bioethanol distillation process.

Figure 23: Ozone Depletion Potential (ODP)


46

8.1.4.4 Acidification potential (AP)

Most of the AP contribution comes from the feed and cattle phases. Specifically, fertilizers used on feed crops and manure and urine from cattle were the major causes. Other contributors to AP included emissions from combustion in electricity production and on-site boiler use, transportation, and pre-chain impacts from corrugated cardboard.

As can be seen in Figure 24 below, AP declined 3% from 336 to 327 g SO2-eq/CB between 2005 and 2011. There was an increase in AP from the cattle phase as a result of distiller’s grains being used in the cattle diets, which results in increased NH3 emissions from cattle urine. However, decreases in AP associated with the use of distiller’s grains in the feed phase negated the increase in the cattle phase and resulted in an overall slight reducing effect on AP from distiller’s grains use. Additionally, there were significant decreases in AP in the feed phase as a result of increased crop yields (and corresponding decreased fertilizer use and associated field emissions as well as fertilizer pre-chain energy emissions per unit of feed produced). Additionally, other smaller AP reductions were realized from energy efficiency improvements across the value chain, increased use of recovered biogas in the harvesting phase, and packaging optimizations resulting in decreased pre-chain emissions.

Figure 24: Acidification Potential (AP)

8.1.5 Water emissions

The main water emissions from the beef value chain were from the feed phase, which accounted for 78% of total water emissions in 2005 and 81% in 2011. Of the feed emissions, approximately 42% was a result of nitrogen runoff and leaching, 20% from phosphorous runoff, 35% from heavy metal runoff and leaching (associated with fertilizers), and 3% from Chemical Oxygen Demand


47

(COD). Other main water emissions were a result of runoff and leaching from cattle pastureland, direct wastewater emissions from the harvesting and case-ready facilities, pre-chain impacts from cardboard packaging production, and water emissions associated with end-of-life landfill disposal for production waste and packaging waste at all phases of the post-farm value chain.

As shown below in Figure 25, there was a 10% reduction in water emissions from 4,981 to 4,487 L grey water-eq/CB from 2005 to 2011. This reduction is mainly a result of increased crop yields between the alternative periods that results in decreased fertilizer and pesticide use and associated runoff and leaching per unit of feed produced. Additional water emissions reductions were associated with the move to distiller’s grains, packaging optimizations in the case-ready phase (reduction of pre-chain water emissions), reduced emissions from reduced packaging waste that went to landfill in the post-farm value chain due to those same packaging optimizations, and harvesting facility direct wastewater emission reductions.

Figure 25: Water Emissions

8.1.6 Solid waste generation

Since waste that was directly generated throughout the beef value chain was analyzed according to ultimate disposal (recycling, incineration, or landfilling), all of the solid waste shown below in Figure 26 was associated with pre-chain waste. All direct waste was therefore evaluated above for final ecosphere emissions to water and air based on final fate degradation.

Solid waste generated from pre-chain production declined by 7% from 0.19 to 0.18 kg/CB between 2005 and 2011. This was due to greater use of biogas at the harvesting facilities and numerous other efficiency improvements throughout the value chain mentioned throughout this analysis. As a result of replacing


48

more purchased diesel with biogas and the other value chain efficiency improvements, less materials were utilized overall, which had a direct relation to reducing all pre-chain environmental impacts including solid waste generation. The use of distiller’s grains as feed had no noticeable impact on solid waste changes from 2005 to 2011.

Figure 26: Solid Waste Generation

8.1.7 Land use

The most significant phase associated with land use was the feed phase due to the pasture and crop land required to grow the feed and this represented approximately 95% of the land required for the total beef value chain. Of that 95%, 70% was solely from pastureland (two-thirds of land requirements for the total beef value chain). Other notable impacts associated with land use were the pre-chain impacts associated with packaging (cardboard) and diesel consumption. As can be seen below in Figure 27, land use declined by 4% from 21.4 to 20.5 m2-years/CB between 2005 and 2011. Most of this decline was associated with increased crop yields. Other notable declines were associated with the use of distiller’s grains, packaging optimization (reduced pre-chain impacts, particularly associated with cardboard), increased use of recovered biogas that reduced pre-chain impacts associated with diesel, and other energy efficiency improvements across the value chain that reduced associated pre-chain impacts.


49

Figure 27: Land Use

8.1.8 Toxicity potential

Inventories of all relevant materials were quantified in a manner consistent with the BASF EEA methodology for assessing the human health impact of these materials (ref. Section 6.8 of Part A submittal). This toxicity potential analysis included consideration of the production of all materials that are in the study boundary scope, the use of all materials used as direct inputs to the beef value chain (i.e., human health exposure to employees of the beef value chain), as well as toxicity of materials disposed of throughout the value chain according to the boundary scope. A detailed scoring table was developed for each alternative broken into life cycle stages. This scoring table with all relevant material quantities considered the H-phrase and pre-chain toxicity potential scores and was provided to NSF International as part of the EEA model submitted as part of this verification. Figure 28 shows how each module contributed to the overall toxicity potential score for each alternative. The values have been normalized and weighted.

The major influencing factor for toxicity potential was the manufacturing impact of agricultural chemicals (fertilizers and pesticides) and the impacts from application. Other major contributors to toxicity potential included fossil energy (natural gas, coal, and diesel) pre-chain and use factors that were utilized throughout the beef value chain for utilities and transportation.

As shown below in Figure 28, the normalized and weighted toxicity potential remained essentially the same from 2005 to 2011. There were reductions in toxicity potential from increased use of recovered biogas from lagoons at harvesting facilities (requiring less purchased diesel), decreased LDPE (reducing pre-chain toxicity potential), and other energy efficiency improvements throughout the value chain that resulted in lower fossil energy use. However, these reductions were effectively neutralized due to the toxicity associated with increased ammonia releases from urine as a result of the use of distiller’s grains in 2011 as well as


50

some small toxicity contribution from the bioethanol distillation associated with the distiller’s grains generation in the feed phase.

Figure 28: Toxicity Potential

8.1.9 Risk (Occupational Illnesses and Accidents potential) All of the materials and activities in the various life cycle stages were assigned specific NACE codes39. NACE (Nomenclature des Activities Economiques) is a European nomenclature which is very similar to the NAICS codes in North America. The NACE codes are used in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the business economy and is broken down by specific industries. Specific to this impact category, the NACE codes track, among other metrics, the number of working accidents, fatalities, illnesses and diseases associated with certain industries (e.g. chemical manufacturing, petroleum refinery, inorganics etc.) per defined unit of output. By applying these incident rates to the amount of materials required for each alternative, a quantitative assessment of risk was achieved. In addition to the NACE analysis for all of the inputs, in order to derive a better representation of change over time in Occupational Illnesses and Accidents potential, U.S. Bureau of Labor (BLS) data were analyzed for the direct industry activity in each of the beef value chain phases as outlined in the assumptions discussion above in Section 6. As also discussed above in the assumptions discussion in Section 6, additional risk categories of Animal Welfare (on both the cattle and harvesting phases), Food Safety (on the harvesting phase), and Community Nuisance Odors and


51

Emissions (on the harvesting phase) were considered as part of the total risk analysis in this study. While these additional risks were considered at the percentages in the applicable phases outlined in Section 6, in the total study, these additional risks were weighted as follows: 1) Food Safety: 7.2%; 2) Animal Welfare: 3.1%; and 3) Community Nuisance Odors and Emissions: 1.3%. These final weightings were a result of the aggregated phase risk weightings. Occupational Diseases were weighted at 48.4%, Fatal Accidents at 27.5%, and Non-fatal Accidents at 12.6% of total study risk. As shown in Figure 29, total risk declined by 32% between 2005 and 2011. Outside of the additional risks analyzed according to expert analysis, the Occupational Illnesses and Accidents reductions are according to the BLS data for each alternative and are representative of the changes noted in each industry that was analyzed on a direct basis as well as a result of any pre-chain input reductions per CB that results in a corresponding decline in associated illnesses and accidents per CB. Some of these reductions can be attributed to the use of distiller’s grains.

Figure 29: Risk Potential (Occupational Illnesses and Accidents)

8.1.10 Environmental Fingerprint

Following normalization or normalization and weighting according to the BASF EEA methodology, the relative impact for all seven of the main environmental categories for each alternative is shown in the environmental fingerprint in Figure 30. As discussed in each of the individual impact analyses above, there was at least a small decline in each of the seven main environmental impact categories and these are reflected in the environmental fingerprint. The largest category improvement is associated with Risk (Occupational Illnesses and Accidents) as


52

shown below, which had a significant contribution to the overall environmental impact reduction of 7% for the beef value chain.

Figure 30: Environmental Fingerprint

8.2. Economic Cost Results

The life cycle cost data for the U.S. Beef EEA were generated as defined in Section 7 of the BASF EEA methodology and described in the overall study assumptions in Section 6 of this report. The results of the life cycle cost analysis based on a present value approach demonstrated an increase of 6% between 2005 and 2011 and are depicted in Figure 31. Again, the consumer prices used to reflect total cost/CB are not associated with USMARC operations but are intended to reflect general value chain cost/CB to the point of retail sale. To reflect current market conditions and pricing, 2005 pricing was adjusted to 2011 dollars.


53

Figure 31: Life Cycle Costs

8.3 Eco-efficiency Analysis Portfolio

The eco-efficiency analysis portfolio for the U.S. Beef EEA was generated as defined in Section 9.5 of the BASF EEA methodology. Utilizing relevance and calculation factors, the relative importance of each of the individual environmental impact categories were used to determine and translate the fingerprint results to the position on the environmental axis for each alternative shown. For a clearer understanding of how weighting and normalization is determined and applied, please reference Section 8 of BASF’s Part A submittal to P352. Specific to this study, the worksheets “Relevance” and “Evaluation” in the EEA model provided to NSF as part of this verification process should be consulted to see the specific values utilized and how they were applied to determine the appropriate calculation factors. Environmental relevance factors and social weighting factors for the USA (national average) were applied to this study. The environmental relevance values utilized were last reviewed in 2012 and the social weighting factors were recently updated in 2011 by an external, qualified third party organization40.

Figure 32 displays the eco-efficiency portfolio for the base case analysis and shows the 2011 U.S. beef value chain to be more eco-efficient than that in 2005. While there was an increase in price of beef of 6% between 2005 and 2011, there was a simultaneous decrease in the overall environmental impacts from the U.S. beef value chain of approximately 7%. Following weighting and normalization per above, the EEA portfolio below results in a 5% improvement in overall eco-efficiency.


54

Figure 32: Eco-efficiency Portfolio: U.S. Beef – Phase 1

9. Scenario #1: 1970 On-Farm Scenario

9.1 General Discussion

As stated above, an assessment was performed for 1970 for the feed and cattle (or on-farm) phases of the study using the available IFSM data. While the original intent of this study was to perform an EEA analysis of three alternatives (1970, 2005, and 2011) for the entire beef value chain, due to the lack of available data for post-farm phases, this was not possible. However, the following discussion demonstrates some of the changes over time that have occurred between 1970 and the 2005 and 2011 alternatives for the on-farm phases as analyzed at USMARC. This detail provides the potential for assessing what changes have taken place on the feed and cattle phases since 1970 and demonstrates further opportunities for learning best practices that have taken place over a longer period of time in these areas.

This is particularly important since for six of the seven high-level impact categories (and correspondingly all six of the environmental impact categories), either the feed or cattle phase was the major contributor. The only impact category that did not have most of the impact from the feed or cattle phase was the Risk category. Additionally, all of the sub-environmental (emissions) categories except ODP had the majority of the impact from the feed or cattle phase. Therefore, while all phases play an integral role in improving the sustainability attributes of the U.S. beef value chain, the largest potential opportunities clearly lie within the feed and cattle phase.


55

Understanding what has changed since 1970 can provide great insight for future management improvement opportunities within these key phases of the beef value chain.

While as with the entire base case analysis, there are smaller trends or changes to understand in greater detail, the following are selected impacts that show a significant or important trend to highlight.

9.2 Cumulative Energy Demand

As shown below in Figure 33, there was a 9% increase in energy use from 408 MJ/CB in 1970 to 447 MJ/CB in 2005 (or 8% from 1970 to 2011 with a value of 444 MJ/CB). This increase demonstrates a classic water-energy (and GWP) nexus and is mainly due to increased irrigation of the pasture and the associated energy necessary to power the water pumps for the pivot irrigation systems. Irrigation was less frequently used in 1970 and it was used only for the corn produced at USMARC. As shown in the base case scenario, the majority of the energy used is associated with the gross energy of the feed. In this scenario here, feed gross energy is 93% of the CED in 1970 for the feed and cattle phases. The remaining CED is mainly associated with irrigation, transportation, and pre-chain energy consumption.

Figure 33: Cumulative Energy Demand for 1970 On-Farm Scenario

9.3 Consumptive Water Use

As can be seen below in Figure 34 (assessed consumptive water use) and Figure 35 (absolute consumptive water use), there was a 29% increase in consumptive water between 1970 with a value of 56,194 L-eq/CB or 1,681 L/CB absolute and 2005 with a value of 79,529 L-eq/CB or 2,380 L/CB absolute (or 27% from between 1970 and 2011 with a value of 76,917 L-eq/CB or 2,302 L/CB absolute). As mentioned above with the CED trend, there was a significant increase in irrigation water from 1970 to 2005


56

because some of the pasture is now irrigated at USMARC and more irrigated corn is produced. As with CED, the large increase in consumptive water negated some gains in efficiency that were noted on the cattle phase due to utility efficiency improvements.

Figure 34: Assessed Consumptive Water Use for 1970 On-Farm Scenario

Figure 35: Absolute Consumptive Water Use for 1970 On-Farm Scenario

9.4 Global Warming Potential

As can be seen below in Figure 36, there was a 5% decrease in GWP from 19.6 to 18.6 kg CO2-eq/CB between 1970 and 2005 (and essentially the same 5% from 1970 to 2011 with negligible change between 2005 and 2011 with a 2011 value of 18.7 kg CO2-eq/CB). This decline was mainly attributed to the fact that the cattle were fed a higher forage diet in 1970 that produced higher enteric methane. Additionally, a


57

small decline was also attributed to increased crop yields on the feed phase (less direct field N2O emissions as well as reduced pre-chain emissions from reduced chemical input per unit of feed). At the same time, the increased use of irrigation added some GWP on the feed phase (corresponding with increased energy use in pumping). However, the increased crop yields still led to an overall reduction in GWP in the feed phase.

Figure 36: Global Warming Potential for 1970 On-Farm Scenario

9.5 Acidification Potential

As shown below in Figure 37, there was a 31% decline in AP from 458 to 315 g SO2-eq/CB between 1970 and 2005 (and 33% from 1970 to 2011 with a value of 308 g SO2-eq). This large decline was mainly a result of increased crop yields that allowed for reduced fertilizer inputs in the feed phase. Utilities and transportation efficiency improvements also led to some smaller reductions in AP on the farm system.


58

Figure 37: Acidification Potential for 1970 On-Farm Scenario

9.6 Water Emissions

As shown below in Figure 38, there was a 5% reduction in water emissions from 4,245 to 4,050 L grey water-eq/CB between 1970 and 2005 (9% from 1970 to 2011 with a value of 3,860 L grey water-eq/CB) and this was again mainly as a result of increased crop yields (less direct water emissions from crop inputs per unit of land).

Figure 38: Water Emissions for 1970 On-Farm Scenario


59

9.7 Land Use

As shown below in Figure 39, there was a 7% reduction in land use from 22.3 to 20.7 m2-years/CB between 1970 and 2005 (10% from 1970 to 2011 with a value of 20.0 m2-years/CB). This was again mainly a result of increased crop yields on the feed phase.

Figure 39: Land Use for 1970 On-Farm Scenario

9.8 Environmental Fingerprint

As can be seen below in Figure 40, the on-farm Environmental Fingerprint shows declines in all of the main impact categories since 1970 with the exceptions of energy and consumptive water use as discussed above. A 10% reduction in environmental impact is noted on this cradle to farm-gate scenario analysis from 1970 to 2005 and a 12% reduction from 1970 to 2011.


60

Figure 40: Environmental Fingerprint for 1970 On-Farm Scenario

9.9 Eco-Efficiency Analysis Portfolio

Figure 41 below shows the EEA Portfolio with the on-farm scenario, which shows a clear improvement in the environmental portfolio overall, while at the same time there has been a small reduction in operational costs.

Note that the costs considered to analyze the economic portion of the EEA portfolio were total operational costs related to the feed and cattle phases as opposed to consumer price of beef that was used for the base case analysis of the full beef value chain. There was a 5% decrease in operational costs from 1970 to 2005 and a 6% decrease from 1970 to 2011. At the same time, as noted above, there was a 10% reduction in environmental impact from 1970 to 2005 and a 12% reduction from 1970 to 2011. Following weighting and normalization, the EEA portfolio below shows approximately a 14% improvement in overall eco-efficiency from 1970 to 2011. The EEA portfolio for the periods of 2005 and 2011 show similar eco-efficiency attributes because the two alternative periods are within less than 5% of one another.


61

Figure 41: EEA Portfolio for 1970 On-Farm Scenario 10. Data Quality Assessment

10.1 Data Quality Statement

The data used for parameterization of the EEA was sufficient with most parameters of high to medium data quality. Moderate (medium) data is where industry average values or assumptions pre-dominate the value. No critical uncertainties or significant data gaps were identified within the parameters and assumptions that could have a significant effect on the results and conclusions. Inputs to the study were comprehensive and the exclusions to the study described in Section 5.3 and noted in Figure 13 would not have a significant impact on the overall study. Eco-profiles used for the study as represented in Table 8 were reviewed for completeness and appropriateness. Eco-profiles that are greater than 10 years old were deemed to be still reflective of current technology and industry practices. Table 9 provides a summary of the data quality for the EEA.


62

Phase Quality Statement Comments

Feed High -

Medium

Mainly IFSM data of high quality for 2005 and 2011. Data for 1970 contains more assumptions as all practices on farm have not been fully documented nor known.

Cattle High-Medium

Mainly IFSM data of high quality for 2005 and 2011. Data for 1970 contains more assumptions as all practices on farm have not been fully documented nor known.

Harvesting High Primary data from harvesters whose facilities represent 60% of

the industry.

Case-Ready High-

Medium While the data was primary, the data source was only from one of the harvesting facilities that also had case-ready data.

Retail Medium None of the retail data was primary data but based off of industry averages from literature and industry reports.

Consumer Medium None of the consumer data was primary data but was based off of averages from literature and industry reports.

Table 9: Data Quality Evaluation for EEA Parameters

11. Sensitivity and Uncertainty Analysis

11.1 Sensitivity and Uncertainty Considerations

A sensitivity analysis of the final results indicates that the environmental impacts were more influential or relevant in determining the final relative eco-efficiency positions of the alternatives. This conclusion is supported by reviewing the BIP Relevance (or GDP-Relevance) factor calculated for the study. The BIP Relevance indicates for each individual study whether the environmental impacts or the economic impacts were more influential in determining the final results of the study. For this study, the BIP Relevance indicated that the environmental impacts were more influential in impacting the results than the economic impacts (reference the “Evaluation” worksheet in the Excel model for the BIP Relevance calculation).

As the data quality related to these main contributors of the environmental impacts was of at least moderate-high quality, this strengthened our confidence in the final conclusions indicated by the study. As expected from a study with large influence from agriculture, the impact categories with highest environmental relevance were water emissions, acidification potential, consumptive water use, and land use. The AP factor also had environmental relevance related to cattle emissions (manure and urine) as did GWP from enteric methane emissions.

The calculation factors shown in Figure 42, which considers both the social weighting factors and the environmental relevance factors, indicate which environmental impact categories had the largest effect on the final outcome. Calculation factors were utilized in converting the environmental fingerprint results (Figure 30) into the final, single environmental score as reflected in our portfolio (Figure 32). The input parameters that were related to these impact categories have sufficient data quality to support a conclusion that this study has a low uncertainty.


63

The social weighting factors did have an influence in shifting the relative weightings of a few impact categories represented in the emissions and air emissions sub-categories. While the environmental and societal impacts of consumptive water use, land use, and water emissions remained high along with toxicity potential and risk, lower societal relevance for AP and GWP caused a decrease in their respective weighting.

Figure 42: Calculation Factors Used in the Sensitivity and Uncertainty Analyses

11.2 Critical Uncertainties

There were no significant critical uncertainties from this study that would limit the findings or interpretations of the study. The data quality, relevance and sensitivity of the study support that the input parameters and assumptions are appropriate and justified.

11.3 Sensitivity Analyses

11.3.1 Scenario #1: Distiller’s Grains Mass Allocation

As represented in the base case analysis, an economic allocation was used that placed 21% of the bioethanol distillation environmental burden onto distiller’s grains. For this scenario, a mass allocation was used instead and this resulted in 62% of the bioethanol distillation process environmental burden being allocated to the distiller’s grains. This 62% was based upon a distillation conversion factor ratio of 479 kg distiller’s grains: 378 L bioethanol41 (or 299 kg with a density for ethanol of 0.79 kg/L).

As expected, the results using a mass allocation of the distiller’s grains were significantly changed in the feed phase compared to the base case economic allocation that would have a direct noticeable impact on the total beef value chain results. For example, Figure 43 below demonstrates a near 3% increase in


64

total value chain GWP as opposed to a 1% decrease on the economic allocation base case analysis. At the same time, Figure 44 shows a 25% increase in total value chain water emissions as opposed to an 11% decrease on the economic allocation base case analysis. Finally, the environmental fingerprint in Figure 45 shows significant movement in the opposite direction on total emissions, land use, toxicity potential, and resource consumption. While there is significant variation with the mass allocation, since we are considering all of the harvesting by-products with an economic allocation and since distiller’s grains is a by-product of the distillation process, we maintained the economic allocation in order to keep consistent with allocation of all by-products. Additionally, with current pricing used in the economic allocation, as is demonstrated in Section 11.3.2 below, a scenario that considered energy allocation further validated the 21% economic allocation factor.

Figure 43: GWP for Distiller’s Grains Mass Allocation Scenario


65

Figure 44: Water Emissions for Distiller’s Grains Mass Allocation Scenario

Figure 45: Environmental Fingerprint for Distiller’s Grains Mass Allocation Scenario

11.3.2 Scenario #2: Distiller’s Grains Energy Content Allocation

As represented in the base case analysis, an economic allocation was used that placed 21% of the bioethanol distillation process environmental burden onto distiller’s grains. For this scenario, an energy content allocation was used instead and this resulted in 21% of the bioethanol distillation process


66

environmental burden being allocated to distiller’s grains.42 Because this value was essentially the same as the economic allocation factor, no further analysis was completed to study the impact of using the energy content allocation approach. One could make the argument that energy content is a constant physical attribute that should be used for calculating the allocation of the distiller’s grains, as opposed to economics, which exhibits fluctuation. However, since we are considering all of the harvesting by-products with an economic allocation and since distiller’s grains is a by-product of the distillation process, we maintained the economic allocation in order to keep consistent with allocation of all by-products. Additionally, with current pricing used in the economic allocation, the energy allocation result further validated the 21% economic allocation factor.

11.3.3 Scenario #3: Economic Allocation for Retail and Consumer Refrigeration and Retail Refrigerant Leakage

As represented in the base case analysis, a volumetric allocation was used to analyze the burden of the retail and consumer phase refrigeration and the retail phase refrigerant leakage.

As expected, the results of using an economic allocation of the retail and consumer phase refrigeration and the retail refrigerant leakage resulted in noticeable changes in environmental impacts. For example, Figure 46 below demonstrates a near 6% increase in total value chain CED as compared to the volumetric allocation base case analysis. At the same time, Figure 47 shows a 20% increase in total value chain GWP compared to the volumetric allocation base case analysis (mainly as a result of the increased GWP of the refrigerant leakage). As can be seen in the environmental fingerprint in Figure 48 for the economic allocation scenario, there was little change in total environmental impact. The largest change with this alternate allocation method was on GWP. However, GWP is only weighted 3% of total beef value chain environmental impacts and this is the reason for little overall movement on the larger picture.

While there are significant differences between the economic and volumetric allocation for retail and consumer refrigeration as demonstrated, the volumetric allocation provided a physical allocation metric that was a more realistic representation of the refrigeration associated specifically with beef. Because a physical allocation metric was not reasonably possible or logical for the other impacts analyzed for the retail and consumer phases, the economic allocation was used since this was the only alternative available.


67

Figure 46: CED for Retail and Consumer Economic Allocation Scenario

Figure 47: GWP for Retail and Consumer Economic Allocation Scenario


68

Figure 48: Environmental Fingerprint for Retail and Consumer Economic Allocation Scenario

12. Conclusions

As presented in the eco-efficiency portfolio analysis, there has been a 5% improvement in the eco-efficiency of the U.S. beef industry between 2005 and 2011 as represented by the system boundary of this study. This correlates to a 6% increase in cost (based on consumer retail price) and a 7% decrease in environmental impact (as represented by the environmental fingerprint analysis) over that same timeframe.

While environmental impacts stem from all phases of the beef value chain as represented throughout the study analysis, the majority of the impacts are attributed to on-farm processes in the feed and cattle phases. Likewise, many of the impact reductions that have been made through the feed and cattle phases relate directly to the improvement in on-farm efficiencies. For example, improved yield of feed crops result in less system inputs being required per unit of land in order to achieve the same desired output of edible beef. The main reason that 2005 was selected to be analyzed as an alternative period was because 2005 was the last year that distiller’s grains was not widely used as a feed source. As a result of using distiller’s grains (with associated allocation) in place of corn and urea in 2011, there were improvements from 2005 in consumptive water, water emissions, AP, land use, and risk (Occupational Illnesses and Accidents). Distiller’s grains is also a more cost effective feed, which has a contribution to reducing on-farm operational costs. At the same time, the use of distiller’s grains in 2011 caused increases in impacts associated with energy, GWP, ADP, and ODP. However, in general, it appears that the overall eco-efficiency of the beef value chain is improved to at least a small extent with the use of the distiller’s grains. Additionally, using distiller’s grains as a


69

feed source provides a beneficial use of a by-product of bioethanol processing, thus providing additional environmental benefit outside of the beef value chain. The impacts associated with the post-farm phases of harvesting, case-ready, retail, and consumer, while generally contributing less overall value chain impacts, present significant opportunities for improvement. Additionally, these opportunities generally may be more straightforward in terms of implementation as seen in this study with examples such as biogas capture and recovery at the harvesting facilities, packaging optimizations, and energy efficiency opportunities throughout. These eco-efficiency analysis results provide the roadmap to identify and prioritize opportunities and to allow better understanding of the specific practices that can be used to further reduce the environmental impacts of the beef value chain, while maintaining the overall economic value proposition. As made clear already, one must realize that there can be significant regional impact differences in the industry, especially related to the feed and cattle phases. Additionally, specific changes in eco-efficiency noted in this study may not be fully representative of the industry as a whole. For example, while the data used from the harvesting facilities comes from companies whose operations represent 60% of the industry, it is not known with a high level of certainty if some of the opportunities such as biogas recovery, which resulted in notable impact reductions throughout the harvesting phase, are fully indicative of the entire industry. Future research is already underway to better understand some of these regional differences in the feed and cattle phase as well as to gather more specific data points to obtain an even higher quality dataset for ongoing measurement and improvement of the U.S. beef industry. Planned ongoing sustainability programs within the industry will provide future communications as data refinement continues to be made.

13. Limitations of EEA Study Results

These eco-efficiency analysis results and its conclusions are based on the specific comparison of the production, use, and disposal, for the described customer benefit, alternatives and system boundaries. Transfer of these results and conclusions to other production methods or products is expressly prohibited. In particular, partial results may not be communicated so as to alter the meaning, nor may arbitrary generalizations be made regarding the results and conclusions.


70

IV. SEEBALANCE® REPORT

1. Introduction

The SEEbalance® tool builds upon the Eco-Efficiency Analysis (EEA) scope to evaluate various social impacts. While the EEA methodology analyzes environmental and economic parameters, the SEEbalance® methodology evaluates all three “pillars” of sustainability by also considering social aspects as is shown below in Figure 49. The EEA scope, boundaries, inputs, and assumptions that were used in the U.S. Beef Phase 1 EEA were maintained in their entirety for the purposes of completing the SEEbalance® study. Therefore, one should consider this SEEbalance® study to simply be an expansion of the original EEA above.

Figure 49: SEEbalance® includes EEA as well as social aspects.43

2. Methodology

Social data was collected for all of the impact categories that are considered within the SEEbalance® methodology as shown in Figure 50, with the exception of child labor (as it was assumed that U.S. law does not allow child labor). This data was collected for each of the industry sectors that are directly part of the U.S. beef value chain according to the scope and boundaries as defined in the U.S. Beef Phase 1 EEA. Data collection was completed by PRé North America consultants for the beef checkoff and this data was then fed into the SEEbalance® study by BASF. The methodology and sources used for this data collection are described in the final report from PRé North America.44


71

Figure 50: SEEbalance® social impact categories.

3. SEEbalance® Analysis Results and Discussion

The SEEbalance® results for the U.S. Beef Phase 1 study are shown below by social impact category. Note that the working accidents, fatal working accidents, occupational diseases, and toxicity potential are exactly what were represented in the EEA. While aggregated in the risk category for EEA, working accidents, fatal working accidents, and occupational diseases are broken out individually here. When the SEEbalance® study is completed, these impacts were migrated to the social impact analysis and are therefore repeated here. Other than the categories that were migrated from the EEA (working accidents, fatal working accidents, occupational diseases, and toxicity potential), detailed analysis of the trending in social impacts is extremely difficult due to lack of quality data at a national and industry sector level. We believe that there are two main reasons for this lack of quality data. Firstly, there appear to be changes over time in the way that data is aggregated (i.e., how industry sectors are assigned to industry codes). This has an impact on raw sector data. Additionally, in order to put each social impact in terms of the CB, PRè North America’s methodology relied on both national economic data as well as per-unit product pricing. Both changes in economic conditions between periods that can result in significant price variation as well as the difficulty of deriving a standard per-unit pricing for non-commodity type products used as inputs to the beef system can result in significant variability that is not directly correlated with the beef production system. Much of the trending is attributed to national data trending of available data. Therefore, specific trending analysis for most of the social impacts below is limited.


72

3.1 Employees

3.1.1 Working Accidents, Fatal Working Accidents, and Occupational Diseases

All of the materials and activities in the various life cycle stages were assigned specific NACE codes45. NACE (Nomenclature des Activities Economiques) is a European nomenclature which is very similar to the NAICS codes in North America. The NACE codes are used in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the business economy and is broken down by specific industries. Specific to this impact category, the NACE codes track, among other metrics, the number of working accidents, fatalities, illnesses and diseases associated with certain industries (e.g. chemical manufacturing, petroleum refinery, inorganics etc.) per defined unit of output. By applying these incident rates to the amount of materials required for each alternative, a quantitative assessment of risk was achieved.

In addition to the NACE analysis for all of the inputs, in order to derive a better representation of change over time in Occupational Illnesses and Accidents potential, U.S. Bureau of Labor (BLS) data were analyzed for the direct industry activity in each of the beef value chain phases as outlined in the assumptions discussion above in Section 6.

As shown in Figures 51, 52, and 53, accidents declined 27%, fatal accidents declined 13%, and occupational diseases declined 42% between 2005 and 2011. The reductions follow direct trending in BLS data for each alternative and are representative of the changes noted in each industry that was analyzed on a direct basis as well as a result of any pre-chain input reductions per CB that results in a corresponding decline in associated illnesses and accidents per CB. Some of these reductions can be attributed to the use of distiller’s grains.

Figure 51: Working Accidents


73

Figure 52: Fatal Working Accidents

Figure 53: Occupational Diseases

3.1.2 Toxicity potential

Inventories of all relevant materials were quantified in a manner consistent with the BASF EEA methodology for assessing the human health impact of these materials (ref. Section 6.8 of Part A submittal). This toxicity potential analysis


74

included consideration of the production of all materials that are in the study boundary scope, the use of all materials used as direct inputs to the beef value chain (i.e., human health exposure to employees of the beef value chain), as well as toxicity of materials disposed of throughout the value chain according to the boundary scope. A detailed scoring table was developed for each alternative broken into life cycle stages. This scoring table with all relevant material quantities considered the H-phrase and pre-chain toxicity potential scores and was provided to NSF International as part of the EEA model submitted as part of this verification. Figure 28 shows how each module contributed to the overall toxicity potential score for each alternative. The values have been normalized and weighted.

The major influencing factor for toxicity potential was the manufacturing impact of agricultural chemicals (fertilizers and pesticides) and the impacts from application. Other major contributors to toxicity potential included fossil energy (natural gas, coal, and diesel) pre-chain and use factors that were utilized throughout the beef value chain for utilities and transportation.

As shown below in Figure 54, the normalized and weighted toxicity potential remained essentially the same from 2005 to 2011. There were reductions in toxicity potential from increased use of recovered biogas from lagoons at harvesting facilities (requiring less purchased diesel), decreased LDPE (reducing pre-chain toxicity potential), and other energy efficiency improvements throughout the value chain that resulted in lower fossil energy use. However, these reductions were effectively neutralized due to the toxicity associated with increased ammonia releases from urine as a result of the use of distiller’s grains in 2011 as well as some small toxicity contribution from the bioethanol distillation associated with the distiller’s grains generation in the feed phase.

Figure 54: Toxicity Potential


75

3.1.3 Wages and Salaries

The main contributor to wages and salaries was the cattle ranching and harvesting sectors. The electricity sector also played a noticeable role in wages and salaries in each of the sectors. As can be seen in Figure 55, wages and salaries increased 51% between 2005 and 2011. The main increases were associated with the cattle ranching and electricity sectors (particularly on retail).

Figure 55: Employee Wages and Salaries

3.1.4 Material and External Costs for Further Professional Training

The main contributor to material and external costs for further professional training was cattle ranching with transportation also having some contribution in the harvesting, case-ready, and retail phases. As can be seen in Figure 56, investment costs for further professional training were shown to decrease 73% between 2005 and 2011 and this was associated mainly with changes in the cattle ranching sector.


76

Figure 56: Material and External Costs for Further Professional Training

3.1.5 Lost Working Hours Due to Strikes and Lockouts

The main contributors to lost working hours due to strikes and lockouts were in the case-ready and harvesting sectors as well as in the transportation sector associated with the case-ready, harvesting, and retail phases. As can be seen in Figure 57, lost working hours due to strikes and lockouts decreased by 71% between 2005 and 2011. The main reductions are associated with reductions in the transportation sector on the harvesting, case-ready, and retail sectors. Additionally, other contributions to lost working hours from strikes and lockouts were associated with packaging in the case-ready phase as well as corn production in the feed phase.


77

Figure 57: Lost Working Hours Due to Strikes and Lockouts

3.2 Local and National Community

3.2.1 Employees

The main contributor to the total number of employees was the harvesting sector. Employment in the transportation sector played a noticeable role in the harvesting, case-ready, and retail phases as well. As can be seen in Figure 58, employment numbers went up 29% between 2005 and 2011. The main contributors to the increase are the chemicals sector associated with the harvesting and case-ready phases as well as the transportation sector associated with the harvesting, case-ready, and retail phases. There is also some contribution from the corn production sector.


78

Figure 58: Employees

3.2.2 Qualified Employees

The main contributor to the numbers of qualified (skilled) employees was the harvesting sector. The transportation, waste management, and electricity sectors also had a noticeable contribution.

As can be seen in Figure 59, qualified employees decreased 21% between 2005 and 2011. The main areas associated with the decline were the harvesting and retail sectors. There was also some contribution to the decrease from the chemical sector associated with the harvesting and case-ready phases and with corn production at the feed phase.


79

Figure 59: Qualified Employees

3.2.3 Gender Equality (as represented by Female Managers)

The main contributor to the total number of female managers was the harvesting sector. Female managers in the transportation, waste, and electricity sectors played a noticeable role along with some contribution from the corn production sector in the feed phase.

As can be seen in Figure 60, numbers of female managers declined 14% between 2005 and 2011. The main decline was noted in the case-ready sector associated with LDPE packaging. Additionally, some additional decline was noted in both the harvesting and retail sectors.

Figure 60: Female Managers


80

3.2.4 Integration of Disabled Employees

The main contributor to the total number of disabled employees was the cattle ranching sector. Disabled employees in the transportation and electricity sectors played a noticeable role in the harvesting, case-ready, and retail phases as well.

As can be seen in Figure 61, numbers of disabled employees that were integrated declined by 19% between 2005 and 2011. The main decline took place in the cattle ranching sector.

Figure 61: Disabled Employees

3.2.5 Part-Time Workers

The main contributor to the total number of part-time employees was the cattle ranching sector. Part-time employment in the transportation and electricity sector played a noticeable role in the harvesting, case-ready, and retail phases as well.

As can be seen in Figure 62, numbers of part-time workers declined by 64% between 2005 and 2011. The main decrease took place in the harvesting and case-ready sectors. Additional reductions were noted in the transportation, electricity, and waste disposal sectors.


81

Figure 62: Part-Time Workers

3.2.6 Family Support

Due to lack of data availability, the proxy indicator of paid time off was used to represent family support. The main contributor to this category was the harvesting sector with additional noticeable contributions from the corn production, transportation, electricity, and waste management sectors.

As can be seen in Figure 63, family support (paid time off) increased by 6% between 2005 and 2011. The main contributor to this increase was the transportation sector.

Figure 63: Family Support


82

3.3 Future Generations

3.3.1 Trainees

The main contributors to the total number of trainees was the electricity sector (particularly noticed in the retail phase) as well as the harvesting sector.

As can be seen in Figure 64, the number of trainees declined by 15% between 2005 and 2011. The main declines took place in the harvesting and case-ready sectors as well as the waste sector.

Figure 64: Trainees

3.3.2 R&D Expenditures

The main contributor to the total R&D expenditures was the cattle ranching sector. R&D in the harvesting sector also played a noticeable role.

As can be seen in Figure 65, there was a decline of 4% in R&D investments between 2005 and 2011. The main area associated with the decrease was in the waste and cattle ranching sectors.


83

Figure 65: R&D Expenditures

3.3.3 Capital Investments

The main contributors to capital investments were the transportation and electricity sectors. Capital investments made in the feed sector (including for distillers grains) and cattle ranching sector also had a noticeable contribution.

As can be seen in Figure 66, capital investments declined by 18% between 2005 and 2011. The main decline in investments was noted in the cattle ranching sector followed by declines in the transportation and feed production sectors.

Figure 66: Capital Investments


84

3.3.4 Social Security

The main contributor to the social security category (representing compulsory payments made by industry) were from the harvesting sector. Other noticeable contributors to this impact were from the feed production, transportation, electricity, and waste sectors.

As can be seen in Figure 67, social security (compulsory payments) declined 5% between 2005 and 2011. The main declines were noted in the harvesting, case-ready, and transportation sectors as well as some contribution from the feed production sector.

Figure 67: Social Security

3.4 International Community

3.4.1 Developing Country Foreign Direct Investment

The main contributors to foreign direct investment (FDI) in developing countries were the harvesting and case-ready sectors. FDI from the chemical sector was also significant.

As can be seen in Figure 68, FDI went up 46% between 2005 and 2011. The main areas related to increase are the same areas that are main contributors to FDI including the harvesting, case-ready, and chemical sectors.


85

Figure 68: Developing Country Foreign Direct Investment

3.4.2 Imports from Developing Countries

The main contributor to imports from developing countries was the cattle ranching sector. The harvesting, case-ready, and chemicals sector also played a significant role.

As can be seen in Figure 69, imports from developing countries increased by 15% between 2005 and 2011. Main increases were noted in the harvesting, case-ready, and chemicals sectors.

Figure 69: Imports from Developing Countries


86

3.5 Social Fingerprint Figure 70 shows that there were both declines and increases in the main categories of the social fingerprint. There were noticeable improvements in the international community and employees categories. At the same time, a negative trend was associated with the categories of local and national community and future generations.

Figure 70: Social Fingerprint

Note that the weighting factors used in this study for the social fingerprint, the SEEbalance® fingerprint and the SEEbalance® portfolio (the latter two discussed in the next section) were calculated based upon a similar approach taken with the EEA study. These factors were based on regional relevance factors that considered both the specific values from this beef value chain study and compared to U.S. national data overall. A social relevance factor was then considered along with the regional relevance factors and like the EEA was obtained through a social poll to understand the importance of the impact category to society. Note that while the EEA social poll included values that were specific to the U.S., this data was not available for the social impact categories for SEEbalance®. Instead, social relevance factors were applied using German data. This assumption was considered valid given similar social conditions between Germany and the U.S.

Final calculation or weighting factors that were applied are shown below in Table 10.


87

Employees 32% Working Accidents 6% Fatal Working Accidents 10% Occupational Diseases 11% Toxicity Potential 25% Wages and Salaries 21% Professional Training 24% Strikes and Lockouts 4% Local and national community 16% Employment 39% Qualified Employees 22% Gender Equality (female managers) 26% Integration (number of disabled employees) 6% Number of Part-time Workers 7% Family Support 0% Future generations 22% Number of Trainees 42% R&D (company expenditures) 8% Capital Investments 16% Social Security 34% International community 9% FDI (in developing economies) 64% Imports from Developing Economies 36%

Table 10: SEEbalance® Study Weighting Factors

3.6 SEEbalance® Fingerprint and Portfolio As can be seen in Figures 71 and 72, there was a slight overall improvement in the socio-eco-efficiency of the beef value chain between 2005 and 2011. Figure 71 demonstrates that this slight improvement was noted in the social and environmental fingerprints. Note that the weighting factors for the SEEbalance® fingerprint and portfolio are identified above in Table 10.


88

Figure 71: SEEbalance® Fingerprint

Figure 72: SEEbalance® Portfolio


89

4. 1970 On-Farm Scenario Coupled with the data used for the EEA related to environmental impacts in 1970, a scenario was created for the SEEbalance® study for the 1970 on-farm data (feed and cattle phases only). Some select social impacts are analyzed below for this scenario, followed by the social and SEEbalance® fingerprints and SEEbalance® portfolio for the scenario.

4.1 Wages and Salaries As shown below in Figure 73, there was a 43% decline in wages and salaries between 1970 and 2005 (and a 14% increase from 1970 to 2011). The feed sector had a noticeable higher contribution to wages and salaries in 1970.

Figure 73: 1970 Scenario Wages and Salaries

4.2 Lost Working Hours Due to Strikes and Lockouts As shown below in Figure 74, there was nearly a 100% decline in lost working hours due to strikes and lockouts between 1970 and 2005 / 2011. Strikes and lockouts were much more commonplace in 1970 in the feed and cattle sectors as well as the transportation sector.


90

Figure 74: 1970 Lost Working Hours Due to Strikes and Lockouts

4.3 Employees

As shown below in Figure 75, there was a 99% decline in the number of employees between 1970 and 2005 (and an 81% decrease from 1970 to 2011). Both the feed and cattle sectors had significantly higher numbers of employees in 1970.

Figure 75: 1970 Scenario Employees


91

4.4 Female Managers

As shown below in Figure 76, there was a 98% decline in female managers between 1970 and 2005 (and a 97% decrease from 1970 to 2011). Both the feed and cattle ranching sectors had significantly higher numbers of female managers in 1970.

Figure 76: 1970 Scenario Female Managers

4.5 Trainees

As shown below in Figure 77, there was nearly a 100% decline in the number of trainees between 1970 and 2005 / 2011. Both the feed and cattle ranching sectors had significantly higher numbers of trainees in 1970.


92

Figure 77: 1970 Scenario Trainees

4.6 Imports from Developing Countries

As shown below in Figure 78, there was a 58% decline in imports from developing countries between 1970 and 2005 (and a 44% decrease from 1970 to 2011). The cattle ranching sector in particular had significantly higher imports from developing countries in 1970.

Figure 78: 1970 Scenario Imports from Developing Countries


93

4.7 Social Fingerprint

As shown below in Figure 79, there was a dramatic shift in the social fingerprint from 1970 to 2005 and 2011. While the social fingerprint for employees demonstrated a noticeable improvement from 1970 to 2005 (with some slight increase in 2011), the social fingerprint for the international community, local and national community, and future generations was significantly better in 1970 than in 2005 and 2011. Note that weighting factors for the social fingerprint are identified above in Table 10.

Figure 79: 1970 Scenario Social Fingerprint

4.8 SEEbalance® Fingerprint and Portfolio

As can be seen below in Figure 80, the societal fingerprint was substantially better in 1970 than in 2005 and 2011. At the same time there was little movement on the economic fingerprint and some improvement on the environmental fingerprint. Because of the dramatic decline in the societal fingerprint from 1970 to 2005 and 2011, the 1970 on-farm SEEbalance® portfolio is better than 2005 and 2011 and represented in Figure 81. Note that the weighting factors for the SEEbalance® fingerprint and portfolio are identified above in Table 10.


94

Figure 80: 1970 Scenario SEEbalance® Fingerprint

Figure 81: 1970 Scenario SEEbalance® Portfolio


95

5. Conclusions

While the base case analysis shows some overall improvement in the socio-eco-efficiency attributes of the U.S. beef industry from 2005 and 2011 as demonstrated above (though completely the opposite for the 1970s on-farm scenario), the overall analysis is deemed to be inconclusive due to the state of U.S. industry sector data quality. The data that were originally provided by PRè North America were verified for accuracy according to what data are currently available. However, due to extreme trending that was noted throughout several of the social impacts analyzed, significant lack of data quality for the available data is evident. As discussed, we believe that the main reasons for the lack of data quality include changes over time in the way that data is aggregated as well as the reliance on both national economic data and per-unit product pricing in order to derive social impacts per CB. As a result of these issues, it is extremely difficult to pinpoint specific and reliable trends at this time that relate to the social attributes of the U.S. beef industry. We recommend that the U.S. beef industry revisit a social analysis once national data is more readily available in outputs that allow correlation at the product level. In the meantime, while the current analysis from this research is inconclusive, the work can be used as a starting point to support future social research for the U.S. beef industry once national data becomes more standardized and accessible that allows a reliable product social evaluation.

V. ECO-EFFICIENCY MANAGER

Background and Introduction As part of the deliverables of Phase 1 of the More Sustainable Beef Optimization Project, BASF created a web-based and online tool called an Eco-Efficiency Manager (EEM) for the U.S. beef harvesting and case-ready sectors. Phase 2 will include the development of EEM for other sectors along the beef value chain. The purpose of the EEM is to allow beef operators to perform ongoing analysis of optimizations that have been made or could be made to their operations and facilities that would have an impact on the eco-efficiency profile of their business as well as the U.S. beef value chain. The tool will enhance decision-making so that tradeoffs of potential optimizations can be realized and operators can realize where they are creating or might create negative or positive changes to environmental impacts. This will also foster an industry-wide direction to better understand and document improvements that have been made as well as opportunities and lessons learned that can be shared. In the end, the goal is to use this tool to demonstrate continual improvement in the eco-efficiency profile of U.S. beef.


96

The initial “packer” EEM includes both the 2005 and 2011 data that was utilized for the Phase 1 EEA so that packers can benchmark their data with the industry “average” that was utilized for the original study. This will also allow users of the tool to understand how they can help to facilitate change in the eco-efficiency attributes of the industry. The EEM can be accessed by users at www.v2.eeaman.com. Login information is unique to the user and the user may request an account and temporary user ID and password from the beef checkoff Sustainability Program project manager at the National Cattlemen’s Beef Association (NCBA). Highlights of the EEM The EEM allows a user to enter data for up to 8 additional alternatives in addition to the 2005 and 2011 base case study data that is pre-defined in the EEM. Screen shots of the EEM are shown below in Figures 82 and 83. Figure 82 shows the general data page for the harvesting phase and Figure 83 shows the Utilities and Land Use data entry page for the harvesting phase. As can be seen in Figure 82, each category of inputs that were analyzed in the original EEA has data entry pages for both the harvesting and case-ready phases. The user may analyze the harvesting and/or case-ready phases.

Figure 82: EEM Harvesting General Data Page


97

Figure 83: EEM Utilities and Land Use Data Entry Page Future Development of the EEM Further development to expand the EEM to represent the full value chain will be completed as part of Phase 2 of the More Sustainable Beef Optimization Project. The research team is currently exploring how to integrate appropriate outputs from the USDA Integrated Farm Systems Model (IFSM) for the on-farm phases of the beef value chain. Additionally, the EEM will be expanded with the primary data that is integrated in Phase 2 with the retail and restaurant sectors.


98

VI. CONCLUSIONS FOR PHASE 1 OF THE MORE SUSTAINABLE BEEF OPTIMIZATION PROJECT

The objective of the Phase 1 of the More Sustainable Beef Optimization Project was to begin to take the U.S. beef industry on a pathway towards greater sustainability. The project consisted of four major steps. First, a qualitative Hot Spot Analysis (HSA) was completed on the U.S. beef industry to provide an analysis of stakeholder perceptions of the industry based upon desktop literature research and stakeholder interviews. Second, an Eco-Efficiency Analysis (EEA) was performed that provides a quantitative life cycle assessment of the environmental and economic impacts of the U.S. beef industry. Third, a SEEbalance® analysis that expands the EEA and considers social impacts of the U.S. beef industry was completed. The EEA and SEEbalance® provided quantitative data assessments that allow the beef industry to recognize current sustainability attributes of U.S. beef as well as associated changes over time. Finally, a web-based and on-line Eco-Efficiency Manager (EEM) was created to provide an interface that allows users to analyze how changes in their individual operations could positively or negatively impact the sustainability attributes of the industry.

The HSA provided the perceptions of stakeholders and highlighted 7 environmental, 7 social, and 7 economic impacts of either high or medium relevance to the beef industry. The EEA demonstrated that there has been a 5% improvement in the eco-efficiency of the U.S. beef industry between 2005 and 2011 (according to the scope and boundaries established in the study). The SEEbalance® study is inconclusive due to the lack of data availability across the U.S. The EEM for the harvesting and case-ready phases of the beef value chain is now ready for use by the industry and additional work on the remainder of the value chain will take place in Phase 2. The work completed in Phase 1 of the More Sustainable Beef Optimization Project provides the U.S. beef industry the baseline qualitative information from the HSA as well as quantitative data and results from the EEA that is necessary to understand perceptions, trends, and opportunities for improving the sustainability attributes of the beef industry.


99

VII. References 1 U.S. Census Bureau. 2012. World Population: 1950-2050. http://www.census.gov/population/international/data/idb/worldpopgraph.php 2 International Organization for Standardization. 2006. Life Cycle Assessment – Requirements and Guidelines (ISO 14044:2006).

3 U.S. Department of Agriculture. 2012. Loss-adjusted food availability for meat, poultry, fish, eggs, and nuts. http://www.ers.usda.gov/data-products/food-availability-(per-capita)-data-system/.aspx

4 U.S. Department of the Interior. 1998. Estimated Use of Water in the United States in 1995. http://water.usgs.gov/watuse/pdf1995/html/

5 U.S. Environmental Protection Agency. (2010). Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures for 2010. http://www.epa.gov/epawaste/nonhaz/municipal/pubs/msw_2010_rev_factsheet.pdf

6 U.S. Department of Agriculture. 2012. Historical beef, pork and broiler price data. http://www.ers.usda.gov/datafiles/Meat_Price_Spreads/history.xls

7 Rotz, C.A., M.S. Corson, D.S. Chianese, F. Montes, S.D. Hafner and C.U. Coiner. 2012. Integrated Farm System Model: Reference Manual. University Park, PA: USDA Agricultural Research Service. http://www.ars.usda.gov/SP2UserFiles/Place/19020000/ifsmreference.pdf

8 Rotz, C.A., D.R. Buckmaster, and J.W. Comerford. 2005. A beef herd model for simulating feed intake, animal performance, and manure excretion in farm systems. J. Anim. Sci. 83:231-242.

9 Rotz, C.A., et al. 2012. 10 Rotz, C.A., et al. 2012. 11 Rotz, C. A. 2012. “Environmental Footprints of Beef Production at MARC.” Presentation at the 2013 Beef Sustainability Research Summit. Denver, Colorado. January 16, 2013.

12 USDA/ERS. 2011. Data sets: fertilizer use and price. USDA Economic Research Service, Washington, D.C. http://www.ers.usda.gov/data-products/fertilizer-use-and-price.aspx 13 Literature searches and discussions with experts yielded no sources of reliable data on pesticide use in 1970 at USMARC or wider industry practices. However, outside of toxicity differences, it is not likely that there are differences in other environmental impacts related to the production of pesticides that would have a substantive impact on this study.


100

14 IPCC (Intergovernmental Panel on Climate Change). 2006. Volume 4. Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf 15 European Commission Joint Research Centre. 2008. SALCA Heavy Metals Tool. http://lca.jrc.ec.europa.eu/lcainfohub/tool2.vm?tid=226

16 Department of Agriculture and Food. 2008. Potential Uses for Distiller’s Grains. http://www.agric.wa.gov.au/objtwr/imported_assets/content/sust/biofuel/potentialusesgrains042007.pdf 17 U.S. Department of Labor. 2012. Injuries, Illnesses, and Fatalities. http://www.bls.gov/iif/ 18 Beef Quality Assurance. 2012. Beef Quality Assurance. http://www.bqa.org/default.aspx 19 Expert opinion scoring for animal welfare analysis from James O. Reagan, PhD (Senior Vice President, National Cattlemen’s Beef Association, a contractor to the beef checkoff), John Paterson, PhD (Executive Director, Producer Education, National Cattlemen’s Beef Association, a contractor to the beef checkoff), and Kim Stackhouse-Lawson, PhD (Director of Sustainability Research, National Cattlemen’s Beef Association, a contractor to the beef checkoff) based on the Beef Quality Assurance program information referenced. 20 U.S. Centers for Disease Control. 2012. Foodborne Diseases Active Surveillance Network (FoodNet). http://www.cdc.gov/foodnet/ 21 Expert opinion scoring for food safety compiled by James O. Reagan, PhD (Senior Vice President, National Cattlemen’s Beef Association, a contractor to the beef checkoff) based on the U.S. Centers for Disease Control data referenced. 22 See for example the auditing program from Dr. Temple Grandin: http://www.grandin.com/survey/survey.html 23 Expert opinion scoring for Community Nuisance Dust and Odors compiled by Scott Harter (Vice President of Environmental, Health, and Safety, Cargill) and Kim Stackhouse-Lawson, PhD (Director of Sustainability Research, National Cattlemen’s Beef Association, a contractor to the beef checkoff) based on direct industry practices knowledge. 24 U.S. EPA. 2012. Supermarkets: An Overview of Energy Use and Energy Efficiency Opportunities. http://www.energystar.gov/ia/business/challenge/learn_more/Supermarket.pdf 25 U.S. EPA. 2012. Profile of An Average U.S. Supermarket’s Greenhouse Gas Impacts From Refrigeration Leaks Compared to Electricity Consumption. http://www.epa.gov/greenchill/downloads/GC_AverageStoreProfile_FINAL_JUNE_2011_REVISED.pdf 26 U.S. Department of Agriculture. 2012. Loss-adjusted food availability for meat, poultry, fish, eggs, and nuts. http://www.ers.usda.gov/data-products/food-availability-(per-capita)-data-system/.aspx


101

27 Food Marketing Institute. 2012. Supermarket Sales: Supermarket Sales by Department – Percent of Total Supermarket Sales. http://www.fmi.org/docs/facts-figures/grocerydept.pdf?sfvrsn=2 28 U.S. Department of Agriculture. 2005. Daily intake of food at home and away from home: 2003-2004. http://www.ers.usda.gov/data-products/food-consumption-and-nutrient-intakes.aspx 29 U.S. Department of Transportation. 2011. 2009 National Household Travel Survey. http://nhts.ornl.gov/2009/pub/stt.pdf 30 Association of Home Appliance Manufacturers. 2011. Average Household Refrigerator Energy Use, Volume, and Price Over Time. http://www.appliance-standards.org/sites/default/files/Refrigerator%20Graph_July_2011.PDF 31 National Cattlemen’s Beef Association, a contractor to the beef checkoff. 2011. Overcoming Defrosting Barriers. http://www.beefresearch.org/CMDocs/BeefResearch/MR_Presentations/Overcoming_Defrosting_Barriers_2011.pdf 32 U.S. Energy Information Administration. 2005. Residential Consumption Energy Survey (RECS). http://www.eia.gov/consumption/residential/data/2005/ 33 U.S. Department of Agriculture. 2012. Food Availability Data. http://www.ers.usda.gov/data-products/commodity-consumption-by-population-characteristics.aspx 34 U.S. Department of Agriculture. 2005. Daily intake of food at home and away from home: 2003-2004. http://www.ers.usda.gov/data-products/food-consumption-and-nutrient-intakes.aspx

35 Boustead Consulting Ltd UK. The Boustead Model 5.1.2600.2180 LCA database. 36 Swiss Center for Life Cycle Inventories. Ecoinvent v2.2. http://www.ecoinvent.org/database/

37 U.S. National Renewable Energy Laboratory. 2012. U.S. Life Cycle Inventory Database. https://www.lcacommons.gov/nrel/search

38 Pfister, S., Koehler, A., and Hellweg, S. 2011. Assessing the Environmental Impacts of Freshwater Consumption in LCA. Environmental Science and Technology. 43: 4098-4104. 39 European Commission, eurostat http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Glossary:Statistical_classification_of_economic_activities_in_the_European_Community_(NACE)

40 TNS Infratest Landsberger. Strasse 338, Munich, Germany 80687.


102

41 Department of Agriculture and Food. 2008. Potential Uses for Distiller’s Grains. http://www.agric.wa.gov.au/objtwr/imported_assets/content/sust/biofuel/potentialusesgrains042007.pdf

42 Lory, John, et al. 2008. Comparing the Feed, Fertilizer, and Fuel Value of Distiller’s Grains. http://www.plantmanagementnetwork.org/pub/cm/review/2008/distillers/ 43 Kölsch, Daniela, Peter Saling, Andreas Kicherer, Anahi Grosse-Sommer, and Isabell Schmidt. 2008. How to Measure Social Impacts? A socio-eco-efficiency analysis by the SEEBALANCE® method. Inernational Journal of Sustainable Development, Vol. 11, No. 1: 1-23. 44PRé North America. 2012. Social Impact Data Gathering for the North American Beef Value Chain. Version 1, December 26, 2012. 45 European Commission, eurostat http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Glossary:Statistical_classification_of_economic_activities_in_the_European_Community_(NACE)

more sustainable beef optimization project phase 1 final ......phase 1 final report june 2013...

Documents