Life Cycle Energy and Greenhouse Gas Analysis ... - ACS Publications

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Life Cycle Energy and Greenhouse Gas Analysis of a Large-Scale Vertically Integrated Organic Dairy in the United States Martin C. Heller* and Gregory A. Keoleian Center for Sustainable Systems, School of Natural Resources and Environment, University of Michigan, 440 Church Street, Dana Building, Ann Arbor, Michigan 48109-1041, United States

bS Supporting Information ABSTRACT: In order to manage strategies to curb climate change, systemic benchmarking at a variety of production scales and methods is needed. This study is the first life cycle assessment (LCA) of a large-scale, vertically integrated organic dairy in the United States. Data collected at Aurora Organic Dairy farms and processing facilities were used to build a LCA model for benchmarking the greenhouse gas (GHG) emissions and energy consumption across the entire milk production system, from organic feed production to post-consumer waste disposal. Energy consumption and greenhouse gas emissions for the entire system (averaged over two years of analysis) were 18.3 MJ per liter of packaged fluid milk and 2.3 kg CO2 equiv per liter of packaged fluid milk, respectively. Methane emissions from enteric fermentation and manure management account for 27% of total system GHG emissions. Transportation represents 29% of the total system energy use and 15% of the total GHG emissions. Utilization of renewable energy at the farms, processing plant, and major transport legs could lead to a 16% reduction in system energy use and 6.4% less GHG emissions. Sensitivity and uncertainty analysis reveal that alternative meat coproduct allocation methods can lead to a 2.2% and 7.5% increase in overall system energy and GHG, respectively. Feed inventory data source can influence system energy use by -1% to þ10% and GHG emission by -4.6% to þ9.2%, and uncertainties in diffuse emission factors contribute -13% to þ25% to GHG emission.

1. INTRODUCTION The World Resources Institute estimates that agriculture is responsible for 13.8% of global anthropogenic greenhouse gas emissions; animal agriculture alone is directly accountable for over 5% of the total.1 A report by the Food and Agriculture Organization of the United Nations claims livestock is responsible for 18% of total greenhouse gas emissions, including landuse changes.2 According to the U.S. Environmental Protection Agency, agriculture is responsible for 6% of total U.S. greenhouse gas emissions, and 33% of total releases of CH4 from anthropogenic activities are livestock related.3 When one considers the carbon footprint of food consumption of the average U.S. household, dairy products make up 18% of the total, whereas red meat consumption represents 30%.4 Meanwhile, the U.S. organic food sector has consistently grown between 15% and 20% annually over the past decade. Retail sales of organic dairy in particular have grown between 16% and 34% in recent years.5 While such growth is in general lauded as an environmental success, there is a great need for systemic benchmarking of the environmental impact of organic agriculture in the United States in order to provide guidance for continual improvements in the sustainability of this rapidly growing sector. Life cycle assessment (LCA) is a method for integral analysis of the environmental impact of products, processes, or services by including all phases of the life cycle, from cradle to grave. Originally developed for the evaluation of industrial products and processes, LCA has proven a useful tool for evaluating complex agricultural systems such as dairy production.6 LCA methodology has been used to compare the environmental performance of r 2011 American Chemical Society

conventional and organic milk production in Sweden,7 Germany,8 Finland,9 and The Netherlands10 and to assess the greenhouse gas (GHG) emissions from milk production in Ireland,11 the Unites States,12,13 and globally.14 The entire milk supply chain (farm production, transport, milk processing, and packaging) in Norway,15 Spain,16 and Sweden 17 has also been analyzed with LCA methods. A review of the key environmental issues for the dairy industry was recently published by the International Dairy Federation.18 This article describes method and model development, as well as energy use and GHG emission results, for a LCA of a largescale, vertically integrated organic dairy in the United States. Aurora Organic Dairy (AOD) is a leading U.S. provider of private-label organic milk and butter, managing over 14000 milking cows and processing over 84 million liters (22 million gallons) of milk annually. Milk from AOD farms (three in Colorado and three in Texas) is processed in a state-of-the-art processing facility in Colorado and then distributed to retail outlets across the country. Recent growth and a commitment to sustainability have led AOD to evaluate its life cycle GHG emissions and explore reduction strategies. The objective of this study is to benchmark the life cycle energy and greenhouse gas emissions across the fluid milk life cycle of AOD’s production system and identify key stages and processes driving impacts. Received: August 30, 2010 Accepted: December 28, 2010 Revised: December 13, 2010 Published: January 11, 2011 1903

dx.doi.org/10.1021/es102794m | Environ. Sci. Technol. 2011, 45, 1903–1910

Environmental Science & Technology

Figure 1. Schematic of milk production life cycle showing major system processes. Double-lined arrows signify included transportation; dashedline arrows signify flows of coproducts allocated from the milk life cycle.

Further, we investigate abatement strategies and explore the implications of coproduct allocation methods and other key modeling approaches. AOD also provides a unique case study for analysis: vertical integration offers internal consistency of data across a large portion of the fluid milk life cycle. In addition, data were collected for two consecutive years, providing a temporal depth atypical for LCA. This effort represents the first comprehensive LCA of a large-scale, vertically integrated organic dairy in the United States. Feed inventory data sources, local feed sourcing scenarios, and manure management emission uncertainty were also investigated to enhance our characterization of the results.

2. METHODS 2.1. System Boundary. Figure 1 shows the major processes included in the milk production system. The system was analyzed over two consecutive annual time periods (April 2007-March 2008 and April 2008-March 2009). The LCA starts with the production of feed on supplier farms and ends with postconsumer waste disposal; it includes all activities at AOD’s six dairy farms (one farm came online June 2007, while another was decommissioned January 2008) and their company milk processing plant. Transport of animal feed and all milk products are accounted for in the study. Estimates of transport and refrigeration

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at the retail and consumer level are included. Emissions from manure (raw or composted) are included until the point that the manure is exported from AOD farms: at this point, the manure is considered an input into another production system, but no coproduct credit is given to the dairy production system. Major building materials for farm and processing plant buildings are included and amortized over 50 years for farm buildings and 30 years for milk plant buildings. For completion, estimates of employee transport as well as corporate office operation are also included. 2.2. Functional Unit. The functional unit for the entire milk production system is 1 L of packaged fluid milk. “Packaged fluid milk” represents a mix of AOD’s products, ranging from skim to whole milk packaged in both half-gallon and gallon cartons. 2.3. System Description. Organic milk is produced on six AOD-owned dairy farms (three in Central and eastern Colorado, two in Central Texas, and one in the Texas Panhandle). The farms primarily purchase organic alfalfa (as well as silages, when available) for roughage fodder. All farms purchase the same organic grain premix consisting of, on average, 40% corn, 10% barley, 12% wheat midds, 21% soybean meal, and 5% minerals. The overall diet for a typical mature dairy cow is 42% alfalfa, 42% grain premix, and 16% other grass hays and silages. Feed supplier farms are generally located in the western United States. Specific locations (and therefore, specific transport distances) were known for many producers, but some feed came through a consolidator with no information on geographic origin of production. In these instances, transport distance was from the consolidator, plus an additional assumed transport to the consolidator (80 km for alfalfa and 724 km for grain premix). All animals grazed regularly on pasture for at least 120 days per year during the grazing season; therefore, pasture intake represented a portion of the cow’s overall feed energy, but not a majority. Pasture acreages are given in the Supporting Information. The inventory accounted for irrigation, manure spreading, and seeding of pastures, as well as diffuse emissions (N2O, CH4) from manure spread or deposited on pastures. All raw milk processed at AOD’s continuous-flow, ultrapasteurization milk plant, located in Central Colorado, originates from AOD farms. The processing plant produces only fluid milk; excess cream is shipped to a copacking facility for butter production and is allocated away from the fluid milk life cycle in this study. Because California law requires higher milk solids than the rest of the country, milk destined to California is supplemented with milk powder; this powder originates from AOD farms but is processed in a copacking facility and is accounted for in this study. Final fluid milk is packaged primarily in half-gallon gable-top cartons constructed of plastic coated paperboard, with a small amount (about 2%) being packaged in gallon high-density polyethylene (HDPE) jugs that are manufactured at the AOD plant via blow molding. Milk cartons are aggregated in corrugated cardboard boxes, wrapped in lowdensity polyethylene (LDPE) film, and shipped on wooden pallets. All fluid milk products are shipped first to a nearby cold storage site, and then sent to regional distribution centers throughout the United States via refrigerated tractor-trailer trucks. Plant wastewater is treated onsite before being discharged for municipal treatment. 2.4. Life Cycle Assessment Model and Data Collection. A model was created to calculate the GHG emissions and energy usage associated with the production of 1 L of packaged, delivered milk. The assessment model was constructed using 1904

dx.doi.org/10.1021/es102794m |Environ. Sci. Technol. 2011, 45, 1903–1910

Environmental Science & Technology the LCA software, SimaPro 7.1.6, in accordance with the ISO 14040:2006 LCA standards.19 Global warming potentials were characterized with the IPCC 2007 methodology using a 100 year time horizon (25 and 298 for CH4 and N2O, respectively).20 Energy resource impact was assessed using the Eco-indicator 95 (version 2.04) characterization. Energy flows are reported on a LHV basis. 2.4.1. AOD Production. A large portion of the model data was collected onsite at AOD’s farms and processing plant. These primary data include amount of feed, electricity, fuels, supplies, and packaging used over the analysis year. Transportation distances for the shipment of feed, raw milk, and final packaged milk to distribution centers were collected from AOD records. Life cycle GHG emissions and energy consumption from production of fuels, building materials, dairy supplies, and packaging materials were calculated using databases available through SimaPro (detailed in the Supporting Information). 2.4.2. Feed Production. Direct inventory of feed production was not possible within the scope of this project, so feed production was modeled with Ecoinvent version 2.0 data sets available in SimaPro.21 Life cycle data for the U.S. production of organic feed crops are not available, and U.S. data sets for conventional crop production are limited. This study used U.S. conventional (i.e., nonorganic) data sets for corn, soybeans, and soybean meal, and Swiss organic data sets for alfalfa, grass hay, corn and grass silage, and wheat. Note that the Swiss data set for alfalfa and grass hay production contained a large energy contribution for in-barn drying of the hay crop: this was deemed inapplicable for U.S. practices and removed. Carbon dioxide taken up by crop biomass was assumed to be released back to the air during animal and human consumer respiration so that net CO2 production approached zero. Thus CO2 taken up by biomass was not accounted for in this study. A carbon balance applied to the dairy farm inventory would warrant an input (credit) of biogenic CO2 to offset the release of biogenic CH4, but because IPCC does not include the indirect radiative effect of CO2 resulting from the oxidation of CH4 in the atmosphere, these indirect products must also be accounted for in the inventory, thus offsetting the biogenic CO2 credit. Organic carbon levels in cropping soils are dependent on a variety of factors beyond the scope of this study, and thus, no net change in soil carbon was assumed. 2.4.3. Fuel Cycles. Fuel consumption and related GHG emissions from transportation were modeled using average U.S. tractor-trailer (25.4 tonne) data sets from Franklin Associates, as presented in ref 21. Refrigerated transport was estimated to consume an additional 1.89 L of diesel per hour of operation,22 with only final packaged products refrigerated during transport. Product cold storage was estimated to consume 82 kWh per 10000 L of storage.22 Regional electricity grids specific for the farms, plant, and cold storage locations were modeled according to Kim and Dale.23 Electricity of unknown origin (for example, in-home refrigeration) was modeled using the U.S. national average grid.21 Biodiesel production (soy methyl ester) and renewable electricity generation were modeled with Ecoinvent data sets.21 Emission factors and energy intensities of the fuel cycles used in the study are reported in Table S3 of the Supporting Information. 2.4.4. Distribution, Consumption, and Disposal. Transport from distribution centers to retail outlets was assumed to be 80 km in refrigerated tractor-trailers, modeled as above. Consumer transport distances were assumed to be 21.6 km roundtrip,24 with

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milk accountable for 3.87% of the grocery trip, based on an economic allocation.25 At-home (consumer) refrigeration assumed a 0.54 m3 refrigerator with 53% empty space;26 empty space is allocated to the milk product based on volume fraction of the occupied space. All cartons (paperboard and HDPE jugs) were disposed of in a landfill, with an assumed 16 km waste management transport.27 Landfill production of CH4 from paper-based products was modeled according to IPCC,28 with no landfill gas recovery considered. 2.4.5. Diffuse Emissions. GHG emissions due to enteric fermentation, manure management, managed pastures, and plant wastewater treatment were estimated according to chapters 10, 11, and 6 of the 2006 IPCC Guidelines for National Greenhouse Gas Inventories.28 Tier 2 methodologies were employed; an enteric methane conversion factor of 5.5% (percent of gross energy in feed converted to methane) was used. 2.5. Coproduct Allocation. Multiple economic outputs, or coproducts, are common in agricultural systems. While system expansion is recommended to avoid coproduct allocation,29 it is often not possible or practical for agricultural systems. Coproduct feedstuffs (e.g., soybean meal, a coproduct in the production of soybean oil) were allocated as described in Table S4 of the Supporting Information. Additional allocation methods are described below. 2.5.1. Bull Calf and Culled Cow Allocation. In previous studies, allocation between meat (bull calf and cull cow) and milk coproducts has been based on economics,9,10,16 mass,9 or energetics.7,11,15 In this study, the allocation of impacts between milk and meat was made on an energy basis at the farm level. The energy for producing bull calves, which are sold shortly after birth on AOD farms, was estimated using the net energy requirement for pregnancy according to eqs 2-19 from the National Research Council’s Nutrient Requirements of Dairy Cattle,30 based on an average calf birth weight of 45 kg. Given that modern dairy genetics and management practices are geared toward maximizing milk conversion, only the culled cow actual embodied energy was allocated to the meat coproduct. Assuming a 635 kg cow with a body condition score of 3, NRC 30 estimates the cow’s empty body mass to be 18.8% fat and 16.8% protein, with the remaining percentages composed of ash and water. Fat and protein are assumed to be the primary energy embodiment of the cull cow; using energy densities of 39.3 MJ/ kg of fat and 23.4 MJ/kg of protein,30 an embodied energy for the cull cow was estimated. The above energy estimates were then multiplied by the respective numbers of animals (bull calves and culled cows) sold on each farm and summed to give the energy of animal (meat) coproduct. Emeat,

farm i

¼ Epregnancy ðbull calvesÞfarm i þ Eembodied, ðculled cowsÞfarm i

culled cow

ð1Þ The energy in the milk coproduct was estimated by first correcting for fat and protein content according to Bernard31 and then multiplying by a standard energy content of 2.90 MJ/kg of milk (3.5% fat, 3.2% protein)26 Emilk,

farm i

¼ 2:90½0:3246 þ 12:86ðfat fractionÞfarm i

þ 7:04ðprotein fractionÞfarm i ðkg milk producedÞfarm i ð2Þ 1905



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Figure 2. Distribution of primary energy consumption across the fluid milk life cycle on a functional unit basis. Data for consecutive years are shown.

The fraction of the farm burdens allocated to the meat coproduct is then Allocation fractionmeat , farm i ¼ Emeat , farm i =ðEmeat, þ Emilk,

farm i Þ

farm i

ð3Þ

This allocation fraction was applied to feed and bedding production and transport, enteric fermentation, manure management, managed soils, and pasture operations. It was not applied to farm utilities, purchased supplies, farm building embodied energy, and farm employee transport, which were allocated wholly to the fluid milk coproduct because these operational burdens exist solely for the production of milk. Weight- and economic-based meat coproduct allocations are also reported for comparison. Farm gate organic milk prices have been highly variable in recent years: AOD does not report a farm gate price discovery because of vertical integration. For this comparative analysis, prices of 0.51 USD/kg organic milk ($23/ cwt) and 1.13 USD/kg meat ($51.44/cwt) were used (cwt = 100 lbs). For both weight- and economic-based allocations, allocation fractions equivalent to eq 3 were calculated and applied in the same way as the energy allocation. 2.5.2. Cream and Milk Powder Allocation. Burdens associated with excess cream shipped from the milk plant to a butter copacker were allocated from the fluid milk life cycle on a milk solids basis as described by Feitz et al.,32 using a milk solids concentration factor of 3.8. Similarly, the raw milk necessary to provide supplemental milk powder was determined using a milk solids concentration factor of 7.7.

3. RESULTS The distribution of primary energy consumption across the fluid milk production life cycle is shown in Figure 2. The total energy consumption is 18.6 MJ/liter milk and 18.0 MJ/liter milk for year 1 and year 2, respectively. About 7% of this primary energy could be considered “renewable” (biomass, hydropower, wind, or solar), with 85% of that renewable in the form of wood feedstock for packaging (fiberboard cartons and cardboard). Farm-level activities (that is, milk production) contribute 30%

to the total energy consumption. Feed and bedding production alone accounts for 12% of the total system energy consumption. Figure 3 shows the distribution of GHG emissions across the life cycle. The total global warming potential is 2.39 kg CO2 equiv/liter milk and 2.22 kg CO2 equiv/liter milk for year 1 and year 2, respectively. Farm-level activities are responsible for 62-65% of the total, with enteric methane being the largest single contributor. Contributions to GHG emissions across the life cycle come from CO2, 43%; CH4, 32%; and N2O, 25% (distribution slightly different in year 2). Modern dairies are optimized for the conversion of feed energy into milk production. Thus, while meat coproducts do exist, they do not represent a large portion of the resource consumption and environmental emissions. Still, the method of allocation between dairy farm coproducts remains in question. We have chosen an energy-based allocation in this study as it is felt to best represent the causal relationship of resource use. Table 1 compares common allocation methods with the energybased allocation chosen here. Note that the milk:meat allocation applies only to the farm-level inventory and therefore does not impact the rest of the life cycle. Economic allocation, weight allocation, and no allocation methods increase life cycle GHG emissions by 2.5%, 5.0%, and 7.5%, respectively, over results based on the energy allocation method (year 1 data).

4. DISCUSSION Access to data from consecutive annual time periods offers a rare test of data quality and modeling robustness. Still, the dairy system under question was not static throughout the analysis period, and much of the differences between analysis years can be explained by examining changes in animal populations. The fraction of the net herd (including bulls) that was productive (in milk) trends upward over the two year time period (Figure S1 of the Supporting Information). All animals consume resources (feed) and contribute to emissions, but only those in milk contribute directly to the functional unit. Thus, as the fraction of the total herd that is in milk increases, life cycle impacts per functional unit decrease. One large farm began operation partway through the first year of data collection (June 2007) and 1906



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Figure 3. Distribution of GHG emissions across the fluid milk life cycle on a functional unit basis. Data for consecutive years are shown.

Table 1. Influence of Milk:Meat Allocation Method on Life Cycle Energy and GHG Emissions weighted average milk

energy use

GHG

allocation fraction

(MJ/liter packaged milk)

(kg CO2 equiv/liter packaged milk)

allocation method

year 1

year 2

year 1

year 2

year 1

year 2

energy

0.90

0.93

18.6

18.0

2.39

2.22

weight

0.97

0.98

18.8

18.2

2.51

2.30

economics

0.94

0.95

18.7

18.1

2.45

2.26

no allocation

1

1

19.0

18.3

2.57

2.33

Table 2. Life Cycle Energy and GHG Emissions of Major Crop Production Compared across Data Sets and Impact of Data Set Choice on Full Milk Life Cycle crop production life cycle alfalfa

a

MJ/kg

kg CO2 equiv/kg

base case

0.94

0.152

all organic (Swiss data) from ref 21

0.94

0.152

conventional (U.S. data) from ref 33

1.24b

0.089

corn MJ/kg

kg CO2 equiv/kg

3.99 12.3 1.99b

soybeans MJ/kg

kg CO2 equiv/kg

fluid milk full life cyclea MJ/liter

kg CO2 equiv/liter

0.435

1.61

0.509

18.6

2.39

1.27

5.01

1.26

20.5

2.61

0.246

1.98b

0.159

18.3

2.28

b

Based on year 1. Reported as HHV.

experienced a start-up period of below average fraction of cows in milk. Data also show that the milk production per cow in milk trends upward over the time period (Figure S1 of the Supporting Information), further contributing to the overall life cycle “efficiency.” 4.1. Feed Production. With the exception of pasture, AOD farms purchase all of their organic feeds from off-farm. Information on specific practices of these feed production farms was not available. In addition, life cycle data for crop production, especially in the United States, are extremely limited. We have been unable to find appropriate life cycle data for organic crop production in the United States. The base case reported here combines data representing conventional U.S. production of corn and soybeans along with data representing organic Swiss production of other feed types. Table 2 shows the life cycle

energy and GHG emissions for production of alfalfa, corn, and soybeans, the major feed types consumed on AOD farms. The base case is compared with Swiss organic crop production data,21 as well as data from Kim and Dale33 representing conventional crop production in the Midwest United States. Table 2 also shows the impact of changing these crop production data sets (alfalfa, corn, and soy only) on the overall fluid milk life cycle. While the Swiss data shows increased energy use and GHG emissions under organic conditions, other studies34,35 have shown the opposite. This life cycle study would certainly benefit from data for regionally appropriate organic crop production, but it is likely that other agronomic parameters such as tillage practices, fertilization, and irrigation rates (see, for example, Snyder et al.36) will have a greater impact on GHG emissions and energy use than does organic certification. Improved accuracy in feed 1907


Environmental Science & Technology production impacts will require additional characterization of cropping practices. There is a strong need for U.S. life cycle data for the production of major feed crops, preferably differentiated by geographic region and production method. Such life cycle documentation would not only provide guidance to sustainability efforts in the production of these feed crops, but would also improve analysis of animal-based and other food system life cycles. 4.2. Diffuse Emission Uncertainties. Methane and N2O emissions from livestock are a major contributor to global warming potential. In the fluid milk life cycle considered here, CH4 from enteric fermentation and manure account for 27% of total system GHG emissions. N2O emissions from manure management account for an additional 15% of total system GHG. Our study relies on emission estimates based on IPCC Tier 2 methodologies 28 compiled at monthly time steps. Whereas animal populations, feed characteristics, and manure management practices are based directly on AOD records and are therefore highly accurate, methods are also based on emission factors that, according to IPCC, have significant uncertainties. CH4 emission factors for both enteric fermentation and manure management have reported uncertainties of (20%. Reported uncertainties for direct N2O emissions are -50% to þ100%, and factors contributing to indirect N2O emissions also have significant uncertainties. Propagating uncertainties in manure management emissions gives a GHG emission range of 0.25 - 0.96 kg CO2 equiv per liter fluid milk (base-case value = 0.46 kg CO2 equiv per liter fluid milk; year 1 data). Combined with the uncertainty in enteric emissions ((0.095 kg CO2 equiv per liter), this gives an uncertainty range for total system GHG emissions of 2.09-2.98 kg CO2 equiv per liter fluid milk. While mechanistic models for prediction of methane emissions are beyond the scope of this paper, they have the potential to improve upon uncertainties as well as evaluate diet-based mitigation options.37 4.3. Transportation. Transportation, including transport of feed and bedding, raw milk, packaged milk, and employees, represents 29% of the total system energy use and 15% of the total GHG emissions. AOD product is distributed throughout the United States: the weighted average transport distance from the plant to distribution centers was 2080 km. The average kg of raw milk delivered from farms to the plant traveled 528 km (467 km in year 2). Still, the total system transportation energy demand is similar to the energy demand for total farm activities, and GHG emissions due to transportation is less than half the contribution from enteric fermentation and manure management. Hypothetically “localizing” feed sources as well as final product markets helps demonstrate the influence of transportation on overall system performance. Reducing feed and bedding transport to within a 161 km radius of the farms (that is, all transport greater than 161 km was set equal to 161 km) causes a 7% reduction in the overall system energy use and a 4% reduction in GHG emissions. Reducing the weighted average distance to finished product distribution centers to 805 km and 161 km results in total system energy reductions of 8% and 12%, respectively, and GHG emission reductions of 5% and 7%, respectively. Such “localizations” are likely not practical because of limits in productive farmland surrounding existing AOD dairy farms and limits in product demand in the region surrounding the processing plant, but they do provide a context for the relative impacts of transportation on the overall fluid milk life cycle.

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Table 3. Potential Reduction in Life Cycle Energy Use and GHG Emissions through Renewable Transport Fuels and Electricity Generation % reduction in overall system performance abatement strategy a

energy use

GHG

20% biodiesel

3.6%

1.7%

100% biodiesela

18%

8.7%

Renewable electricityb

12%

4.7%

a

Soy biodiesel inventory does not include Indirect Land Use Change (ILUC). b Modeled as a 50/50 mix of solar and wind generation. Does not include electricity use in distribution center, retail, and in-home refrigeration.

It should be noted that while most preretail transport distances were known with certainty, much of the grain premix and alfalfa was purchased from a consolidator, and the ultimate origins of the feedstuffs were unknown. Removing the additional transportation to the consolidator for these feedstuffs reduces the overall system energy demand by 2.2%. 4.4. Abatement Scenarios. Methane production from enteric fermentation remains a standout contributor to GHG emissions in the milk life cycle. Microbial processes in the rumen that allow cows to digest complex carbohydrates such as grasses also naturally produce CH4 as a byproduct. Ongoing research suggests that manipulations of the animal diet can reduce CH4 production. Grainger et al. demonstrated a 12% reduction in CH4 production with the addition of whole cottonseed to the diet,38 corroborating earlier evidence of reduced CH4 emissions through the addition of fats.39 Such studies are difficult to draw generalized conclusions from, as results are often dependent on the base diet of the animals, and there is concern of the effectiveness of the treatment diminishing over time. In addition, market availability of appropriate organically certified dietary supplements are limited. For demonstration purposes, however, a 12% reduction in enteric fermentation CH4 production would lead to a 2.4% reduction in the overall system GHG emissions. Lowering the carbon intensity of transport fuels and electricity generation can also lead to considerable reductions in system GHG emissions and fossil energy use. Table 3 shows the potential system reduction of energy use and GHG emissions achieved by displacing petro diesel with soy methyl ester diesel (biodiesel) in on-farm use and in the transport of raw milk to the plant and finished product to distribution centers. While 100% biodiesel may not be mechanically practical, especially in colder climates, these results demonstrate an upper bound to the abatement potential from biodiesel substitution. Table 3 also shows the abatement potential of replacing grid electricity at the farms, processing plant, and cold storage with a 50/50 mix of solar and wind generation. Again, this is intended to give an upper bound to the abatement potential from renewable electricity generation. The GHG emission contribution from enteric fermentation is considerably larger than that from system diesel or electricity use, but the abatement potential achievable through the adoption of available renewable energy technologies appears to be greater than what can be expected through animal diet manipulation. 4.5. Comparisons with Published Studies. Comparisons between LCA studies are made with caution: differences in system boundaries, allocation procedures, and other methodological nuances can have strong influences on final results. 1908


Environmental Science & Technology In addition, real differences in climate as well as management practices make comparisons between systems challenging. It is with these cautions that we offer in the Supporting Information comparisons with literature-reported LCAs of the production of bovine milk for human consumption (references included in comparison: 6-9,11-16,40-42. For the total life cycle, the range of GHG values (including only those studies that report the whole life cycle) is 0.51-2.4 kg CO2 equiv/liter packaged milk (ave = 1.37, median = 1.33, n = 8) and energy values range from 3.6 to 18.6 MJ/liter packaged milk (ave = 8.4, median = 6.3, n = 8). Our study lies notably on the high side of the range, especially with regards to energy use, and the reason for this discrepancy remains unclear. The comparison in the Supporting Information also suggests that there is not a clear distinction between conventional and organic milk production, reiterating our previous statement that other agronomic parameters (e.g., starch to fiber ratios in cow rations, crop fertilization rates, tillage practices, and manure management practices) may have a greater influence on overall system energy and GHG emission performance than organic certification. Rotz et al12 have begun to explore the influence of these parameters on the carbon footprint using farm simulation models. Significant improvements in data quality and representativeness, particularly with respect to feed production and methane emissions, are required before more definitive comparisons can be made between agricultural production methods. This study demonstrates the significance of different modeling approaches, model parameters, and data sources on primary energy and GHG results, which should be useful in guiding future milk life cycle assessments.

’ ASSOCIATED CONTENT

bS

Supporting Information. Table S1: Comparison of literature-reported LCA studies of milk production and processing. Figure S1: Temporal trends in the fraction of net herd in milk and milk production per cow. Table S2: Characteristics of AOD farms operations. Table S3: Emission factors, energy intensity, and original sources of ancillary system components. Table S4: Allocation factors. This information is available free of charge via the Internet at http://pubs.acs.org/.

’ AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]; phone: 734.764.1412; fax: 734.647.5841.

’ ACKNOWLEDGMENT The authors are grateful to the Aurora Organic Dairy Foundation for financial support and to the University of Michigan School of Natural Resources and Environment Masters’ students who contributed to the study: Sarah Cashman, Keri Dick, Derek Przybylo, William Walter, Jennifer Gough, Amy Kolodzy, Blake Marshall, and Daniel Wilson. The views, opinions, conclusions, and recommendations presented here do not necessarily reflect those of Aurora Organic Dairy. ’ REFERENCES (1) World Greenhouse Gas Emissions, 2005. World Resources Institute. http://www.wri.org/chart/world-greenhouse-gas-emissions2005.

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