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This article presents a cradle-to-grave analysis of the United States fluid milk supply chain greenhouse gas (GHG) emissions that are accounted from fertilizer production through consumption and disposal of milk packaging. Crop production and on-farm GHG emissions were evaluated using public data and 536 farm operation surveys. Milk processing data were collected from 50 dairy plants nationwide. Retail and consumer GHG emissions were estimated from primary data, design estimates, and publicly available data. Total GHG emissions, based primarily on 2007 to 2008 data, were 2.05 (90% confidence limits: 1.77–2.4) kg CO2e per kg milk consumed, which accounted for loss of 12% at retail and an additional 20% loss at consumption. A complementary analysis showed the entire dairy sector contributes approximately 1.9% of US GHG emissions. While the largest GHG contributors are feed production, enteric methane, and manure management; there are opportunities to reduce impacts throughout the supply chain.
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Greenhouse gas emissions from milk production and
consumption in the United States: A cradle-to-grave life
cycle assessment circa 2008
Greg Thoma
, Jennie Popp
, Darin Nutter
, David Shonnard
, Richard Ulrich
Marty Matlock
, Dae Soo Kim
, Zara Neiderman
, Nathan Kemper
Cashion East
, Felix Adom
Ralph E. Martin Department of Chemical Engineering, University of Arkansas, 3202 Bell Engineering Center, Fayetteville, AR 72701-1201, United States
Department of Agricultural Economics and Agribusiness, University of Arkansas, 217 Agriculture Building, Fayetteville, AR 72701, United States
Department of Mechanical Engineering, University of Arkansas, 204 Mechanical Engineering Building, Fayetteville, AR 72701, United States
Department of Chemical Engineering and Sustainable Futures Institute, Michigan Technological University, 1400 Townsend Drive,
Houghton, MI 49931-1295, United States
Department of Biological and Agricultural Engineering, University of Arkansas, 203 Engineering Hall, Fayetteville, AR 72701, United States
article info
Article history:
Received 31 October 2011
Received in revised form
6 May 2012
Accepted 30 August 2012
This article presents a cradle-to-grave analysis of the United States uid milk supply chain greenhouse gas
(GHG) emissions that are accounted from fertilizer production through consumption and disposal of milk
packaging. Crop production and on-farm GHG emissions were evaluated using public data and 536 farm
operation surveys. Milk processing data were collected from 50 dairy plants nationwide. Retail
and consumer GHG emissions were estimated from primary data, design estimates, and publicly
available data.Total GHG emissions, based primarily on 2007 to 2008 data, were 2.05 (90% condencelimits:
1.77e2.4)kg CO
e per kg milk consumed, which accounted for loss of 12%at retail and an additional 20% loss
at consumption. A complementary analysis showed the entire dairy sector contributes approximately 1.9%
of US GHG emissions. While the largest GHG contributorsare feed production, enteric methane, and manure
management; there are opportunities toreduce impacts throughout the supply chain.
Ó2012 Elsevier Ltd. All rights reserved.
1. Introduction
Consumers are growing more aware of their impact on the
environment, and this may result in changes to their consumption
habits. To that end, the US dairy industry is working to further
improve the environmental performance of its supply chain in
a way that is also economically sustainable. In 2007, approximately
17.4 Tg (million metric tons) of uid milk of all types were
consumed (USDA, 2010a). Analysis of the dairy supply chain from
production through ultimate disposal of packaging was necessary
to provide the industry with a documented baseline of the carbon
footprint of uid milk an analysis of the dairy supply chain from
production through ultimate disposal of packaging was performed.
These results serve to identify reduction opportunities through best
practices and creation of decision-support tools for producers,
processors, and others throughout the dairy supply chain that can
be used to foster the long-term viability of the industry. This life
cycle analysis (LCA) was performed in compliance with ISO
14040:2006 and 14044:2006 standards, with the exception that
a single impact assessment method, global warming potential, was
adopted. ISO standards call for a wider range of impact assessment
categories. The Innovation Center for US Dairy is addressing addi-
tional impact categories in other projects; thus this work repre-
sents one aspect of the dairy industrys sustainability evaluation.
A number of life cycle assessment studies have focused on dairy
in the past decade (Guinard, Verones, & Loerincik, 2009). There are
a variety of methodological approaches used in these studies. Some
report on an energy-corrected milk, or fat and protein-corrected
basis, and each uses a slightly different milk-to-beef allocation
procedure; many have focused on production only, but a few have
included additional post-farm components in the analysis. Much of
this research has been done in Australia (Lundie, Feitz, Jones,
Dennien, & Morian, 2003), Scandinavia (Berlin, 2005;Eide, 2002),
and Western European countries (Hospido, Moreira, & Feijoo, 2003).
Previous LCAs for dairy have focused primarily on agricultural
production (e.g., Basset-Mens, Ledgard, & Boyes, 2009;de Boer,
2003;Capper, Cady, & Bauman, 2009;Cederberg & Flysjö, 2004;
*Corresponding author. Tel.: þ1 479 575 7374.
E-mail address: (G. Thoma).
Contents lists available at SciVerse ScienceDirect
International Dairy Journal
journal homepage:
0958-6946/$ esee front matter Ó2012 Elsevier Ltd. All rights reserved.
International Dairy Journal 31 (2013) S3eS14
Cederberg & Mattsson, 2000;Flysjö, Henriksson, Cederberg,
Ledgard, & Englund, 2011a;Haas, Wetterich, & Kopke, 2001;
Thomassen, Dolman, van Calker, & de Boer, 2009). A large body of
research exists on packaging in general, with some emphasis on
milk packaging in particular (Keoleian & Spitzley, 1999). Little
research on transportation of milk has been published. Life cycle-
based analysis of dairy processing is less common (Berlin,
Sonesson, & Tillman, 2008;COWI, 2000;Tomasula & Nutter,
2011;Xu & Flapper, 2009). Research on the life-cycle of milk from
retail to consumer to end-of-life has been minimal (Cederberg,
Sonnesson, Henriksson, Sund, & Davis, 2009;Eide, 2002;Gerber,
Vellinga, Opio, Henderson, & Steinfeld, 2010;Guinard et al.,
2009). Table 1 summarizes several full life cycle study results.
The majority of the studies report similar greenhouse gas (GHG)
emissions at the farm gate, ranging from approximately 0.75e1.5 kg
e per kg milk and 75e90% allocation of burdens to milk
compared to the co-product beef. A recent comparative study of
allocation methods supports this range, noting that system
expansion results in somewhat lower (as low as 63%) allocation to
milk, while mass allocation is as high as 98% (Flysjö, Cederberg,
Henriksson, & Ledgard, 2011b). One of the lowest reported carbon
footprints is the study by Eide (2002) that reports 65% allocation to
milk and 0.45 kg CO
e per kg milk. The Food and Agriculture
Organization of the United Nations (FAO) reports the US average
farm-gate GHG emission as 1 kg CO
fat and protein
corrected-milk (FPCM) (Gerber et al., 2010). These studies point to
enteric fermentation and manure management as primary sources
of GHG emissions in dairy production. Some studies point to on-
farm management as a critical area for investigating improve-
ment opportunities (Henriksson, Flysjö, Cederberg, & Swensson,
2011;Thoma et al., 2012b). Other studies investigate simplied
approaches to evaluating the potential impact/importance of
different parameters in the contribution to carbon footprint
(Asselin-Balençon et al., 2012;Flysjö et al., 2011a).
Retail businesses are beginning to engage their supplychains to
encourage adoption of best practices and development of innova-
tive solutions that reduce environmental impacts and maintain
value. Understanding both the sources of and the factors inu-
encing these environmental impacts is an important rst step to
identify the most effective reduction opportunities. More detailed
analysis of on-farm GHG emissions as well as emissions associated
with milk transportation, processing, packaging, distribution are
presented elsewhere (Nutter, Ulrich, Kim, & Thoma, 2012;Thoma,
Jolliet, & Wang, 2012a;Ulrich, Thoma, Nutter, & Wilson, 2012).
The goal and scope of this work were:
Goal: Determine GHG emissions associated with consumption
of 1 kg of milk by US consumers. To account for differences in
production at the farm and processing, we used the National
Research Council (2001) approach for FPCM.
Scope: Cradle-to-grave. The system boundaries included
the energy use and GHG emissions associated with every step
of the life cycle from production of fertilizer through to either
landll or municipal waste incineration of the milk packaging.
We explicitly excluded infrastructure in this analysis. Inci-
dental activities such as employee commutes, accounting, legal
services, executive air travel, and the cost of heating the
farmers residence, were not included. The primary time frame
for the study was 2007e2008.
2. Methods
2.1. Functional unit
The functional unit of this study is 1 kg of milk consumed by US
consumers. National scale reference ows for US uid milk prod-
ucts are presented in Table 2 (USDA, 2007). We created a national
averagemilk as the sales volume-weighted average of the four
milk-fat content varieties for post-processor calculations. Product
loss in the supply chain, including wasted or spoiled milk by
consumers and out-of-date milk at retail, is equivalent to 29.6% loss
of all milk produced prior to it being consumed (USDA, 2010a). This
loss is reported as 12% at retail and 20% at consumption and is not
differentiated by milk-fat content. These losses in the supply chain
affect the reference ows of upstream processes; specically, the
required ow into the consumption phase is approximately 1.25 kg
per kg consumed, and the ow into the retail channel is approxi-
mately 1.14 kg per kg delivered to retail. This results in a reference
ow of 1.42 kg milk from the farm to milk processing. Because
much of the energy in dairy feed is converted to milk solids (e.g., fat
and protein), and not all farms produce milk with standard fat and
protein composition, we have normalized on-farm production to
a standard milk (4% fat, 3.3% protein) using the National Research
Council approach (NRC, 2001) for FPCM; this is also referred to as
Energy Corrected Milk (ECM) because the calculation is based on
the ratio of the energy content, as determined by fat and protein
Table 1
Summary of previously published life cycle analyses for uid milk production and consumption (beyond farm gate).
Study Functional unit (FU) kg CO
per FU
% to milk
factors (CO
Study description
Cederberg et al. (2009) kg ECM at retail 1.08 85% 1, 25, 298 Sweden, 1990 versus 2005
Eide (2002) kg milk at end-of-life w0.54
to 0.65
65% eNorway, study of 3 dairies
Gerber et al. (2010) kg FPCM at retail 2.4 w90% 1, 25, 298 International average
Gerber et al. (2010) kg FPCM at retail 1.3 w90% 1, 25, 298 US average
Guinard et al. (2009) kg milk at end-of-life 1.2 Economic 1, 25, 298 Literature review of 60
studies, primarily European
Table 2
Total US sales of uid milk products in 2007.
Product Fat
content (%)
solids (%)
Water (%) Density
(kg L
Total sales
(million kg)
Whole milk
3.26 8.6 88.13 1.0333 7398
Reduced fat milk (2%)
1.94 8.8 89.21 1.0346 8742
Low fat milk (1%)
0.96 9.1 89.92 1.0360 5257
Fat-free milk (skim)
0.11 9.1 90.84 1.0364 3971
Includes sales of avored and organic milk.
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14S4
concentrations, of produced milk to standard milk. The following
relationship was used to convert farm production to a fat- and
protein-corrected basis:
FPCM ¼0:0929Fþ0:05882Pþ0:192
0:0929 ð4%Þþ0:05882 ð3:3%Þþ0:192
0:7576 (1)
In Equation (1),Fis the percentage milk fat in the produced milk
and Pis the percentage protein in the produced milk. While it is
important to account for differences in fat and protein concentrations
at the farmlevel, for post-farm stages of the supplychain a volumetric
basis is more appropriate for calculations because the milk processing
and handling is not different for milk with different fat content. We
haveused the reported sales forwhole, 2%,1%, and skim milkfrom this
table to determine reference ows for each of these main uid milk
products leaving the processor gate. For purposes of this study we
have aggregated avored milk with the appropriate typebased on its
fat content; in addition, because the density of the different varieties is
nearlythesame(Watson & Tittsler, 1961), we have not explicitly
accounted for any potential density variation effects.
2.2. Allocation
Attributional LCA is intended to quantify the material and energy
inputs and outputs associated with a specic system. In practice,
industrial processes usually result in more than one valuable
product emilk, cream, and beef in this study. Here it is necessary to
apply allocation to the LCA model. Allocation is the partitioning of
the input and output ows of a system among the multiple system
products. ISO 14040/14044 guidelines recommend a hierarchy of
procedures for addressing this issue. System expansion has not been
used for this project, primarily due to the lack of LCA for potential
substitute products that could be used as a basis for this approach.
Physical relationships have been adopted where feasible and
economic value or mass allocation has been used as our third option,
as described fully by Thoma et al. (2012a).InTable 3, we present
a summary of the allocation ratios and approaches adopted for this
study. We have made an effort to be consistent with allocation
methodologies at each stage of the value chain, i.e., using economic
allocation across all feed byproducts. When possible, we have
adopted causal relationships to dene the allocation ratio.
2.2.1. Feed co-products allocation
Allocation of burdens between co-products arising from feed
crop processing (e.g., between distillers grains and ethanol or grain
meals and oils) is necessary. In such situations, we have adopted
economic allocation as the standard method. For the economic
approach, where available, we have used a ve-year average price
to dene the allocation ratio.
2.2.2. Milkebeef allocation
An important allocation for this study is the allocation of GHG
emissions between the co-products of beef and milk from virtually
all dairy facilities. For this study, we have chosen to allocate impacts
between the co-products milk and meat according to a biological
relation following Thoma et al. (2012a).
2.2.3. Creamemilk allocation
To derive allocation factors, we have used national milk
consumption rates for different milk fat content products coupled
with a milk fat solids mass balance (Thoma et al., 2012a). Informa-
tion from the USDA regarding the standard composition of various
milk products was used to determine the composition of each type
of milk on a fat-free basis (USDA, 2010b). Water is primarily a carrier
for milk fat and proteins. Since the principal difference between the
co-products is the fat content (Table 2), our approach to allocation
for these co-products was to assign the entire raw milk burden to
milk solids. Then using mass conservation principles, our approach
was to assign the appropriate milk protein and milk fat burdens to
each of the different milk fat products based on the reported milk fat
content of each co-product. The data do not provide sufcient
resolution at the processor level to allocate milk processing impacts
among the different fat content milk products; in fact, most of the
energy requirements are volume- or mass-based. Pasteurization
energy is insensitive to milk fat content.
Table 3
Summary of allocation ratios and types used in this study; the base case methodology has been shaded in the table.
Co-products Economic
Mass allocation Other
Soy oil/soy meal 31:69 19.5:80.5
Soy oil/meal/hulls 56.7:41.2:2.1 19.4:74:6.6
Cottonseed/lint 19:81 38.5:61.5
Milk/beef 94.5:5.5 95:5 (Protein
88.6:11.4 (causal by feed nutrient content)
Distillers Grains (dry)/ethanol 30:70 52:48
Distillers Grains (wet)/ethanol 24:76 51:49
Corn/corn silage
Region 1 47:53 Causal relationship based
on crop nitrogen requirements
determined from reported yield
Region 2 88:12
Region 3 94:6
Region 4 92:8
Region 5 No data
Fluid milk/cream ee 80:20 (mass balance of milk solids)
Refrigeration ee
Milk:other refrigerated
retail (electricity &
1.62:98.38 (shelf space occupied)
Home (electricity) 1.62:98.38 (space allocation)
HDPE (recycled) ee System expansion using ecoinvent unit process
We have adopted economic allocation unless a particular stage allowed an allocation approach higher on the International Organization for Standardization (ISO)
This is not an allocation of burdens of co-products, but an allocation of fertilizer and fuel inputs, which are reported in aggregate, between the two crops. The inputs are
used as technosphere ows into separate unit processes for each crop. The large differences between regions are primarily determined by the relative production of each crop.
More silage is grown in Region 1 compared with corn grain than the other regions, and therefore the allocation of shared inputs is not nearly equal.
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14 S5
2.2.4. Retail and consumer refrigeration
GHG burdens associated with the retail stage are primarily
associated with electricity for compressors and loss of refrigerants
due to leakage. The allocation approach for these burdens, between
milk and other refrigerated goods, is based on space-occupied and
sales velocity metrics. Shelf space occupied, as a fraction of either
refrigerated or total store shelf space, is used to allocate annual
whole-store GHG emissions to the total sales of the item.
2.3. Data Sources
Data were collected from many sources including the USDAs
National Agricultural Statistical Service (NASS) and Economic
Research Service, peer-reviewed literature related to LCA of milk,
othertechnicalliterature,consultationwithexpertsin differentelds,
and an extensive nationwide survey of dairy farm operations. The
survey was distributed to over 5000 producers, of which there were
536 voluntary responses (Popp et al., 2012;Thoma et al., 2012b).
SimaPro 7.1 was used as the primarymodeling software and the
ecoinvent database was used to provide information on the
upstream burdens associated with production of materials
including primary fuels and refrigerants (Frischknecht & Rebitzer,
2005). Background, or upstream processes included from the
ecoinvent database, which is primarily of European origin, is
believed to have a relatively small inuence on the calculated
footprint because the unit processes in the ecoinvent database are
typically representative of modern technologically advanced
systems. In addition, a test was performed using a modied version
of the ecoinvent database in which all of the background electricity,
to the extent feasible has been replaced with the US primaryenergy
production mix. No signicant difference in results was observed.
Technosphere ows used in the model, including materials and
energy purchased from a supplier, were characterized with
a combination of the inherent variability found during the statis-
tical data aggregation and the pedigree matrix of data quality used
by the ecoinvent database (Weidema, 1998). Thus inventory ows
can be assigned a probability density function (PDF) that describes
the likelihood of a particular inventory ow occurrence. The
SimaPro software platform enables Monte Carlo Analysis for
calculation of propagation of inventory uncertainty to impact
uncertainty by choosing inventory ows from the PDF and aggre-
gating over multiple runs. Data from the surveys and other US-
specic information were incorporated into the model to the
extent available. We have adopted the IPCC (2006) carbon dioxide
equivalency factors of 25 for methane and 298 for nitrous oxide.
2.3.1. Dairy rations
In the assessment of GHG emissions associated with production
of the principal grains used in animal rations (Adom et al., 2012a;
Adom, Workman, Thoma, & Shonnard, 2012b), two main sources of
agricultural data were used: crop production data in terms of
annual yield, and agricultural chemical use statistics including
annual fertilizer and pesticide totals, both reported at the state level
from the USDA National Agricultural Statistics Service (USDA,
Few data have been collected at aggregate levels for cattle
forage. GHG emissions estimates from cattle forage production
were created from crop production budgets produced by state
agriculture extension specialists who provided estimates for the
inputs needed to produce alfalfa and grass hay, silage, and pasture.
After compiling inputs for specic crop production, these were
entered as inputs to a new unit process in the LCA software plat-
form. Detailed tables of regional rations by animal class are given as
supplementary materials by Thoma et al. (2012b).
2.3.2. On-farm emissions
On-farm methane measurements are not feasible as a general
method for estimation of enteric methane production. As discussed
by Thoma et al. (2012b), a comparison of models led to selection of
an enteric methane emissions model based on dry-matter intake
(Ellis et al., 2007). The American Society of Agriculture Engineers
Standard on manure characteristics (ASAE, 2005) was used to
estimate the quantity of manure generated. IPCC (2006) emission
factors for methane were applied to calculate the total manure
management methane emissions. Daily nitrogen excretion, based
on reported crude protein content of rations, ranged from
approximately 0.2 kg day
for open heifers up to 0.43 kg day
multiparous lactating cows. These estimates were combined with
Tier-2 emission factors for specic manure management technol-
ogies (Table 10.21, IPCC, 2006), including an accounting for direct
deposition on pasture, to estimate the total on-farm N
O emissions
associated with manure management.
2.3.3. Farm to processor transportation
Raw milk is delivered by unrefrigerated insulated tank trucks
from one or more farms to a processing plant (Ulrich et al., 2012).
Rail transport is used only for processed dairy products that are not
highly perishable and do not have to be delivered on a strict
schedule such as ice cream, yogurt and canned milk. The truck
makes a round trip, picking up milk at each farm along its route,
then delivering the load to the plant. Tank capacities may reach
34 m
(9000 gallons) with 22.7 m
(6000 gallons) being the most
common. This study considered only deliveries made by full
22.7 m
trucks delivering an average of 22 m
For this study a value of 2.4 km L
was used (5.7 miles per
gallon), which results in GHG emissions of 1.33 kg CO
(2.13 kg CO
e mile
). Because the deliveries included were
restricted to full loads with the same capacity, the emissions-per-
delivered was only a function of the round-trip distance. The
average round-trip length was 829 km.
Several large dairy co-operatives have provided an extensive
database that includes individual truck delivery from farms to uid
milk processing plants. The combined database has over 300,000
records from 2007 to 2008 and provides average transport
2.3.4. Milk processing, packaging, and distribution
Starting in February 2008, an extensive survey was sent to eight
milk processing companies. Surveys were returned for 50 indi-
vidual processing plants for their production during calendar year
2007 (Nutter et al., 2012). These 50 plants were responsible for
approximately 25% of the entire uid milk volume processed in
2007. Information requested in each survey included plant energy
consumption, truck eet fuel consumption, refrigerant purchases
for both the plant and truck eet, on-site milk packaging produc-
tion, packaged milk type and sizes, and annual production values
for total plant uid, uid milk, and packaged milk.
2.3.5. Retail
After distribution from the processor to the retail gate, uid milk
is displayed for consumer purchase. During this phase, there are
three distinct emissions streams: refrigerant leakage, refrigeration
electricity, and overhead electricity. For the purposes of this LCA,
milk sales were aggregated through three channels: supermarkets,
mass merchandisers (big boxstores) and convenience stores. One
of the major challenges associated with evaluation of the retail stage
is the allocation of whole-store energy consumption tomilk. Data on
the sales volume, space occupancy, and energy demands of milk
were analyzed to allocate the burden of this supply chain stage for
uid milk (EPA, 2008b,2009;ICF Consulting, 2005;USEIA, 2003).
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14S6
According to data from the Interaction Research Institutes database
(Mateen, Innovation Center for US Dairy, Chicago, IL, USA: personal
communication, 2009), 65% of uid milk sold in the US is sold
through the supermarket channel, 21% through mass merchandise
channel and 14%is distributed through drug and convenience stores.
Detailed information for the supermarket channel is provided
below; additional information for mass merchandizing and conve-
nience stores is reported by Thoma et al. (2010).
Supermarket channel. These stores sell a broad mix of food and
a limited mix of general merchandise. Reference information for
supermarkets used to allocate retail burdens is provided in Table 4.
They have a large land footprint and most use direct expansion
refrigerant systems (EPA, 2006). Common refrigerants are R-22, R-
404A, and R-507A; for mixtures of these refrigerants, the compo-
sition of the mixture was used to determine the appropriate global
warming potential (
htm). It was assumed that R22 and R404A were used at 54% and
46%, respectively (EPA, 2008b). These systems have a compressor
that is housed separately from the refrigeration units, and the
refrigerant is pumped in through a pipe network. This piping
system is the source of most leaks, due to catastrophic events (e.g.,
a broken pipe). Supermarkets typically have 1814 kg of refrigerant
in a system that leaks 18% annually (EPA, 2009). The USEPA
GreenChill Program reports information regarding the sources and
causes of refrigerant loss in supermarket systems (EPA, 2009).
Burdens were allocated on an occupied-space basis, using
consumer-facing linear shelf length as the space metric. Consumer-
facing shelf space information was obtained from a proprietary
database available through the project sponsor (Table 5). Milk was
allocated a share of refrigerated space and a share of total grocery
space to account for the refrigeration and overhead burdens,
respectively. Overhead electricity demands allocated to milk
include ventilation, lighting, cooling, space heating, water heating,
and plug loads. Table 6 presents the fractional consumption of
electricity and natural gas in grocery retail outlets (USEIA, 2003);
this information coupled with data published by the American
Society of Heating, Refrigerating and Air-Conditioning Engineers
were used to generate estimates of the overhead burden (ASHRAE,
Mass merchandisers. Mass merchandisers sell 21% of the milk in
the US (Mateen, personal communication 2009). This channel
typically sells a large mix of general merchandise as well as grocery
items. It is assumed that the grocery section within a mass
merchandiser is typical of an average supermarket with a smaller
land footprint 3900 m
for a mass merchandise grocery compared
to 4343 m
for a supermarket (Table 7). Burdens associated with the
grocery section of mass merchandising were allocated in a manner
consistent with the supermarket channel.
Convenience stores. Convenience stores sell 14% of the milk in the
US (Mateen, personal communication 2009). These stores sell a mix
of grocery and general merchandise items and have the smallest
land footprint of 434 m
. This study included all stores within the
channel, and did not distinguish between fuel-selling and non-fuel-
selling locations. Convenience stores differ from typical grocery
stores in that the refrigeration systems are fully-enclosed units,
indicating that the compressors for the refrigerants are housed
within the unit itself, eliminating the need for external piping and
virtually eliminating the risk of system leakage. Current regulations
require that refrigerants be captured to be recycled or destroyed at
the end of useful life of the refrigeration system. We have assumed
that any small leakage from this recovery process is below the cut-
off criterion for the study.
2.3.6. Consumer transportation, storage and waste
For the consumer contribution, we allocated a fraction of in-
home refrigeration (1.62% of refrigeration use) to milk storage.
We allocated fuel for consumer travel based on an estimate of the
sales percentage that milk represents in typical grocery purchases
Table 5
Proportion of refrigerated space dedicated to milk.
Parameter Length (m) Percent
attributed to milk
Milk refrigerated shelf length 21.3 100
Refrigerated shelf length, total 1311 1.62
All store shelf length, total 6934 0.31
Table 6
Electricity and natural gas allocation at retail.
End use all channels Electricity usage (%) Natural gas usage (%)
Refrigeration 43
Cooking 2 13
Allocated by shelf space 45 13
Ventilation 4
Lighting 13
Cooling 14
Plug Loads 17
Space heating 2 74
Water heating 1 13
Other 4
Allocated by sales (overhead) 55 87
Sources are EPA (2008b) and USEIA (2003).
Table 7
Average annual electrical energy consumption for retail channel outlets.
Sales channel 25th
Grocery and convenience 347 565 586
Convenience store 467 743 847
Convenience store w/gas station 406 667 850
Grocery store/food market 347 566 586
Units are kW h m
; data source is ASHRAE (2007).
Table 4
Reference data for supermarket retail outlets.
Parameter Amount Reference
Refrigerant load 1814 kg ICF Consulting (2005)
Annual leak rate 18% ICF Consulting (2005)
Linear refrigerated
milk space
21.3 m Mateen Personal Communication,
Innovation Center for US Dairy,
Chicago, IL (2009)
Linear refrigerated
total space
1311 m Mateen (2009)
Linear grocery
total space
6934 m Mateen, (2009)
kW h per square meter 556.5 kW h m
ASHRAE (2007)
Total area of store 4343 m
(in 2008)
FMI (2012)
Refrigeration demand
(% of total)
43% USEIA (2003)
Overhead demand
(% of total)
55% USEIA (2003)
Natural gas per
square meter
15.3 m
USEIA (2003)
Overhead demand
(natural gas)
87% USEIA (2003)
Total milk sales, average
US grocery
746551 US$ Mateen (2009)
Average price, liter
of milk
0.99 US$ USDA (2007)
R-22 faction in stores 54% ICF Consulting (2005)
R-404A fraction
in stores
46% ICF Consulting (2005)
Additionalinformationfor other sales channelsis presentedby Thomaet al. (2010).
Personal communication (Innovation Center for US Dairy, Chicago, IL, USA).
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14 S7
(0.307%), and an assumed transportation distance of 10.9 km
round-trip to the grocery store; 175 trips and 229.2 kg purchased
per household (Table 8). An assumption in this analysis is that
convenience stores, supermarkets, and mass merchandise outlets
all have the same transportation distances and associated milk
purchases. It is likely that customers of convenience stores will
typically purchase smaller quantities of milk and customers of
supermarkets on each trip. Thus it is possible that there is an
overestimate of the transportation burden for this sales channel;
however, it mayalso be the case that trips to the convenience store
are not dedicated to purchase of groceries, and that some fraction of
the entire trip should actually be allocated to other consumer
activities. For this reason we have simply taken the average values
as described above.
We modeled waste scenarios in SimaPro with unit processes
from ecoinvent to model consumer disposal of milk packaging
material. Industry data provided for conventional white milk shows
that 89% of milk is delivered in high density polyethylene (HDPE)
containers, 11% in paperboard, and 1% in polyethylene tere-
phthalate (PET) (Klein, personal communication 2009). Franklin
Associates (2008) reports that HDPE is recycled at a 29% rate,
while the paperboard gable top cartons are not currently being
recycled to any measurable degree. This report also indicates that
an estimated 14% of post-consumer waste is incinerated with
energy recovery. We have modeled the incineration of these
materials, however have not accounted for energy recovery, as it
will fall below the one percent cutoff.
2.3.7. Product loss
The Economic Research Service (ERS) of the USDA publishes
estimates of food loss for most commodity foods, including dairy
(USDA, 2010a). These estimates are the best available, and report
12% loss of uid milk from retail to consumer, and an additional 20%
loss due to cooking loss, spoilage and waste at the consumer. We
have accounted for disposal as a volumetric ow equal to the
volume of waste milk loss to municipal sewage treatment. For the
milk loss at retail, our assumption is that it is further processed into
dry milk solids that are used as a feed supplement for cattle or other
animals. We have assumed that it displaces dried whey as an
animal feed. Because milk is approximately 13% solids, the waste
ow from retail into the return channel is taken as 13% of the out-
of-date milk weight.
3. Results and discussion
3.1. Supply chain analysis
3.1.1. Cradle-to-farm gate
Thoma et al. (2012b) present a detailed analysis of the cradle-to-
farm gate GHG emissions. The GHG emissions were reported as
1.23 kg CO
FPCM. Due to the scaling required by the product
loss in the system, 1.42 kg FPCM is required at the farm gate to
provide one kg consumed. Thoma et al. (2012b) present a summary
of the farm-gate results by production region: Region 1: Northeast;
Region 2: Southeast; Region 3: Upper Midwest; Region 4: South-
west plus High Plains; Region 5: West Coast (Popp et al., 2012).
3.1.2. Farm to processor transportation
Emissions from this phase of the value chain are dominated by
tailpipe emissions from the trucks. Loss of refrigerants is negligible
since the trucks are not refrigerated according to Ulrich et al.
(2012). The average round-trip length was 829 km giving average
emission levels of 0.049 kg CO
milk delivered to the
processor gate (Ulrich et al., 2012). Due to milk loss in the supply
chain, the transportation burden is 0.0696 kg CO
3.1.3. Processing, packaging and distribution
GHG emissions associated with processing, packaging, and
distribution are provided in Table 9. All major emission sources,
from raw milk entering the refrigerated storage silo, through
delivery of packaged uid milk to the retailer, are accounted. The
gate-to-gate cumulative GHG emission is 0.203 (0.017) kg
of packaged uid milk. This is reported as the mean with
95% condence interval and represents the inherent variability in
this supply chain stage. The contribution of the processing supply
stage is 0.288 kg CO
milk consumed.
3.1.4. Retail channels
The retail stage of the supply chain contributes 0.099 kg
milk refrigerated, or 0.141 kg CO
milk consumed
assuming that all milk (including out of date returns) at retail was
refrigerated. As shown in Table 10, the burden from electricity,
refrigerants, and natural gas contribute 64.4%, 35.5%, and 0.15%,
3.1.5. Consumer transportation and storage
As described previously, in-home allocation fractions are based
on the assumption that the generic American food pantry is roughly
equivalent to grocery store stock, i.e., there is a nearly steady state
ow of food through retail outlets and the American consumers
home. This will slightly overestimate the burden as there are items
that must be refrigerated once opened that are not refrigerated at
the retail outlet. We used a consumer transportation distance of
10.9 km round-trip to the grocery store for each shopping as shown
in Table 8 (FHA, 2009).
Table 8
Energy consumption for in-home refrigeration and consumer transport.
Energy consumption area Amount
In-home refrigeration
Annual refrigeration energy (USEIA, 2005) 1345 kW h y
Per capita milk consumption (USDA, 2010a) 76.4 kg y
(calculations assume
3 persons per household, thus
229.2 kg y
Burden allocated to milk (1.62%) 0.097 kW h kg
Consumer transport
Travel for shopping from home
(FHA, 2009)
10.9 km trip
; 175 trips per year
Attributed to dairy 0.307% retail shelf space allocation
Table 9
Summary of greenhouse gas (GHG) emissions from milk processing.
Unit process Gate-to-gate GHG emissions
(kg CO
packaged milk)
Purchased electricity 0.054 (0.0090)
Onsite fuel combustion 0.022 (0.0044)
Refrigerant loss 0.001 (0.0014)
Total 0.077 (0.0109)
Raw material 0.034 (0.0034)
Container formation 0.020 (0.0012)
Total 0.054 (0.0044)
Mobile fuel combustion 0.058 (0.0091)
Refrigerant loss 0.014 (0.0037)
Total 0.072 (0.0102)
Overall 0.203 (0.0174)
Numbers in parentheses indicate 95% condence interval of mean,
but do not account for propagation of input uncertainty.
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14S8
Fig. 1 presents the summary of the retail and consumer use
phases. The bar in the natural gas column for consumers represents
the gasoline consumption associated with travel to the store for
groceries; natural gas consumption at retail is too small to appear.
The use phase also includes the impact of disposal of wasted milk to
a municipal wastewater treatment facility;the ow was taken to be
the estimated volume of spoiled milk disposed. Fig. 1 accounts for
product loss in the supply chain, whereas Table 10 only accounts for
gate-to-gate retail contributions.
3.1.6. Post-consumer waste management
End-of-life analysis of the packaging was based on the distri-
bution of primary packaging materials used for milk delivery in the
US and is summarized in Fig. 2. Of total HDPE packaging, 29% was
recycled and an avoided product credit was accounted. No recycling
credit for PET or paperboard was claimed; all non-recycled waste
packaging was considered to be disposed in a landll or incinerated
(86% of all non-recycled waste was assumed to be disposed in
a landll and the remaining 14% incinerated). We used standard
ecoinvent unit processes for waste management, including incin-
eration of post-consumer waste. Incineration unit processes
contribute less than 0.4% of GWP when summed across the entire
supply chain. Spoiled or wasted milk at the consumption phase was
assumed to be disposed into a municipal wastewater treatment
facility modeled with an ecoinvent unit process.
3.1.7. Supply chain loss/waste
In this project, we have explicitly accounted for estimated
product loss through the supply chain. Based on USDA food loss
reports, we accounted for a 12% loss (return) at retail and an
additional 20% loss from cooking, spoilage and waste at the
consumer stage. Fig. 3 shows the effect of different waste rates on
the footprint of consumed milk. The best-case scenario (3% retail
loss and 5% consumer loss) results in a 23% decrease in GHG
emissions compared with the base case. This appears to present
a signicant reduction opportunity; however, interpreting the
consequences of changes in consumer behavior is difcult. For
example, if consumer waste is reduced because children stop
leaving half-full cereal bowls, there will be essentially no change in
the overall system emissions, with the exception of a small
reduction in municipal wastewater treatment emissions, because
there will be no change in the quantity of milk moving through the
Fig. 1. Greenhouse gas emissions from retail and consumer use phases. The turquoise
bar under natural gas is transport by consumers from retail to their home.
Fig. 2. Packaging disposal greenhouse gas emissions for national mix of primary
packaging. Reforming refers to electricity necessary for the recycling process of high
density polyethylene containers.
Fig. 3. Product loss is modeled at the retail and consumption stages. This gure
presents a sensitivity analysis of the carbon footprint as it is inuenced by the degree
of loss/waste.
Table 10
GHG burdens at retail stage of milk value chain.
Component kgCO
milk Total MT CO
e annual
Refrigerants 0.0423 472,571
Electricity 0.0529 591,252
Natural Gas 0.0001 1515
Total 0.0953 1,065,338
Mass merchandizer
Refrigerants 0.0273 96,360
Electricity 0.0342 120,560
Natural Gas 0.0001 309
Total 0.0616 217,229
Refrigerants N/A
Convenience store
Electricity 0.1311 320,538
Natural Gas 0.0002 587
Total 0.1313 321,126
All channels
Refrigerants 568,931 (35.48%)
Electricity 1,032,351 (64.37%)
Natural Gas 2411 (0.15%)
Total 1,603,693 (100%)
Values are per kg milk sold (not per kg milk consumed eretail losses are
accounted, but not consumer losses).
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14 S9
system. However, if parents were to put less milk on the cereal,
their milk purchase would last longer, and their consumption of
milk would remain constant, but they would purchase less to meet
that consumption demand. Thus, while the effect of consumer
waste makes a large difference in the footprint of the consumed
milk, the effects of change in this behavior on the cumulative GHG
emissions are difcult to predict.
3.2. National scale impact of uid milk consumption
Fig. 4 shows the ow of GHG emissions associated with the
production of uid milk in the United States. The contribution of
each stage in the supply chain is denoted both numerically and by
the width of each arrow. Each regions contribution was deter-
mined from the USDA reported milk production (corrected to kg
FPCM) and regional GHG emission intensity (kg CO
milk) to
create a national scale contribution due to production of uid milk.
The arrows leaving the top of the chart represent allocation of
cumulative up-stream burden to co-products that is removed from
the uid milk supply chain at that point. The calculations also
account for the different quantities of milk (whole, skim, 1%, and
2%) consumed in the United States and for product loss across the
supply chain. In 2007, approximately 17.4 Tg of uid milk of all
types were consumed (USDA, 2007). This resulted in an estimated
35.4 Tg CO
e in GHG emissions. The breakdown of these emissions
by supply chain stage and source is presented in Fig. 5. Enteric
methane contributes approximately 8.8 Tg CO
e and manure
management 8 Tg CO
e (6.2% of all ruminant enteric methane re-
ported as 140.8 Tg in 2005, and 13.5% of GHG emissions from
manure management, reported as the 62.1 Tg in 2005) (EPA,
2008a). These values are for uid milk only, and, of course,
exclude the allocation to co-products beef and excess cream.
The cumulative GHG emission is 2.05 kg CO
e per kg milk
consumed (17.6 pounds CO
e per gallon of milk consumed). Fig. 6
presents the breakdown of GHG emissions across the supply
chain. There is, of course, natural variability in production and
Fig. 4. Greenhouse gas ow through the milk supply chain. All values (including beef and cream allocation) are Tg CO
e for all uid milk consumed in 2007. This diagram includes
an accounting for both the regional production of milk destined for consumption as a beverage and the national mix of whole, skim, 1%, and 2% milk for 2007.
Fig. 5. Greenhouse gas emissions by supply chain stage. Values are reported in Tg, and values smaller than 0.04 Tg are not reported to maintain readability of the chart.
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14S10
supply chain activities as well as uncertainty in reported values
for many parameters necessary for computation of the footprint.
To account for this, a Monte Carlo uncertainty analysis was per-
formed, as described in Section 2.3, with the result that the 90%
condence band varies from 1.77 to 2.4 kg CO
consumed. Of total burden associated with consumption of uid
milk, 72% is accrued by the dairy farm gate. This highlights the
signicant opportunity for the industry in on-farm improvements,
specically in terms of manure management and controlling
enteric methane emissions. These emissions sources as well as
the incoming burden of the feed are signicantly inuenced by
the on-farm feed conversion efciency. Improving conversion
efciency reduces GHG emissions from all three sources. Manure
management offers additional potential reductions through
adoption of management technologies that reduce methane
emissions, such as digesters or wider adoption of daily spread and
other low emission approaches. Even though the majority of
emissions occur during production, it should be noted that some
extraemissions are induced by waste in the downstream supply
chain due to product loss or waste.
3.3. Dairy sector contribution to the US greenhouse gas inventory
In an effort to place the entire dairy sector into context of overall
emissions of GHG in the United States, we have combined estimates
from several sources (EPA, 2011;Thoma et al., 2012b;USCB, 2011)
to create an estimate of the relative contribution of the dairy sector
to the overall US greenhouse gas inventory in 2008. To maintain
comparability from year-to-year reports of GHG emissions, the EPA
reports the GHG equivalents based on an earlier version of the IPCC
global warming potentials (IPCC, 1995). This analysis was based on
the global warming potentials published (for the IPCC) by Forster
et al. (2007).Table 11 presents a comparison of the global warm-
ing potentials from the two IPCC reports.
Table 12 presents the GHG inventory for the United States for
the years 2007 through 2009 based upon the two IPCC reports.
There is an approximately 1.6% increase in the estimate of GHG
emissions based on the revised global warming potentials. Note
that the values reported in Table 12 do not include sequestration-
associated forestry because the current study does not include
sequestration associated with the crop production for the dairy
Fig. 6. Supply chain contribution to carbon footprint of genericmilk. Generic milk refers to regional-production-weighted (raw milk input) and purchase-volume-weighted (milk
fat content) average milk consumed in the US during 2007. Note that fuels, fertilizer, and milling are included in the feed stage. In addition, there is a 12% product loss accounted in
retail, and an additional 20% wasted product in the consumption phase.
Table 11
Comparison of second and fourth IPCC global warming potential equivalency factors.
Gas SAR (IPCC, 1995) AR4 (Forster
et al., 2007)
21 25
O 310 298
HFC-125 2800 3500
HFC-134a 1300 1430
HFC-143a 3800 4470
HFC-152a 140 124
SF6 23,900 22,800
Table 12
Comparison of US greenhouse gas emission inventory for the second (SAR; IPCC,
1995) and fourth (AR4; Forster et al., 2007) Intergovernmental Panel on Climate
Change (IPCC) reports; sequestration of carbon dioxide is not included.
Gas SAR (Tg CO
e) AR4 (Tg CO
2007 2008 2007 2008
6120 5921 6120 5921
665 677 791 806
O 325 311 313 299
HFCs 130 129 127 131
17 16 16 15
Total 7263 7061 7366 7172
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14 S11
ration, and therefore is a more directly comparable baseline for
estimation of the dairy sector contribution.
3.3.1. Estimation of overall dairy sector GHG emissions
The USDA reported 82.67 Tg of milk produced in 2008 with an
average milk fat content of 3.68%. As reported by Thoma et al.
(2012b) and Ulrich et al. (2012) the GHG emissions up to the
processor receiving silos is approximately 1.28 kg of CO
FPCM. This assumes roughly equivalent transportation distances for
both uid milk and non-uid milk uses, which is probably a slight
overestimate for milk destined for cheese production facilities,
resulting in 105.8 Tg CO
e emissions. Based on information from
the US Census Bureau, shown in Table 13, it is possible to estimate
the GHG emissions from the manufacturing stages for non-uid
milk dairy products. Combining these estimates with the retail
and consumption information reported elsewhere in this paper,
and using the assumption that retail and consumption for non-uid
milk dairy products is in the same proportion to manufacturing as
for uid milk (approximately 70% as shown in Fig. 4), the total dairy
sector GHG emissions is estimated to be 133.6 Tg, or 1.9% of the US
total for 2008 from Table 12 (7172 Tg, AR4). The breakdown by
supply chain stage is presented as Fig. 7.
4. Conclusions
This work establishes a sound and defensible baseline for GHG
emissions associated with production and consumption of uid
milk in the United States during 2007e2008. In the future, as the
industry moves to meet its 2020 goals of 25% reduction in GHG
emissions, progress can be assessed against this baseline
emission level. An important caveat is that this LCA exclusively
focuses on carbon emissions, and does not account for other
environmental impacts, such as water or air quality. Decisions
made solely on the basis of GHG emissions do not account for
potential trade-offs with other environmental impacts, and
caution should be exercised in making decisions based on a single
metric. Future comparison against the baseline developed this
study will be facilitated by the availability of the life cycle inventory
model in public US databases. The data are currently being
prepared for submission in the near future.
The results of this study show that, on average, 2.05 kg CO
emitted per kg milk consumed, but a more accurate interpretation
is that based upon knowledge uncertainty and characteristic vari-
ability, we can be 90% condent that the GHG footprint of milk lies
between 1.77 and 2.4 kg CO
milk consumed. These error
bounds are a combination of both knowledge uncertainty and
characteristic variability.
Other LCA studies for dairy production have been performed
over the past decade and report similar GHG emissions at the farm
gate (Basset-Mens et al., 2009;Capper et al., 2009;Cederberg &
Flysjö, 2004;Cederberg & Mattsson, 2000;Cederberg et al., 2009;
Eide, 2002;Gerber et al., 2010;Guinard et al., 2009;Haas et al.,
2001;Thomassen et al., 2009). A recent UN Food and Agricultural
Organization report states that the US average farm-gate GHG
emission is 1.0 kg CO
FPCM (Gerber et al., 2010), which is
approximately 20% lower than that found in this study: 1.23 kg
FPCM (90% CI: 1.1e1.45 kg CO
The LCA results give insight into the innovationopportunities of
the dairy supply chain; however, these opportunities should be
carefully evaluated and explored. Innovations and changes in supply
chain activities may yield unintended results, or may simply shift
burdens in the system without causing any real reduction in envi-
ronmental impact. A major conclusion of this study is that there is
opportunity to reduce impacts throughout the entire supply chain,
across feed and milk production, processing, distribution and
consumption. The on-farm and processing GHG emissions showed
signicant variability (Nutter et al., 2012;Thoma et al., 2012b).
Identication and recognition of this variability suggests that
opportunities exist for improvement of those lower performers.
There is signicant value in understanding the sources and causes
of variability. Uncertainty in the system indicates opportunities for
improving modeling techniques and further exploration of the
methodology used for those calculations.
This work has pointed to manure management, feedproduction,
and enteric methane as three areas for innovation research (Thoma
et al., 2012b). Nutrient management strategies on the dairy farm
that link inorganic fertilizer use with application of manure for crop
production should be integral to any GHG reduction approach.
Anaerobic lagoons on larger farms and deep bedding on smaller
farms are manure management systems for which the GHG emis-
sions are signicantly greater than other systems, such as dry lot
and solid storage. On the surface, this seems to indicate that a shift
in practices could result in emission reductions; however, both the
economic cost and potential environmental burden shifting that
could result from changing to a different system must be consid-
ered. Methane digesters have great potential as a way to capture
and potentially utilize methane that is otherwise lost to the
atmosphere, and should be considered a high priority for these
larger systems; however, the possibility of additional land
requirements for managing nutrients retained in the digester solids
must be evaluated. Because this study only considers GHG emis-
sions, there is need for further study to understand the full range of
environmental impacts before making decisions.
Table 13
Energy consumption by dairy manufacturing industry sector.
Sector description Electricity
(MW h)
e Purchased
fuels (US$1000)
31151N Fluid milk and butter 3,905,802 3.23 $236,516 0.198
311513 Cheese 3,765,102 3.11 $284,802 2.06
311514 Dry, condensed, and
evaporated product
1,524,350 1.26 $187,245 1.36
31152 Ice cream and frozen
1,317,111 1.09 $27,397 1.71
NAICS, North American Industry Classication System.
Based on 2008 national average cost of natural gas of US$9.65 MCF
Fig. 7. Estimated contribution to the US greenhouse gas emissions inventory in 2008.
Sequestration of CO
has not been accounted for in this analysis.
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14S12
The analysis of uid milk processing plant GHG emissions
suggests some opportunities to reduce individual emissions.
Therefore, a focus on truck eet fuel usage and plant electricity
consumption is prudent since these two components are the
greatest GHG contributors.
Implementation of standard energyefciency practices should be
considered for the refrigeration system, compressed air system,
motors,and lighting. Similarly, plant fuel reductions could be realized
through improved steam system efciency and operating practices.
Emission savings for packaging could come from improved bottle
designs resulting in reduced material use and upgrades to modern,
energy-efcient formation equipment. As an example, changing the
bottle cap manufacturing process from injection-molding to ther-
moforming may lower environmental burdens, as has been recom-
mended for theyogurt cup manufacturing process (Keoleian, Phipps,
Dritz, & Brachfeld, 2004). Finally, careful study of plant specicopti-
mization of the transport distances (i.e., truck miles) and the future
selection of transport refrigeration systems using low GWP refriger-
ants may reduce emissions for the uid milk industry.
This work was funded by the Innovation Center for US Dairy. The
quality and thoroughness of the ISO review panel was invaluable to
the project team. The review panel consisted of Olivier Jolliet from
the University of Michigan, Robert Anex from the University of
Wisconsin, and Pascal Lesage from Interuniversity Research Center
for the Life cycle of Products, Processes and Services (CIRAIG), École
Polytechnique de Montréal.
The Innovation Center played an instrumental role in collection
of the on-farm data, as well as the information gathered from the
processing facilities and dairy cooperatives. Without the strong
industry commitment to collect high quality data across the supply
chain, this study would not have been possible. The Innovation
Center also assisted with testing of the survey instrument for the
on-farm data collection and provided valuable editing to the survey
questions, enabling collection of relevant data. Finally, the Inno-
vation Center has worked with the publisher in the establishment
of this special issue.
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... However, the dairy industry also has an impact on the environment through production of greenhouse gases (GHG) and reactive nitrogen, while utilizing natural resources such as land, water, and fossil fuel. According to Thoma et al. (2013), the US dairy sector produced about 2% of the total anthropogenic GHG in the country in 2007. As in many sectors of the economy, the dairy industry has made commitments to reduce its environmental impact and improve land and water stewardship of dairy production systems. ...
... The allocation factor for milk was calculated as [1 − 6.04 × (kg of live weight sold/kg of milk produced)]. The system boundary was cradle-to-farmgate because most GHG emissions (>70%) for milk consumption originate from on-farm sources (Thoma et al., 2013), and post-farmgate process are not affected by feed additive inclusion. The system boundary and processes included in this study are shown in Figure 1. ...
... Chemical compositions of regional lactating cow diets (Table 3) were calculated using NRC (2001) from the dietary feed ingredient compositions collected from the specific region. Chemical compositions of dry cow and heifer diets (Table 4) were also calculated using NRC (2001), using typical BW, DMI, and dietary ingredient compositions for heifers and dry cows (Thoma et al., 2013;Naranjo et al., 2020). These dietary ingredient compo-sitions were used to calculate total requirement for each feed ingredient for each model, which were later used for feed production-related GHG calculations. ...
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It is estimated that enteric methane (CH 4) contributes about 70% of all livestock greenhouse gas (GHG) emissions. Several studies indicated that feed additives such as 3-nitrooxypropanol (3-NOP) and nitrate have great potential to reduce enteric emissions. The objective of this study was to determine the net effects of 3-NOP and nitrate on farmgate milk carbon footprint across various regions of the United States and to determine the variability of carbon footprint. A cradle-to-farmgate life cycle assessment was performed to determine regional and national carbon footprint to produce 1 kg of fat-and protein-corrected milk (FPCM). Records from 1,355 farms across 37 states included information on herd structure, milk production and composition, cattle diets, manure management , and farm energy. Enteric CH 4 , manure CH 4 , and nitrous oxide were calculated with either the widely used Intergovernmental Panel on Climate Change Tier 2 or region-specific equations available in the literature. Emissions were allocated between milk and meat using a biophysical allocation method. Impacts of nitrate and 3-NOP on baseline regional and national carbon footprint were accounted for using equations adjusted for dry matter intake and neutral detergent fiber. Uncertainty analysis of carbon footprint was performed using Monte Carlo simulations to capture variability due to inputs data. Overall, the milk carbon footprint for the baseline, nitrate, and 3-NOP scenarios were 1.14, 1.09 (4.8% reduction), and 1.01 (12% reduction) kg of CO 2-equivalents (CO 2-eq)/kg of FPCM across US regions. The greatest carbon footprint for the baseline scenario was in the Southeast (1.26 kg of CO 2-eq/kg of FPCM) and lowest for the West region (1.02 kg of CO 2-eq/kg of FPCM). Enteric CH 4 reductions were 12.4 and 31.0% for the nitrate and 3-NOP scenarios, respectively. The uncertainty analysis showed that carbon footprint values ranged widely (0.88-1.52 and 0.56-1.84 kg of CO 2-eq/kg of FPCM within 1 and 2 standard deviations, respectively), suggesting the importance of site-specific estimates of carbon footprint. Considering that 101 billion kilograms of milk was produced by the US dairy industry in 2020, the potential net reductions of GHG from the baseline 117 billion kilograms of CO 2-eq were 5.6 and 13.9 billion kilograms of CO 2-eq for the nitrate and 3-NOP scenarios, respectively.
... [5] Information adapted from the LCA study of (Thoma et al., 2013). ...
... [8] Information adapted from the LCA study of (Thoma et al., 2013). and 52% respectively (Fig. 3a). ...
... In the consumption stage, energy utilisation is mainly required for transportation from the retail outlets to the households and refrigeration (Fig. 3d). The average electricity demand per kg of refrigerated milk is estimated to be 0.35 MJ/kg (Thoma et al., 2013). Regarding the travel distance for grocery shopping, the average route was estimated to be equal to 10.9 km per trip with 175 trips taking place annually per 3-person household in the US (Thoma et al., 2013). ...
Background The dairy industry requires substantial energy resources at all stages of production and supply to meet consumer needs in terms of quantity, quality and food safety. The expected future climate change effects will cause serious uncertainty to the dairy industry. Adapting to these upcoming conditions is a challenge and one that is compounded by the continuous increase in food demand, as a result of global population growth. Predictably, under current conditions, this situation might lead to a significant increase in the energy requirements of the dairy industry. Therefore, there is a clear need to mitigate energy use through enhanced energy conservation, waste reduction and waste management. Scope and approach This review paper presents and discusses alternative dairy operations and mitigation strategies that have the potential to lead the dairy industry towards net-zero carbon emissions. Further, the focus of this work turns to supply chain energy modelling (SCEM) as means to mitigate energy use, while relevant work in the literature is reviewed. Key findings and conclusions Supply chain energy models can provide a complete overview of the energy demand and the energy mix of a dairy supply chain. Additionally, they can highlight the most energy consuming processes and allow the evaluation of alternative energy-saving operations that can lead towards the net-zero carbon target. Overall, the development or use of computational tools for simulating the energy demand in the industry has strong potential for improving sustainability across the dairy supply chain.
... These positives have allowed the US to shrink livestock and pork herds to historically low levels. For example, the US has gone from 25 million dairy cows in 1950 to 9 million today with a concomitant 60% increase in milk production and 33% decrease in the carbon footprint (48). The same is true for beef, poultry, and all other ASF production where productivity improvements have drastically shrunk environmental impacts (49). ...
... High quality data are needed for such analyses. For example, a dairy sector study uncovered a wide variation in environmental impact, revealing not just one milk carbon footprint, but hundreds (48). Categorizing various factors showed that feed, manure, and enteric CH 4 were the biggest contributors. ...
... Sin embargo, del mismo modo que la agricultura industrial animal en general, la producción láctea en particular está ligada a diferentes problemas globales, relacionados, entre otros, con el medio ambiente (Van der Werf et al., 2009;Leip et al., 2010;Thoma, G. Et al., 2013;Rotz, C.A., 2018), con la ética animal (Desaulniers, É., 2015;Eisen, J., 2017;Gillespie, K., 2018;Kolbe, K., 2018;Wicks, D., 2018) y el bienestar animal (Young, R. 2003;EFSA, 2009;Eurogroup for Animals & Compassion in World Farming, 2015;DG Health and Food Safety, 2017;Broom, 2017). ...
... La explotación de las vacas no solo tiene consecuencias nefastas para las vidas de estas y las de sus crías, sino que también afecta al medio ambiente del planeta en diversos modos (van der Werf et al., 2009;Leip et al., 2010;Thoma, G. Et al., 2013;Rotz, C.A., 2018), El rol de la industria láctea es particularmente importante en términos de emisiones de gases de efecto invernadero, aunque si se compara la leche procedente de las ubres de las vacas con sus sustitutos vegetales más populares, la conclusión es que la leche de origen animal tiene un impacto mayor tanto en emisiones de gases de efecto invernadero como en el uso de la tierra y el uso del agua (Poore, J. y Nemecek, T., 2019), y los estudios científicos más recientes apuntan a que hay suficiente evidencia como para alentar a un cambio dietario, de productos lácteos animales a alternativas vegetales, solo basándose en razones ambientales (Carlsson Kanyama et al., 2021). en la lista mundial de empresas con mayor facturación del sector. ...
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En el presente artículo se presentan los principales resultados de la investigación doctoral de la autora (Ruiz Carreras, 2021), cuyo objetivo general es estudiar el discurso de los grupos de presión de la industria láctea europea (ILE) desde una perspectiva no especista. Para ello se examina a la ILE como actor económico y de influencia, identificando a las principales empresas y grupos de presión, y se analiza el discurso que construyen con respecto a las vacas y a las recomendaciones nutricionales que acaban en las guías alimentarias. El análisis demuestra que la ILE adapta su narrativa a las preocupaciones actuales relacionadas con la ciencia, salud, medio ambiente y bienestar animal, al mismo tiempo que los contradicen. Destaca la negación de los intereses de los animales explotados por sus segregaciones maternas a través de una representación que les cosifica y obvia su capacidad de sintiencia, autonomía e individualidad.
... Es evidente que los valores generados acá por kg de proteína láctea son intermedios al rango de 12-140 kg CO 2 -eq sugerido por Havlík et al. (2014) y Herrero et al. (2013, superiores a los 61 kg CO 2 -eq reportados en Estados Unidos (Thoma et al. 2013) y a los 66,6 kg CO 2 -eq, en Costa Rica (Vega 2016). No obstante, los valores reportados por la investigación abarcan el promedio de 81 kg CO 2 -eq reportado en Nicaragua por Gaitán et al. (2016). ...
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The study aimed to characterize production dynamics and greenhouse gas (GHG) emissions from 61 dairy farms in five regions in Honduras. Farm data were collected through individual surveys during the initial and final phases (IP; FP). Using Microsoft Excel®, data was incorporated into the global livestock environmental assessment model-interactive (GLEAM-i, FAO) life cycle framework to estimate annual emissions of methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) at the farm system level. Animal emissions (GHG/animal) were derived in Excel® from the GLEAM-i predictions. Together, farms during the IP emitted 25.038 t CO2 equivalent (CO2-eq) while these emissions were 10,5% lower in the FP. Emissions of GHG/animal (2,85 ± 0,08 t CO2-eq) and GHG/kg of milk protein (96,91 ± 4,50 kg CO2-eq) during the IP were 13% and 21% lower in the FP, respectively. Methane and N2O emission values (52,82 ± 1,64 vs 2,66 ± 0,10 kg/animal) were 13% and 17% higher in the IP than in FP. The South-Central region emitted the lowest amount of CH4 and N2O (42,95 ± 2,37 kg/animal vs 1,82 ± 0,15 kg/animal) while 27% lower GHG/kg milk protein was observed between the IP and FP of the Western and Northern regions. It was concluded that the used methodology identified productive and environmental impacts derived from implemented technical interventions in dairy production systems in Honduras.
... We set for the SCOR KPI, a range of [0-100]% as explained by (Petersen et al., 2016). The CO2 balance varies according to policies of countries where nodes are located as well as OEM environmental strategies but range between 30-45 Teragram (Tg) (Thoma et al., 2013). Since the dairy products are easily perishable, dairy SCs are not dispersed. ...
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Supply Chain (SC) modeling is essential to understand and influence SC behavior, especially for increasingly globalized and complex SCs. Existing models address various SC notions, e.g., processes, tiers and production, in an isolated manner limiting enriched analysis granted by integrated information systems. Moreover, the scarcity of real-world data prevents the benchmarking of the overall SC performance in different circumstances, especially wrt. resilience during disruption. We present SENS, an ontology-based Knowlegde-Graph (KG) equipped with SPARQL implementations of KPIs to incorporate an end-to-end perspective of the SC including standardized SCOR processes and metrics. Further, we propose SENS-GEN, a highly configurable data generator that leverages SENS to create synthetic semantic SC data under multiple scenario configurations for comprehensive analysis and benchmarking applications. The evaluation shows that the significantly improved simulation and analysis capabilities, enabled by SENS, facilitate grasping, controlling and ultimately enhancing SC behavior and increasing resilience in disruptive scenarios.
... Vários estudos vêm sendo conduzidos no exterior com foco na questão da sustentabilidade da atividade leiteira com o objetivo de auxiliar na construção de um sistema de produção com menor impacto ambiental (THOMA et al., 2013;RODHE et al., 2015;WATTIAUX et al., 2019). No entanto, no âmbito nacional esse tema ainda carece da geração de informações para adequação ambiental e consequentemente sustentabilidade da atividade (MARTINS et al., 2015). ...
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RESUMO: A atividade leiteira é considerada um dos principais segmentos do agronegócio brasileiro, sob o ponto de vista econômico e social. O estudo tem por objetivo contribuir na compreensão dos parâmetros ambientais envolvidos na formação do perfil sustentável de propriedades leiteiras. Os menores índices ambientais foram devido ao manejo inadequado de dejetos sólidos e líquidos, bem como do solo em área de preservação permanente e evidências de erosão. Como medidas mitigadoras do impacto ambiental, sugere-se o planejamento de demanda dos recursos financeiros para a adequação e construção de estrumeiras cobertas para tratamento correto dos dejetos antes da aplicação ao solo, tratamento da água de consumo, recomposição gradual das áreas de preservação permanente e controle de erosão. Por fim, a destinação correta e o tratamento dos dejetos oriundos da bovinocultura de leite devem ser estratégias empregadas, concomitantemente com o armazenamento das embalagens de todos os produtos utilizados na propriedade. Nesse sentido, o planejamento da propriedade torna-se de suma importância para o sucesso e o incremento da sustentabilidade na atividade leiteira. As adequações em torno do planejamento da atividade leiteira e seus resíduos devem ser priorizados, a fim de aumentar os índices ambientais, e por consequência tornar essa atividade agrícola mais sustentável. Palavras-chave: Parâmetros sustentáveis. Produção de leite. Sustentabilidade ambiental. ABSTRACT: Dairy activity is one of the main sectors in Brazilian agribusiness from the economic and social point of view. Current study intends to contribute towards an analysis of environmental parameters in the formation of sustainable profiles of dairy farms. Lowest environmental rates were due to inadequate management of solid and liquid wastes, soil in the permanent preservation area and evidence of erosion. The planning of financial resources and the construction of covered dung deposits are mitigating measures on environmental impacts for the correct treatment of waste prior to its application in the soil, treatment of water for consumption and gradual re-composition of permanent preservation areas and control of erosion. Correct final deposit and the treatment of wastes from dairies should be strategies concomitantly employed with storage of packages of all products used on the farm. Farm planning is highly important for the success and the growth of sustainability in dairy activities. Adequacies with regard to planning of dairy activities and their wastes should be given priority to increase environmental indexes and make the agricultural activity more sustainable.
... LCA studies are very data demanding, requiring a significant amount of financial resources and labour to be completed (Thoma et al., 2013). This feature leads researchers to take two different paths to compare the environmental impact of dairy systems. ...
Dairy production is a vital part of the Brazilian agri-food system, providing food security, employment, and income in rural areas. Nevertheless, rearing dairy cattle leads to greenhouse gases (GHG) emissions which may contribute to global warming and consequently climate change. The remarkable heterogeneity among dairy farms as well as the lack of representative research pose constraints to the development of effective actions to reduce GHG emissions in emerging countries. In this study, we explore a large farm survey to group farms and derive their carbon footprint (CF). Cluster analysis and life cycle assessment are applied to a sample of 911 farms. The results of the analysis categorized the farms into four groups. Statistical comparisons indicated a significant difference in the CF between groups for producing one kg of fat and protein corrected milk (FPCM). The mean CF results ranged from 1.75 kg CO2eq. (kg FPCM)⁻¹ in Group 1 (G1) to 3.27 kg CO2eq. (kg FPCM)⁻¹ in Group 4 (G4). While G1 was composed of larger farms, on average having more access to technologies and technical support, G4 was composed of less specialized producers, owning dual-purpose herds. We also identified and discussed key strategies and management practices that can be adopted by farmers for reducing the CF of dairy farming. Research and policy should strive to accelerate farmers’ adoption of intensification technologies and practices, though following sustainable intensification practices that also account for regional socioeconomic development.
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Significance Agricultural methane emissions must be decreased by 11 to 30% of the 2010 level by 2030 and by 24 to 47% by 2050 to meet the 1.5 °C target. We identified three strategies to decrease product-based methane emissions while increasing animal productivity and five strategies to decrease absolute methane emissions without reducing animal productivity. Globally, 100% adoption of the most effective product-based and absolute methane emission mitigation strategy can meet the 1.5 °C target by 2030 but not 2050, because mitigation effects are offset by projected increases in methane. On a regional level, Europe but not Africa may be able to meet their contribution to the 1.5 °C target, highlighting the different challenges faced by high- and middle- and low-income countries.
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The usefulness of food packaging is often questioned in the public debate about (ecological) sustainability. While worldwide packaging-related CO2 emissions are accountable for approximately 5% of emissions, specific packaging solutions can reach significantly higher values depending on use case and product group. Unlike other groups, greenhouse gas (GHG) emissions and life cycle assessment (LCA) of cereal and confectionary products have not been the focus of comprehensive reviews so far. Consequently, the present review first contextualizes packaging, sustainability and related LCA methods and then depicts how cereal and confectionary packaging has been presented in different LCA studies. The results reveal that only a few studies sufficiently include (primary, secondary and tertiary) packaging in LCAs and when they do, the focus is mainly on the direct (e.g., material used) rather than indirect environmental impacts (e.g., food losses and waste) of the like. In addition, it is shown that the packaging of cereals and confectionary contributes on average 9.18% to GHG emissions of the entire food packaging system. Finally, recommendations on how to improve packaging sustainability, how to better include packaging in LCAs and how to reflect this in management-related activities are displayed.
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Purpose A greenhouse gas emissions analysis (carbon footprint) was conducted for cultivation, harvesting, and production of common dairy feeds used for the production of dairy milk in the USA. The goal was to determine the carbon footprint (grams CO2 equivalents (gCO2e)/kg of dry feed) in the USA on a regional basis, identify key inputs, and make recommendations for emissions reduction. Methods Commonly used dairy feeds in the USA, such as soybeans, alfalfa, corn, and others, were identified based on a recent literature review and information from dairy farm surveys. The following input data for the cultivation and harvesting of dairy feeds were collected for five US regions: crop production data, energy input, soil amendments, and crop protection chemicals. Life cycle inventory input data were mainly collected from the US Department of Agriculture National Agricultural Statistical Service on a state-by-state basis as well as from state extension services forage crop budget estimates. In addition to consulting other life cycle assessment studies and published articles and reports, this cradle-to-farm gate carbon footprint analysis was conducted using the Ecoinvent™ unit processes in SimaPro version 7.1© (PRé Consultants 2009). Results The final carbon footprint results (gCO2e/kg of dry dairy feed) varied regionally depending on a number of factors such as lime and fertilizer application rates. The average national US carbon footprint results of the main feeds were: corn grain (390), corn silage (200), dried distillers grains with solubles (910 dry mill, 670 wet mill), oats (850), soybeans (390), soybean meal (410), winter wheat (430), alfalfa hay (170), and forage mix (160). Conclusions and recommendations The southeast dairy region generally showed a relatively high level of carbon footprint for most feeds, and this is attributable to the higher application rates of both synthetic fertilizers and lime. The highest contributor to carbon footprint for most regions (apart from soybeans and soybean meal) was due to the application of inorganic nitrogen fertilizer. Efficient transfer of knowledge to farmers with regards to fertilizer best management practices such as precision application of farm nutrients may contribute significantly to reducing regional crop carbon footprints.
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A gate-to-gate life cycle assessment was conducted to evaluate the Global Warming Potential associated with USA fluid milk processing. Data collected from 50 fluid milk processing plants were used to construct a life cycle assessment model for the greenhouse gas (GHG) emissions across the milk processing system, from raw milk entering the plant’s refrigerated storage silo through delivery of packaged fluid milk to retail store’s loading dock. Carbon dioxide equivalent (CO2e) emissions associated with the processing, packaging, and distribution in the processing of packaged fluid milk were investigated. Upstream emissions associated with raw materials, extraction, and transportation were included. Average GHG emissions for processing, packaging and distribution were 0.077, 0.054 and 0.072 kg CO2e kg−1 packaged fluid milk, respectively. Overall GHG emissions were 0.203 (±0.017) kg CO2e kg−1 packaged fluid milk with major individual GHG contributors being plant electricity usage (27% of total) and truck fleet tailpipe emissions (29% of total).
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A carbon footprint analysis was conducted for a single dairy feed mill located in Michigan, USA with the aim of developing a preliminary assessment of dairy feed mill operations. The goal was to determine the greenhouse gas (GHG) emissions for 1 kg of milled dairy feed. Inputs and activities identified in this analysis included production of feed ingredients, onsite energy, and transportation of feed inputs to the milling site and mill output to dairy farms. Feed mill GHG emissions were calculated to be 0.62 and 0.93 kg CO2-eq (equivalent) kg−1 of milled dairy feed for economic and mass allocation, respectively. The highest emissions were due to the feed ingredient inputs that contributed 73–82% toward the carbon footprint, depending on the allocation method. Energy and transportation impacts together contributed between 8 and 12%. Scenarios investigated feed ingredient inputs likely to represent different USA mill locations.
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Greenhouse gas (GHG) emissions were evaluated from crop production through the on-farm portion of the milk supply chain for five production regions in the USA derived from publicly available data and from 536 surveys of farm operations collected from dairy operations nationwide. The production weighted national average footprint at the farm gate was 1.23 kg carbon dioxide equivalent (CO2e) per kg of fat and protein corrected milk (fat, 4%; protein 3.3%). Regional differences in GHG emissions per kg milk produced can be primarily traced to differences in production and management practices. Feed-to-milk conversion efficiency is shown to be the single most important explanatory variable, followed by choice of manure management technology. While there is no one-size-fits-all solution, GHG emissions reduction opportunities exist across the spectrum of dairy management options. However, as with all decisions, it is important to weigh potential trade-offs with other environmental and economic impacts.
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This study presents an analysis of the cradle to farm gate greenhouse gas footprint of milk. Compared with the detailed model, we aim to accurately represent the variations in carbon footprint across farms, while being more parsimonious in terms of data needs. The simplified model strongly reduces the farm-specific data requirement from 162 animal-rations in the detailed survey to 12 feed rations for lactating cows, while explaining 91% of the variability in feed print and 98% of the variability in total footprint across 531 farms. The additional 95% confidence interval on an individual farm footprint is less than 10%. Feed efficiency and manure management are key determinants of the footprint per kg milk. A 15% reduction in the average footprint can be achieved by a 10% reduction for the 50th percentile of the best farms and by a higher and targeted reduction for the less efficient farms.
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An analysis of greenhouse gas emissions (carbon dioxide equivalents, CO2e) was conducted from 2007 databases for 211,216 round trips of tank trucks that delivered raw milk from farms to processing plants in the United States of America. The total amount of milk was 4.81 × 109 kg, or about 17.4% of the 2007 total USA production for use as fluid milk products. Average round trip distance was 850 km resulting in tailpipe emissions of 0.050 kg CO2e kg−1 milk delivered or 0.071 kg CO2e kg−1 milk consumed representing 3.5% of the total greenhouse gas emissions for fluid milk consumed. Based on this we estimate the total emissions for fluid milk delivery from farm to processor in the US at 1.3 × 109 kg CO2e y−1. Some overall reduction in total delivery distance could be realized by realigning farm-to-processor relationships, especially in regions where farms are equally distant from multiple processors.
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In 2008 the US dairy industry committed to reducing green house gases (GHG) associated with milk production. Understanding the contributions of dairy practices to GHG requires the collection of extensive complex farm level data. The purpose of this paper is three fold: 1) to describe the innovative model of data collection involving an expansive collaborative process, 2) discuss survey response rates and 3) offer lessons learned that will facilitate the replication of this method for data collection needs associated with other agricultural industries or other agriculturally related research questions.
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Data from 536 United States of America dairy farms were used to test algorithms for milk to beef allocation. A wide range of rations was represented, from pasture-based to large confined animal operations. Variety in the animal classes sent to beef provided a very robust dataset. We report an empirical rela-tionship for the causal allocation ratio (AR c) based on detailed analysis of farm rations, to allocate whole farm emissions between milk and beef: AR c ¼ 1e4.39 • BMR; with BMR defined as the kg beef sold per kg milk sold annually. USA dairy farm green house gas emissions allocated to milk using this approach was, on average, 91.5%, compared with economic (94.4%) and the protein-based (95%) allocation methods. We include an analysis of the allocation between fluid milk and excess cream at the processing plant. This analysis shows 19.8% of the post-farm (after allocation to beef) milk production burden allocated to the excess cream.
The paper describes the general structure of the ecoinvent database developed by the Swiss Centre for Life Cycle Inventories. The database accommodates more than 2500 background processes often required in LCA case studies.Quality guidelines, established in order to ensure coherent data acquisition and reporting across the various institutes involved, are described. These include aspects such as the reporting of pollutants (e.g., heavy metals), or the nomenclature of processes and elementary flows.The data (exchange) format is also described. Processes are documented with the help of meta-information and flow data (including both unit process raw data and aggregated LCI results). The structure of the data format corresponds to the ISO/TS 14048 data documentation format. Data exchange between project partner institutes and between the database and its customers (database users) is based on XML-technology. Matrix inversion is used to calculate the cumulative LCA data using efficient algorithms and making use of the fact that LCA matrices are usually sparse.