Submission for
Verification of Eco-efficiency Analysis Under
NSF Protocol P352, Part B
U.S. Beef – Phase 2
Eco-efficiency Analysis
September 2015

Submitted by:
BASF Corporation
100 Park Avenue, Florham Park, NJ, 07932
Prepared by:Thomas Battagliese, Metrics Manager, Applied Sustainability
Juliana Andrade, Sustainability Specialist
Rafael Vinas, Sustainability Specialist
Kim Stackhouse-Lawson, PhD, Executive Director Global Sustainability, NCBA
C. Alan Rotz, PhD, Agricultural Engineer, USDA
Jasmine Dillon, Research Assistant, Penn State University

Copyright © 2015 BASF Corporation

Contents
1.

Purpose and Intent of this Submission ..................................................... 32.

Content of this Submission ....................................................................... 33.

BASF’s EEA Methodology .......................................................................... 3
3.1 Overview ....................................................................................................... 3
3.2 Preconditions ................................................................................................. 4
3.2.1 Environmental Burden Metrics ...................................................................... 4
3.2.2 Economic Metrics ........................................................................................ 5
3.3 Work Flow ..................................................................................................... 5

4.

Study Goals, Decision Criteria and Target Audience ................................. 5
4.1 Study Goals ................................................................................................... 5
4.2 Context & Decision Criteria .............................................................................. 6
4.2.1 Study Drivers .............................................................................................. 7
4.2.2 Geography.................................................................................................. 7
4.2.3 Scenario and Horizon .................................................................................. 7
4.2.4 Engagement ............................................................................................... 8
4.2.5 Life Cycle ................................................................................................... 8
4.2.6 Product and Market ..................................................................................... 8
4.2.7 Economy .................................................................................................... 8
4.2.8 Innovation .................................................................................................. 8

5.

Customer Benefit, Alternatives and System Boundaries ........................... 9
5.1 Customer Benefit (CB) .................................................................................... 9
5.2 Alternatives.................................................................................................... 9
5.3 System Boundaries ......................................................................................... 9
5.4
Scenario Analyses.......................................................................................10

6.

Input Parameters and Assumptions ....................................................... 11
6.1 Input Parameters ..........................................................................................11
6.1.1 Overall Study Assumptions ..........................................................................11
6.1.2 USMARC Feed Production and Pasture .........................................................13
6.1.3 USMARC Cattle Production ..........................................................................18
6.1.3.1 Cow-Calf Operations ..................................................................................19
6.1.3.2 Feedlot Operations ....................................................................................20
6.1.4 Harvesting .................................................................................................20
6.1.5 Case-Ready ...............................................................................................22
6.1.6 Retail ........................................................................................................22
6.1.7 Consumer ..................................................................................................23
6.1.8 Restaurant ................................................................................................23

7.

Data Sources ........................................................................................... 24

8.

Eco-efficiency Analysis Results and Discussion ...................................... 27
8.1. Environmental Impact Results ........................................................................27
8.1.1. Cumulative Energy Demand ........................................................................28
8.1.2. Abiotic Depletion Potential (ADP) .................................................................30

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8.1.3
Consumptive Water Use ..........................................................................31
8.1.4 Air Emissions .............................................................................................33
8.1.4.1
Global Warming Potential (GWP) ..............................................................33
8.1.4.2
Photochemical ozone creation potential (POCP) .........................................34
8.1.4.3
Ozone depletion potential (ODP) ..............................................................34
8.1.4.4
Acidification potential (AP) .......................................................................35
8.1.5 Water emissions.........................................................................................36
8.1.6 Solid waste generation ...............................................................................37
8.1.7 Land use ...................................................................................................38
8.1.8 Toxicity potential........................................................................................39
8.1.9 Risk Potential (Occupational Illnesses and Accidents) ....................................40
8.2. Economic Cost Results ...................................................................................42
9.

Pennsylvania Grass-finished Beef Scenario ............................................ 42
9.1 Overview ......................................................................................................42
9.2 Description of systems ...................................................................................43
9.3
Pennsylvania Grass-finished Eco-Efficiency Analysis Results .............................44
9.3.1 Cumulative Energy Demand ........................................................................45
9.3.2 Abiotic Depletion Potential (ADP) .................................................................46
9.3.3 Consumptive Water Use..............................................................................47
9.3.4 Air Emissions .............................................................................................48
9.3.4.1 Global Warming Potential (GWP) ................................................................48
9.3.4.2 Photochemical Ozone Creation Potential (POCP) ..........................................49
9.3.4.3 Ozone Depletion Potential (ODP) ................................................................49
9.3.4.4 Acidification Potential (AP) .........................................................................50
9.3.5 Water Emissions ........................................................................................50
9.3.6 Solid Waste Generation ..............................................................................51
9.3.7 Land Use ...................................................................................................51
9.3.8 Toxicity Potential........................................................................................52
9.3.9 Risk (Occupational Illnesses & Accidents) .....................................................52
9.4
Economic Cost Results ..................................................................................53

10. Data Quality Assessment ........................................................................ 53
10.1 Data Quality Statement ..................................................................................53
11. Sensitivity and Uncertainty Analysis........................................................ 54
11.1 Sensitivity and Uncertainty Considerations .......................................................54
11.2 Sensitivity Analyses ........................................................................................54
11.2.1 Scenario #1: WDGS Mass Allocation ............................................................54
11.2.2 Scenario #2: WDGS Energy Content Allocation .............................................56
11.2.3
Scenario #3: Economic Allocation for Consumer Refrigeration ....................56
12. Conclusions ............................................................................................. 58
13. Limitations of EEA Study Results ............................................................ 59
14. References .............................................................................................. 59

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Copyright © 2015 BASF Corporation

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 – Phase 2 Eco-efficiency Analysis was performed by BASF according to themethodology validated by NSF International under the requirements of Protocol P352. The
one exception to the Protocol P352 for this Eco-efficiency Analysis is that it is noncomparative in nature. Due to the fact that there was no historical data available frommultiple primary partners in the value chain, the study is presented as a baseline analysis.
However, a web-based tool (Eco-Efficiency Manager) is being developed for the U.S. beefindustry that will use the baseline results shown in this report and will allow comparative
scenarios to be developed that will automatically activate the comparative nature of the
Eco-efficiency methodology.
More information on BASF’s methodology and the NSF validation can be obtained at
http://www.nsf.org/services/by-industry/sustainability-environment/claims-validation/ecoefficiency/ or https://www.basf.com/en/company/sustainability/management-andinstruments/quantifying-sustainability/eco-efficiency-analysis.html

2. Content of this Submission
This submission outlines the study goals, procedures, and results for the U.S. Beef Ecoefficiency 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 productor 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.

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3.2

Preconditions
The basic preconditions of this eco-efficiency analysis are that all alternatives thatare being evaluated are being compared against a common functional unit orcustomer benefit. This allows for an objective comparison between the various
alternatives. (As mentioned, since this study is non-comparative, an objective
comparison may be made in the future using this baseline life cycle data). The
scoping and definition of the customer benefit are aligned with the goals andobjectives of the study. Data gathering and constructing the system boundaries are
consistent with the functional unit and consider both the environmental andeconomic impacts of each alternative over their life cycle in order to achieve thespecified customer benefit. An overview of the scope of the environmental andeconomic 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 wateruse, and land use. These are shown below in Figure 1. Metrics shown in blue
represent the seven main categories of environmental burden that are used toconstruct the environmental fingerprint; burdens in green represent all elementsof the emissions category; and burdens in pink represent specific air emissions
impact categories considered.

Figure 1: Environmental Burden Metrics for BASF Eco-efficiency Methodology

4Copyright © 2015 BASF Corporation

3.2.2 Economic Metrics
It is the intent of the BASF EEA methodology to assess the economics ofproducts or processes over their life cycle and to determine an overall total costof ownership for the defined customer benefit ($/CB).

3.3

Work Flow
A representative flowchart of the overall process steps and calculations conducted
for this eco-efficiency analysis is summarized in Figure 2 below. Note that due to thenon-comparative nature of this study, the eco-efficiency portfolio is not developed inthis study.

Figure 2: Overall Process Flow for U.S. Beef EEA Study

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 offeeding more than 9 billion people by the year 2050. 1 Experts estimate that thisfuture 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 value
chain of the U.S. beef industry. This provides a starting point for ongoing analysis
and a journey of continuous improvement within the industry. Any establishedopportunities will be used to set the U.S. beef industry on a more sustainable
pathway, which may include sharing and communicating best practices, embedding
improvement opportunities throughout the industry, prioritizing solution-oriented

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research on sustainability criteria that are determined to be critical, and empowering
the industry through ongoing education.
This EEA submission is the second phase (Phase 2) of an ongoing study of the U.S.
beef industry. Phase 1 established a historical perspective of the eco-efficiency of the
U.S. beef industry and considered 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 is intended to build upon the Phase 1 base case study
for the year 2011 and includes refined data, additional datasets, and an expanded
focus of the beef value chain for data that is representative of the period between
2011 and 2013. Individual data point timeframes are associated with when data
were collected or for which year data were actually available. (Additional detail in
Section 4.2.3 and Table 1 below).
Major specific changes or additions to the Phase 1 base case study that are reflectedin this Phase 2 study include:
•

•
•
•
•

updated farm-level data for the U.S. Meat Animal Research Center (USMARC)
that refines segregation of impacts at the actual cattle phases (Phase 1
represented total cattle impacts at the cattle phase. This study updates that data
to segregate impacts according to actual operations at the USMARC facility andare represented according to the cow-calf operations and feedlot, which includes
integrated backgrounding.),
expanded aggregated data into the case-ready sector analysis from an additional
case-ready partner,
replacement of literature average data in the retail sector phase analysis to now
include primary data from retail partners,
expansion of the study to include the restaurant sector with addition of primaryindustry data from restaurant partners, andvalue chain scenario analysis using farm data from Pennsylvania grass-finishedsystems.

Phase 2 is considered to be a baseline study for the industry of current practices.
There is no historical comparison as was completed in Phase 1 because historical
data were not available for much of the expanded data considered in Phase 2.
The study will continue over the next few years in order to compile a complete
aggregated national farm-level dataset that will consider all major beef producing
regions in the U.S. Additionally, future data for the industry will continue to becollected and analyzed to better understand the changes that occur within thesustainability profile of the industry, to pinpoint opportunities for impact reduction
across the industry, and to drive the sustainability performance of the U.S. beefindustry.

4.2

Context & Decision Criteria
The study goals, target audience, and context for decision criteria used in this study
are displayed in Figure 3.

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4.2.1 Study Drivers
The purpose of the study was to quantify and baseline the current eco-efficiency
profile of the U.S. beef industry 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 notinclude beef exported from or imported to the U.S. It is not possible to have adataset for the full value chain that is representative of the U.S. beef industry
without aggregating regionalized on-farm data. For Phase 2 of the U.S. Beef EEA,
the post-farm data is representative of the U.S. beef industry. However, the onfarm 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 this phase of the U.S. Beef EEA because asa research center USMARC has extensive data, which were very difficult to find ina centralized manner elsewhere in the industry.
The USMARC is a research facility so its production practices do not fully
represent the beef industry as a whole. In reality, 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 thepractices 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 beefindustry 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 stockis primarily allocated to the dairy industry. This allocation of resources andemissions greatly reduces the environmental footprint of cattle raised from dairy
calves. Because dairy cattle are not part of the USMARC system due to the factthat it is a beef cattle research farm, no allocation is made in this study andtherefore, the analysis of this USMARC system does not receive this benefit. As aresult, the USMARC farm impacts of this study are considered to be likely
conservative as compared to commercial operations. Other minor differences inlabor and resource use will exist for this government facility, but thesedifferences will have little effect on the environmental impact of the cattle
produced.
Representative regionalized data will be collected, aggregated, and analyzed infuture phases of the U.S. Beef EEA.

4.2.3 Scenario and Horizon
The study considered the eco-efficiency attributes of the total value chain forbeef that was produced by the U.S. beef industry (according to the geographicalscope defined in Section 4.2.2) during the period of 2011-2013. Data wereselected from this period based upon availability and timing of data collection
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Copyright © 2015 BASF Corporationand this period is considered to be representative of current beef industry
practices. Specific years for which each of the value chain phase data wereanalyzed are shown below in Table 1.
Phase

Source Data Year

Feed (USMARC)2011

Cow-Calf (USMARC)

2011

Feedlot (USMARC)

2011

Harvesting

2011

Case-Ready

Combination of 2011 & 2013

Retail2013

Consumer

2011

Restaurant

Combination of 2011 & 2013

Table 1: Source Data Year by Value Chain Phase

4.2.4 Engagement
The study is intended to be used by the entire value chain of the U.S. beefindustry 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 and inrestaurants that is produced by the U.S. beef industry according to thegeographical 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 and in restaurants. Future updates to the study will
include regionalized on-farm data.

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.

8Copyright © 2015 BASF Corporation

Scenario and Horizon
Geography

Engagement

40 years
globalNGO/ external

regional

10 years
supplier/customer
local

technology

Drivers

3 years

regulatorycompetitve

internal

supply chain
1 product 1 market

incremental

Value Chain

post-consumer

consumerfull life cycle

consumeremerging
gap closure
survival

step change

Innovation

all products/markets

Product / Material

EconomyFigure 3: Context of U.S. Beef EEA Study

5. Customer Benefit, Alternatives and System Boundaries

5.1

Customer Benefit (CB)The functional unit or Customer Benefit (identified also as CB) applied for thisanalysis is one pound of consumed, boneless, edible beef. This CB was selected inorder to capture a relative average of the beef industry. Because there are so manydifferent types of beef cuts and further-processed beef products, it is not reasonably
feasible to analyze all types of beef produced in the U.S. Additionally, in order tounderstand the impacts specific to beef, boneless beef was evaluated. Finally, inorder to evaluate the entire beef life cycle, the CB considers beef consumed.

5.2

Alternatives
There are no alternatives analyzed in this study. While Phase 1 evaluated thehistorical progression of beef, Phase 2 is considered to be a baseline study for theindustry of current practices. There is no historical comparison as was completed inPhase 1 because historical data was not available for much of the expanded datathat was considered in Phase 2. As a result of the study being non-comparative,
there is no environmental fingerprint or eco-efficiency analysis portfolio associatedwith this study. As mentioned, this study can be used for future comparative
scenario analysis.

5.3

System Boundaries
The system boundary for this study is presented in Figure 4 below. Dairy cattle were
not included in the scope of this study because they are not included in the beef9

Copyright © 2015 BASF Corporation

production system at USMARC. Additionally, as is common practice in life cycle
analysis, capital equipment, buildings, and infrastructure and repair andmaintenance material, parts, and supplies were excluded. Office & administrative
impacts, employee commutes, seeds for feed, cattle veterinary medicines, andcleaning chemicals used at the retail sector were excluded according to the cut-off
criteria defined in the BASF EEA Methodology. These aspects have a de minimus
impact on study results, contributing individually less than 1% and collectively lessthan 3% to the overall value chain impacts in this study.

Figure 4: System boundary for U.S. Beef – Phase 2

5.4

Scenario Analyses

In addition to the base case analysis, the following scenario analyses wereconsidered:•

Scenario #1: Analysis of Wet Distillers Grains with Solubles (WDGS) using a
mass allocation.

•

Scenario #2: Analysis of Wet Distillers Grains with Solubles (WDGS) using anenergy content allocation.

•

Scenario #3: Consumer phase refrigeration 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 of10

Copyright © 2015 BASF Corporation

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 Throughallocation, a percentage of impacts are assigned to the scope product system andthe 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 samefacility as the beef itself;•

analysis of distillers grains, which are a by-product of the bioethanol
distillation process;•analysis of retail and restaurant primary data, which were not available at a
product level; and•analysis of consumer refrigeration because refrigeration for beef in this phaseis 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 foreach phase of the scope of the study are included below.

6.1.1 Overall Study Assumptions
The following assumptions were used:
1. Table 2 presents the dressing percentage (yield of carcass from live animal)
and value chain loss values that were applied in order to obtain the CB ofweight of consumed, boneless, edible beef. The dressing percentage value
was based upon an industry average of 62% with a 3% reduction to accountfor cull cows and bulls. Loss values used were from the USDA Economic
Research Service 3, with the exception of retail loss values, which are basedon primary data. Note that the total loss is not a simple sum of eachindividual phase loss, but instead, each loss is calculated from the previous
phase.

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Dressing percentage
Losses at harvesting & case-ready phase (fat, bone,and shrink)
Loss at retail phase (shrink & spoilage)
Loss at consumer phase (cooking losses, spoilage,
plate waste)

Total losses from live animal weight sent from cattle
phase

59%
33%
7%
20%

71%

Table 2: 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.
b.
c.
d.

Industrial use: 25%
Agriculture: 70%
Livestock: 55%
Thermoelectric Utilities: 50%

3. 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% isincinerated with energy recovery. This assumption was based on 2010 EPA
national waste data. 5
4. In order to avoid allocation and the potential for double-counting credits andimpacts 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.
5. Based upon data from study partners, the packaging used directly or
indirectly for the beef product that was purchased at retail (non-restaurant)
was assumed to be 63% completed in the case-ready phase (i.e., packaged
into a retail-ready output) and 37% at the retail store directly.
6. Based upon industry average data, it was assumed that 47% of beef isconsumed at home and 53% is consumed in a restaurant. 6 Impacts for eachof these consumption points were divided accordingly.
7. 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. According to the data collected from the primary partners, 100% ofcorrugated cardboard is recycled. In order to avoid allocation andpotential for double-counting credits and impacts of the recycling
system, a closed-loop recycling process was assumed and the cut-off
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method was applied. Therefore, the impacts of the recycling process
were considered to be the burden of the purchaser of the recycledmaterial. For this study specifically for example, the harvesting
facilities surveyed purchased corrugated cardboard that contained
30% recycled fiber content. Therefore, to be consistent, the burdenof 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 bedisposed 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.
8. For cost analysis, the present value (2011 dollars since all data through theharvesting phase is representative of 2011) consumer price of the beef was
utilized and assumed to reflect the full cost of the value chain up to the pointof sale at the retailer. These values were not associated with the operationalcosts of the beef value chain. However, using the consumer price was seenas the best possible approach to achieve a total cost that was representativeof 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. 7

6.1.2 USMARC Feed Production and Pasture
The feed production phase accounted for the life cycle of the feed (i.e., theagricultural crops and pastureland) that was consumed by the animals raised in thebeef system. Input parameters for the feed phase were considered mainly basedon modeling data produced by the U.S. Department of Agriculture’s (USDA)
Integrated Farm Systems Model (IFSM). This approach was utilized as someprimary data availability for on-farm production is limited.
The IFSM is a research tool used to assess and compare the environmental andeconomic sustainability of farming systems. Crop production, feed use, and thereturn of manure nutrients back to the land are simulated for many years ofweather on a crop, beef, or dairy farm. 8 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, andfeeding operations predict resource use, timeliness of operations, crop losses, andnutritive quality of feeds. Feed allocation and animal responses are related to thenutrient contents of available feeds and the nutrient requirements of the animalgroups making up the herd. For beef operations, the animal groups can include
cows, calves, replacement animals, stockers, backgrounding and finishing cattle. 9
The quantity and nutrient contents of the manure produced are a function of thefeeds consumed and herd characteristics.

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Nutrient flows are tracked through the farm to predict losses to the environment
and potential accumulation in the soil. 10 Environmental losses include ammonia
emission, denitrification and leaching losses of nitrogen, erosion of sediment acrossthe farm boundaries, and the runoff of sediment-bound and dissolved phosphorus.
The sum of the various forms of nitrogen loss provides a total reactive nitrogenloss. 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 aredetermined as the sum of nutrient imports in feed, fertilizer, deposition, andlegume fixation minus the nutrient exports in milk, excess feed, animals, manure,and losses leaving the farm.
The IFSM boundaries are depicted below in Figure 5.

Figure 5: Boundaries, major components and nutrient flows
Integrated Farm System Model 11

simulated with theNote 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 andanimal production. Simulation of this production system provided system inputs aswell 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 3 with USMARC simulated data compared to actual reported feeduse, which represents some of the IFSM data directly used in this study.

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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

Distillers grain

1,841

1,837

0.2

Total

18,307

18,290

0.0

Table 3: Actual reported vs. IFSM Simulated feed production at USMARC for 2011 12

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 basedon moisture content. IFSM data were used for all direct system inputs and directemissions 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 life cycle inventory analyses as well as for some
additional direct emissions.
The USMARC facility included about 5,000 acres of irrigated farmland used for feed
production.
Feed production at USMARC included alfalfa/grass (preserved as silage or hay),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 asother animals that were not part of the beef cattle system boundaries were
removed from the boundary conditions so that only the beef system and theassociated feed production required were considered.
While most feed used at USMARC was produced directly on-site, some feed waspurchased from off-site sources and was also considered in this study. The
purchased feed consisted of 1,790 tons of Wet Distillers Grains with Solubles(WDGS).
The following is a list of additional assumptions for the feed phase that werenecessary to complete this study:
1. USMARC raises other species of animals for which some of the feed is used.
Resource use and emissions from feed crop production were allocatedamong 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 matter15

Copyright © 2015 BASF Corporation

produced provides the allocation factor. Through simulation of the various
production systems with the IFSM, the portion of the total feed used bycattle within the USMARC system and assigned as the associated allocation
factor was found to be 82.5%.
2. 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. Emissions from the manure were considered.
3. Primary data from the IFSM simulations was used to obtain the following
emission factors for corn production:
Runoff loss

lb P/ton P appliedlb N/ton N applied

0.3
1.2

Air emissions(direct + crop
residue)lb N 2 O/ton N applied

0.41

Table 4: Corn Direct Emissions

Table 5 presents emission factors used to calculate additional emissions
from USMARC not included in the IFSM simulations. Note that for N 2 O
emissions, direct emissions were analyzed with IFSM in the above point.
Indirect N 2 O emissions related to leaching and volatilization are shown
below as N direct conversion and volatilization to NH 3 -N and conversion to
N 2 O.
Emission
Leached N to N 2 O-N
CO 2 from urea
CO 2 from limestone
Volatilization of NH 3 from fertilizer-N

Factors13
0.75% (0.00225 kg N 2 O-N /kg fertilizer-N)
0.20 kg CO 2 -C / kg (NH 2 ) 2 CO
0.12 kg CO 2 -C / kg CaCO 3
10%

Table 5: Additional Field Emission Factors

Note that direct N 2 O background emissions from soil were not included in
the N2O emissions in this study. Only emissions associated with manureand fertilizer N application to soil were considered.
Chemical Oxygen Demand (COD) for pesticides was calculated based on thechemical 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.
4. 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 were16

Copyright © 2015 BASF Corporation

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 leachingrates were assumed as representative values and this asssumption would
have a de minimus impact on the overall results. The analysis includes both
runoff and leaching of heavy metals. 14
5. With the exception of enteric methane, biogenic carbon was not modeled inthis 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 theatmosphere at some point along the chain. However, because entericmethane is modeled in the cattle phase, a 1 CO 2 -eq credit was applied to
the global warming potential (GWP) factor of methane (thus utilizing a GWP
of 24 CO 2 -eq for methane as opposed to the standard factor of 25 CO 2 -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 entericmethane 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.
6. The only impacts associated with irrigation within the USMARC system werethe consumptive water value itself (since the water was well water fromwithin 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.
7. Transport distance was assumed to be an average of 20 miles from thedistillery to the feedlot for the WDGS based on the location of the distillery
from where USMARC purchased WDGS (assumed average of 250 miles forcorn to distillery for the WDGS).
8. WDGS is a by-product of the bioethanol distillation process (from corn). In
order to derive an appropriate impact analysis of just the WDGS, since theimpacts of the WDGS 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
Distillers Grains with Solubles (DDGS). The drying energy was thendeducted from the DDGS profile (according to the distillation
ecoinvent profile) to derive an appropriate profile for WDGS.
b. Additionally, the corn profile in ecoinvent associated with thebioethanol profile was replaced with a non-irrigated corn profile from
Iowa. Yield of the corn was adjusted to 2011 yield values shown
above of 168 bu/acre.

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Copyright © 2015 BASF Corporationc. An adjustment factor of 1.55 was then applied to the profile toaccount for the fact that 1.55 times the weight of WDGS is produced
compared to DDGS from the distillation process. 15
d. The final profile of WDGS was then created by assuming an
economic allocation associated with the current pricing of ethanol
and WDGS, which resulted in 21% of the burden of the distillation
process (and pre-chain impacts) being allocated to WDGS.
9. Gross bioenergy, or the energy released if the feed biomass were
combusted, was accounted for in all crops used for feed. While the amountof feed was based on IFSM simulated outputs and includes losses fromproduction to consumption, the gross bioenergy content was based uponecoinvent 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 6 tocorrespond with feed inputs already on a DM basis.
Crop ecoinvent Profile
Silage maize IP, at farm/CH S
Corn, at farm/US U
Hay intensive IP, at farm
Grass from natural meadow extensive IP, atfield/CH S

Gross Bioenergy(MJ/kg DM)
18.6
18.5
17.8
18.5

Table 6: Gross Bioenergy of Crops

10. For the Occupational Illnesses and Accidents risk values, in addition to thestandard pre-chain impacts, direct impacts for the feed category wereassessed using the U.S. Bureau of Labor Statistics (BLS) data for theindustry category of “Oilseed and Grain Farming.” 16

6.1.3 USMARC Cattle Production
Parameters for the cattle production operations at USMARC 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.
Due to the integrated nature of the USMARC operations, it is difficult to separately
model the impacts associated with backgrounding as both the backgrounding andfinish phases occur at the same feedlot. Therefore, the backgrounding/finish
phases are integrated in the analysis of this study and identified as Feedlot
Operations.
1. The impacts of all calves, heifers, cows, bulls, and beef cattle were includedin the study.

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2. All harvested and grazed forage and grains used as feed were included in thefeed phase of the study. Only supplementary feeds were included in the
cattle phase.
3. Emissions from the cattle operations were modeled in IFSM including:a. CH 4 emissions – enteric and manure emissions. The manure
emissions in the feed phase were a result of manure being applied tothe cropland, while manure emissions in the cattle operations
included manure deposits on the pastureland and feedlot.
b. N 2 O emission – pastureland and manure emission from feedlot.
c. NH 3 emission – urine and manure emission on pastureland andfeedlot.
Note that these emissions were predicted through simulation of the biologicaland physical processes modeled within the IFSM.
4. 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.
5. For Occupational Illnesses and Accidents risk values, in addition to thestandard pre-chain impacts, direct impacts for the cattle category wereassessed using the U.S. Bureau of Labor Statistics (BLS) data for the industry
category of “Cattle Ranching and Farming.”
6. 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 andaccidents) considered for the cattle phase, which was animal welfare. Expert
opinion, supported by the national Beef Quality Assurance (BQA) program 17,
evaluated this additional risk category on a scale of 1:10 with 1 representing
the most risk and 10 the least risk. 18 The expert opinion scale score applied
to the animal welfare category was 7.5. 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.

6.1.3.1 Cow-Calf Operations
The cow-calf operation is used to describe the portion of the cattle phase inwhich 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 thensent to the backgrounding program on the feedlot. The USMARC cow-calf
operations handled about 6,600 cows on 24,000 acres of grazing pasture, someof which was irrigated. The animals were fed hay and silage during the wintermonths. Note that pasture inputs were included in the feed phase of thisanalysis.
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6.1.3.2 Feedlot Operations
The USMARC facility also included a 3,700 head feedlot operation. Cattle werebackgrounded (i.e., taken from weaned calves to yearlings) for 3 months on ahigh forage diet (hay silage and distillers grain) and finished in the feedlot(confined drylot) for 7 months on a high grain diet (corn silage, corn, anddistillers 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.
The mass value of body weight of the cattle sent to harvesting included finishedcattle, cull cows, and cull bulls.

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 forthe harvesting phase. These data were collected through on-site facility visits
and follow-up discussion and were based on measured data for primary inputs aswell as measured or calculated data for operational emissions and waste. The
producers selected represented both large and small operations so that scale ofoperations was properly considered. Data were then aggregated in a weightedaverage 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 thescope of the study for the harvesting phase. Primary data associated with thetransportation of cattle, waste, paper, plastics (packaging), and liquid carbon
dioxide were used. For all other raw material and supply inputs, an averagetransport 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 thatwere necessary to complete this study:
1. An economic allocation that credits the final beef produced for the byproducts 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 byproducts and edible beef received from the packing sector collaborators. The
allocation credit applied to the beef value chain was 11.7%, respectively (i.e.

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11.7% of the harvesting impacts were allocated to the beef system byproducts).
2. Corrugated cardboard used for packaging had a recycled fiber content of30%.
3. Of the packaging used as inputs to the product system (corrugatedcardboard and plastics), 96% went directly to either the case-ready or retailphase. Therefore, end-of-life impacts for this 96% were included at therespective phase. The remaining 4% of packaging plastic consumed in theharvesting plant was included as part of the total facility waste profile for
end-of-life impact analysis. For the 4% corrugated cardboard intended forrecycling, it was assumed there was no impact from recycling within thescope 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 thestandard pre-chain impacts, direct impacts for the harvesting category wereassessed 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) wereconsidered 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.
a. Food Safety: Food safety was measured as contamination from
pathogens as well as recalls. Based on data from the Centers for
Disease Control 19 and expert opinion, the scale scoring applied to thefood safety category was 8 out of a possible score of 10. 20 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. 21 The expert opinion scale scoring applied to theanimal welfare category was 7.5 out of a possible score of 10. 22 The
total risk weighting for animal welfare was considered to be 8.5% andthis 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 consideredthrough trends observed as voluntary best practices to mitigate thesecommunity impacts in the industry over time. The expert opinion
scale scoring applied to the community nuisance dust and odors was21

Copyright © 2015 BASF Corporation

7 out of a possible score of 10. 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 ispackaged into a retail-ready output. As mentioned earlier, for purposes of thisstudy, 63% of the U.S. beef was assumed to be packaged in a case-readysystem.
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) as
well as an additional stand-alone case-ready and distribution operation. For the
harvesting partner with case-ready operations, the primary data collected
included inputs for energy, packaging, waste, and consumable items. Based on
industry expert opinion and direct operations knowledge from this partner,
certain other data values, such as certain cleaning chemicals and waste, wereassumed to be 10% of the average of the harvesting facility data from the three
producers surveyed. For the partner with separate case-ready operations,primary data were applied for the entire operation.
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 theharvesting 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 thestandard pre-chain impacts, direct impacts for the case-ready category wereassessed 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 packaged beef is sold to theconsumer.
Primary data were collected from three retail partners, which range in size fromsmall to large. Primary data was representative of total retail sales from thesepartners. Retail data that was collected represents 8% of the total beef sold in
the U.S. at a retail level.
Because retailers sell more than just beef, in order to derive beef-specific retailvalues, an economic allocation was performed based on beef:total store sales.
Additionally, since refrigeration energy and leakage are only associated withrefrigerated perishable retail sales (that include beef), these aspects were further

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refined with average industry factors regarding retail refrigeration energy 24 andperishable sales. 25
As mentioned earlier, the packaging used directly or indirectly for the beef
product that was purchased at retail was assumed to be 63% completed in thecase-ready phase (i.e., packaged into a retail-ready output) and 37% at theretail store directly.
For the Occupational Illnesses and Accidents risk values, in addition to thestandard pre-chain impacts, direct impacts for the retail category were assessedusing 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.
Based on industry sales data, it was assumed that 47% of U.S. beef is consumed
at home (from retail purchase).
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, 26 electricity consumption associated with refrigeration, 27
repackaging of beef by the consumer, 28 cooking energy, 29 and consumer beef
waste. 30
A volumetric allocation based on the average U.S. diet was applied in order todetermine an appropriate allocation for consumer refrigeration associated withbeef. The volumetric allocation was derived from an analysis of USDA Economic
Research Service data on U.S. food consumption at home and associateddensities. 31

6.1.8 Restaurant
The restaurant phase considers beef that is sold to the consumer at therestaurant level. This includes both quick service restaurants and sit-down
restaurants. The data used within the study at the restaurant level is primarydata from three restaurant partners (two quick service restaurants and one
casual sit-down restaurant). Restaurant data that was collected represents
approximately 6% of the total beef sold in the U.S. at the restaurant level.
Since restaurants sell more than just beef, in order to derive beef-specificrestaurant values, an economic allocation was performed based on beef:total
restaurant sales.
Based on industry data, it was assumed that 53% of beef is consumed inrestaurants.

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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, Boustead, 32
and ecoinvent. 33 Overall, the quality of the data was considered medium-high to high. Noneof the eco-profiles data were considered to be of low data quality. A summary of the ecoprofiles used by phase 2 is provided below in Table 7.
Note that an asterisk (*) in the General Utility & Waste Profiles, Feed, and Cow-calf Phasesin the below eco-profile names designates that the eco-profile was also used for thePennsylvania Grass-finished Scenario that is reviewed below in Section 9 of this report.
Eco-Profile

General Utility & W aste Profiles
Water from well*
Electricity use*
Natural gas use
Diesel use*
Gasoline use*
Lubricating oils*
Transportation (diesel; longhaul)*Municipal wastewater treatment
Municipal solid waste landfill

Feed Phase

Urea fertilizer*
Glyphosate
Dicamba
Dimethenamide pesticide
Atrazine
Metolachlor
Acetochlor
Pyraclostrobin
Single superphosphate fertilizer*
Potassium fertilizer*
Fludioxinol fungicide
Mefanoxam fungicide
Clothianidin insecticide
2,4-Dichlorophenoxyacetic acid*
Chlorpyrifos insecticide
Paraquat dichloride
Clopyralid herbicide

Source, YearBASF, 2010
BASF, 2011
BASF, 1999
BASF, 1999
BASF, 1999
BASF, 1999
US LCI, 2011 34
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
BASF, 2005
BASF, 1997
BASF, 1999
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
BASF, 1997
BASF, 2011
BASF, 2006
BASF, 1997
BASF, 1997
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 201024

Comments

Profile based on 2011 U.S. Energy Information
Administration electricity grid profile data.

Ecoinvent profile: Treatment, sewage, towastewater treatment, class 3/CH U
Ecoinvent profile: municipal solid waste, 22.9%
water, to sanitary landfill/CH U

Ecoinvent profile: Pesticide unspecified, atregional storehouse/RER U
Ecoinvent profile: Atrazine, at regional
storehouse/RER U

Ecoinvent profile: Nitrile compounds, at regional
storehouse/RER U
Ecoinvent profile: Pyretroid compounds, atregional storehouse/RER U
Ecoinvent profile: Organophosphorus
compounds, at regional storehouse/RER U
Ecoinvent profile: 2,4-D, at regional
storehouse/RER U
Ecoinvent profile: Organophosphorus
compounds, at regional storehouse/RER U
Ecoinvent profile: Pesticide unspecified, atregional storehouse/RER U
Ecoinvent profile: Pesticide unspecified, atCopyright © 2015 BASF Corporation

Eco-Profile
Picloram herbicide
Carbaryl insecticide
Ammonium sulfate
Calcium oxide*
Bioethanol from corn
Corn

Cow -Calf Phase

Source, Year

Ecoinvent 2.2, 2010
BASF, 2002
BASF, 1996
BASF, 1997
Ecoinvent 2.2, 2010
BASF, 2011

Calcium oxide*
Magnesium oxide*
Sodium chloride*
Zinc sulfate*Molasses*Corn
Dicalcium phosphate*
Potassium fertilizer*
Iodine*BASF, 1997
Boustead, 1996
Boustead, 1996
BASF, 2003
BASF, 2000
BASF, 2011
BASF, 2003
BASF, 1997
BASF, 2006

Urea
Copper chloride
Sodium selenite
Thiamine mononitrate
Calcium oxide
Magnesium oxide
Sodium chloride
Zinc sulfate
Molasses

BASF, 2005
BASF, 1998
BASF, 2003
BASF, 2003
BASF, 1997
Boustead, 1996
Boustead, 1996
BASF, 2003
BASF, 2000

PropaneBoustead, 1996

Feedlot Phase

Harvesting Phase
Biogas

Tallow
Phosphoric acidAcetic acid
Lactic acid
Nitric acidSulfamic acid
Chlorine
Detergent
Sodium hypochlorite
Sodium chlorite
Sodium hydroxide
Antifoam
SilicaCitric acid
Calcium hypochlorite
Hydrogen peroxide
Carbon dioxide
Sodium chloride
Anhydrous ammonia
Sodium bicarbonate
Triazine pesticide
HDPE

Ecoinvent 2.2, 2010
Food LCA db, 2008
Boustead, 1996
Boustead, 1996
BASF, 2003
Boustead, 1996
Boustead, 1996
Boustead, 1990
BASF, 1996
BASF, 2002
Boustead, 1996
BASF, 2003
BASF, 2002
Boustead, 2000
BASF, 1998
BASF, 2013
Boustead, 1996
BASF, 1996
Boustead, 2000
Boustead, 1996
BASF, 1999
Ecoinvent, 1996
BASF, 2007

25

Comments
regional storehouse/RER U
Ecoinvent profile: Pesticide unspecified, atregional storehouse/RER U

Ecoinvent profile: Ethanol, 95% in H2O, fromcorn, at distillery/US

Ecoinvent profile: Biogas, from slurry, atagricultural co-fermentation, covered/CH U

Copyright © 2015 BASF Corporation

Eco-Profile
Steel
PVC
CottonNylon
Iron
Laundering
LDPE
Aluminum alloy
Cardboard, virgin
Cardboard, recycled
Paper
Polypropylene
Wood pallets

Case-Ready Phase
Nitric acid
Sodium hydroxide
Antifoam
SilicaSteel
CottonNylon
Laundering
LDPE
Aluminum alloy
Cardboard, virgin
Cardboard, recycled
Paper
Polypropylene
Wood pallets
R-134a Refrigerant
Alcohols, C13-C15, ethoxylated
N,N-Dimethylcyclohexylamine
Paraffin
Phosphoric acid
Potassium silicate
Propylene glycol
Sodium hypochlorite
Alkylbenzene sulfonate
Latex

Retail PhaseR-143a Refrigerant
R-134a Refrigerant
PropaneSource, YearBASF, 2010
BASF, 1996
BASF, 2003
BASF, 2002
BASF, 1999
BASF, 2005
BASF, 2005
BASF, 1996
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
BASF, 1996
Ecoinvent 2.2, 2010
Boustead, 1996
BASF, 2003
BASF, 2002
Boustead, 2000
BASF, 2010
BASF, 2003
BASF, 2002
BASF, 2005
BASF, 2005
BASF, 1996
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
BASF, 1996
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
BASF, 2008
BASF, 2005
Ecoinvent 2.2, 2010
Boustead, 1996
Boustead, 1996
BASF, 2010
BASF, 2002
BASF, 2008
Ecoinvent 2.2, 2010
BASF, 2002
Ecoinvent 2.2, 2010
Boustead, 1996

26

Comments

Ecoinvent profile: Corrugated board, fresh fibre,
single wall, at plant/RER U
Ecoinvent profile: Corrugated board, recycling
fibre, double wall, at plant/RER U
Ecoinvent profile: Paper, woodfree, uncoated,
at non-integrated mill/RER U
Ecoinvent profile: Wood container and pallet
manufacturing (of project USA Input Output
Database)

Ecoinvent profile: Corrugated board, fresh fibre,
single wall, at plant/RER U
Ecoinvent profile: Corrugated board, recycling
fibre, double wall, at plant/RER U
Ecoinvent profile: Paper, woodfree, uncoated,
at non-integrated mill/RER U
Ecoinvent profile: Wood container and pallet
manufacturing (of project USA Input Output
Database)
Ecoinvent profile: Refrigerant R134a, atplant/RER S
Ecoinvent profile: Paraffin, at plant/RER S

Ecoinvent profile: Latex, at plant/RER S
Ecoinvent profile: Refrigerant R134a, atplant/RER S

Copyright © 2015 BASF Corporation

Eco-Profile
Polypropylene
LDPE
Cardboard, virgin
Paper
Polystyrene
Nylon

Consum er Phase

Source, YearBASF, 1996
BASF, 2005
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
BASF, 2009
BASF, 2002

LDPE

BASF, 2005

Polypropylene
LDPE
HDPE

BASF, 1996
BASF, 2005
BASF, 2007

Restaurant Phase

Cardboard, virgin
Aluminum
Polystyrene
Polyvinyl chloride
Alcohols, C13-C15, ethoxylated
Citric acid
Coco trimethyl ammonium
chloride
Sodium hypochlorite
Sodium hydroxide
Urea
Alkylbenzene sulfonate
Sodium tripolyphosphate
Potassium carbonate
Sodium lauryl sulphate
Glass oxide
PropaneEcoinvent 2.2, 2010
Boustead, 1996
BASF, 2009
Boustead, 1996
BASF, 2008
BASF, 1998
BASF, 2000
BASF, 2002
BASF, 2003
BASF, 2005
BASF, 2008
Ecoinvent 2.2, 2010
Ecoinvent 2.2, 2010
BASF, 2007
BASF, 2011
Boustead, 1996

Comments

Ecoinvent profile: Corrugated board, fresh fibre,
single wall, at plant/RER U
Ecoinvent profile: Paper, woodfree, uncoated,
at non-integrated mill/RER U

Ecoinvent profile: Corrugated board, fresh fibre,
single wall, at plant/RER U

Ecoinvent profile: Sodium tripolyphosphate, atplant/RER
Ecoinvent profile: Potassium carbonate, atplant/GLO

Ecoinvent profile: Refrigerant R134a, at
Ecoinvent 2.2, 2010 plant/RER S
Steel
BASF, 2010
CottonBASF, 2003
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.
R-134a Refrigerant

Table 7: 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. Figure 6 below summarizes theenvironmental impact results as represented by percent contribution from each value

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chain phase. As can be readily seen in Figure 6, the majority of the environmental
impacts occur within the feed and animal phases.
Absolute environmental impact data are then presented below with further
discussion in Sections 8.1.1 through 8.1.10.

Figure 6: Summary of environmental impact data on percentage basis.

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 83% of thetotal Cumulative Energy Demand (CED). Additionally, while all phases of the beefvalue chain contributed to CED through fossil energy consumed for utilities andtransportation, the retail and consumer energy requirements were clearly higher
as a result of more energy required per pound of beef due to scale (associatedwith refrigeration, cooking, and transport).
As can be seen below in Figure 7, CED was 503 MJ/CB. Figure 8 demonstrates
the impact of the gross bioenergy from the feed as represented by the majorityof the contribution from renewable bio-based energy. Since this energy is abiological requirement for the animals and cannot be changed, it is important torecognize that the main opportunities for energy reduction are found with theremaining energy (most of which is currently non-renewable as is associatedwith the current U.S. energy grid and transportation system).

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Figure 7: Cumulative Energy Demand where current is represented by data from the period 20112013 as defined in Table 1.

Figure 8: Renewable and Non-Renewable Energy Breakout where current is represented by datafrom the period 2011-2013 as defined in Table 1.

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8.1.2. Abiotic Depletion Potential (ADP)
Zinc in the cattle phase (used as an essential mineral supplement) was the mostdominant 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 thecurrent 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, andcoal) that was used for fertilizers in the feed phase and throughout the entire
beef value chain for utilities and transportation fuels.
ADP was 4.7 mg Ag-eq/CB. Figure 9 represents the ADP by phase while Figure
10 represents the ADP by resource.

Figure 9: Abiotic Depletion Potential by phase where current is represented by data from theperiod 2011-2013 as defined in Table 1.

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Figure 10: Abiotic Depletion Potential by resource where current is represented by data from theperiod 2011-2013 as defined in Table 1.

8.1.3 Consumptive Water Use
Nearly 98% of the consumptive water was consumed in the feed phase and thiswas associated mainly with the irrigation of crops. Electricity and pre-chain water
consumption (especially from pre-chain impacts from materials such as
corrugated cardboard) had a significant contribution on consumptive water aswell as direct water consumption within the harvesting process.
The assessed consumptive water use was 1,160 L-eq/CB and the absoluteconsumptive water use is 2,325 L/CB.
The consumptive water use is shown both at an assessed value as well as an
absolute value in Figures 11 and 12 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 water consumed in the study was 0.499, which is representative of
the entire U.S. as a midpoint indicator (note that this factor was modified fromapplication of an endpoint characterization factor in Phase 1 in order to decrease
uncertainty and to maintain better consistency from a methodological standpointthat considers midpoint factors as the norm). 35

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Figure 11: Assessed Consumptive Water Use where current is represented by data from theperiod 2011-2013 as defined in Table 1.

Figure 12: Absolute Consumptive Water Use where current is represented by data from the period
2011-2013 as defined in Table 1.

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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 47% of total GWP. N 2 O from manureon the feedlots and pastureland was the second largest contributor, with 27% of
the total value chain emissions. Other significant contributors included fieldemissions from fertilizers on the feed phase, refrigerant leakage on the retail andrestaurant phases, and cooking on the consumer and restaurant phases. Less
significant GWP contributors included corrugated cardboard and LDPE packaging
pre-chain emissions.
As can be seen in Figure 13, the GWP was 22.0 kg/CB.

Figure 13: Global Warming Potential (GWP) where current is represented by data from the period
2011-2013 as defined in Table 1.

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8.1.4.2 Photochemical ozone creation potential (POCP)
The main contributors to POCP included VOCs from silage feed (as well as some
contribution from high moisture corn and WDGS), enteric methane, fossil energy
emissions (especially diesel), and packaging pre-chain emissions from corrugatedcardboard and LDPE.
As can be seen in Figure 14, the POCP was 66 g C 2 H 4 -eq/CB.

Figure 14: Photochemical Ozone Creation Potential (POCP) where current is represented by datafrom the period 2011-2013 as defined in Table 1.

8.1.4.3 Ozone depletion potential (ODP)
While ODP was a small overall value, the most significant contributors to ODP
were halogenated hydrocarbons used for refrigerants in the case-ready,restaurant and retail sectors, vinyl gloves used in the restaurant sector, and
LDPE pre-chain emissions.
As can be seen in Figure 15 below, ODP was 0.76 mg CFC-eq/CB.

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Figure 15: Ozone Depletion Potential (ODP) where current is represented by data from the period
2011-2013 as defined in Table 1.

8.1.4.4 Acidification potential (AP)Most of the AP contribution comes from the feed and cattle phases. Specifically,ammonia from fertilizers used on feed crops and manure and urine from cattle
were the major causes. Other contributors to AP included emissions fromcombustion in electricity production and on-site boiler use, transportation, andpre-chain impacts from corrugated cardboard.
As can be seen in Figure 16 below, AP was 329 g SO 2 -eq/CB.

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Figure 16: Acidification Potential (AP) where current is represented by data from the period 20112013 as defined in Table 1.

8.1.5 Water emissions
The main water emissions from the beef value chain were from the feed phase,
which accounted for 90% of total water emissions. Of the feed emissions,approximately 34% was a result of nitrogen runoff and leaching, 19% from
phosphorous runoff, and 33% from heavy metal runoff and leaching (associatedwith fertilizers). Other main water emissions were a result of runoff and leachingfrom cattle pastureland, direct wastewater emissions from the harvesting andcase-ready facilities, pre-chain impacts from cardboard packaging production,
and water emissions associated with end-of-life landfill disposal for productionwaste and packaging waste at all phases of the post-farm value chain.
As shown below in Figure 17, were 3,095 L diluted water-eq/CB.

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Figure 17: Water Emissions where current is represented by data from the period 2011-2013 as
defined in Table 1.

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), allof the solid waste shown below in Figure 18 was associated with pre-chain
waste. All direct waste was therefore evaluated above for final ecosphereemissions to water and air based on final fate degradation. The most significant
contributions came from the pre-chain waste associated with dicalcium
phosphate (for supplements), transport fuels (diesel and gasoline), andelectricity.
The solid waste generated was 0.17 kg municipal waste-eq/CB.

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Figure 18: Solid Waste Generation where current is represented by data from the period 20112013 as defined in Table 1.

8.1.7 Land use
The most significant phase associated with land use was the feed phase due tothe 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 thepre-chain impacts associated with packaging (cardboard) and diesel
consumption.
As can be seen below in Figure 19, land use was 21.5 m2a-eq/CB. The units are
expressed first in m2a (where a = time in years) and are then weighted. As
outlined in the BASF EEA Methodology, the absolute land use values areweighted according to land use occupation and transformation type in order toaccount for ecosystem damage and biodiversity factors.

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Figure 19: Land Use where current is represented by data from the period 2011-2013 as definedin Table 1.

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 thesematerials (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 andwas provided to NSF International as part of the EEA model submitted as part ofthis verification. Figure 20 shows how each phase 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 impactof agricultural chemicals (fertilizers, especially urea and lime, and pesticides) andthe impacts from application. Other major contributors to toxicity potential
included fossil energy (natural gas, coal, and diesel) pre-chain and use factorsthat were utilized throughout the beef value chain for utilities and transportation.
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The normalized and weighted toxicity potential values are shown below in Figure
20.

Figure 20: Toxicity Potential where current is represented by data from the period 2011-2013 as
defined in Table 1.

8.1.9 Risk Potential (Occupational Illnesses and Accidents)
All of the materials and activities in the various life cycle stages were assigned
specific NACE codes. 36 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 thebusiness economy and is broken down by specific industries. Specific to this
impact category, the NACE codes track, among other metrics, the number ofworking 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 ofmaterials required for each alternative, a quantitative assessment of risk wasachieved.

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In addition to the NACE analysis for all of the inputs, in order to derive a better
representation of Occupational Illnesses and Accidents potential, U.S. Bureau ofLabor (BLS) data were analyzed for the direct industry activity in each of thebeef value chain phases as outlined in the assumptions discussion above in
Section 6.
As also discussed above in the assumptions discussion in Section 6, additionalrisk categories of Animal Welfare (on both the cattle and harvesting phases),
Food Safety (on the harvesting phase), and Community Nuisance Odors andEmissions (on the harvesting phase) were considered as part of the total risk
analysis in this study. While these additional risks were considered as thepercentages 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 48.5%, Fatal Accidents at 25.2%, andNon-fatal Accidents at 14.8% of total study risk.
The normalized Risk Potential values are shown below in Figure 21. The majorityof the risk is associated with direct accidents and illnesses with each phase.

Figure 21: Risk Potential (Occupational Illnesses and Accidents) where current is represented bydata from the period 2011-2013 as defined in Table 1.

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8.2.

Economic Cost Results

The life cycle cost data for the U.S. Beef EEA were generated as described in theoverall study assumptions in Section 6 of this report. As shown in Figure 22, the life
cycle costs in 2011 were $5.55/CB.

Figure 22: Life Cycle Costs where current costs are consumer prices of beef in 2011.

9. Pennsylvania Grass-finished Beef Scenario

9.1

Overview
The representative Pennsylvania grass-finishing operations were modeled based
upon data collected from a combination of 17 survey responses and 4 farm visits tograss-finished beef producers in the state. The survey responses indicated that the
average operation was 44.5 ha in size, with a range from 1.6 to 170 ha. The average
number of cows on each farm was 22 (range: 2 to 60) and the average number offinishing animals was 13 (range: 1 to 49).
Grass-finished beef (also referred to as grass-fed, forage-fed, and forage-finished) is
defined as a meat product from cattle who, for the duration of their life, consumed
only forages with the exception of the mother’s milk. From the data collected,
representative farms were modeled using the Integrated Farm System Model andenvironmental impact results were compiled using weighted average values forgrass-finished beef production in the state. Weighting factors were developed based42

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upon the number of cows represented by the modeled management scheme, thenumber of cows within the region of the state being modeled, the age at finish, theuse of manure or fertilizer, and the inclusion of a small grain forage in the feeding
strategy. Six systems were developed according to common management schemes
identified in the survey data. Descriptions of the modeled systems follow.
Results of these simulations should not be extrapolated to be representative of
national grass finished beef production due to regional climate, management,
animal, and scale differences. These results should be interpreted in a regional
context, understanding that water, energy use, and productivity values are only
meaningful in regional contexts based upon the environmental, economic, and social
factors influencing agricultural production in a given landscape. In general, farms in
the northeastern U.S. tend to be smaller—in terms of land area and herd size—thanthose in the central U.S. due to greater variability in the terrain in the region.
Smaller land area per farm has implications on impacts such as energy use
efficiencies, as the land base may limit the number of animals that can be produced
using the same quantities of resources. In addition, beef cattle are often maintainedon marginal soils or sloping terrain which may be less suitable for row crop
production. A final consideration is that agriculture in the northeastern U.S. is rainfed
with no irrigation use due to high precipitation and low evapotranspiration rates in
the region.

9.2

Description of systems
In all systems, cattle are assumed to be grazed in some form of a managed rotation
through multiple paddocks in order to allow rest for plant regrowth after the grazing
period. Animal characteristics, herd composition, and stocking rates are set
according to averages calculated from producer survey responses. All systems withthe exception of System 1 and System 6 produce all of the forage consumed by the
animals produced.
Many of these systems are managed to meet organic standards. As such, chemicalfertilizer and pesticide inputs are limited. To represent the small amounts of urea use
reported in the survey and farm visits, urea was applied to 3% of perennial pasture,
3% of perennial hay land, 13% of annual pasture, and 13% of annual hay land.
Pesticide use is typically limited to fence line application and spot spraying fortreatment of weeds which are difficult to control using grazing and clipping. 2,4Dichlorophenoxyacetic acid was applied to 1% of perennial and annual pasture eachto represent the minimal use reported.
•

System 1: All of the grazed forage required is produced on the farm and alfalfa
silage is purchased to meet supplemental feed requirements. No haying
operations are conducted. Lime, imported manure, or chemical fertilizers are notapplied to pastures. Cattle are finished at 30 months of age. This system issimulated in western Pennsylvania using historical daily weather data from
Pittsburgh (1981-2005), and the weighting factor is 17.5%.

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System 2: Hay is produced from the same land base where cattle are grazed.
Neither lime nor chemical fertilizers are applied to pastures. Cattle are finished at
24 months of age. This system is simulated in western Pennsylvania using
weather data from Pittsburgh, and the weighting factor is 18.5%.

•

System 3: Similar to System 2, grazing and haying is done on the same landbase. In addition to hay produced from the grazed land, hay is also produced
from a separate, non-grazed hay field. Cattle are finished at 24 months of age.
This system is simulated in western Pennsylvania using weather data from
Pittsburgh, and the weighting factor is 18.5%.

•

System 4: All feed required is produced on the farm, which includes grazed
forage, perennial grass hay and silage, alfalfa hay, and oat bale silage. Some ofthe perennial hay fields are ungrazed, and the alfalfa and oat fields are
harvested and fed as preserved forage. The oat field is fertilized with poultry
manure to meet N requirements for the oats. Cattle are finished at 24 months ofage. This system is simulated in central Pennsylvania using weather data fromState College (1986-2010), and the weighting factor is 12.9%.

•

System 5: The operation is the same as that in System 4 with the exception of
the application of poultry manure. Instead of poultry manure, this system isapplying synthetic P and K fertilizers to the oat field. Residual N from thepreceding grazed or fertilized perennial grass crop is the sole N source for oat
production in this system. The weighting factor is 11.9%.

•

System 6: Some supplemental feed is produced as perennial grass hay from thegrazed land base. In addition to grazing perennial pastures, a rye crop is alsograzed before head emergence. A small amount of alfalfa silage is purchased as
supplemental feed, and cattle are finished at 24 months of age. This system issimulated in eastern PA using historical weather data for Lancaster, PA (19792003), and the weighting factor is 20.7%.

Assumptions about dressing percentages and other value chain losses are identicalto those used for traditional beef production. Though the processing of grassfinished beef carcasses is assumed to be the same as that of traditional beef, thesedata may be updated in future analyses as post-harvest data specific to grassfinished beef carcasses become available.

9.3

Pennsylvania Grass-finished Eco-Efficiency Analysis Results
The sources of the main impacts are essentially the same as what are discussed
above in the baseline scenario with USMARC data (simply different scale ofcontribution for each phase in many cases). Therefore, the results for thisPennsylvania Grass-finished scenario are presented below. The inputs considered in
the boundary scope are the same as those defined in Figure 4 for the USMARC
analysis. As mentioned, the data for the post-farm phases of the value chain are
assumed to be the same as that associated with traditional beef (and used in the44

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baseline analysis that considered the USMARC data). The data collected to represent
the feed and cattle phases of the Pennsylvania Grass-finished scenario were from2014. Specific years for which each of the value chain phase data were analyzed for
the Pennsylvania Grass-finished scenario are shown below in Table 8.

Phase

Source Data Year

Feed (PA Grass-finished)

2014

Cattle (PA Grass-finished)

2014

Harvesting

2011

Case-Ready

Combination of 2011 & 2013

Retail2013

Consumer

2011

Restaurant

Combination of 2011 & 2013

Table 8: Pennsylvania Grass-finished Source Data Year by Value Chain Phase

9.3.1 Cumulative Energy Demand

Figure 23: Cumulative Energy Demand, 486 MJ/CB, where PA Grass is represented by data fromthe period 2011-2014 as defined in Table 8.

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Figure 24: Cumulative Energy Demand, Renewable vs. Non-renewable, where PA Grass isrepresented by data from the period 2011-2014 as defined in Table 8.

9.3.2 Abiotic Depletion Potential (ADP)

Figure 25: Abiotic Depletion Potential, 5.2 mg Ag-eq/CB, where PA Grass is represented by datafrom the period 2011-2014 as defined in Table 8.

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Figure 26: Abiotic Depletion Potential by resource, where PA Grass is represented by data fromthe period 2011-2014 as defined in Table 8.

9.3.3 Consumptive Water Use

Figure 27: Consumptive Water Use, 27 L-eq/CB, where PA Grass is represented by data from theperiod 2011-2014 as defined in Table 8.

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Figure 28: Consumptive Water Use, Absolute, 55 L/CB, where PA Grass is represented by datafrom the period 2011-2014 as defined in Table 8.

9.3.4 Air Emissions
9.3.4.1 Global Warming Potential (GWP)

Figure 29: Global Warming Potential, 27.4 kg CO 2 -eq/CB, where PA Grass is represented by datafrom the period 2011-2014 as defined in Table 8.

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9.3.4.2 Photochemical Ozone Creation Potential (POCP)

Figure 30: Photochemical Ozone Creation Potential, 49 g C 2 H 4 -eq/CB, where PA Grass isrepresented by data from the period 2011-2014 as defined in Table 8.

9.3.4.3 Ozone Depletion Potential (ODP)

Figure 31: Ozone Depletion Potential, 0.71 mg CFC11-eq/CB, where PA Grass is represented bydata from the period 2011-2014 as defined in Table 8.

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9.3.4.4 Acidification Potential (AP)Figure 32: Acidification Potential, 223 g SO 2 -eq/CB, where PA Grass is represented by data fromthe period 2011-2014 as defined in Table 8.

9.3.5 Water Emissions

Figure 33: Water Emissions, 5,141 L diluted water-eq/CB, where PA Grass is represented by datafrom the period 2011-2014 as defined in Table 8.

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9.3.6 Solid Waste Generation

Figure 34: Solid Waste Generation, 0.24 kg municipal waste-eq/CB, where PA Grass isrepresented by data from the period 2011-2014 as defined in Table 8.

9.3.7 Land Use

Figure 35: Land Use, 29.7 m2a-eq/CB, where PA Grass is represented by data from the period
2011-2014 as defined in Table 8.

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9.3.8 Toxicity Potential

Figure 36: Toxicity Potential, where PA Grass is represented by data from the period 2011-2014
as defined in Table 8.

9.3.9 Risk (Occupational Illnesses & Accidents)

Figure 37: Risk, where PA Grass is represented by data from the period 2011-2014 as defined inTable 8.

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9.4

Economic Cost Results

Figure 38: Life Cycle Costs, $9.71/CB (Industry Data), where PA Grass costs are consumer pricesof grass-finished beef in 2011.

10. Data Quality Assessment

10.1

Data Quality Statement

The data used for parameterization of the EEA was sufficient with most parametersof high to medium data quality. Moderate (medium) data is where industry averagevalues or assumptions pre-dominate the value. No critical uncertainties or significant
data gaps were identified within the parameters and assumptions that could have asignificant effect on the results and conclusions. Inputs to the study werecomprehensive and the exclusions to the study described in Section 5.3 and noted in
Figure 4 would not have a significant impact on the overall study. Eco-profiles used
for the study as represented in Table 7 were reviewed for completeness andappropriateness. Eco-profiles that are greater than 10 years old were deemed to be
still reflective of current technology and industry practices. Table 9 provides asummary of the data input quality used for the EEA.

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Phase
Feed
Cow-Calf
Feedlot
Harvesting
Case-Ready

Quality Statement
High-Medium
High-Medium
High-Medium
High
High

RetailHigh-Medium

Consumer

MediumRestaurantHigh-Medium

Comments
Mainly IFSM data of high quality.
Mainly IFSM data of high quality.
Mainly IFSM data of high quality.
Primary data from harvesters whose facilities represent
60% of the industry.
Primary data specific to beef from case-ready sector.
Primary data from the retail sector but data had to beallocated on an economic basis in order to assign beefspecific values.
None of the consumer data was primary data but was
based off of averages from literature and industry
reports.
Primary data from the restaurant sector but some data
had to be allocated on an economic basis in order toassign beef-specific values.

Table 9: Data Quality Evaluation for EEA Parameters

11. Sensitivity and Uncertainty Analysis

11.1

Sensitivity and Uncertainty Considerations

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

11.2

Sensitivity Analyses

11.2.1 Scenario #1: WDGS Mass Allocation
As represented in the base case analysis, an economic allocation was used thatplaced 21% of the bioethanol distillation environmental burden onto WDGS. For
this scenario, a mass allocation was used instead and this resulted in 62% of thebioethanol distillation process environmental burden being allocated to the
WDGS. This 62% was based upon a distillation conversion factor ratio of 479 kg
WDGS : 378 L bioethanol 37 (or 299 kg with a density for ethanol of 0.79 kg/L).
As expected, the results using a mass allocation of the WDGS were significantly
changed in the feed phase compared to the base case economic allocation thatwould have a direct noticeable impact on the total beef value chain results. For
example, Figure 39 below demonstrates a near 4% increase in total value chain
GWP when applying a mass allocation. At the same time, Figure 40 shows a 53%
increase in total value chain water emissions with application of a mass
allocation.

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While there is significant variation with the mass allocation, since we areconsidering all of the harvesting by-products with an economic allocation andsince WDGS is a by-product of the distillation process, we maintained theeconomic allocation in order to keep consistent with allocation of all by-products.
Additionally, with current pricing used in the economic allocation, as isdemonstrated in Section 11.3.2 below, a scenario that considered energy
allocation further validated the 21% economic allocation factor.

Figure 39: GWP for WDGS Mass Allocation Scenario

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Figure 40: Water Emissions for WDGS Mass Allocation Scenario

11.2.2 Scenario #2: WDGS Energy Content Allocation
As represented in the base case analysis, an economic allocation was used thatplaced 21% of the bioethanol distillation process environmental burden onto
WDGS. For this scenario, an energy content allocation was used instead and thisresulted in 21% of the bioethanol distillation process environmental burden being
allocated to WDGS. 38 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 physicalattribute that should be used for calculating the allocation of the WDGS, as
opposed to economics, which exhibits fluctuation. However, since we areconsidering all of the harvesting by-products with an economic allocation andsince WDGS is a by-product of the distillation process, we maintained theeconomic 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.

11.2.3 Scenario #3: Economic Allocation for Consumer Refrigeration
As represented in the base case analysis, a volumetric allocation was used toanalyze the burden of the consumer phase refrigeration.

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The results of using an economic allocation of the retail and consumer phase
refrigeration and the retail refrigerant leakage resulted in some small but
material changes in environmental impacts. For example, Figure 41 below
demonstrates a near 2% increase in total value chain CED as compared to thevolumetric allocation base case analysis. At the same time, Figure 42 shows a
3% increase in total value chain GWP compared to the volumetric allocation basecase analysis. The largest change with this alternate allocation method was on
GWP. However, GWP is only weighted 3.8% of total beef value chain
environmental impacts.
While there are differences between the economic and volumetric allocation for
consumer refrigeration as demonstrated, the volumetric allocation provided aphysical allocation metric that was a more realistic representation of therefrigeration associated specifically with beef. Because an alternate physicalallocation metric was not reasonably possible or logical for the other impacts
analyzed for the consumer phase, the economic allocation is presented since thiswas the only alternative available.

Figure 41: CED for Consumer Economic Allocation Scenario

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Figure 42: GWP for Consumer Economic Allocation Scenario

12. Conclusions
While environmental impacts stem from all phases of the beef value chain as represented
throughout the study analysis and in Figure 6, the majority of the impacts are attributed to
on-farm processes in the feed and cattle phases.
The impacts associated with the post-farm phases of harvesting, case-ready, retail,consumer, and restaurant, while generally contributing less overall value chain impacts,
present material opportunities for improvement. Additionally, these opportunities generally
may be more straightforward in terms of implementation. One very significant opportunity
to which the entire value chain can contribute is on the issue of food waste. Based on theloss data analyzed, spoilage at the point of sale (retail and restaurant sectors) as well as
spoilage and plate waste at the point of consumption (at-home consumption and inrestaurant) are estimated to represent more than 10% of the edible beef that is processed.
This is a huge opportunity if one considers all of the value chain inputs that are necessary to
make this wasted beef. Additionally, it is a huge opportunity to contribute positively to theever-growing global food supply demand. While reducing this impact will entail consumer
behavior change, eliminating or at least greatly reducing this impact is likely one of thelargest single opportunities for impact reduction.
In terms of the Pennsylvania Grass-finished scenario presented here, while not directlycomparable to the baseline farm data from USMARC due to regional differences, there are58

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trade-offs for both conventional and grass-finished beef. Additional data and research isneeded for both conventional and grass-finished beef, which should include an expanded
focus on lesser understood but longer-horizon sustainability issues such as biodiversity andsoil integrity.
This eco-efficiency analysis of current U.S. beef industry value chain practices provides thebaseline data that can be used to benchmark value chain improvements in the future.
Future research is already underway to better understand some of the regional differencesin the feed and cattle phase as well as to gather more specific data points to obtain an evenhigher quality dataset for ongoing measurement and improvement of the U.S. beef industry.
Planned ongoing sustainability programs within the industry will result in futurecommunications as data continues to be refined.
13. Limitations of EEA Study Results
These eco-efficiency analysis results and its conclusions are based on the specificcomparison of the production, use, and disposal, for the described customer benefit,
alternatives and system boundaries. Transfer of these results and conclusions to otherproduction 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 maderegarding the results and conclusions.

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

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)-datasystem/.aspx
3

U.S. Department of the Interior. 1998. Estimated Use of Water in the United States in 1995.
http://pubs.er.usgs.gov/publication/cir1200
4

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
5Meat Solutions, Inc. 2014. Technomic Foodservice Volumetric Study; Freshlook/IRI
Categorized by VMMeat System; Meat Solutions Annual Beef Consumption Report, prepared forthe beef checkoff.

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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
8Rotz, 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. 83:231242.
9Rotz, C.A., et al. 2012. Because these are hyperlinked I couldn’t use the comment-but weneed to have complete sources here10

11

Rotz, C.A., et al. 2012.

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

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

13European Commission Joint Research Centre. 2008. SALCA Heavy Metals Tool.
http://eplca.jrc.ec.europa.eu/ResourceDirectory/tool.vm?tid=226
14Department of Agriculture and Food, Government of Western Australia. 2008. Potential Uses
for Distillers Grains.
15

16

U.S. Department of Labor. 2012. Injuries, Illnesses, and Fatalities. http://www.bls.gov/iif/

17

Beef Quality Assurance. 2012. Beef Quality Assurance. http://www.bqa.org/default.aspx

Expert opinion scoring for animal welfare analysis from James O. Reagan, PhD (Former
Senior Vice President, National Cattlemen’s Beef Association), John Paterson, PhD (NCBA
Executive Director, Producer Education, National Cattlemen’s Beef Association), and Kim
Stackhouse-Lawson, PhD (Director of Sustainability Research, National Cattlemen’s Beef
Association) based on the Beef Quality Assurance program information referenced.
18

U.S. Centers for Disease Control. 2012. Foodborne Diseases Active Surveillance Network(FoodNet). http://www.cdc.gov/foodnet/
19

Expert opinion scoring for food safety compiled by James O. Reagan, PhD (Former SeniorVice President, National Cattlemen’s Beef Association) based on the U.S. Centers for Disease
Control data referenced.
20

See for example the auditing program from Dr. Temple Grandin:
http://www.grandin.com/survey/survey.html
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Expert opinion scoring for animal welfare analysis from James O. Reagan, PhD (Former
Senior Vice President, National Cattlemen’s Beef Association), John Paterson, PhD (NCBA
Executive Director, Producer Education, National Cattlemen’s Beef Association), and Kim
Stackhouse-Lawson, PhD (Director of Sustainability Research, National Cattlemen’s Beef
Association) based on the Beef Quality Assurance program information referenced.
22

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) based on directindustry practices knowledge.
23

U.S. EPA. 2012. Supermarkets: An Overview of Energy Use and Energy Efficiency
Opportunities.
https://www.energystar.gov/sites/default/files/buildings/tools/SPP%20Sales%20Flyer%20for%
20Supermarkets%20and%20Grocery%20Stores.pdf
24

Food Marketing Institute. 2012. Supermarket Sales: Supermarket Sales by Department –Percent of Total Supermarket Sales. http://www.fmi.org/docs/factsfigures/grocerydept.pdf?sfvrsn=2
25

U.S. Department of Transportation. 2011. 2009 National Household Travel Survey.
http://nhts.ornl.gov/2009/pub/stt.pdf
26

Association of Home Appliance Manufacturers. 2011. Average Household Refrigerator
Energy Use, Volume, and Price Over Time. http://www.appliancestandards.org/sites/default/files/Refrigerator%20Graph_July_2011.PDF
27

28

National Cattlemen’s Beef Association. 2011. Overcoming Defrosting Barriers.

U.S. Energy Information Administration. 2005. Residential Consumption Energy Survey(RECS). http://www.eia.gov/consumption/residential/data/2005/
29

U.S. Department of Agriculture. 2012. Food Availability Data.
http://www.ers.usda.gov/data-products/commodity-consumption-by-populationcharacteristics.aspx
30

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-nutrientintakes.aspx
31

32Boustead Consulting Ltd UK. The Boustead Model 5.1.2600.2180 LCA database.

Swiss Center for Life Cycle Inventories. Ecoinvent v2.2.
http://www.ecoinvent.org/database/database.html
33

U.S. National Renewable Energy Laboratory. 2012. U.S. Life Cycle Inventory Database.
https://www.lcacommons.gov/nrel/search
34

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Pfister, S., Koehler, A., and Hellweg, S. 2011. Assessing the Environmental Impacts ofFreshwater Consumption in LCA. Environmental Science and Technology. 43: 4098-4104.
35

European Commission, eurostat
http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Glossary:Statistical_classificatio
n_of_economic_activities_in_the_European_Community_(NACE)
36

Department of Agriculture and Food, Government of Western Australia. 2008. Potential Uses
for Distillers Grains.
37

38

Lory, John, et al. 2008. Comparing the Feed, Fertilizer, and Fuel Value of Distillers Grains.

Crop Management. 7.

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