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Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
Environmental impacts and resource use of Australian beef and lamb exported to the USA determined using life cycle assessment
Stephen Wiedemann a *, Eugene McGahan a, Caoilinn Murphy a, Ming-Jia Yan a, Beverley Henry b, Greg Thoma c, Stewart Ledgard d
a FSA Consulting, PO Box 2175, Toowoomba, Qld 4350, Australia
b Institute for Future Environments, Queensland University of Technology, Brisbane, Qld 4000, Australia c Ralph E. Martin Department of Chemical Engineering, University of Arkansas, Fayetteville, AR 72701, USA d AgResearch Ruakura Research Centre, Private Bag 3123, Hamilton 3240, New Zealand
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ARTICLE INFO
Article history:
Received 8 September 2014
Received in revised form
21 January 2015
Accepted 21 January 2015
Available online 31 January 2015
Keywords:
Carbon
Energy
Footprint
ABSTRACT
Australia is one of the two largest exporting nations for beef and lamb in the world and the USA is a major export market for both products. To inform the Australian red meat industry regarding the environmental performance of exported food products, this study conducted the first multi-impact analysis of Australian red meat export supply chains including all stages through to warehousing in the USA. A large, integrated dataset based on case study farms and regional survey was used to model beef and lamb from major representative production regions in eastern Australia. Per kilogram of retail-ready red meat, fresh water consumption ranged from 441.7 to 597.6 L across the production systems, stress-weighted water use from 108.5 to 169.4 L H2O-e, fossil energy from 28.1 to 46.6 MJ, crop land occupation from 2.5 to 29.9 m2 and human edible protein conversion efficiency ranged from 7.9 to 0.3, with major differences observed between grass finished and grain finished production. GHG emissions excluding land use and direct land use change ranged from 16.1 to 27.2 kg CO2-e per kilogram, and removals and emissions from land use and direct land use change ranged from -2.4 to 8.7 kg CO2-e per kilogram of retail retail ready meat.
Process based life cycle assessment shows that environmental impacts and resource use were highest in the farm and feedlot phase. Transportation contributed <5% of greenhouse gas emissions, water and land, confirming that food miles is not a suitable indicator of environmental impacts for red meat transported by ocean shipping. The contribution of international transportation to total energy demand was higher, ranging from 14 to 23%. These beef and lamb supply chains were found to rely on small volumes of water from stressed water catchments, and occupied only small amounts of crop land suited to other food production systems. Production of high quality protein foods for human consumption used only small amounts of protein from human edible grain.
© 2015 Elsevier Ltd. All rights reserved.
1. Introduction
Agricultural systems such as livestock production face the challenge of maintaining and increasing production in the future with constrained natural resources and pressure to reduce environmental impacts. Globally, meat demand is expected to increase 74% by 2050 because of expanding global population and increased wealth (FAO, 2009). However, global targets also exist to reduce greenhouse gas (GHG) emissions (Stocker et al., 2013) and concerns
* Corresponding author. Tel.: +61 (0)7 4632 8230; fax: +61 (0)7 4632 8057. E-mail address: stephen.wiedemann@fsaconsulting.net (S. Wiedemann).
http://dx.doi.org/10.1016/jjclepro.2015.01.073 0959-6526/© 2015 Elsevier Ltd. All rights reserved.
exist regarding the use of scarce water resources (Rockstrom et al., 2007; WHO, 2009) and arable land (UNEP, 2014). Australia is one of the two largest exporting nations for beef and lamb in the world, closely following Brazil in total volume of beef and New Zealand in lamb (FAO, 2011). The United States of America (USA) is a major export market for both products (DFAT, 2012). Product life cycle assessment (LCA) is an important method for understanding the impacts associated with food products and particularly for determining what stages in the supply chain contribute to impacts. Despite long transport distances, LCA studies of red meat have shown that transportation distance, or 'food miles' (Paxton, 2011) is not a good indicator of environmental impacts in several instances (Webb et al., 2013; Weber and Matthews, 2008). However, 'food
List of acronyms:
ABARES Australian Bureau of Agricultural and Resource
Economics and Sciences
CSF case study farms
GHG greenhouse gas
GWP global warming potentials
HEP-CE human edible protein conversion efficiency
LCA life cycle assessment
LF long fed
LU Land Use
dLUC direct land use change
MF mid fed
NSW New South Wales
QLD Queensland
RAF regional average farms
SA South Australia
VIC Victoria
WSI water stress index
Farm services (energy, purchased
_feed, etc.)
Water supply (bore, dam, river)
Land occupation (cultivated arable, arable pasture, non-arable)
Grain/ hay
miles' is still taken as a proxy indicator for environmental impact in popular media communications and a greater understanding of the relationship between impacts and transport distance is sought by the users of Australian beef and lamb in the USA.
To date, there has been no holistic environmental analysis of Australian beef and lamb supply chains to the USA. Life cycle assessment studies of Australian production have focussed on case study farms (Eady et al., 2011; Peters et al., 2010a, 2010b), theoretical production systems (Ridoutt et al., 2012a) or controlled production systems found on research farms (Brock et al., 2013) that could not be considered representative of markets that draw from large production regions. These studies predominantly focussed on one or two impacts only. Recent farm gate studies of beef (Wiedemann et al., 2015c) and lamb (Wiedemann et al., 2015d) cover larger regions representative of Australia's export markets through to the farm gate, and were the basis for this expanded supply chain analysis. The present study aimed to determine major environmental impacts and resource use from the production, processing and transport of Australian beef and lamb to the USA by extending two existing farm-gate LCA studies by the same authors, which used large, integrated datasets based on case study farms and regional survey datasets. The study aimed to report on major environmental impacts and resource use indicators with new methods and to provide a robust assessment of impacts and hotspots in the supply chain, with particular attention to the role of transportation.
2. Materials and methods
The study included beef and lamb production from major representative production regions in eastern Australia, through the whole supply chain to the point of distribution to retail in the USA. The functional unit was chosen as one kilogram of retail ready cuts of Australian beef and lamb, at the regional storage centre in the USA. The system boundary included all stages of production, processing, transport and cold storage on the east coast of the USA, as well as distribution to the point of retail (Fig 1).
2.1. Production system characteristics
Australia's sheep and cattle industries have been developed to utilise some 3.5 M km2 of native vegetation grazing land (Lesslie
Fig. 1. Illustration of beef and lamb supply chains in the study. Shaded boxes indicate co products.
and Mewett, 2013), or 46% of continental land area. The majority of sheep are produced in the south-east states of South Australia (SA), Victoria (VIC) and New South Wales (NSW), representing 73% of Australia's sheep flock (MLA, 2013a) and the vast majority of export lamb production. The majority of beef cattle are produced in the states of Queensland (QLD) and NSW. Central and southern QLD, and northern and central NSW represent 35% of Australia's beef herd and the major regions exporting premium beef to the USA market and were the focus of the study. Premium beef and lamb exports to the USA must meet specific market requirements. Export lambs are >22 kg carcase weight (CW) and beef cattle destined for premium markets may be grass-fed or grain-finished. The study investigated beef bred in rangeland areas and finished on pastures (grass-fed), and steers finished on grain for either 115 days (Mid-fed - MF) or 330 days (Long-fed - LF). The LF category is tailored to the production of a high quality, niche beef product, predominantly from Angus or Wagyu breeds, for the USA restaurant trade.
2.1.1. Indicators
The study investigated GHG emissions using the IPCC AR4 global warming potentials (GWP, 100 years) of 25 for methane and 298 for nitrous oxide (IPCC, 2007). Greenhouse gas emissions associated with land use (LU) and direct land use change (dLUC) were included and reported separately.
Fossil fuel energy demand was assessed by aggregating all fossil fuel energy inputs throughout the system and reporting these per mega joule (MJ) of energy, using Lower Heating Values (LHV). Modelling methods and processes used are described below. Fresh water consumption (an inventory method — Bayart et al., 2010) was assessed, covering all sources and losses associated with livestock production in both foreground and background systems. Fresh water consumption refers to evaporative uses or uses that incorporate water into a product that is not subsequently released back into the same river catchment (ISO, 2014). Stress-weighted water use was assessed using the water stress index (WSI) of Pfister et al. (2009) reported in water equivalents (L H2O-e) after Ridoutt and Pfister (2010). Land occupation was reported using a disaggregated inventory based on land type and suitability. Four land
types are specified, crop land, arable pasture land, non-arable pasture land and industrial land occupation. No characterisation factors were applied, and land occupation data were reported in square metre years (m2 yr). Human edible protein conversion efficiency was determined by dividing the protein content of the retail ready product by human edible protein in the feed consumed and methods are explained in more detail in the following sections.
2.2. Life cycle inventory and modelling
The inventory stage was divided into segments covering the production phase in Australia, the meat processing stage in Australia, and transport and warehousing through the supply chain to the USA. Data collection and modelling methods are outlined for each stage.
2.2.1. Farm and feedlot
Inventory data covering livestock production, inputs of purchased feed, fertiliser, fuel and services, and land occupation for the farm stage of the supply chain were from regional survey data collected by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) and a survey of case study farms (CSF). The ABARES dataset covered 345 beef producers and 203 specialist lamb producers annually for the five year period of 2006-2010 (ABARES, 2013a, b). Case study farm data were sourced from other publications (Wiedemann et al., 2015c, 2015d). Feedlot inventory data were from a survey of the Australian feedlot industry (Davis et al., 2010a, b). In addition to purchased inputs, Davis et al. (2010a) provided an inventory of measured water use and feed requirements for feedlot finishing. Material inputs for the farm and feedlot are provided in the supplementary material. For each region, human edible protein consumed by livestock was determined from the inventory of grain use throughout the supply chain based on the mass of human edible grain for each grain type, and the average protein content of the grain. Impacts associated with farm infrastructure were excluded based on the findings of a scoping study showing the contribution from these sources was <1% (Wiedemann, unpublished data). Inventory data were aggregated for reporting relative to livestock feed intake.
For each major region, a regional average farm model was developed based on the herd and flock inventory data, which determined total production outputs from the farm stage. This model was also used to determine total feed requirements (dry matter intake — DMI) for the grazing herds and flocks in each region, using the prediction equation of Minson and McDonald (1987) for cattle, and the AFRC (1990) feed intake model for sheep as they are applied in the Australian NGGI (DCCEE, 2012a). Results from the regional herds and flocks were aggregated to provide a market average for the USA export market with ratios for beef of 65% from NSW and 35% from QLD for the grass-fed and mid-fed grain finished supply chains. The long-fed supply chain was only supplied by NSW where a greater number of suitable cattle are bred. Lamb exports were modelled with equal proportions of lamb from SA, VIC and NSW. Herd and flock modelling methods are explained in detail in other publications (Wiedemann et al., 2015c, 2015d) and key herd and flock parameters are provided in Table 1. The herd and flock model also predicted livestock GHG emissions using methods from the Australian NGGI (DCCEE, 2012a) or global inventory methods (Dong et al., 2006) as applied in other publications (Wiedemann et al., 2015c, 2015d). Key model parameters are specified in Table 2. Emissions and removals from Land Use and direct Land Use Change were modelled for each region using methods outlined in previous related publications (Henry et al., Submitted for publication; Wiedemann et al., 2015c, 2015d). These methods are outlined in the supplementary material.
Table 1
Animal productivity characteristics of the grass-fed beef and lamb supply chains on a per farm basis at the farm gate.
Grass-fed beef Lamb
Breeding animals (no. cows/ewes) 222.8 1245
Breeding animal culling rate (%) 16.8 23
Breeding animal mortality rate (%) 2.1 4
Breeding animal average weighta (kg LW/head) 469 65
Weaning percent (%) 81.1 97
Weaning weight (kg LW) 208.7 n.r.
Average steer weight at sale (kg LW/head) 554.2 n.a
Average lamb weight at sale (kg LW/head) n.a 51
Average daily growth rate of steers/lambs 0.53 0.14
(kg LW/head/day)
Annual cattle sales (kg LW) 85,889 n.a.
Annual sheep and lamb sales (kg LW) n.a. 63,965
Annual wool sales (kg greasy) n.a 8284
Biophysical allocation for sheep LW (%) n.a. 71
a LW = live weight, n.a. = not available, n.r. = not reported.
Each regional farm model estimated livestock drinking water based on livestock numbers, feed intake and climate. Farm water supply losses and irrigation water use were determined for each region using water balance methods and datasets described in detail by Wiedemann et al. (2015a, 2015b). For each regional farm, a disaggregated model of land occupation was developed to differentiate between crop land, arable pasture land and non-arable pasture land, using methods described in other publications Wiedemann et al. (2015a, 2015b).
Impacts associated with purchased inputs such as fertiliser and fuel were modelled using processes from the Australian LCI database (Life Cycle Strategies, 2007) where available, or the European Ecoinvent (2.0) database (Frischknecht et al., 2005). Feed grain inventory data were obtained from Wiedemann et al. (2010a) and Wiedemann and McGahan (2011). Impacts associated with services such as communications, insurance and accounting were modelled based on expenditure using economic input—output data (Rebitzer et al., 2002).
2.2.2. Meat processing
Inputs and impacts associated with beef and lamb processing (Table 3) were collected from an industry survey of meat processing plants in Australia (GHD, 2011) which included recorded energy inputs, cleaning inputs, water use and waste water production per unit of output.
Two meat processing plant models were developed to determine flows of co-products and waste from processing of beef and lamb, and to determine emissions from waste treatment. Factors used to determine product mass, co-product mass and waste production are described in Wiedemann and Yan (2014). Key model parameters included dressing percentages (i.e. from live animal to hot carcase) of 55% and 45%, cutting and chilling losses of 3% and 4%, and retail yield (from cold carcase to retail meat) 74% and 88% for boneless beef and bone-in lamb respectively. Human edible protein conversion efficiency was determined by dividing the mass of protein in boneless beef and bone-in lamb, determined from the edible yield of each product (Wiedemann and Yan, 2014) and a dry mass protein content of 0.2 kg/kg red meat. Protein mass was therefore 0.19 and 0.15 kg/kg retail product for beef and lamb respectively, and these values were used to determine by the total human edible protein yield. Emissions from anaerobic treatment of effluent were included using effluent production rates and characteristics reported by GHD (2011) and from data collected at three meat processing plants by the authors, and Australian inventory factors (DCCEE, 2012a).
Table 2
Greenhouse gas (GHG) methods and uncertainty estimates used in the study.
Emission source Key parameters/model Uncertainty (%) Reference
Enteric methanea (kg/sheep) kg DMI/head x 0.0188 + 0.00158 ±20 DCCEE (2012a).
Enteric methane (CH4 yield — % of GElb grazing cattle) 6.5% ±20 Dong et al. (2006).
Enteric methanec (MJ CH4 feedlot cattle) 3.406 + 0.510*SR + 1.736*H + 2.648*C ±20 DCCEE (2012a).
Manure methane (grazing) (kg)d kg DMI* (1 - DMD)* MEF ±20 DCCEE (2012a).
Manure nitrous oxide (grazing) Urinary N - 0.004 kg N2O-N/kg N in urine ±50 DCCEE (2012a).
Faecal N - 0.005 kg N2O-N/kg N in faeces.
Manure ammonia (grazing) 0.2 kg NH3-N/kg N of excreted in manure ±20 DCCEE (2012a).
Manure methane (kg/hd feedlot beef)e VS (kg/head)* Bo* MCF* p ±20% DCCEE (2012a).
Manure nitrous oxide (feedlot cattle) Faecal and urinary N - 0.01 kg N2ON/kg N excreted ±50% Muir (2011).
Manure ammonia (feedlot cattle) 0.75 kg NH3-N/kg N excreted ±20% Watts et al. (2012).
lndirect nitrous oxide — ammonia 0.01 kg N2O-N/kg N lost as ammonia-N ±50 DCCEE (2012a).
lndirect nitrous oxide — Leaching and runoff 0.0125 kg N2O-N/kg N lost in leaching and runoff ±50 DCCEE (2012a).
a DMI = Dry Matter intake.
b GEI = Gross energy intake assumed to be 18.6 MJ/kg dry matter intake (DMI). c SR = soluble residues, H = hemicellulose, C = cellulose.
d DMD = Dry matter digestibility; MEF = Methane emission factors (temperate — 1.4 x 10~5 kg CH4/kg DM manure, warm — 5.4 x 10~5 kg CH4/kg DM manure). e VS = volatile solids; Bo = 0.17 m3 CH4/kg VS; MCF = 0.05 in Queensland, 0.015 in NSW. Density of methane (p) = 0.622 kg/m.3.
2.2.3. Transport and warehousing
Transport stages were included throughout the supply chain based on an inventory of transport distances and representative truck types and load specifications (Table 4). International transport of chilled beef and lamb was via ship to the USA. Transport distances represented export from the port of Brisbane and import to the port of Philadelphia (PH) based on trade data (https:// usatrade.census.gov/). Alternative modelling to the port of Los Angeles was completed for comparison. An inventory of inputs associated with storage in refrigerated warehouse was based on micro data from the Energy Information Agency Commercial Buildings Energy Consumption Survey, ASHRAE design guidelines (ASHRAE, 2004; EIA, 2003), and an interview of plant managers to determine electricity consumption, conducted as part of the project (Table 4).
Impacts associated with transport were modelled for each different transportation type using database processes corresponding to the specific inventory stage. Australian truck processes were modelled using the AustLCl database (Life Cycle Strategies, 2007). International ocean liner transport was modelled using data from Webb et al. (2013). GHG emissions from refrigerant losses were not included because of a lack of data and their expected minor contribution (Webb et al., 2013). Ships were assumed to carry goods both ways because Australia operates a large trade deficit with the USA (DFAT, 2012). For transport within the USA, US lifecycle inventory (National Renewable Energy Laboratory, 2012) long-haul truck processes were applied. To understand the sensitivity of modelling assumptions in transportation, baseline parameters (Table 4) used to model the transportation from Australia to the USA and warehousing in USA were varied as part of the sensitivity analysis.
2.3. Methods for handling co-production
The study applied allocation methods following the (ISO, 2006) hierarchy and guidance from (LEAP, 2014) where clear and suitable methods were available. System subdivision and allocation prior to the farm-gate was explained in Wiedemann et al. (2015c, 2015d), using a system separation where possible, and biophysical allocation between wool and live weight in the sheep system after Wiedemann et al. (2015b).
At the meat processing plant, raw hides, tallow, meat and blood meal are co-generated. Allocation between meat products and co products at the point of meat processing was handled using economic allocation (LEAP, 2014) based on Australian market data (MLA, 2013b, c) and allocation fractions are provided in Table 5.
To understand the sensitivity of the co-product handling at meat processing, an alternative, hybrid approach was analysed. In this approach, we divided the rendering process from the rest of meat processing system. Biophysical allocation was used between retail meat, edible offal and raw hides, and system expansion was applied the rendering products (pet food, protein meals, and tallow). Production of soybean meal (equally sourced from Brazil and Argentina) was substituted for production of pet food, and protein meals. Tallow was substituted for Malaysian palm oil. The hybrid approach was chosen as an alternative to more closely match the biophysical approach taken at the farm stage where wool and live weight allocation was performed for sheep. A detailed explanation of meat processing allocation and system expansion factors and methods is described in Wiedemann and Yan (2014).
2.4. Analysis
Modelling and the uncertainty analysis was conducted in Simapro 7.3 (Pré-Consultants, 2012). Uncertainty associated with purchased inputs was determined using a pedigree matrix (Frischknecht et al., 2005), and was assessed using Monte Carlo analysis in SimaPro 7.3. One thousand iterations provided a 95% confidence interval for the results. Differences between datasets were assessed using comparative Monte Carlo analysis in SimaPro 7.3.
3. Results
Results are presented for resource use and impact categories of retail ready beef and lamb in the following sections.
Table 3
Major inputs associated with meat processing used in this study.
Major inputs units Per tonne carcase weight (beef) Per tonne carcase weight (lamb)
Fresh water consumption L 8743 5973
(100% consumptive)
Electricity kWh 318 329
LPG MJ 83 533
Diesel MJ 40 19
Petrol MJ 7 14
Coal MJ 693
Fuel oil MJ 1184
Natural Gas MJ 1230 2346
Table 4
Assumptions and emission factors used for transportation modelling from meat processing plant to regional storage centre.
References
Baseline parameters
Distance from meat processing plant to port Distance form custom clear to warehouse Warehousing period
Distance from warehouse to regional retail centre
Shipping distance from Australian port to USA Emission factors
Transoceanic shipping Energy
GHG emissions Warehousing Electricity Natural gas Ammonia
MJ/tkm kg CO2 e/tkm
kWh/(m3,yr)
Btu/(m3,yr)
kg/(m3,yr)
350 30 0.08 600 18120
0.2—0.27 0.02—0.0215
30.4—512.8
21019—535820
0—7.7
Based on average distances from Rockhampton, Kilcoy, Dinmore, Oakey, Inverell, Tamworth to Brisbane port
Expert judgement based on a review of meat import processes and interviews with importers
Expert judgement based on a review of meat import processes and interviews with importers
Expert judgement based on a review of meat import processes and interviews with importers
Oceanic distance between Brisbane and Philadelphia
Webb et al. (2013). Webb et al. (2013).
(ASHRAE, 2004; EIA, 2003) (ASHRAE, 2004; EIA, 2003) (ASHRAE, 2004; EIA, 2003)
3.1. Fresh water consumption and stress-weighted water use
Fresh water consumption ranged from 441.7 to 597.6 L/kg boneless beef depending on the production system and averaged 463.8 L/kg bone-in lamb (Fig 2). Drinking water and irrigation were the largest sources of fresh water consumption. With supply losses included, drinking water supply contributed 63 and 56% of fresh water consumption, and irrigation contributed 31 and 61% for the average of the beef and lamb supply chains respectively. Water supply losses were much higher than livestock drinking water due to the large evaporative loss from farm dams, which were the major drinking water supply. Irrigation losses were lower than irrigation water use due to the major supply source being larger, more efficient supply dams or bores. Stress-weighted water use ranged from 108.5 to 124.9 L-e/kg boneless beef and averaged 169.4 L-e/kg bone-in lamb. Stress-weighted water use was strongly influenced by regional water stress indexes, which averaged 0.22 (0.02—0.85) and 0.37 (0.01 —0.82) in the beef and lamb production regions. This resulted in higher stress-weighted water use for lamb than beef, despite the higher average fresh water consumption for the latter.
3.2. Land occupation
Total land occupation varied from 199.4 to 432.5 m2/kg boneless beef, 88% of which was from non-arable land (Fig 2). Crop land occupation from the grass and grain finished beef supply chains
Table 5
Meat products and co products per 1000 kg of live weight of beef and lamb processed.
Products Beef Lamb
Mass of Economic Mass of Economic
product (kg) allocation product (kg) allocation
Retail cutsa 442.1 91.4% 426.7 88.4%
Edible portion 95% 76%
of retail cutsb
Hides 88.4 5.5% 75.0 7.2%
Meat, blood and 61.9 1.5% 69.8 1.6
bone meal
Tallow 33.6 1.1% 81.0 2.3
Pet food 13.3 0.5% 15.0 0.5
Totals 639.3 100% 667.5 100%
a For beef, retail cuts refer to boneless beef and edible offal; for lamb, this refers to bone-in lamb and edible offal. b Wiedemann and Yan (2014).
varied from 3.2 to 29.9 m2/kg boneless beef, with the small amount of crop land used for grass-fed beef contributed by forage or supplement production. Grain production was the predominant contributor to crop land occupation. Consequently, Beef MF and LF occupied 5.1 to 9.5 times more crop land than grass finished beef. Crop land occupation from the lamb supply chain averaged 2.5 m2/ kg bone-in lamb.
3.3. Fossil fuel energy demand
Total fossil fuel demand from the grass and grain finished beef supply chains ranged from 32.3 to 46.6 MJ/kg boneless beef (Fig 2). The largest contribution was from the farm and feedlot stage (averaging 63%), followed by meat processing (averaging 20%), and international transportation (predominantly shipping) of meat from Australia to the warehouse in the USA (averaging 17%). Fossil fuel demand associated with transportation in the production system contributed a small amount (1—2%) of total energy demand. Grain-finished supply chains (Beef MF and LF) were found to have higher energy intensity than the grassland-finished supply chain. Total fossil fuel energy demand for the lamb supply chain averaged 28.1 MJ/kg bone-in lamb (Fig 2). The largest contribution was from the farm production stage (46%), followed by meat processing (31%) and international transportation (23%).
3.4. Greenhouse gas emissions
Emissions (excl. LU, dLUC) from the grass and grain finished beef supply chains ranged from 23.4 to 27.2 kg CO2-e/kg beef. The predominant contribution was from primary production (averaging 93%), followed by meat processing (4%) and transportation of meat from Australia to the warehouse in the USA (3%). By source, enteric methane was the single largest emission (averaging 70%), followed by carbon dioxide from the burning of fossil fuels (averaging 11%), and manure nitrous oxide (averaging 10%). Total GHG emissions from lamb supply chain averaged 16.1 kg CO2-e/kg bone-in lamb. Relative contributions of components were similar between the beef and lamb supply chains. Lamb primary production was also the predominant contributor (90%) and 75% of emissions were from enteric methane. Contributions from carbon dioxide were slightly higher (14%) while manure nitrous oxide (9%) was slightly lower for lamb GHG emissions relative to beef emissions.
Mean LU and dLUC emissions from the low and high emission scenarios were 8.3, 8.7 and 4.1 kg CO2-e/kg boneless beef for grass-
. 700.0 a e
o, - 600.0
g| 500.0 S u
S M 400.0 S*
* Hl 300.0 ï
£ 200.0 100.0
0.0 Beef grass Beef MF Beef LF Lamb
□ Other minor use 11.2 19.5 28.9 10.0
□ Meat processing 10.1 10.1 10.1 7.1
□ Irrigation losses 15.1 42.2 25.4 58.2
□ Irrigation 63.4 165.8 97.8 223.7
□ Drinking losses 272.1 217.1 163.7 126.5
□ Drinking 168.6 142.9 115.9 38.2
s s S " «if
500.0 450.0 400.0 350.0 300.0 250.0 200.0 150.0 100.0 50.0 0.0
Beef MF
Beef LF
Beef grass Beef MF BeefLF Lamb
■ Warehouse 0.03 0.03 0.03 0.03
S International transport 6.43 6.43 6.43 6.43
□ Meat processing 7.66 7.66 7.66 8.85
□ Farm&Feedlot 18.24 24.00 32.54 12.81
ii ■S i 2t
« o 0 u
□Non arable pasture 407.8 303.1 161.0 252.9
■Arable pasture 21.5 16.0 8.5 13.3
s Crop land 3.2 16.1 29.9 2.5
■ Warehouse 0.002 0.002 0.002 0.002
□ International transport 0.756 0.756 0.756 0.756
□ Meat processing 0.980 0.980 0.980 0.916
□ Farm & Feedlot 25.485 21.620 23.823 14.395
Fig. 2. Contribution of processes to fossil energy, fresh water consumption, land occupation, and greenhouse gas (GHG, excluding land use and direct land use change) emissions per kg of retail ready Australian beef and lamb exported to USA. Grass = grass-fed, MF = medium fed grain (115 days), LF = long-fed grain (330 days). Different letters on bars indicate significant differences (P < 0.05) between cases assessed using comparative Monte Carlo analysis.
fed, mid-fed and long-fed grain finished beef respectively when soil organic carbon stock change was assumed to be zero under pasture. When soil carbon sequestration was included for pastures for beef produced in NSW, values declined slightly to 7.3, 8.1 and 3.5 kg CO2-e/kg boneless beef for grass-fed, mid-fed and long-fed beef respectively. Results from the two scenarios for lamb showed average emissions of 0.4 kg CO2-e/kg bone-in lamb, or removals of -2.4 kg CO2-e/kg lamb where soil carbon sequestration under improved pastures was included.
3.5. Human edible protein conversion efficiency
Total HEP-CE was 7.9 for grass finished boneless beef and 2.9 for bone-in lamb, indicating that these products yield more human edible protein than they utilise throughout the system via consumption of animal feeds that could potentially be fed to humans. Grain finished boneless beef utilised more human edible protein inputs, resulting in lower efficiencies of 0.3 (LF) and 0.5 (MF).
4. Discussion
Livestock production, processing and transport requires water and energy inputs, and utilises land resources. While a number of studies have investigated resource use at the farm-gate level (Ridoutt et al., 2012a; Wiedemann et al., 2015c, 2015d; Williams
et al., 2006), fewer have included a detailed analysis of meat processing and the associated allocation processes required. We found the primary production phase of the supply chain to dominate GHG emissions, fresh water consumption and land occupation, while fossil fuel energy demand was more evenly distributed across the supply chain.
The farm-gate component of this study extended the assessment of fresh water consumption by taking into account consumptive losses associated with water supply throughout the supply chain, accounting for more loss pathways than previously included by some authors (e.g. Peters et al., 2010b). Farm-gate water use was slightly lower than reported for the national average for Australian beef (Wiedemann et al., 2015a), reflecting differences between the specific regions supplying the USA prime beef market and a broader assessment of Australian beef production. Several published studies from the United States do not report their coverage of the supply chain, but report higher water use of ~2300 L/kg boneless beef (converted from carcase weight — Capper, 2011) to 3682 L/kg boneless beef (Beckett and Oltjen, 1993). Comparative stress weighed water use was found to be much lower than fresh water consumption, reflecting the relatively low water stress conditions existing in most Australian livestock producing regions. Similar findings were made for Australian lamb by Ridoutt et al. (2012b).
We found Australian livestock production to rely predominantly on non-arable rangelands, reflecting the land capability in
Australia, where less than 6.5% of land mass is considered arable (FAOSTAT, 2014). While total land occupation in the study was higher than found by others (e.g. Nguyen et al., 2010; Williams et al., 2006), comparisons are not meaningful without understanding land capability or disturbance. Further research investigating biodiversity impacts is required to determine impacts from land occupation more accurately, but at a minimum differentiating the use of crop land (high disturbance) from pasture land (low disturbance) is informative. Production of the major grass-finished beef and lamb exports was found to utilise very little grain, yielding more HEP than consumed by the animals. This study found energy intensity per kilogram of retail ready meat to be lower from grass finishing than grain finishing. The higher energy intensity from grain finishing primarily relates to the additional inputs required to produce, transport and mill the feed inputs. In comparison, the grass finishing systems used few inputs for pasture production. Together with the land occupation results, this finding shows the importance of ruminant livestock in producing food products from low value land and grasses unsuitable for alternative food production. This is particularly relevant for Australia, where the area of arable land is small comparative to the area of natural rangelands.
Greenhouse gas emissions are known to be dominated by the production phase of the supply chain in ruminants (Ledgard et al., 2011). Results from the production phase were similar to previous Australian studies of beef (Eady et al., 2011; Peters et al., 2010a) and lamb (Brock et al., 2013; Eady et al., 2012) when GWP values and enteric methane prediction models were standardised. Impacts from lamb were lower than beef at the farm gate predominantly because a proportion of impacts are allocated to wool (see supplementary material) and because the sheep systems had higher productivity, resulting in lower livestock emissions. Emissions from Australian beef excluding LU and dLUC were similar or slightly lower than results from suckler beef production in Europe (e.g. Casey and Holden, 2006; Nguyen et al., 2012; Williams et al., 2006) and North America (e.g. Beauchemin et al., 2010; Lupo et al., 2013; Pelletier et al., 2010). This is mainly due to lower energy inputs on Australian cattle farms, and lower nitrous oxide emissions from cropping and manure in Australia than international defaults (DCCEE, 2012a). Differences in the impacts from beef and lamb diminished when the different yield of edible product (Table 5) was taken into account; per kg of edible product, GHG emission intensities ranged from 24.5 to 28.6 per kg beef and 21.1 per kg lamb.
Fewer studies have assessed impacts from LU and dLUC sources though these may be significant. Estimation and attribution of emissions and removals is complex, and results were regionally variable, contributing 4.1 —8.7 kg CO2-e/kg beef. Removals or modest emission levels from the sheep system ranged from -2.4 to 0.4 kg CO2-e/kg lamb, in response to modest rates of soil carbon sequestration under pastures in southern Australia. Emissions from dLUC have declined rapidly in Australia (DCCEE, 2012b) in response to changed management practices and legislation introduced over the last decade. These changes are only realised slowly when applying a retrospective analysis and 20-year amortization. We explored the expected future emissions from Australian beef by projecting the clearing rate data forward over the period from 2006 to 2026, when the full impact of reduced deforestation will take effect. This showed that dLUC emissions for Australian beef will decline to between 0.4 and 0.7 kg CO2-e/kg beef, with potential for reforestation on previously cleared land to result in net removal of carbon dioxide in this time period (Henry et al. submitted). Rates of GHG removals associated with increased soil carbon levels in improved pasture for the lamb production system are expected to decline as a new equilibrium is reached. Reported dLUC in the literature range from 5 to 8 kg CO2-e/kg beef for dairy calves produced in the EU (Nguyen et al., 2010) to >700 kg CO2-e/kg beef in
Brazil (Cederberg et al., 2011). In contrast to these results, Pelletier et al. (2010) investigated soil carbon sequestration in grazing systems and suggested that removals may be up to 8 kg CO2-e/kg beef for cattle raised on improved pastures in the USA, though a complete investigation of LU and dLUC emissions and removals was not included. Considering the limited data within the LCA literature, further research is required to understand the role of LU and dLUC on beef production.
4.1. Handling co-production
Processing and transport are minor stages and are occasionally overlooked in studies of beef and lamb. Results from a number of recent and older studies (e.g. Lupo et al., 2013; Williams et al., 2006) report results using a carcase weight functional unit without including impacts or allocation processes involved in meat processing, showing a mismatch between the system boundary and reference flow or functional unit of the study (Wiedemann and Yan, 2014). By accounting for the value of co-products and impacts from the meat processing stage, impacts to grass-fed beef, for example, were in the order of 10% lower than if co-products were ignored in the present study. To understand the sensitivity of economic allocation at the meat processing, a hybrid approach was also performed to divide impacts between the primary meat products while using system expansion to account for the minor rendering products. While it has been accepted for some time in LCA practice that multiple methods of allocation may be used at different points in the same study, here this was applied for a closely related co-product system coming from the same process. The hybrid approach resulted in an average of 8% lower GHG emissions, fossil fuel use, fresh water consumption, and stress-weighted water use, and 21% lower crop land occupation for the primary products from beef and lamb supply chains. This highlighted the sensitivity of allocation at meat processing and the contribution of valuable co products from meat processing.
4.2. Transportation
When averaged across the beef and lamb supply chains, international transportation contributed contributed <5% to GHG emissions, land occupation and fresh water consumption, while the contribution to energy demand was higher, ranging from 14% to 20% for beef and 23% for lamb. The higher relative contribution for lamb was related to the lower energy intensity at farm gate (Fig 2). To test assumptions, we investigated alternative transport distances to port and within the USA. Importing beef and lamb into the closer port (Los Angeles) reduced GHG by 0.3% and a modest impact on energy use (-4%). Increasing the distance of post-warehouse transport from 600 km to 2000 km increased the overall energy use by 6—7%, while changes to GHG were ~1%. The minor role of transport in assessing impacts from globally traded red meat has been shown previously (Ledgard et al., 2011; Webb et al., 2013) where transport contributed similar levels to GHG and energy as found here. Thus, transport distance or 'food miles' was found to be a poor predictor of the environmental impacts and resources of red meat transported by ship.
5. Conclusions
This study is the first multi-impact analysis of Australian red meat supply chains through to the USA. Impacts and resource use associated with red meat supply are heavily influenced by the production system and less by other components of the supply chain such as transportation or meat processing. Transportation was found to contribute <5% of GHG emissions, and water and land
resource use, confirming that food miles is not a suitable indicator of environmental impacts for red meat. Emissions from livestock, soil and fossil fuel consumption were similar in aggregate to other production regions in the world, and LU and dLUC emissions were found to be a moderate emission source for beef, but not lamb, in the current analysis. Deforestation emissions have declined significantly for Australian beef and as a consequence, the emissions attributed to beef from historic clearing events will continue to decline. Reforestation and soil carbon sequestration were shown to have potential to represent a small offset for emissions from beef and lamb production. Water use was found to be relatively low and was drawn predominantly from low water stress catchments. In the first Australian analysis of regional land occupation and consumption of human edible protein inputs, red meat products were found to be produced predominantly from non-arable rangeland areas with small amounts of arable land occupation. Human edible grain inputs were modest for the grass finished systems, displaying a high degree of resource efficiency in the production of high quality food from low quality grass resources, with a low degree of competition with human food sources or other grain users. These results underscore the valuable role in the global food supply chain that ruminants can provide when managed to exploit marginal land which is poorly suited for arable production.
Acknowledgements
This study was funded by Meat and Livestock Australia, project number B.CCH.2072. Review and input to the project scope from Dr. Tom Davison was appreciated. The contribution of data from the many case study farmers involved in the study was greatly appreciated.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jclepro.2015.01.073.
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