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Agricultural Systems
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S agricultural Systems
Resource use and greenhouse gas intensity of Australian beef production: 1981-2010
S.G. Wiedemanna*, B.K. Henryb, E.J. McGahana, T. Grantc, C.M. Murphya, G. Niethed
a FSA Consulting, 11 Clifford Street, Toowoomba, QLD, Australia
b Institute for Future Environments, Queensland University of Technology, Brisbane, Australia c Life Cycle Strategies, Melbourne, VIC, Australia d Niethe Consultancies, Brisbane, QLD, Australia
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ARTICLE INFO
Article history: Received 3 July 2014
Received in revised form 4 November 2014 Accepted 5 November 2014 Available online 21 November 2014
Keywords:
Carbon
Energy
ABSTRACT
Over the past three decades major changes have occurred in Australia's beef industry, affecting productivity and potentially the amount of resources used and environmental impacts from production. Using a life cycle assessment (LCA) approach with a 'cradle-to-farm gate' boundary the changes in greenhouse gas (GHG) emission intensity and key resource use efficiency factors (water use, fossil fuel energy demand and land occupation) are reported for the 30 years from 1981 to 2010, for the Australian beef industry. The analysis showed that over the three decades since 1981 there has been a decrease in GHG emission intensity (excluding land use change emissions) of 14% from 15.3 to 13.1 kg CO2-e/kg liveweight (LW). The improvement was largely due to efficiency gains through heavier slaughter weights, increases in growth rates in grass-fed cattle, improved survival rates and greater numbers of cattle being finished on grain. However, the increase in supplement and grain use on farms, and the increase in feedlot finishing, resulted in a twofold increase in fossil fuel energy demand for beef production over the same time. Fresh water consumption for beef production dropped to almost a third from 1465 L/kg LW in 1981 to 515 L/kg LW in 2010. Three contributing factors for this dramatic reduction in water use were: (i)an increase in the competitive demand for irrigation water, resulting in a transfer away from pasture for cattle to higher value industries such as horticulture, (ii) an initiative to cap free flowing artesian bores in the rangelands, and (iii) an overall decline in water available for agriculture compared to industrial and domestic uses. While there was higher uncertainty relating to estimates of land occupation and emissions from land use (LU) and direct land use change (dLUC), an inventory of land occupation indicated a decline in non-arable land occupation of about 19%, but a sevenfold increase in land occupation for feed production, albeit from a low base in 1981. GHG emissions associated with LU and dLUC for grazing were estimated to have declined by around 42% since 1981, due largely to legislated restrictions on broad-scale deforestation which were introduced progressively between 1996 and 2006. This paper discusses the prospects and challenges for further gains in resource use efficiency and reductions in greenhouse gas intensity for Australian beef production.
© 2014 Elsevier Ltd. All rights reserved.
1. Introduction
The major global challenges of food security and climate change have generated greater interest in understanding and monitoring the environmental impacts and resource depletion from food production systems. To address these challenges, there has been an increased focus on sustainable intensification - the production of more food from fewer resources with lower impacts. However, the capacity to monitor the impacts of food systems on the environment has been hampered by the lack of practical methods of
* Corresponding author. Tel.: +61 746328230; fax: +61 746328057. E-mail address: Stephen.wiedemann@fsaconsulting.net (S.G. Wiedemann).
http://dx.doi.org/10.1016Zj.agsy.2014.11.002 0308-521X/© 2014 Elsevier Ltd. All rights reserved.
assessment, and the lack of suitable data to quantify these impacts over time and identify impact hotspots for improvement. Life cycle assessment (LCA), initially a tool for the industrial sector ISO (2006), has increasingly been applied to agricultural products to quantify environmental impacts, to meet this need. In this paper, an LCA approach is used to assess the change in key environmental impacts and resource use efficiency of Australian beef production over the past three decades (1981-2010). This period covers a period of significant change in beef production systems in Australia and coincides with availability of more reliable data on animal numbers, their movements and productivity than in earlier years.
The Australian beef industry has evolved from meagre beginnings with European settlement in the late eighteenth century to a national herd of approximately 26.5 million head of beef cattle
for a broad spectrum of markets and climatic conditions. Australia is now the world's 8th highest beef producing country and third largest beef exporter. These developments have inevitably been accompanied by changes in the interaction of beef production with the environment. The industry continues to develop in response to market, climate and policy drivers, improved production technology and changing socio-cultural values (Bindon and Jones, 2001). These changes influence, and are affected by, environmental impacts and resource constraints. The beef industry in Australia remains characterised by relatively low input production systems utilising native or naturalised grasslands in the extensive rangelands and woodlands. Australian beef producers also operate within a high degree of climatic variability, driven largely by the El Nino-Southern Oscillation (McKeon et al., 2004). Low input management and a high degree of flexibility are management strategies employed to manage the influence of high climate variability, particularly in the north where frequent droughts limit pasture growth and, in turn, cattle productivity (Herrero et al., 2013). An example of change in the industry over the last 40 years has been the move in the northern subtropical and tropical regions to Bos indicus breeds which are favoured for their capacity to handle heat, poor quality feed and parasites. In the extensive north, these breeds have lower mortality rates but also lower weaning rates than Bos taurus breeds. In the southern part of the continent, the temperate climate and Mediterranean weather patterns have historically delivered more reliable rainfall and better feed conditions. The southern regions are more productive in terms of weaning rates, growth rates and beef produced per unit area of land, though inputs such as fertiliser are significantly higher than in the north. Other major structural changes influencing the interaction of beef cattle in the Australian environment over the past half century have included advances in animal genetics and the rapid expansion of the feedlot industry from 1980 onwards. The influence of these changes on resource use and environmental impacts across the industry has not been assessed to date.
The environmental impact of animal agriculture receiving most attention over recent years has been its contribution to global warming, and quantifying greenhouse gas (GHG) emissions and trends is critical to assessing the environmental performance over time. For beef cattle, GHG emissions arise from enteric fermentation (the rumen digestive process) and from manure management. At a national scale, these direct animal emissions contribute approximately 10% of Australia's total GHG emissions as estimated for Kyoto Protocol reporting (DCCEE, 2013a). In addition, indirect emissions result from fossil fuel energy demand, energy and emissions associated with manufacture of production inputs, soil emissions from nitrogen fertiliser use and emissions associated with land use (LU) and direct land use change (dLUC).
Another prominent concern globally is the stress on fresh water resources (Rockström et al., 2007; WHO, 2009). In Australia, agriculture is attributed with using 65-70% of extracted water, primarily for irrigation (ABS, 2006b), which is similar to the situation globally. Water requirements of cattle vary greatly depending on the moisture content of the feed, the climatic conditions and the physiological state of the animal (CSIRO, 2007; Springell, 1968). Other contributions to water use for beef production include evaporative losses from farm water supply, and irrigation for pasture, fodder and grain production (Wiedemann et al., 2015). Land, particularly arable land, is a limited resource globally and is included in analysis of the environmental impacts of production as the area of'land occupation' to produce a product. For agricultural products, assessment of the area and type of land (e.g. arable or non-arable) used contributes to reducing the risk of unintended trade-offs in managing environmental impacts (Ridoutt et al., 2011). However, analysis of trends in land occupation over time is complicated by the lack of a consensus method of assessment and the movement of land parcels between production systems or between agriculture and
other uses (e.g. conservation reserve or infrastructure). While a number of studies have assessed GHG emission intensity and water use for beef production in Australia (Eady et al., 2011; Peters et al., 2010a, 2010b; Ridoutt et al., 2012) these studies focussed on only one or two case study farms or used theoretical production estimates. The recent study by Wiedemann et al. (2015) studied two major Australian beef production regions, but did not provide comprehensive coverage of the whole industry. Hence, no previous Australian studies have quantified changes in impacts over time or provided broad regional coverage of the beef industry.
Changes in the Australian beef industry undertaken for productivity benefit directly or indirectly affect environmental impacts and resource use efficiency. Improvements in feed quality and quantity using grain finishing or flexible stocking rates to preserve pastures or selection of animals with higher feed conversion efficiency will all increase productivity and, by earlier finishing and heavier slaughter weight, may also decrease the GHG intensity of the product (Capper, 2011; Peters et al., 2010a). However, the trend in GHG intensity or apparent resource efficiency of a product will reflect deliberate management improvements and also factors less under the control of the producer. For example resource use may be influenced by regulation as well as more efficient management, and by seasonal climatic conditions. This study aimed to quantify the trend in GHG emissions, fossil fuel energy demand and water use for the Australian beef cattle industry for the period 1981-2010 using a LCA approach. The study included estimates for LU and dLUC GHG emissions, although data limitations meant the estimates were based on a semi-quantitative approach using best available data. The study identified impact and resource hotspots and considered possible management, policy and natural factors contributing to the trends.
2. Methods
Key sources of data for this analysis included the Australian Bureau of Agricultural and Resource Economics (ABARES) which undertakes independent research and data analysis for agriculture; the Australian Bureau of Statistics (ABS) which provides national and regional scale statistics based on surveys and census data; industry data primarily from Meat and Livestock Australia and the Australian government's National Greenhouse Gas Inventory (NGGI) report to the United Nations framework Convention on Climate Change (UNFCCC) and Kyoto Protocol reporting.
2.1. LCA approach
2.1.1. System boundaries and functional unit
The product system included the national beef herd producing cattle processed in Australia, and specifically excluded beef from dairy cattle and beef from herds supplying the live export market (Fig. 1). Analysis of herd and processing statistics in this study indicated that beef from the dairy herd in Australia contributes only 8-12% to total beef production. Assessment of change in environmental impacts focussed on the beef herd and its changes over the study period. The study excluded not only live export animals but also the herd supporting their production. Australia exports beef cattle from the northern production regions for finishing in feed-lots overseas prior to processing, with Indonesia being the largest market. While the live export market is highly relevant to Australia, collecting inventory data for the transport and finishing of these cattle in their country of destination was beyond the scope of the present study.
The study examined the primary production system (i.e. cradle to farm gate) using a functional unit of 'one kilogram of live weight (LW)' on-farm, immediately prior to processing (Fig. 1). The choice
Fig. 1. System boundary diagram showing coverage of the cradle to farm gate primary production system producing beef cattle processed in Australia (dashed line) and excluded production systems.
of LW as a functional unit aligns with the system boundary (at the farm/feedlot gate) and the focus of the study.
2.1.2. Impact categories and resource use
The study included investigation of GHG emissions, energy demand, fresh water consumption and land occupation. The GHG assessment applied the Intergovernmental Panel on Climate Change (IPCC) AR4 Global Warming Potential (GWP) equivalence factors of 25 for methane and 298 for nitrous oxide (IPCC, 2007). Emissions from LU and dLUC were estimated and reported separately from the agriculture sector emissions following ISO TS 14067 (ISO, 2013) recommendations. Energy demand was assessed using the fossil fuel energy demand method (Frischknecht et al., 2007) and is reported in MJ of oil-equivalents (lower heating values - LHV). Water use was assessed using the fresh water consumption use indicator (Bayart et al., 2010) and impact assessment methods were not applied. Land occupation was assessed from a broad analysis of national statistics.
2.2. Inventory data
2.2.1. Herd modelling and prediction of feed intake
A spatially defined national herd inventory was developed from statistics of livestock numbers and cattle processed (ABS, 2013) and the annual ABARES survey (ABARES, 2013) for livestock productivity parameters. The herd was modelled in periods of 5 years (1 July-30 June) corresponding to ABARES datasets. The age of young cattle at processing is required to determine average daily gain (ADG), a critical component of herd productivity, but because no datasets give this statistic explicitly, the age of the herd was determined by constructing a national herd production model based on weaning, mortality and cattle slaughter, and calibrating this model against the standing inventory numbers for steers, heifers and cows reported by the ABS (ABS, 2013). These two datasets enabled a
prediction of the mean age of cows, steers and heifers. The predicted age at processing of steers and surplus heifers was verified by comparison with market requirements for age (as indicated by dentition) and weight. The methods and definitions reported in these datasets are given in the explanatory report by ABARES (2011) and further details are provided in the supplementary materials. Beef production from the dairy industry was excluded by modelling dairy herd production numbers based on data from the annual ABARES survey (ABARES, 2013) and removing the contribution of cull dairy cows and progeny to meat production. Productivity data as summarised in Table 1 enabled estimation of feed intake using the Australian NGGI feed intake model of Minson and McDonald (1987).
2.2.2. Greenhouse gas emissions calculations
Enteric methane emissions from cattle grazed at pasture were calculated using methods appropriate to major regions and feeding systems. Enteric methane from the temperate southern regions was determined using the Blaxter and Clapperton (1965) model which is based on feed intake and pasture digestibility. For consistency with the Australian NGGI, the digestibility characteristics used in Australian NGGI calculations for southern Australia were assumed. In the northern regions, the prediction equation of Kennedy and Charmley (2012) which is based on feed intake only was applied. Enteric emissions from feedlot cattle were estimated using the model of Moe and Tyrrell (1979), as used in the Australian NGGI (see supplementary material for details). For grazing cattle, emissions from manure were predicted using methods consistent with the Australian NGGI that include both direct emissions (N2O and CH4) and, where relevant, indirect emissions from ammonia volatilisation, leaching and runoff. Feedlot manure emissions were modelled using a mass balance approach and emission factors from recent Australian research as reviewed by Watts et al. (2012) (see supplementary material for details). Nitrogen fertiliser use for beef production in Australia is primarily associated with the production of feed grain.
Table 1
Annual herd production parameters for 5 year periods over the study period 1981-2010.
Production parameter 1981-1985 1986-1990 1991-1995 1996-2000 2001-2005 2006-2010
Females - mated (hd) 7,806,726 7,785,274 8,945,291 8,486,621 9,247,685 9,217,722
Females - not mated (hd) 1,552,465 1,513,321 1,575,859 1,563,842 1,568,719 1,522,643
Bulls (hd) 312,269 311,411 357,812 339,465 369,907 368,709
Steers > 1 year (hd) 4,006,410 3,962,470 4,281,868 4,182,244 4,139,809 4,053,108
Calves branded (hd) 5,994,399 6,058,721 6,632,298 6,727,739 7,145,714 7,160,021
Weaning per cent (%) 76.8% 77.8% 74.1% 79.3% 77.3% 77.7%
Mortality rate (%) 4.0% 3.1% 3.0% 2.5% 2.7% 2.7%
Av. age of steers at processing (yrs) 2.41 2.36 2.34 2.28 2.20 2.18
Av. LW of steers at processing (kg LW) 474 540 538 586 597 574
Av. daily gain of steers - birth to processing (kg/d) 0.39 0.55 0.55 0.68 0.76 0.72
Av. age of surplus heifers at processing (yrs) 2.04 1.96 1.96 1.86 1.83 1.81
Av. LW of surplus heifers at processing (kg LW) 405 438 440 453 426 414
Av. daily gain of surplus heifers at processing (kg/d) 0.36 0.50 0.49 0.61 0.52 0.49
Nitrous oxide emissions were determined using the Australian NGGI emission factors and grain production processes previously reported by Wiedemann and McGahan (2011). Emissions from fossil fuel combustion were determined from the inventory of purchased inputs and direct farm fossil fuel energy use.
Greenhouse gas emissions associated with LU and dLUC were calculated and reported separately as recommended in ISO TS 14067 (ISO, 2013). The major LU and dLUC sources for beef production were (a) the clearing of trees (deforestation) to promote pasture growth; and (b) soil carbon loss due to cultivation for feed grain or fodder production, associated with land management and the conversion of pasture to crop land. Emissions arise from savannah burning which is commonly practiced by beef cattle farmers, though the attribution of these emissions to beef production is not clear because wildfires are endemic in the natural environment and should not be directly attributed to the beef farming enterprise. As noted in the Australian national greenhouse gas inventory, stopping anthropogenic fires would not necessarily reduce GHG emissions as they would be replaced by wildfires (DCCEE, 2010). It is generally understood that emissions from controlled, early dry season savannah fires are lower than emissions from the uncontrolled wild-fires that are endemic to the natural system. This implies a possible net reduction in emissions from managed fires compared to the reference (natural) system, but in the absence of quality data distinguishing managed fires and natural wild-fires, a conservative approach was adopted by excluding emissions and removals associated with changed fire management. Deforestation and soil carbon losses were assessed using ISO TS 14067 recommended methods and assumptions consistent with Australian GHG inventory reporting where available. Data sources for deriving areas of deforestation and carbon stock change were the Australian NGGI (for 1990 to 2010) and supporting data from UNFCCC reporting (AGO, 1998) for 1981 to 1990. The DSITIA (2012) provided more detailed data for Queensland (QLD), the state with the highest rates of land clearing during the study period. Reliable estimates of GHG emissions from LUC in Australia are available from 1990 to 2010 in the NGGI which uses a Tier 3 approach. Over these 20 years LUC emissions have been dominated by clearing of woodlands and forest in QLD to promote pasture production for extensive grazing. Using these national clearing statistics and, independently available data from the QLD Government State wide Land cover and Trees Study (SLATS) program, an estimate was made of the proportion of total deforestation for beef production for 1990-2010 (AGO, 1998). For the period 19811990, GHG emissions were estimated using the revised 1996 IPCC Guidelines for the Australian NGGI (IPCC, 2007) methods, rates of clearing reported from state government data and extensive expert consultation (AGO, 1998), to give as consistent a time series as possible. The rate of conversion of forest to grassland is constrained by remotely sensed and internationally reviewed data on areas of forest, and on-ground verification of woody vegetation clearing and post-clearing land management in the state of Queensland (DSITIA,
2012). Confidence in these estimates is limited by the assumptions involved in attribution of clearing for grazing for cattle and sheep and lack of spatial data on changes in land management following clearing or re-clearing.
Factors contributing to uncertainty in estimates of GHG emissions for dLUC include the coarser spatial scale of some data and lack of sufficient information for attribution of clearing to beef cattle or sheep grazing. The total land occupied for cropping in major grain producing regions across the 30 year time period of analysis (ABS,
2013) provided an estimate of land conversion from pasture to cultivated grain production based on grain and concentrate use in the herd (including feedlots). Emissions from soil carbon loss were assessed using factors from Dalal and Chan (2001) with more detail provided in the supplementary material. The possible impact of reduced tillage or no-till farming practices on soil carbon emis-
sions has not been included in this analysis because of the uncertainty in impact in Australian soils and rate of practice change for feed grain production (McLeod et al., 2013; Page et al., 2013). Thus the estimated GHG emissions may represent a minor overestimate in regions where no-till farming has been widely adopted.
2.2.3. Fossil fuel energy demand
Fossil fuel energy demand was modelled from farm input data such as farm fuel use, feed inputs, fertiliser, services, transport of cattle throughout the supply chain, which were collated from ABARES (2013). These data were cross checked with case study farm data, from different regions of Australia previously modelled by the authors (Wiedemann et al., 2015).
2.2.4. Fresh water consumption
Water use assessment was based on an inventory of fresh water consumption uses after Bayart et al. (2010), covering all sources and losses associated with beef production both in 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). Degradative water consumption was not estimated and impact assessment was not included. Primary sources of fresh water consumption use for beef cattle production arise from livestock drinking requirements and irrigation water used to grow feed fed to cattle. While irrigation water data were available on a national scale to inform the analysis, there is no equivalent dataset reporting drinking water for livestock and these were modelled based on herd data.
2.2.4.1. Drinking water. Drinking water for grazing cattle was predicted from the livestock inventory by region, using a prediction equation derived from CSIRO (2007) by Ridoutt et al. (2012) which is based on LW, feed intake, moisture content of feed and ambient temperature. Drinking water requirements for feedlot cattle were determined from feed intake and ambient temperature using Winchester and Morris (1956), which was found to correspond well with measured feedlot drinking water data from a range of Australian feedlots, as reported by Davis et al., 2009.
2.2.4.2. Drinking water supply losses. Appreciable losses may also arise from the water supply system (Wiedemann et al., 2015) and are highly dependent on the actual system supplying the water. No definitive dataset was available reporting the proportion of drinking water supplied from different sources (bore, creek/river, dams) across the major grazing regions in Australia. In the absence of these data a survey was conducted of industry experts across all regions to determine the proportion of water drawn from different sources. Loss rates were determined for different sources, with the highest losses arising from uncapped bores flowing freely to open, unlined drains. Evaporation losses from farm dams were also estimated using methods outlined in Wiedemann et al. (2015).
2.2.4.3. Irrigation water. Irrigation water use was determined from the area of land irrigated on beef farms from the ABARES survey, and national irrigation datasets collated by the ABS (2006a, 2006b) which provided national data for irrigated pasture used for beef cattle, and irrigation use associated with the production of purchased hay, grain and supplements.
2.2.4.4. Irrigation water supply losses. Water Account Australia reports (1993-1994 to 2009-2010 in ABS, 2000, 2012a) gave the sources of irrigation water supply as distributed sources (46%), bores (27%) and other surface water supplies (24%), with the remaining 3% being reuse water from other industries. Four years of data (2004-2005 to 2010) were available from the national water account, where
supply losses from distributed irrigation sources were specified. The average loss rate for these 4 years was 27.1% of total water extracted from the environment (ABS, 2006a, 2012a). These losses correspond to evaporation losses from state owned supply dams and seepage losses from irrigation channels. Losses from surface water sources (i.e. direct extraction from unregulated creeks and rivers) and bores were assumed to be negligible (see supplementary material for details).
2.2.5. Land occupation
Land occupation for beef cattle production was derived from online statistics from FAO (2011) and ABS (2013). Statistics for non-arable grazing land did not disaggregate grazing land to use for beef cattle and sheep, so this was inferred from location (see supplementary material for details). Land occupation for feed production was estimated from grain and concentrate use in the herd (including feedlots) and total land occupation for cropping in major grain producing regions across the 30 year time period of analysis (ABS, 2013).
2.3. Co-production and treatment of inputs
Where beef, sheep and cereals were co-produced on the same farm, inputs extracted from ABARES datasets were divided on the basis of the proportion of land occupied. To divide between sheep and cattle, predicted feed consumption was used as a measure of land occupation. The functional unit of the study did not differentiate between beef from different animal classes and the system boundary stopped prior to meat processing. Consequently, allocation was not required within the beef herd. Manure nutrients from the feedlot sector were handled using a system expansion process to include the avoided production and application of synthetic fertilisers for cropping systems, as use of feedlot manure for crop production is common (Skerman, 2000).
2.4. Background data
Background data for upstream processes such as generation and supply of energy and purchased products such as fertiliser were sourced from the Australian LCI database (Life Cycle Strategies, 2007). Fossil fuel energy demand associated with the manufacture of purchased inputs such as fertiliser was modelled from either the Australian LCI database (Life Cycle Strategies, 2007) where available, or the European Ecolnvent (2.0) database (Frischknecht et al., 2005). Feed grain data were sourced from the Australian LCI database (Life Cycle Strategies, 2007), Wiedemann et al. (2010) and Wiedemann and McGahan (2011).
2.5. Data limitations
The study relied on data from a number of disparate and discontinuous datasets to construct the herd model from which predictions of GHG and water use were made. A degree of caution should be applied in interpreting these results. The process of calibrating the model with national slaughter statistics, which is the most reliable dataset available, ensured that productivity was not grossly over predicted. However, no definitive statistics are collected in Australia on growth rates in slaughter cattle (particularly grass fed slaughter cattle), and consequently there is a degree of uncertainty in the estimates. Insufficient data were available to inform a quantitative uncertainty analysis. However as the most important results from the study are the trends over time, the uncertainty in the absolute values is less important and the trend data were supported by multiple time steps providing confidence in trends identified. A higher degree of caution is recommended for the land occupation, LU and dLUC emission results presented in the study.
Consistent data were not available for land occupation or land use change for beef cattle in a spatially and temporally disaggregated format, as required to undertake an analysis that fully accounts for movement of land between sheep and cattle production and between grazing and broadacre cropping over the period of analysis.
3. Results
3.1. Greenhouse gas emissions
Greenhouse gas emissions (excluding LU and dLUC) from the defined Australian beef herd were estimated to have risen 19% over the 30 year time period from 1981 to 2010 from 35.8 Gg to 45.1 Gg CO2-e, reflecting a gradual increase in herd numbers. Over the same time period, the GHG intensity declined 14% from 15.3 to 13.1 kg CO2-e/kg LW (Fig. 2). The GHG profile (excluding LU and dLUC) was dominated by enteric methane (84-88%) followed by nitrous oxide (10-11%) and carbon dioxide from fossil fuels (3-5%). Contributions from carbon dioxide increased over the analysis period with the greater use of fossil fuels, while enteric emissions declined proportionally per kg LW.
Over the analysis period, the national average age of finished steers decreased from 2.41 to 2.18 years and finished weight increased from 474 to 574 kg (Table 1). From 2001 to 2010 slaughter weights declined slightly across the herd (Table 1), which may have been in response to widespread drought conditions over this decade. Australia suffered two major droughts in this period, during 20022003 and 2006-2007 (Australian Government, 2014). Below average rainfall limits pasture production and therefore limits livestock growth rates in many regions (Carter et al., 2000). Over the assessment period, average carcase weights increased by 13.5%, which is reflected in the ABS published slaughter data for Australia over this time period. An indicator of the improvement in productivity over this period was the increase in beef production per breeding cow mated, which rose 32% from 301 to 396 kg mainly in response to heavier steer weights. Beef production per breeding cow mated declined slightly in the last decade in response to below average rainfall conditions. Fewer improvements were observed in breeding herd productivity. Mortality rates declined from 4.0% to 2.7%, resulting in slightly higher production. The change in mortality rate was greatest in the northern regions, possibly reflecting a transition from Bos
1981-1985 1986-1990 1991-1995 1996-2000 2001-2005 2006-2010
□ Breeder cattle □ Calves to 12 months
IE Grass finishing ■ Grow out prior to finishing
S3 Grain finishing
Fig. 2. Change in GHG emissions per kilogram of LW at the farm gate from different sectors of the beef herd over the period 1981-2010.
Fig. 3. Estimated annual rate of direct land use change for beef production in States and Territories of Australia over the past 30 years averaged for 5-year periods from 1981 to 2010. (QLD - Queensland, NSW - New South Wales, VIC - Victoria, SA - South Australia, WA - Western Australia, TAS - Tasmania, NT - Northern Territory). Sources of total clearing data: 1981-1990 (AGO, 1998); 1990-2010 (DCCEE, 2012). Insert shows the location of clearing events detected between 1990 and 2011 (DCCEE, 2013b).
taurus to Bos indicus genetics. However, over the study period weaning percentages varied little from the 30 year average (77.2%) and no trend was evident. Breeding efficiency was higher for the southern regions (aggregated av. of 84% over 30 years for southern regions) compared to the northern regions (aggregated av. of 65%).
3.2. Land use and direct land use change greenhouse gas emissions
were drinking water, drinking water supply losses and irrigation water for pasture. Drinking water supply losses declined from 530 to 190 L/kg LW over the analysis period, with the savings mainly related to lower supply losses from artesian bores in the pastoral regions. The decline in irrigation water from 798 to 152 L/kg LW was even more pronounced. In absolute terms, irrigation water use for pasture was estimated to have declined by 1351 GL for the entire Australian herd.
Nationally, dLUC emissions associated with deforestation for beef cattle pastures were estimated to have increased from an annual average of 44.1 Mt CO2-e in the 5 years from 1981 to 1985 to 62.8 Mt in 1986-1990, before declining to an average of 24.2 Mt CO2-e/ year in 2006-2010 (Fig. 3).
Cultivated land occupation for feed grain, predominantly for the feedlot sector, was estimated to have increased more than sevenfold over the analysis period, from 0.2 M ha to 1.7 M ha. Soil carbon losses associated with LU and dLUC were estimated to have increased from an annual average of 225,540 t CO2-e/yr in the 5 years to 1985, to 1,697,963 t CO2-e/yr in the 5 years to 2010. This contributed an additional 0.1-0.5 kg CO2-e/kg LW across the whole herd. The trend analysis indicated that LU and dLUC GHG emissions for beef production decreased by 41% over the 30 year analysis period, noting the uncertainty due to lack of data on movement of grazing land between sheep and cattle production over the analysis period.
3.3. Fresh water consumption
Fresh water consumption declined in both absolute terms and in relation to production over the investigation period. Average fresh water consumption for the 5 years to 1985 was estimated to be 3442 GL, declining to 1773 GL for the average of the 5 years to 2010. Over this period, water use per kilogram of LW declined from 1465 to 515 L/kg LW (Fig. 4). The three largest contributions to water use
3.4. Fossil fuel energy demand
In contrast to GHG and water use, fossil fuel energy demand increased over the analysis period, from 6.3 to 11.7 MJ/kg LW (Fig. 5) before declining slightly in the last 5-year period to 11 MJ/kg LW. The increase in fossil fuel energy demand to 2005 was primarily associated with feedlot production, and smaller increases were also observed in farm fuel use, farm services and fertiliser application. The decline in average annual fossil fuel energy demand in the last 5 years was partly in response to a decline in fertiliser use in this period which may be in response to below average rainfall and associated poor growing conditions for pasture.
3.5. Land occupation
Land occupation for grazing has trended downwards over the past two decades, with land classified by ABARES as non-arable agricultural use dropping from approximately 57% of Australia's total land area in 1981 to 46% in 2010 (ABARES, 2009; ABS, 2013) which represents a decline of 19% relative to land occupation in 1981. Within the non-arable agricultural land category, the predominant use in Australia is extensive grazing of beef cattle with smaller proportions used for sheep grazing. The area of land occupied for cereal cropping increased by 37% in this time period (from ABS, 2013) accounting for some of the decline in grazing land, though most of
tuO 800
_J bOO
A, H
E Irrigation of crops
■ Feedlot water
B Drinking water supply
losses 0 Drinking water
□ Irrigation of pasture
Fig. 4.
1981-1985 1986-1990 1991-1995 1996-2000 2001-2005 2006-2010 Change in fresh water consumption per kilogram of LW from the Australian beef herd from 1981 to 2010.
the land lost from grazing moving to conservation area and other non-agricultural use. The area of arable land occupied directly for beef production was estimated to have increased more than sevenfold over the analysis period, from 0.2 M ha to 1.7 M ha in response to greater requirements for feed grain.
4. Discussion
4.1. The influence of herd management on resource use and emission intensity
A number of herd management factors have been identified as influencing resource use and emission intensity from beef production at the farm scale and national scale. Improving herd productivity via higher weaning rates, higher growth rates, heavier carcase weights and lower mortality rates all increase beef production per unit of feed (i.e. higher feed conversion rate) and water consumed. Total feed intake is the primary factor governing livestock GHG emission intensity (Cottle et al., 2011) and land occupation, and is an important factor governing water consumption. While changes in water consumption in response to herd management were less significant, herd management did have a large effect on livestock greenhouse gas emissions.
The analysis showed ADG and finished weights in young cattle increased over the analysis period, leading to increased beef production per breeding cow and reduced livestock emissions via the dilution of maintenance effect (Johnson et al., 1996). This trend was also observed by Capper (2011) in the USA beef herd. In Australia, increased ADG and finished weights were observed predominantly in response to increased numbers of cattle finished on grain diets in feedlots, and to a lesser extent in the grass finished segment of the industry. For example, national ADG increased by 85% for steers across the whole herd (Table 1) while ADG in the grass finished steers increased <20% and declined between the last two analysis periods in response to low rainfall conditions leading to reduced feed quality. Between 1981 and 2010 the number of cattle finished on grain rose from an estimated 340,000 head (annual average for 1981-1985) to 1.74 M head (annual average for 1996-2000) before numbers stabilised at 2.32-2.37 M head in the decade to 2010. The increase in animals finished on grain over the first 20 years represented a shift from grass-finishing to grain-finishing, which was less apparent in the later 10 year period when the increase was associated with an expansion in herd numbers. In comparison to grass finished steers, grain finished cattle had higher lifetime ADG and higher finished weights. In addition to efficiency improvements, feeding proportions of grain reduce daily methane emissions compared to
1981-1985 1986-1990 1991-1995 1996-2000 2001-2005 2006-2010 Fig. 5. Fossil fuel energy demand per kilogram of LW from the Australian beef herd from 1981 to 2010.
grass feeding (Dong et al., 2006). Including all impacts associated with production across the supply chain, Peters et al. (2010a) and Pelletier et al. (2010) found that finishing cattle on grain compared to grass reduced emissions intensity. Our analysis showed grain finished cattle rose from an average of 8% of the young animals slaughtered in 1981-1985 to 45% for the 5 years to 2010, thus supporting a reduction in emissions across the whole herd. There remains capacity to increase the number of young cattle finished on grain, but barriers exist regarding the economics of grain feeding some classes of cattle in northern and southern Australia.
The analysis of herd reproductive performance performed in this study showed that there has been effectively no improvement in weaning rate over the past 30 years. Improvement in reproductive performance is a major efficiency goal of the beef industry. However, this is hampered by a number of factors including feed availability and quality in response to cyclical droughts (Burns et al., 2010). lncreasing weaning rates to approach a practical maximum potential for Australia of 90% (Hunter and Niethe, 2009) would result in lower livestock emissions and requirements for drinking water and land for feed production.
4.2. The influence of production system management on resource use and emission intensity
Over the analysis period, large changes were observed in the management systems used to produce cattle and this had a demonstrable effect on resource use. The major changes identified were a shift from grass to grain finishing, intensification of land occupation and an industry wide reduction in the utilisation of irrigation for pasture finishing. These changes led to lower total water consumption, reduced land occupation and higher energy demand. Changes in on-farm water and land management also led to reduced water consumption and total emissions from LU and dLUC sources.
Grain finishing resulted in improved herd productivity and reduced feed requirements, explaining part of the 19% reduction in total land occupation requirements observed in the analysis. In contrast, increased grain finishing led to most of the observed increase in fossil fuel energy demand for the herd as a result of added inputs for feed grain production and feedlot operations. Fossil fuel energy demand also increased in response to intensification of production from grazing land, observed from the increase in farm fertiliser and supplementary feed use on grazing farms.
lrrigation water use declined by 81% during the analysis period, in response to a shift in water use from lower value users (such as beef) to higher value users. Agricultural water use has declined substantially over the past two decades as water has shifted to urban and industrial users (ABS, 2000, 2004, 2010, 2012a). Within agriculture, water use has also shifted proportionally from beef to higher value industries such as horticulture (ABS, 2005, 2012b), under the influence of market pressures for land and water. Irrigation is an important resource for the industry, allowing for the production of high quality feed which mitigates the influence of low-rainfall seasons on productivity. However, despite such large reductions in irrigation water and associated high quality feed production, reductions were not observed in total production or productivity. This suggests that productivity losses from this trend were offset by intensification of production on non-irrigated land and the expansion of the feedlot industry. While these changes did not happen in a co-ordinated fashion, the outcome showed a shift in impacts from irrigation water consumption to fossil fuel energy use and greater use of cultivated non-irrigation land.
Water consumption also declined in response to a change in water supply management. Across large areas of inland Australia, livestock water is supplied from artesian bores which flow to the surface under pressure. During the analysis period, management of these water sources moved from free flowing bore drains where losses
are very high, to capped bores and tanks under a government and landholder initiative (DERM, 2011) to reduce water losses. Significant reductions were not apparent from other water supplies.
GHG emissions intensity from LU and dLUC associated with beef production declined by approximately 41% over the analysis period, due largely to legislated restrictions on broad scale land clearing. While emissions may still be attributed to beef cattle for some years, there is expectation to have only modest additional emissions from dLUC in the future. Further, options to mitigate GHG emissions may exist via strategic tree planting on less productive lands to sequester carbon, offsetting livestock emissions (Eady et al., 2011; Paul et al., 2013). The increases in efficiency of water and land occupation, and a decrease in GHG emission intensity of beef production provide an indication of positive environmental stewardship by the industry.
4.3. Comparison with the literature
Enteric methane emissions averaged 17% lower than reported by the Australian NGGl for the analysis period. While it was not possible to compare all details of the modelling with the Australian NGGl, there were some notable differences. Firstly, this study excluded the breeding herd and young cattle associated with the live export trade, resulting in lower emissions. Secondly, the present study applied an alternate enteric methane prediction equation for cattle grazing tropical pastures (Kennedy and Charmley, 2012), which resulted in 30% reduction in predicted enteric methane from cattle in QLD, the Northern Territory (NT) and northern Western Australia (WA). This prediction equation is based on a larger dataset covering more feed types typical of tropical grazing systems in Australia than the equation used by the Australian NGGl. While two specific Australian methods were use to predict enteric methane emissions for the northern and southern herds, average national results were comparable to predictions using the IPCC method (Dong et al., 2006). The emission intensity results presented here were similar to case study data reported for Australian beef production by Peters et al. (2010a) and Eady et al. (2011). These studies have focussed on a limited number of farms or regions, generally over 1-5 years. Greenhouse gas intensity from these studies varied from 11 to 17 kg CO2-e/kg LW when converted from carcase weight results and with standardised GWP values. Wiedemann et al. (2015) reported values for two major beef production regions of eastern Australia using a much larger dataset, and showed a range of emissions from 10.6 to 12.4 kg CO2-e/kg LW for grass finished cattle, excluding LU and dLUC.
Capper (2011) compared USA beef production in the year 1977 with 2007 and showed that GHG emission intensity (excluding LU and dLUC) declined 16.3% over this period. When the values were converted from carcase to LW, using dressing percentages supplied, the change in emission intensity was from 12.6 to 10.4 kg CO2-e/kg LW and was attributed to increased ADG and slaughter weights, and larger numbers of dairy calves entering the beef supply chain. Verge et al. (2008) reported a decline in GHG emission intensity for Canadian beef from 16.4 to 10.4 kg CO2-e/kg LW (excluding LU and dLUC) from 1981 to 2001, attributed to greater production efficiency, increased numbers of grain fed cattle and larger numbers of dairy calves in the beef production inventory. The present study reported a change in emissions intensity from 15.3 to 13.1 kg CO2-e/ kg LW, representing a similar decline to Capper (2011) but a less significant decline than Verge et al. (2008). While beef production in the USA and Canada is very different to Australia in terms of management, land type, climate, production efficiency and intensity and the role of the dairy sector, improved herd productivity was found to contribute to reduced emissions across all countries in the past three decades. However, in contrast to North America, beef production in Australia is not heavily influenced by the dairy sector, which contributes only 10% of total beef produced. The influence
of dairy cattle was not covered in the scope of this study because of the much larger data requirements to complete this analysis, but we expect this to have only a small influence on the trend in impacts for GHG.
The average fresh water consumption results for the last 5 years of this study were higher than theoretical estimates reported for Australian beef by Ridoutt et al. (2012) and case study estimates by Peters et al. (2010b). The regional analysis by Wiedemann et al. (2015) showed higher water use than previous case studies, but did not include water use from major irrigation regions in the southeastern part of Australia, or high loss rates from uncapped bores in the pastoral regions. The present results suggest irrigation contributes larger amounts of water to the whole industry than assessed from case study analyses and included greater losses from supply systems than previously assessed by these case studies. Capper (2011) reported a 12.1% decline in water use from 1175 to 1019 L per kg LW (following conversion from a carcase weight basis) for 1977 and 2007 respectively, though it was not clear from the study whether losses from water supply were taken into account, or whether a detailed analysis of changes in water management associated with feed production was performed. Previous studies of water use associated with USA beef production have indicated higher levels of water use associated with irrigation than found in the present study (Beckett and Oltjen, 1993) largely explaining the observed differences.
5. Conclusions
This study represents the most comprehensive analysis, to date, of trends in the environmental impacts of Australian beef production on a national scale. Estimates of improvements in environmental efficiency reflected enhanced herd productivity and changes in management of key resources such as water and land. Increased herd productivity was shown to directly contribute to the reduction of the global warming intensity of Australian beef production over the past three decades. It also highlights that there has been some slowing of the rate of improvement since 2001 despite the potential for further productivity and environmental improvements in key areas. Realising this potential for environmental benefits aligned to ongoing growth in productivity will require a continuation of the industry commitment to research, development and extension in cooperation with other stakeholders in government and nongovernment sectors. Ongoing monitoring based on improving data availability and quality, and scientifically robust methods, is also needed not only to document trends but also to further understand the trade-offs between environmental criteria and production goals so that informed decisions can be made.
Acknowledgements
We gratefully acknowledge the internal and external reviewers whose comments were instrumental in improving the quality of the analysis and reporting of this study. We acknowledge the funding of Meat and Livestock Australia (B.CCH.2032) that supported the study, and the beef industry officers and producers who assisted with data and understanding of past and current developments in beef production. Dr Tom Davison of MLA is acknowledged for his assistance and input to the project scope.
Appendix: Supplementary material
Supplementary data to this article can be found online at doi:10.1016/j.agsy.2014.11.002.
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