Using Life Cycle Assessment to Quantify the Environmental Impact of Chicken Meat Production. RIRDC Publication No. 12/029

Transcription

1 Using Life Cycle Assessment to Quantify the Environmental Impact of Chicken Meat Production RIRDC Publication No. 12/029

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3 Using Life Cycle Assessment to Quantify the Environmental Impact of Chicken Meat Production By Stephen Wiedemann, Eugene McGahan and Glenn Poad April 2012 RIRDC Publication No.12/029 RIRDC Project Nos. PRJ and PRJ

4 2012 Rural Industries Research and Development Corporation. All rights reserved. ISBN ISSN Using Life Cycle Assessment to Quantify the Environmental Impact of Chicken Meat Production Publication No. 12/029 Project Nos. PRJ and PRJ The information contained in this publication is intended for general use to assist public knowledge and discussion and to help improve the development of sustainable regions. You must not rely on any information contained in this publication without taking specialist advice relevant to your particular circumstances. While reasonable care has been taken in preparing this publication to ensure that information is true and correct, the Commonwealth of Australia gives no assurance as to the accuracy of any information in this publication. The Commonwealth of Australia, the Rural Industries Research and Development Corporation (RIRDC), the authors or contributors expressly disclaim, to the maximum extent permitted by law, all responsibility and liability to any person, arising directly or indirectly from any act or omission, or for any consequences of any such act or omission, made in reliance on the contents of this publication, whether or not caused by any negligence on the part of the Commonwealth of Australia, RIRDC, the authors or contributors. The Commonwealth of Australia does not necessarily endorse the views in this publication. This publication is copyright. Apart from any use as permitted under the Copyright Act 1968, all other rights are reserved. However, wide dissemination is encouraged. Requests and inquiries concerning reproduction and rights should be addressed to the RIRDC Publications Manager on phone Researcher Contact Details Mr Stephen Wiedemann FSA Consulting PO Box 2175 TOOWOOMBA QLD 4350 Phone: Fax: In submitting this report, the researcher has agreed to RIRDC publishing this material in its edited form. RIRDC Contact Details Rural Industries Research and Development Corporation Level 2, 15 National Circuit BARTON ACT 2600 PO Box 4776 KINGSTON ACT 2604 Phone: Fax: Web: Electronically published by RIRDC in April 2012 Print-on-demand by Union Offset Printing, Canberra at or phone ii

5 Foreword Agricultural industries are under increasing pressure to quantify their environmental impacts and resource usage by government, consumers and the general public. Quantification of greenhouse gas emissions is particularly necessary as a first step to adapting the meat chicken industry to the regulatory requirements of the future. Life cycle assessment is a powerful tool for quantifying environmental impacts and resource usage throughout the whole meat chicken supply chain. This report presents the first comprehensive study of its kind for Australian chicken meat production, focussing on two supply chains: Queensland, and South Australia. This project was funded by the RIRDC Chicken Meat Program from producer levies matched by Australian government funds. This report is an addition to RIRDC s diverse range of over 2000 research publications and it forms part of our Chicken Meat R&D program, which aims to support increased sustainability and profitability in the chicken meat industry through focused research and development. Most of RIRDC s publications are available for viewing, free downloading or purchasing online at Purchases can also be made by phoning Craig Burns Managing Director Rural Industries Research and Development Corporation iii

6 Acknowledgements The research team would like to thank the industry partners and their employees for their generous contribution of time and expertise when collating inventory data for this project. Because of confidentiality agreements with the industry partners these people will not be named. The authors also thank Tim Grant of Life Cycle Strategies for his valuable input and review of the project. The authors also thank the RIRDC project managers Dr Vivien Kite and Dr Elisa Heylin for their support in this project. iv

7 Abbreviations ABS CED CH 4 CO 2 -e CW DCCEE FCR FU GHG GWP IPCC LCA LCI LHV LW MJ ML N N 2 O NGGI P TS VS VW WF Australian Bureau of Statistics Cumulative Energy Demand Methane Carbon Dioxide Equivalent Carcase Weight Department of Climate Change and Energy Efficiency Feed Conversion Ratio Functional Unit Greenhouse Gas Global Warming Potential Intergovernmental Panel on Climate Change Life Cycle Assessment Life Cycle Inventory Lower Heating Value Live Weight Mega Joule Megalitre Nitrogen Nitrous Oxide National Greenhouse Gas Inventory Phosphorus Total Solids Volatile Solids Virtual Water Water Footprint v

8 Contents Foreword... iii Acknowledgements... iv Executive Summary... x Introduction... 1 Objectives of the Research...1 Life Cycle Assessment Method Overview...1 Literature Review... 6 Greenhouse Gas Emissions in Agriculture...6 Water Accounting in Australian Agriculture...8 Life Cycle Assessment of Chicken Meat Production...13 Methodology Goal and Scope...16 Supply Chain Descriptions...17 Data Collection and Limitations...18 Scenario Analysis...20 Life Cycle Inventory...20 Allocation...24 Alternative Housing Scenarios...26 Alternative Litter Utilisation Scenarios...26 Impact Assessment...28 Results Energy Use...29 Total GHG...30 Water Use...32 Discussion Farm Gate vs Processor Gate...34 Allocation Methods...35 Comparison of Supply Chains...35 Scenario Analysis...37 Efficiency Improvement Options...40 Australian and International Studies...42 vi

9 Conclusions and Recommendations Implications for the Chicken Meat Industry...45 Recommendations...47 References Appendix Reporting Uncertainty...52 Inventory Data Queensland...52 Inventory Data South Australia...56 Inventory Data Free Range...59 Inventory Data Organic...61 Appendix Feed Inputs...63 Organic Supply Chain Ration...66 Appendix Manure Production...69 Spent Litter Mass Flow...70 Emissions from Conventional and Free Range Grow-out Facilities...71 vii

10 Figures Figure 1 General framework for LCA and its application (ISO 2006a: 14040)... 3 Figure 2. System boundary with foreground system boundary noted within the dashed lines Figure 3. Supply chain diagram detailing data characteristics for the QLD supply chain Figure 4. Figure 5. Figure 6. Figure 7. Figure 8. Contribution of supply chain stages to energy use for chicken meat production in two state supply chains (QLD and SA) and two alternative grow-out systems (FR and Organic) Total GHG emissions associated with production of chicken meat in two state supply chains (QLD and SA), and two alternative grow-out systems (FR and Organic) Contribution of supply chain stages to total GHG emissions for chicken meat production in two state supply chains (QLD and SA) and two alternative grow-out systems (FR and Organic) Contribution of green and blue water to the water footprint of chicken meat production in two state supply chains (QLD and SA) and two alternative grow-out systems (FR and Organic). 33 Total GHG for chicken meat production with spent litter utilisation for energy generation (SA supply chain) Figure 9. Contribution analysis for the QLD supply chain with grower feed removed Figure 10. Nitrogen mass flows from spent litter in the grow-out phase of meat chicken production Tables Table 1. The global warming potential of major greenhouse gases... 6 Table 2. Virtual water use estimates for alternative protein sources Table 3. Summary of meat chicken LCA results and assumptions for total GHG and energy use presented in the literature Table 4. Aggregated average performance data for Queensland and South Australian grow-out farms Table 5. Aggregated average performance data for the free range grow out farms and estimated performance for organic grow-out farms Table 6. Volumetric water use categories used in this project Table 7 Green water use associated with Australian crop production Table 8. Allocation methods applied throughout the supply chain Table 9. Spent litter allocation (system expansion) assumptions applied throughout the supply chain Table 10. Meat processing allocation assumptions applied throughout the supply chain Table 11. Poultry litter characteristics for energy recovery scenarios Table 12. Production system assumptions for anaerobic digestion of poultry litter Table 13. Production system assumptions for combustion of poultry litter Table 14. Table 15. Table 16. Table 17. Energy use for production of chicken meat in two state supply chains (QLD and SA) and two alternative grow-out systems (FR, Organic) Contribution of supply chain stages to energy use for chicken meat production in two state supply chains (QLD and SA) and two alternative grow-out systems (FR and Organic) Water use for production of chicken meat in two state supply chains (QLD and SA) and two alternative grow-out systems (FR and Organic) Total GHG, energy and water use for chicken meat production at the farm-gate (live weight basis) in two Australian supply chains and two alternative grow-out systems viii

11 Table 18. Total GHG and Energy use for chicken meat from the QLD supply chain showing three alternative methods for allocating impacts Table 19. Total GHG and energy use for conventional QLD and organic meat chicken rations Table 20. Comparison of total GHG for FR and organic production systems Table 21. Table 22. Table 23. Table 24. Table 25. Comparison of two methods (MB+IPCC and DCCEE default) for estimating manure emissions from chicken meat production in the QLD supply chain Sensitivity of GHG and energy use to changes in the FCR of meat chickens in the QLD supply chain Total GHG for Australian and international chicken meat production from conventional production systems Energy use for Australian and International chicken meat production from conventional production systems Environmental impacts from the primary output product from four different livestock production systems in Australia Table 26. Major inputs for feed milling (aggregated QLD supply chain) Table 27. Aggregated inputs for chick production (breeding and hatching) (QLD supply chain) Table 28. Aggregated inputs for the grow-out farm (QLD supply chain) Table 29. Aggregated GHG emissions for the grow-out farm (QLD supply chain) Table 30. Aggregated inputs for the meat processing plant (QLD supply chain) Table 31. Major inputs for feed milling (aggregated SA supply chain) Table 32. Aggregated inputs for chick production (breeding and hatching) (SA supply chain) Table 33. Aggregated inputs for the grow-out farm (SA supply chain) Table 34. Aggregated GHG emissions for the grow-out farm (SA supply chain) Table 35. Aggregated inputs for the meat processing plant (SA supply chain) Table 36. Aggregated inputs for the grow-out farm (FR supply chain) Table 37. Aggregated GHG emissions for the grow-out farm (FR supply chain) Table 38. Aggregated inputs for the grow-out farm (Organic supply chain) Table 39. Aggregated GHG emissions for the grow-out farm (Organic supply chain) Table 40. Aggregated, simplified meat chicken ration for the QLD supply chain Table 41. Simplified meat chicken ration for the SA supply chain Table 42. Nitrous oxide emission factors for field crops Table 43. Energy and GHG emissions for minor inputs to the layer and pullet rations Table 44. Simplified ration for organic meat chickens Table 45. Organic wheat / soybean rotations with high and low input and yield scenarios Table 46. Average feed intake and crude protein levels for meat chickens (as-fed basis) Table 47 Methane potential and conversion factors from the DCCEE and IPCC used in this study Table 48. Manure management systems and emission factors for nitrous oxide from the DCCEE and IPCC used in this study Table 49. Manure application emission factors from the DCCEE and IPCC as used in this study Table 50. Aggregated ammonia emissions from egg production systems following the DCCEE and IPCC methods and emission factors ix

12 Executive Summary What the report is about Climate change, resource use and food production are important challenges to society in the present era. Australia, as a member of the global community, has an important role to play in reducing environmental impacts without compromising food security. To achieve this will require a new focus on productive efficiency from all sectors, including food production. In order to reduce environmental impacts while increasing food production, Australia s agricultural sectors need to dramatically reduce the resource use and emissions intensity of food production. The chicken meat industry has an important role to play in this effort, being the largest supplier of meat for domestic consumption in Australia. Food is an important part of the environmental impact of every Australian through the production, processing and consumption phase of the food supply chain. In order to contribute to knowledge of these impacts, and to meet the challenges for improved production efficiency, this report presents research on the environmental intensity of chicken meat production, focussing particularly on greenhouse gas emissions, energy and water use; undertaken through a lifecycle assessment (LCA). Who is the report targeted at? The study was conducted with the Australian public, the research community and the chicken meat industry in mind, and represents an industry first for this information. The chicken meat industry will be able to understand where its most significant environmental impacts occur, as well as to begin discerning where it could make the biggest difference to reduce these impacts. As this is the first LCA for chicken meat in Australia, the data set will be valuable for entry into life cycle inventory data sets for use by other LCA practitioners. This information is also important for consumers and policy makers investigating the environmental impacts associated with food production, processing and consumption. Where are the relevant industries located in Australia? The chicken meat supply chains investigated in this study were from Queensland and South Australia. These were selected to provide the greatest degree of contrast between production systems in Australia. The chicken meat industry is also based in Victoria, Western Australia and Tasmania. Background The chicken meat industry in Australia is dominated by vertically integrated, company-based supply chains with modern, efficient production systems. In addition to constantly improving traditional indicators of production efficiency, the industry is motivated to produce an ecologically efficient and sustainable product. In line with national environmental priorities, the chicken meat industry is seeking to reduce greenhouse gas emissions, reliance on fossil fuels for energy and water use in production. However, to date, these areas have not been thoroughly investigated for the chicken meat supply chain. At the industry level, estimation of emissions and resource usage is the first step towards benchmarking performance and enabling industry-wide improvement. LCA is a tool being used worldwide to determine these impacts for a product of interest, by assessing the environmental impact from cradle-to-grave throughout the whole life cycle of a product. This study presents the first comprehensive LCA for chicken meat production in Australia, based on data collected from commercial chicken meat supply chains. x

13 Aims/objectives The chicken meat industry commissioned a project to conduct an industry-wide LCA, based on the agreed agricultural methodology, encompassing several supply chains and scenarios. The overarching objectives of this project include: 1. Quantifying the environmental impacts of meat chicken production, specifically: a. energy use, b. greenhouse gas emissions, and c. water use. 2. Providing robust data to allow comparison of chicken meat with other similar products, based on environmental performance and efficiency of production. 3. Identifying areas within the supply chain where improvements can reduce resource usage and environmental impacts. 4. Establishing baseline data for benchmarking and reporting industry performance. 5. Reviewing existing chicken meat LCAs. Methods used The study investigated two conventional chicken meat supply chains based in Queensland (QLD) and South Australia (SA). Additionally, the study investigated free range and organic chicken meat production. Foreground data were collected directly from the chicken meat companies for the feed mill, breeder farm, grow out farm and meat processing plant stages. Background (literature) data were used for upstream grain production. Results/key findings The study showed that Australian chicken meat production is a highly efficient form of meat production. Chicken meat production generated low levels of GHG and resulted in moderate levels of energy and water use. Total greenhouse gas emissions ranged from 1.89 ± 0.15 to 2.38 ± 0.16 CO 2 -e / kg carcase weight (CW) for conventional chicken meat production in SA and QLD respectively. Total GHG emissions from free range production (2.19 ± 0.14 CO 2 -e / kg CW) were not significantly different to the average of the SA and QLD conventional production systems, while organic production was found to generate 20% higher emissions (2.86 ± 0.48 CO 2 -e / kg CW) than QLD conventional production. Differences between states related to slightly better productivity (lower feed conversion ratio (FCR)) and lower energy use in SA. South Australia also has a more efficient electricity supply system resulting in lower greenhouse gas burdens from electricity use throughout the supply chain. As a food product, chicken meat is highly efficient. As an example, the total greenhouse gas emissions for the production phase (i.e. not including retail and cooking) for a barbeque chicken are similar to the emissions generated by driving a car to collect the chicken. Energy use (Cumulative Energy Demand, reported as a Lower Heating Value) ranged from 14.7 ± 0.7 to 20.4 ± 1.0 MJ / kg CW for the SA and QLD supply chains respectively. Energy use for the free range production system (16.8 ± 0.8 MJ / kg CW) was not significantly different to the average of the two conventional systems. Organic production (12.8 ± 1.0 MJ / kg CW) used significantly less energy than all other production systems. Water use was assessed using two volumetric classifications. Using an equivalent measure to the ABS water use statistics (ABS equivalent water use), conventional chicken meat production used L / kg CW. The ABS equivalent water use category can be considered comparable to water use in the Australian home. In fact, most of the water use in this category is drawn from the same source as local xi

14 homes. This means that the average roast chicken (1.7 kg) requires less water to produce throughout the whole supply chain than an average 4-minute shower. Total consumptive water use (sometimes termed blue water use) was 96 and 127 L / kg CW for SA and QLD chicken meat respectively. The higher consumptive water figures are associated with water used in imported feed grain production. Implications for relevant stakeholders: The most important areas of environmental impact in the chicken meat supply chain were feed production for growing the meat chickens (46-63% of greenhouse gas and energy), followed by growout housing (including manure production), meat processing and breeding. Impacts associated with feed production are not directly in the control of the chicken meat industry. However, the efficiency of feed use (FCR) is an important driver of environmental efficiency as well as production cost. A scenario analysis showed that reducing FCR by 0.1 resulted in a 2-3% reduction in GHG and energy use. At the grow-out stage of production, electricity use and manure management were the most important contributors to greenhouse gas and energy. Improvements to manure management may offer substantial reductions in greenhouse gas for the chicken meat industry, though further research is required to validate emission factors. One possibility for the industry to reduce greenhouse gas and energy would be to generate electricity from poultry spent litter. This could lead to a 30% reduction in greenhouse gas and energy use. At the meat processing stage, greenhouse gas and energy use were driven by electricity use, making this a major priority for environmental productivity. Improvements to waste treatment may also offer an opportunity to reduce greenhouse gas at the processing stage. Recommendations Being the first study of this kind in the Australian chicken meat industry, a number of recommendations were identified for future research in this area. These relate primarily to improving efficiency and addressing uncertainty in the modelling. Research into electricity use at the grow-out and processing stage is likely to have the largest impact on resource efficiency in chicken meat production. This is an increasingly important cost for the industry, so any improvements to energy efficiency will have a financial offset for the industry. Efficiency in feed use is also an important on-going research priority. In addition to FCR, the feed formulation may also influence manure emissions, though a large degree of uncertainty still exists in this area. Spent litter management is an important environmental priority for the chicken meat industry. One of the largest sources of uncertainty in the study arose from the lack of Australian research of greenhouse gas emissions arising from manure during the housing of grower chickens. Consequently, this is an important priority for the industry if improvements are to be made. Utilisation of spent litter could represent an important offset for the chicken meat industry. Spent litter can be utilised as a fertiliser replacement, providing a credit to the chicken meat industry. There may be scope to improve the efficiency of spent litter as a fertiliser source by investigating ways to reduce nitrogen losses, increasing this offset, and this would warrant further investigation. Emissions from spent litter application also require validation under Australian conditions. The investigation of energy generation from spent litter in this report provided encouraging results. It may be warranted to expand this study by conducting a broader investigation of the environmental impacts of these approaches compared to current utilisation. This could also take into account differences in transport requirements, fertiliser value and carbon sequestration potential. xii

15 Two parts of the present study were based on desktop analyses: data for feed grains and additives, and the organic supply chain grow-out farm. Considering the importance of feed inputs in the chicken meat supply chain, further research is warranted to quantify the impacts associated with grain production. In particular, soil carbon sequestration or losses could significantly influence the GHG emissions profile of chicken meat production, and this source has not been adequately accounted for in this study. Further investigation of organic production systems and organic grain production would be valuable, particularly considering the apparent efficiency of Australian organic production. xiii

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17 Introduction The chicken meat industry in Australia is dominated by vertically integrated, company-based supply chains with modern, efficient production systems. In addition to constantly improving traditional indicators of production efficiency, the industry is motivated to produce an ecologically efficient and sustainable product. In line with national environmental priorities, the chicken meat industry is seeking to reduce greenhouse gas (GHG) emissions, reliance on fossil fuels for energy and water use in production. However, to date, these areas have not been thoroughly investigated for the chicken meat supply chain. At the industry level, estimation of emissions and resource usage is the first step towards benchmarking performance and enabling industry-wide improvement. This information is also important for consumers and policy makers investigating the environmental impacts associated with food production, processing and consumption. Life cycle assessment (LCA) is a tool being used worldwide to determine these impacts for a product of interest, by assessing the environmental impact from cradle-to-grave throughout the whole life cycle of a product. This study presents the first comprehensive LCA for chicken meat production in Australia, based on data collected from commercial chicken meat supply chains. Objectives of the Research The chicken meat industry commissioned a project to conduct an industry-wide LCA, based on the agreed agricultural methodology, encompassing several supply chains and scenarios. The overarching objectives of this project include: 1. Quantifying the environmental impacts of meat chicken production, specifically: a. energy use, b. greenhouse gas (GHG) emissions, and c. water use. 2. Providing robust data to allow comparison of chicken meat with other similar products, based on environmental performance and efficiency of production. 3. Identifying areas within the supply chain where improvements can reduce resource usage and environmental impacts. 4. Establishing baseline data for benchmarking and reporting industry performance. 5. Reviewing existing chicken meat LCAs. Further detailed project goals and scenarios are presented in the methodology section of the report. Life Cycle Assessment Method Overview LCA is a reasonably well established research method, which is defined by a number of international and Australian standards. However, LCA methodologies for agricultural systems are still under development in a number of areas. Additionally, several scientific research fields that support LCA (i.e. GHG measurement and estimation methods) are still under development. The constant development of methods is also related to the very broad scope of LCA, which can include almost every conceivable environmental impact that is created from the production or provision of a product or a service. LCA was developed for use in manufacturing and processing industries and investigates resource usage and environmental impacts over the entire life cycle of a product or service. This covers everything from the extraction and processing of the raw materials needed to make the product to its recycling and disposal. Additionally, the impact associated with every raw material that is used by a 1

18 system (i.e. diesel used in crop production) is attributed to the final product (i.e. grain), which may in turn be attributed to the chicken meat that was grown using that grain. The applications of LCA research are broad, ranging from comparison of the environmental credentials of a product through to system auditing and directing research. LCA can be used as a theoretical approach to compare mitigation scenarios for research or for comparing materials during the evaluation of a new product. Because LCA investigates all the environmental impacts associated with the entire life cycle of a product or function, LCA can be used to prevent three common forms of environmental burden shifting. These are: burden shifting from one stage of the life cycle to another. burden shifting from one sort of environmental impact to another (i.e. reducing water use but increasing energy use). burden shifting from one location to another. LCA research is an iterative process; where the whole assessment is repeated multiple times, each time in more detail. First, a screening analysis is conducted using approximate data; this results in a rapid assessment or hot-spot analysis. Following this, a detailed data collection process is undertaken, followed by a second analysis. This analysis identifies areas where further data may be required or where more detailed interrogation of the data are required. Following the data interrogation state, a final analysis can be completed. International standards have been developed to specify the general framework, principles and requirements for conducting and reporting LCA studies (ISO 2006a: 14040) and (ISO 2006b: 14044). The framework (Figure 1) includes four aspects: Goal and scope definition: The product(s) to be assessed are defined, a functional basis for comparison is chosen and the required level of detail is defined. Inventory analysis: Inputs from the environment (resources and energy) and outputs (product, emissions and waste) to the environment are quantified for each process and are combined in the process flow chart. Allocation of inputs and outputs needs to be clarified where processes have several functions (for example, where one production system produces several products). In this case, different process inputs and outputs are attributed to the different goods and services produced. An extra simplification used by LCA is that processes are generally described without regard to their specific location and time of operation. Impact assessment: The effects of the resource use and emissions generated are grouped and quantified into a limited number of impact categories, which may be weighted for importance. Improvement assessment: The results are reported in the most informative way possible and the needs and opportunities to reduce the impact of the product(s) on the environment are systematically evaluated against the study s goal. LCA differs from other environmental tools (e.g. risk assessment, environmental performance evaluation, environmental auditing and environmental impact assessment) in a number of significant ways. In LCA, the environmental impact of a product, or the function a product is designed to perform, is assessed. An LCA is essentially a quantitative study. Quantitative analysis requires standardised databases of main processes (energy, transport) and software for managing the complexity of the supply chain. 2

19 Figure 1 General framework for LCA and its application (ISO 2006a: 14040) Australian rural industries have recognised the importance of LCA studies in agricultural systems and, as such, are in the process of developing a standardised methodology to assist practitioners undertake LCA studies. Progress will be reviewed in a following section of this report. Functional Units and System Boundaries The functional unit in LCA is a measure of the function of the studied system, which provides a reference to which the inputs and outputs can be related (ISO 2006a). This enables comparison of two different systems. For agricultural products, there are three main types of functional unit that can be used. These are mass (kg product), area (ha or year) or quality adjusted mass (e.g. kg protein). The choice of functional unit is particularly important when comparing different systems. System boundaries determine which unit processes are included in an LCA study. In LCA methodology, all inputs and outputs from the system are based on the cradle-to-grave approach. This means that all inputs into the system should be traced back to flows from the environment, without any transformation from humans. Outputs should also be discarded to the environment without subsequent human transformation (ISO 2006a). Each system considers upstream processes with regard to the extraction of raw materials and the manufacturing of products being used in the system, and it considers downstream processes as well as all final emissions to the environment. Consequential and Attributional LCA There are two basic perspectives in LCA research; attributional or consequential. Attributional studies investigate which impacts should be attributed to a given product. This is a retrospective view up the supply chain. The main question for an attributional LCA is what impacts were created by producing this product? If a study is investigating production for a whole state or nation, every type of system that is currently being used needs to be included to get an accurate and representative result. An alternative approach is to consider a dynamic system, and investigate the consequences of a change in production. In this case, the question might be what impacts would be created if one more kilogram of product were produced? This type of study will focus on the marginal technology and production system, the system that will be used to produce the next unit of output. 3

20 While the attributional study is relatively straight forward to understand, the consequential approach can be more difficult. An example from the pig industry may help to clarify this. Pork in Queensland is currently produced at many different scales of efficiency ranging from small producers with <200 sows, to medium producers ( sows) to large producers (3000+ sows). Over the last 15 years, there has been a substantial increase in herd size and a move towards more efficient, often vertically integrated supply chains. When conducting a consequential analysis for this industry, there would be no need to investigate what is currently happening; the more important question is how the system will change in the future. For example, if the research question was to investigate one more kilogram of pork production in Queensland, only the systems expected to expand to meet demand for pork would be investigated. The study would not investigate a representative farm, but rather would focus on a large-scale integrated system. Importantly, results from a consequential study cannot be used to comment on the current industry or compared with attributional studies, without clear explanation of the differences involved. The present study takes an attributional approach and aims to quantify current production practices for the supply chains investigated. Important Issues Relating to LCA Research Applied Research LCA may be classified as an applied research tool. This means LCA research does not generally involve conducting individual research studies into each impact area associated with the system. Instead, LCA draws from other studies that have been completed in the area, and relates the results to the system being investigated. Where knowledge gaps exist, the LCA practitioner can either conduct a very brief investigation with the aim of determining how significant the contribution may be from the unknown process, or exclude the process until further research has been undertaken. There are strengths and weaknesses with this type of applied research. One strength is that an LCA can develop broad answers long before the detailed research is completed. A second strength is that the broad scope (i.e. all greenhouse gases associated with a production system) allows impacts to be classified in terms of their overall impact. Likewise, mitigation strategies can be evaluated in a holistic manner. This is something that many scientific research programs find difficult to achieve. The weakness of an applied research tool is that it relies on results from external research and modelling, which is less precise than if a full measurement campaign was done. Modelling, or the extrapolation of other research findings, can introduce a source of error if there is a significant difference between the conditions of the original research and the conditions the research is applied to explain in the LCA. It is common for a single product to involve many thousands of processes within the LCA model. Consequently, the process data used for common products (such as diesel or urea for example) are drawn from Australian and sometimes international databases. A distinction in LCA is made between foreground data (or data collected as part of the project from the industries involved), and background data (which is drawn from databases or literature sources). Allocation Most production systems produce both primary and secondary products. Allocation is the process by which environmental burdens are divided between these primary and co-products within the LCA. This process is very important and can have a large bearing on the result. The ISO standard (ISO 2006b) recommends the following methods (in order of preference) for handling co-production: 4

21 Wherever possible, allocation should be avoided by correct delineation of the system boundary or system expansion. Where allocation is not avoidable, inputs and outputs should be partitioned between different functions or products in a way that reflects the underlying physical relationships between them. If the latter is not possible, allocation should be carried out based on other existing relationships (e.g. in proportion to the economic value of products). Depending on the method used, considerable differences in the final result may be observed. Allocation at the point of slaughter is a particularly sensitive process and this is discussed in the methodology section of the report. 5

22 Literature Review Greenhouse Gas Emissions in Agriculture Greenhouse gases refer to a group of compounds that contribute to energy capture in the atmosphere surrounding the earth, the so called greenhouse effect. Several gases contribute to this effect, with the primary gases being water vapour (H 2 O), carbon dioxide (CO 2 ), nitrous oxide (N 2 O), methane (CH 4 ) and ozone (O 3 ). In addition, there is a range of human-made halocarbons (such as perfluorocarbons (PFCs), hydrofluorocarbons (HFCs), chlorofluorocarbons (CFCs) and sulphur hexafluoride (SF 6 ) that exist in small amounts but are very potent. These greenhouse gases occur only at trace levels in the atmosphere, making up only 0.1 per cent of the atmosphere by volume (IPCC 2007). The greenhouse effect is vital for life on earth. However, alterations to the concentration of these gases may lead to warming of the atmosphere and subsequent changes to the earth s climate. Climate change in Australia will lead to increased rainfall variability and higher temperatures, placing pressure on agricultural production systems and leading to volatility in supply of commodities such as grain. This has important ramifications for the chicken meat industry. Hence, any contribution the industry can make to reducing greenhouse gas emissions is important for the long-term viability of the sector. Greenhouse gases contribute to atmospheric warming at different rates. Hence, a scale has been developed to compare gases based on their warming potential (global warming potential GWP) relative to carbon dioxide. This compares the radiative force from a given mass of a greenhouse gas to the radiative forcing caused by the same mass of carbon dioxide and is evaluated for a specific timescale. Global warming potential depends both on the intrinsic capability of a molecule to absorb heat, and the lifetime of the gas in the atmosphere. Global warming potential values take into account the lifetime, existing concentration and warming potential of gases and vary depending on the time period used in the calculation. Global warming potential is used under the Kyoto Protocol to compare the magnitude of emissions and removals of different greenhouse gases from the atmosphere. The Kyoto Protocol establishes legally binding commitments for the reduction of four greenhouse gases (carbon dioxide, methane, nitrous oxide, sulphur hexafluoride), and two groups of gases (hydrofluorocarbons and perfluorocarbons). The GWP of the three most relevant gases to agriculture is shown in Table 1, expressed in kilograms of carbon dioxide equivalence (kg CO 2 -e). Table 1. Greenhouse Gas The global warming potential of major greenhouse gases Kyoto compliant 100 yr GWPs (1990 baseline) applied by the Australian National Inventory (DCCEE 2010) 100 year GWPs IPCC (2007) a Carbon Dioxide 1 1 Methane (24*) Nitrous Oxide a Solomon et al. (2007). * Biogenic methane was given a value of 24 to account for the biogenic nature of the carbon molecule which returns to the biogenic carbon cycle. GWP values have not remained static in the past 20 years. This study applied the IPCC (2007) GWP values. 6

23 GHG Emission Estimation Greenhouse gas emissions may be calculated using different boundaries that define which emissions are counted for a given system. In Australia, four main accounting methods are used for different applications, all of which provide results at a different scale. These are: The Australian National Greenhouse Gas Inventory (NGGI). The National Greenhouse and Energy Reporting System (NGERS) carbon accounting framework. This was established by international protocols and Australian legislation (i.e. the National Greenhouse and Energy Reporting Act). Carbon Footprinting : This is a frequently used, but poorly defined, term that generally relates to the GHG emissions attributable to a product, with varied calculation methods and system boundaries, and Life cycle assessment: This uses the indicator Global Warming, or total GHG to aggregate emissions over the whole production life cycle of a product to a defined end point. This is generally the most comprehensive of all the approaches at the product level. The National GHG Inventory (NGGI) Australia s national emissions are calculated in the National Greenhouse Gas Inventory (NGGI), which provides a breakdown of emissions by source and by industry sector. This inventory is calculated using defined methods (i.e. DCCEE 2010) which are approved by the Intergovernmental Panel on Climate Change (IPCC). By standardising international methods for estimating GHG, quantification of global emissions and benchmarking between countries over time is possible. The NGGI data are Australia s benchmark against our Kyoto commitments. When compiling the NGGI, Australia uses both the IPCC default methods and country-specific methods for some factors (typically for larger, more important emissions). Factors and methods defined by the Department of Climate Change and Energy Efficiency (DCCEE) for the estimation of individual emissions, such as nitrous oxide from poultry production, can also be applied at an enterprise level. However, it is important to note that many of the emission factors used are based on international default values provided by the IPCC and may be based on limited research. These factors also may not reflect the most recent IPCC manuals. National Greenhouse and Energy Reporting System Carbon Accounting The NGERS carbon accounting framework is based on an assessment framework developed by the World Resources Institute (WRI 2004). This framework uses the concept of an operational boundary to help companies better manage the full spectrum of risks and opportunities that exist along the value chain (WRI 2004). The operational boundary defines the scope of direct and indirect emissions for operations that fall within a company s established organisational boundary. These boundaries, or scopes, are categorised as follows: Scope 1: all direct GHG emissions, Scope 2: indirect GHG emissions from consumption of purchased electricity, heat or steam, and Scope 3: other indirect emissions including the extraction and production of materials and fuels, transport related activities in vehicles not owned or controlled by the reporting entity, other electricity activities and outsourced activities. Under the NGERS, businesses in Australia are required to report Scope 1 and 2 emissions related to energy use when established thresholds are exceeded. This does not incorporate direct agricultural 7

24 emissions (i.e. nitrous oxide from litter) and therefore omits a considerable portion of the true GHG emissions related to an agricultural enterprise. For this reason, it is not possible to compare or substitute results from this type of assessment with LCA results. Carbon Footprinting The term - Carbon Footprint - has become widely used for describing the greenhouse gas emissions associated with a product, activity or business. Two approaches have been adopted for carbon footprinting; a business orientated approach with methods similar to the WRI (2004), and a product approach based on LCA (i.e. PAS 2050). Currently, an International Standard (ISO 14067) is being developed and this will cover both approaches to developing a carbon footprint. Because of the close association with LCA, some LCA studies have reported results as a carbon footprint. Life Cycle Assessment LCA has a well developed method for accounting GHG emissions (along with other environmental impacts) which is outlined in a series of International Standards (ISO and ISO 2006a, b). LCA is the most comprehensive assessment tool for a product or a facility, because it accounts for emissions that may have occurred off-site (Scope 3 emissions) and emissions related to all products used by the system. Water Accounting in Australian Agriculture As with GHG estimation, water use can also be calculated using a variety of definitions and methodologies, resulting in a range of results for livestock products. Water use data for agriculture can be grouped under two broad methodological approaches; water engineering and water footprinting. Water Engineering Water Balances A water balance is a method of accounting for all water in a system by measuring or estimating water inputs and outputs. It is the most common method used in water engineering. In its simplest sense, water use is defined as the sum of the water outputs from a system, or the sum of the water inputs minus water captured in storage within the system. Within this definition of water use, delineation is possible between beneficial uses of water and nonbeneficial uses, or losses. Water use may include both beneficial and non-beneficial uses depending on the purpose of the balance calculations. The strength of this approach is that it provides a full assessment of water movements attributable to a system, identifying where improvements can be made by reducing or eliminating losses. Water balances can be applied at any scale depending on the resolution of input data and the required resolution of output data. If water use is to be attributed to a product (a kilogram of grain or chicken meat), the general approach would be to account for all system water inputs (from watercourses, storages, groundwater etc) which are directly related to production. Rainwater may also be included in the balance, though this would be identified separately. Where rainfall is captured on a site because of environmental considerations, this water could be considered a water use attributable to the product, because the water is being restricted from other uses in the environment. A water balance for open systems (such as a farm) will generally include rainwater for completeness. However, this is not reported in the balance as a contributor to water use. For example, when water use is quoted for irrigated cotton, this generally represents the volume of water that was irrigated onto the field to grow the crop, not the 8

25 water actually used by the plants (which would include rainfall that fell during the growing season and stored soil moisture that was present prior to planting). ABS Water Definitions The Australian Bureau of Statistics (ABS) collect comprehensive water use statistics covering broad geographical regions of Australia, reporting water use on an industry by industry basis (see ABS 2008). Water use as reported by the ABS is defined as the sum of distributed water use, selfextracted water use and reuse water use. This is compatible with data available to most water users (i.e. water bills for reticulated supply, meter readings for bores). Distributed and self-extracted water uses are defined as water supplied from engineered delivery systems. Delivery systems vary greatly in size and degree of infrastructure, incorporating a range of systems, from sub-artesian groundwater extraction to water supply from rivers or state-owned dams. Water is classified as distributed if the water is purchased, or self-extracted if not. For water to be considered used, it has either been transferred from a natural watercourse or extracted from groundwater. Hence, water from small overland flow dams are generally not considered in ABS water use calculations or datasets. Reuse water refers to any drainage, waste or storm water that has been used more than once without being first discharged to the environment. It can refer to both treated and untreated water. The ABS definition of water use includes the volume of water lost through supply systems. The attribution of this loss volume to suppliers and consumers depends on the origin of the loss. For example, distribution system losses are considered to be a form of use by the supplier and metering losses are considered to be a form of use by the consumer. Because ABS data are widely available, it is useful to align a water use assessment with these definitions. Water Footprint The term - water footprint (WF) - was first used in the early 2000 s to measure the transfer of virtual water (VW) between nations as a result of trading agricultural commodities. Hoekstra (Hoekstra & Hung 2002) introduced the term water footprint to refine their assessments of virtual water. These authors initially presented data interchangeably under the headings virtual water and water footprint. Virtual water or the water footprint is determined by the physiological requirements of the plant. This takes no account of the source of the water. For crops grown in dryland situations, the physiological water requirement is sourced directly from rainfall stored in the soil. In irrigation regions, a large portion of the water used by the crop may be drawn from surface or groundwater sources. Most publically available VW/WF data released in the last 10 years has not differentiated between these sources. This has resulted in a degree of confusion about the impacts of WF water use from agricultural products. Clearly, the water used by a dryland crop cannot be directly equated with water used by an irrigation crop from the perspective of water resource allocation or the environmental impacts of water use. This is very important when communicating WF results, because in the mind of the general public, water use is typically considered in terms of litres of water used to have a shower or similar. Clearly, the water footprint for an agricultural product involving grain use (i.e. chicken meat) will not be comparable to this. The WF of a range of agricultural products has been compiled by Hoekstra and Chapagain (2007). Additionally, water footprints have been calculated by Wiedemann et al. (2010) and Wiedemann & McGahan (2011) for Australian pork and eggs. Results from these authors are presented in Table 2. To address the problems with aggregated water use terms such as VW/WF, two terms were developed by Falkenmark (Falkenmark 2003) (Falkenmark & Rockstrom 2006) to differentiate between water directly sourced from rainfall and water from surface or groundwater sources. The terms blue water and green water - are defined as follows: 9

26 Blue water describes what is understood to be accessible water, such as water from surface or groundwater supplies. Green water describes evapotranspiration water (i.e. Falkenmark 2003, Falkenmark & Rockstrom 2006) or soil stored moisture from rainfall. These terms have since been adopted and incorporated into standard water footprinting methods (i.e. Hoekstra et al. 2009). Additionally, the most recent water footprint methodology includes Grey Water, which represents water that must be available to assimilate pollutants from a given production system. In the case of Australian livestock production (such as pork or eggs in Table 2), > 98% of the water footprint to the farm gate is contributed by rainwater to grow dryland grains (i.e. green water). Consequently, other methods must be applied to understand the impacts of water use on water resources (i.e. competition for limited supply) and competition with the environment. Table 2. Virtual water use estimates for alternative protein sources Species L / kg * (Australian estimates) Reference Chicken meat 2,914 Hoekstra & Chapagain (2007) Pork 5,909 Hoekstra & Chapagain (2007) Pork 2,753 3,020 (~1.5% blue water) Wiedemann et al. (2010) Eggs 1,844 Hoekstra & Chapagain (2007) Eggs 2,670.5 (~0.7% blue water) Wiedemann & McGahan (2011) Sheep meat 6,947 Hoekstra & Chapagain (2007) Beef 17,112 Hoekstra & Chapagain (2007) Soybeans 2,106 Hoekstra & Chapagain (2007) * Values for meat are assumed to be carcase weight. LCA Water Use Inventory Water in LCA can be classified using the standard classification for abiotic resources based on the regeneration potential. The three main types of freshwater resources thus classified include deposits, funds and flows (Koehler 2008). Freshwater deposits represent non-replenishing groundwater stocks (which are finite resources). Freshwater funds may be characterised as sub-artesian groundwater supplies or dams (exhaustible resources). Freshwater flows refer to streams and rivers (in principle, non-exhaustible). Owens (2002) further defined water use to differentiate between in-stream uses (i.e. hydroelectric generation) and off-stream withdrawal, and suggested classifying water by source from surface water or groundwater. Classification of water return or disposition is then suggested, with the options being: Water use: Water is used off-stream and is subsequently released to the original river basin (downstream users are not deprived of any water volume). Water consumption: Off-stream water use where water release or return does not occur (i.e. evaporation from storage, transpiration from crop production). Water depletion: Withdrawal from a water source that is not replenished or recharged (i.e. a water deposit). 10

27 Building on these definitions, Owens presents five water use and water depletion indicators: In-stream water use indicator (i.e. the quantity of water used for hydro-electric power generation). In-stream water consumption indicator (i.e. evaporative losses from storages and canals in excess of unrestricted river losses). Off-stream water use indicator (i.e. surface withdrawals from sustainable sources that are returned to the original basins and groundwater withdrawn from sustainably recharged aquifers and returned to surface waters). Off-stream water consumption indicator (i.e. evaporative losses and other conveyance losses, and transfers to another river basin). Off-stream water depletion indicator (i.e. withdrawals from overdrawn, unreplenished groundwater sources. For agriculture, most extracted water represents consumptive use (i.e. water is transpired, respired or evaporated). Water depletion may also be relevant for agricultural systems that withdraw water from the Great Artesian Basin (GAB), which may be classified as an unreplenished source in the short to mid-term. The methodology presented by Owens is considered foundational in the field of LCA. The Australian methodology for agricultural LCA (Harris & Narayanaswamy 2009) provides another alternative to defining and measuring water use. The methodology identifies the following water use elements in the inventory phase: collected rainwater (treated and untreated); collected surface water (treated and untreated); groundwater (treated and untreated); saline and hyper-saline water (low quality water for low quality uses); cooling water (treated and untreated) to and from the cooling towers; scheme water (for a centralised water treatment and sewerage works); grey water, potable water (human and animals), irrigation water, etc; and treated and untreated storm water run-on and run-off (if captured and used in processes/production activities). Additionally, the methodology specifies that water flows associated with feed preparation and incorporation, drinking and service water for animals are to be calculated and included over the entire life span of the animals that contribute to the final product. Water use throughout the life cycle of the product should include, but not be limited to: mining and extraction of raw material (mining operations, dust suppression); manufacturing of materials (e.g. chemicals); irrigation and drinking water, cultivation and processing; 11

28 heating and cooling (e.g. evaporative losses); transport; and evaporation, seepage, drainage etc. The methodology proposes presenting water use under two definitions, i) the ABS water use definition outlined previously in this document, and ii) the following two definitions provided by the National Land and Water Resources Audit (NLWRA): Surface water sustainable flow regimes: the volume and pattern of water diversions from a river that include social, economic and environmental needs. Groundwater sustainable yield: the volume of water extracted over a specific time frame that should not be exceeded to protect the higher social, environmental and economic uses associated with the aquifer. The methodology states that the sustainable use of water shall be reported as a percentage of: water removed from rivers as a percentage of sustainable flow regimes, and groundwater abstraction as a percentage of sustainable yield. A weakness of this approach is the lack of comparability with other established methodologies (i.e. Owens 2002) which has been used as a basis for most other water methodology developments in the field of LCA. To date, no studies have been completed using this method and further methodology research is underway in this area (Simon Winter, pers. comm.). LCA Water Use Impact Assessment Recent research in the LCA field has aimed to determine the likely impact of water use to the environment, rather than simply the amount used. Methods have been proposed by Milà i Canals et al. (2009) and Pfister et al.(2009). These methods focus on blue water and exclude green water from the impact assessment. Ridoutt & Pfister (2010) describe the use of a stress-weighted water footprint, which utilises a Water Stress Index (WSI) value to scale water use in different regions according to the degree of stress within the specific water catchment being investigated. Green water is included in the framework as an interim to determining blue water requirements for crop irrigation, and to allow comparisons with VW studies. At the impact assessment stage, green water and non-evaporative blue water resources are not considered. These methods are being tested in a number of Australian and international case studies for agricultural products and may be useful in future assessments of water use for chicken meat production. 12

29 Life Cycle Assessment of Chicken Meat Production A literature review was conducted covering studies from both peer reviewed journals and government reports that were available in the public domain. LCA studies varied in the depth of research and comprehensiveness of reporting. To manage this, a screening process was established with the following criteria: Coverage of the whole supply chain through to the farm gate (at a minimum) - partial supply chain studies were not included. Elaboration of data sources and/or collection methods used for the study with sufficient detail to enable review of the approach taken for estimating GHG emissions. Comprehensive coverage of emission sources throughout the supply chain. Only attributional studies covered. From a total of eight studies scanned, six met the review criteria and are included here. Studies omitted from the review include Weidema et al. (2008) and Bennett et al. (2006). Where multiple production systems were investigated by the same study, results from all systems that met the review criteria were reviewed. Of the studies reviewed, all covered GHG and four studies reported energy use. None of the reviewed studies reported water use. This is common for agricultural LCA, where water use has only recently become an important issue. Table 3 provides a summary of results from these studies. Table 3. Reference Cederberg et al. (2009) Katajajuuri et al. (2008) Summary of meat chicken LCA results and assumptions for total GHG and energy use presented in the literature Study country Sweden Finland Production system Through to the 'farm gate' excluding meat processing Conventional production. Includes supply chain through to retail shelf for broiler fillets. Adjusted total GHG value kg CO 2 -e / kg live weight Adjusted Energy Use (MJ / kg live weight) Co-product handling 1.35 NR No allocation to slaughter by-products allocation by mass of meat in product Pelletier (2008) USA Conventional production, to farm gate N.A Prudêncio da Silva et al. (2008) Brazil Conventional production. Southern Brazil and central-west Brazil production systems Mass-allocated by gross chemical energy content and (system expansion for poultry litter) Verge et al. (2009) Canada Conventional production, to farm gate 1.0 NR N.A Williams et al. (2006) UK/Wales Conventional housing, including slaughter Economic allocation Free Range (non-organic), including slaughter Economic allocation Organic, including slaughter Economic allocation 13

30 System Boundaries, Functional Units and Allocation The studies were split between those that investigated the supply chain only to the farm gate (Cederberg et al. 2009, Pelletier 2008, Verge et al. 2009) and those that included slaughter processes (Katajajuuri et al. 2008, Prudêncio da Silva et al. 2010, Williams et al. 2006). Functional units were either kg live weight at the farm gate (Pelletier 2008, Verge et al. 2009) or kg product at a number of different end points in the supply chain. Cederberg et al. (2009) and Williams et al. (2006) reported impacts per kg of carcase weight at the farm gate, without accounting for energy use or emissions during the slaughtering process, or allocation between meat and slaughter co-products. Both studies calculated results using a dressing percentage of 70% with all environmental impacts attributed to meat. This was considered misleading, as the functional unit does not clearly align with the system boundary. Output at the farm gate is always live weight. To present results on a carcase weight basis both underestimates the contribution from slaughtering processes and does not account for by-products at the point of slaughter. One study (Prudencio da Silva et al. 2008) presented results on a carcase weight basis including slaughter processes. One further study (Katajajuuri et al. 2008) used a functional unit that was for further processed chicken meat (marinated breast fillet). Allocation processes at the point of slaughter were not clear in these studies. For comparison, results were adjusted to use kilograms of live weight (LW) at the farm gate as a standard functional unit (Table 3). This was straightforward for Cederberg et al. (2009) and Williams et al. (2006). Results from Prudencio da Silva et al. (2008) were back calculated assuming 10% of GHG to the post farm gate stage and a 70% dressing percentage with no allocation to slaughter coproducts. Results from Katajajuuri et al. (2008) were back-calculated based on the contribution data presented in the original study and assuming a ratio of filleted meat to LW of Data Collection and Modelling Three main approaches to data collection were used in these studies: National inventory studies (i.e. data collected for the whole country from national statistics and other sources). Simulated supply chain studies (i.e. data collected from a number of sources and constructed into a theoretical farm). Industry data studies (i.e. data collected from commercial facilities). Cederberg et al. (2009), Verge et al. (2009), Pelletier (2008) and Williams et al. (2006) conducted national inventory studies, aiming to provide results that were representative of the country being investigated. These studies drew data from a wide range of sources: literature, national statistics, personal communications with industry representatives and occasionally from commercial facilities. Prudêncio da Silva et al. (2008) was the only study that investigated simulated supply chains, where data were collected from a range of literature sources and commercial facilities. Katajajuuri et al. (2008) was the only study that collected data from commercial facilities throughout the supply chain. These data were supplemented with literature sources and modelling of some parameters. 14

31 Most studies stated that IPCC methods were followed for estimating GHG emissions from litter and land application of litter, but only Cederberg et al. (2009) and Williams et al. (2006) provided details for how the calculations were done. One study (Pelletier 2008) used data from a previous poultry GHG study by Coufal (Coufal et al. 2006) for litter emissions in grow-out sheds in addition to the IPCC emission factors. Production systems Williams et al. (2006) was the only study that compared different production systems (conventional, free range and organic). This study identified higher emissions from organic and free range production systems compared to conventional systems. Higher GHG emissions for the organic and free range systems were attributed to the poorer growth rates and feed utilisation (feed conversion ratio), which led to higher feed requirements and energy usage. Prudêncio da Silva et al. (2008) examined the difference between small-scale, family-based enterprises and larger commercial facilities in Brazil. However, the only data assumptions changed between the two supply chains related to transport and only a small difference was observed. Main contributors to GHG emissions Contributions from feed production ranged from ~45% to 82.4% (Katajajuuri et al. 2008; Pelletier 2008). When contributions of individual GHGs were assessed, nitrous oxide was found to be the dominant emission source, ranging from 49-59%. The majority of nitrous oxide emissions were associated with feed production. Emissions of carbon dioxide throughout the supply chain contributed 39-47% according to Cederberg et al. (2009) and Verge et al. (2009). 15

32 Methodology This chapter outlines the project goal and scope, supply chain descriptions, data collection processes and limitations, inventory methods, allocation choices and impact assessment methods used in the project. Goal and Scope The following goals for the project were set by the industry steering committee and the project team: 1. To provide information on energy and water usage and GHG emissions to the general public for the promotion of chicken meat to consumers. 2. To identify environmental research priorities throughout the supply chain and to validate these research aims (this will inform industry and government research investment). 3. To determine the environmental impacts (in terms of energy and water use and GHG emissions) of different production systems (i.e. free range compared to conventional grow-out farms). 4. To determine the environmental benefits associated with some industry practices such as improvements in feed conversion ratio or changes to shed design. 5. To establish a baseline for resource usage (energy and water) and GHG emissions for the industry to benchmark their performance against. 6. To identify key areas of the supply chain where further metering and monitoring should be undertaken to improve environmental performance. 7. To establish a process of data collection and assessment as a basis for future industry reporting requirements to both consumers and the government. System Boundary, Functional Unit and Impact Categories The study is a retrospective (attributional) study covering two production regions in Australia and three alternative production systems. The study covered the primary production supply chain from breeding (rearing, fertile egg production and hatchery processes) through to the meat processor gate. The end-point was the cold storage unit where chicken meat is stored prior to retail distribution (Figure 2). The functional unit was 1 kg of chilled, whole chicken at the processor gate. Results are also provided at the farm gate level using the functional unit 1 kilogram of live weight. Three impact categories were investigated: 1. Cumulative Energy Demand (CED Lower Heating Values - LHV). 2. Total GHG (measured in CO 2 -e using IPCC (Solomon et al. 2007) Global Warming Potentials (GWPs). 3. Water use (ABS equivalent water use and consumptive fresh water use). 16

33 Figure 2. System boundary with foreground system boundary noted within the dashed lines Supply Chain Descriptions The chicken meat industry identified two production regions to be investigated; Queensland (QLD) and South Australia (SA). These were selected to provide the greatest degree of contrast between production systems in the country. The main contrasts are in feed types used (sorghum based diet in QLD, wheat based diet in SA) and climate (QLD is sub-tropical, SA is Mediterranean). Both regions are characterised by vertically-integrated supply chains where birds are bred and slaughtered by the same company, while the grow-out phase (from day old through to slaughter) is contracted out to contract growers. During the grow-out phase, bird ownership remains with the processing company. Additionally, the chicken meat industry identified two alternative methods of production (free range and organic) to be covered by the study. Both the free range and organic systems shared some sections of the supply chain with conventional production. In particular, the breeding system (production of day-old chicks) and slaughtering processes were shared between these systems. Supply Chain 1 Queensland (QLD) The QLD chicken meat industry is predominantly located in the south east of the state near Brisbane. Production in this region is characterised by modern, tunnel-ventilated sheds and processing plants are located in several locations within 40 km of the city of Brisbane. The QLD supply chain, with tunnel ventilated grow-out housing is defined as conventional production in this report, and is abbreviated as the QLD supply chain. 17

34 Supply Chain 2 South Australia (SA) Meat chicken production in SA is primarily located close to Adelaide and in the Two Wells region to the north of Adelaide. The SA supply chain consisted of breeding, hatchery, grow-out and meat processing stages. Processing plants are located in the Adelaide region. The SA supply chain, with tunnel ventilated grow-out housing is defined as conventional production in this report, and is abbreviated as the SA supply chain. Supply Chain 3 Free Range The free range supply chain included enterprises in QLD and SA. These were averaged to provide a single aggregated result. Free range operations in both states relied on chicks from the conventional breeding system and used similar diets to the conventional production system. Likewise, slaughtering processes were identical to conventional production. The free range birds were housed in tunnel ventilated sheds with access to an outdoor range area after 21 days of age, through to the point of slaughter. Supply Chain 4 Organic Production (Organic) The organic supply chain was a desktop analysis of Queensland organic chicken meat production. The system used chicks bred in the conventional QLD supply chain. The grow-out phase was modelled to reflect industry standards and requirements. Organic production is free range, with birds having access to an outdoor range area from 10 days of age. Organic production relies on organic grains (produced without fertiliser or chemicals), though diets may also contain synthetic amino acids in small quantities. Diets and bird performance were based on advice from organic poultry nutritionists. Water and energy use data were assumed to be equivalent to non-tunnel ventilated conventional systems. Assumptions regarding manure management were the same as the free range system. Data Collection and Limitations For each state and supply chain, foreground data were collected from each company at the following stages in the supply chain; Feed mill (including total annual commodities purchased), Breeder farm (including breeder flock, rearer flocks and hatchery), Grow-out farm (minimum of three farms for each company), and Meat processing plant. At each facility, data relating to inputs and outputs were collected for a 12-month period. A 12-month data collection period was considered sufficient to account for seasonal variation in grain supply and energy use associated with heating and cooling. Aggregated foreground inventory data covering energy use, GHG and water use are presented in Appendix 1. Background (literature) data were used for the assessment of grain production upstream from the feed mill (Figure 3). Methods and assumptions for estimating energy use, GHG and water use associated with grain production (background) are reported in Appendix 1. 18

35 Figure 3. Supply chain diagram detailing data characteristics for the QLD supply chain (foreground data in yellow, background data in grey) Data Limitations Most of the production data supplied for the project were subject to confidentiality agreements. Consequently, sensitive data (such as specific diet formulations) are not presented in this report, and all data are presented in an aggregated format. Data were not collected for some services such as telephone and internet usage, and head-office administration of supply chain logistics. Data collection did not extend to grandparent and great grandparent breeding systems, as these were shown to contribute < 1% in the scoping study. Some limitations existed in the compilation of data for poultry feed. This primarily relates to minor dietary inputs such as sodium bicarbonate and vitamins. Where data were not available, substitutions were made with similar products. Substitution processes in poultry diets are discussed further in Appendix 1. Grain production processes did not include impacts from soil carbon flux, though a preliminary investigation of the importance of these emissions was included in the scenario analysis. Typically, soil carbon flux has not been taken into account in Australian grain LCA studies (Biswas et al. 2008, Eady et al. 2011a, Grant & Beer 2008) Grain production was modelled using the marginal production system, which is zero tillage in all Australian grain growing regions (ABS 2009). There are insufficient data to conclude if zero tillage practices will result in carbon losses or sequestration across Australia s grain growing regions. 19

36 Scenario Analysis A series of scenarios addressing issues of relevance to the industry (as defined by the steering committee and project team) were investigated as part of the project. These included: Changes in feed conversion ratio (FCR) of +/- 0.1, achieved with no change in diet formulation. Development of new, higher yielding feed grain varieties for inclusion in chicken rations. Application of Australian greenhouse gas inventory default methods for manure emission estimation or application of an approach utilising a mass balance approach with updated emission factors from the IPCC. Comparison of cross-ventilated and tunnel ventilated sheds, and of insulated vs non-insulated sheds for the grow-out phase. This scenario was undertaken using data from one farm in the QLD system. Additionally, a scenario investigated the impact soil carbon flux (losses) may have on the GHG emissions from chicken meat production. Life Cycle Inventory The primary assumptions and methods applied in collating the LCI are reported in this section. Detailed inventory data and methodology choices are reported in Appendices 1, 2 and 3. This section should be read with reference to these appendices where further detail is required. Bird Performance Bird performance data were supplied by the grow-out farms involved in the study. Table 4 shows aggregated, average performance data for the QLD and SA supply chain. Table 5 shows performance data for the free range and organic supply chains. Table 4. Aggregated average performance data for Queensland and South Australian growout farms Production data QLD SA Average birds produced/farm birds/yr 949,662 1,955,162 Average bird weight kg Flocks no./year Average bird age at slaughter days Flock FCR (uncorrected for mortalities)

37 Table 5. Aggregated average performance data for the free range grow out farms and estimated performance for organic grow-out farms Production data Free Range Organic Average birds produced/farm birds/yr 590, ,000 Average bird weight kg Flocks year Average bird age at slaughter days Flock FCR (uncorrected for mortalities) Energy and Transport Energy use data were collected from all facilities throughout the supply chains and primarily consisted of electricity use, with smaller amounts of liquid petroleum gas (LPG) and natural gas. Relatively small volumes of diesel and petrol were used at some sites. Transport data were collected for all transfers of materials within the supply chain. Major transport stages included grain and feed input commodities (to the feed mill); transport of prepared ration from the feed mill to the breeder, rearer and grow-out farms; transport of fertile eggs and chicks between the breeder, hatchery and grow-out units and transport of live birds from the grow-out unit to the meat processing plant. Transport data were calculated as tonne kilometres and were classified according to truck type, using AustLCI transport unit processes. Staff transport to / from work for all facilities was calculated from staff records and reported travel distances. Modelling GHG Emissions Greenhouse gas emissions arise from several sources within the poultry production chain, primarily associated with the use of energy and the handling of manure and waste. Emissions associated with energy use were estimated easily from energy consumption data; however emissions from manure and waste are poorly understood at the present time for Australian systems. Manure and waste GHG emissions are in the form of methane (CH 4 ) and nitrous oxide (N 2 O). These emissions arise from manure during housing (CH 4 and N 2 O), manure stockpiling (CH 4 and N 2 O), land application of litter (N 2 O) and from indirect sources (via ammonia volatilisation and nitrate leaching / runoff). Manure emissions are generated at both the breeder and the grow-out production facilities, with the majority of emissions arising from the grow-out facility. Two stages are required to estimate manure-related emissions. These are: Estimation of manure production (specifically, volatile solids VS and nitrogen N), and Estimation of emissions, based on emission potentials (for methane) and emission factors for a range of manure handling systems. A range of emission factors may be applicable for different manure management systems and for different climatic zones. Manure emissions were estimated using two approaches. The primary approach was to apply a mass balance for estimating manure excretion (based on feed and bird production data) and utilise emission factors from the 2006 IPCC methodologies. These are 21

38 referenced as separate chapters; Chapter 10 Emissions from livestock and manure management, referenced as Dong et al. (2006) and Chapter 11 N 2 O emissions from managed soils, and CO 2 emissions from lime and urea application, referenced as De Klein et al. (2006). Further details and specific emission factors applied are documented in Appendix 3. The second estimation approach followed the methods used for the Australian national greenhouse gas inventory, as documented in DCCEE (2010). This is a streamlined, proscriptive method based on the IPCC manuals, though with less detail and in some cases, emission factors that have not been updated to reflect the most recent IPCC (i.e. 2006) manual. This method has been applied directly as outlined by the manual without modification to approximate the national inventory, though at a smaller (supply chain) scale. It is noted that very little research has been done to validate the emission factors recommended by the IPCC or the DCCEE for manure from chicken meat production. At this point, no Australian research has been commissioned in this area. Consequently, emission factors were selected to best reflect the conditions experienced in Australian production systems. In the scoping phase of the project, nitrous oxide emissions from the grow-out shed were identified as the major source of manure GHG (using the DCCEE emission factor of 0.02). On further investigation, the IPCC (Dong et al. 2006) was found to recommend a significantly lower factor (0.001), which was more closely aligned with the peer reviewed research (i.e. Guiziou & Béline 2005, Wathes et al. 1997). While emission research has not been done in Australia, there are two factors that suggest nitrous oxide emissions are likely to be low; firstly, the nitrogen loading in Australian grow out systems is relatively low, because most sheds are cleaned out after each batch of chickens. This minimises nitrogen transformations and losses because the duration time is minimal. Additionally, moisture levels are typically quite low in spent litter from Australian grow-out systems. Craddock & Hollitt (2010) reported average moisture levels of 20-26% from a survey of spent litter from 123 farms, which suggests that denitrifying conditions favouring nitrous oxide emissions may be limited. Detailed methods and assumptions for estimating manure GHG emissions are reported in Appendix 3. In addition to applying two methodological approaches to estimate manure emissions, uncertainties were accounted for as part of the Monte Carlo analysis. This was achieved by defining a range of data for each key emission factor. The data range was taken from the relevant IPCC manual. Water Use Water Use Classifications Water use data were collected to enable the use of a number of volumetric reporting categories. These methods have been developed specifically for this project and are described in Table 6. 22

39 Table 6. Volumetric water use categories used in this project Water use reporting category Units Description Noted exclusions of relevance to this project ABS Equivalent Water Use ABS Equiv. L All Australian water uses from extracted sources (surface water, bore water, etc) including water withdrawals released to sewer (meat processing). Water use drawn from small on-farm storages and evaporation from these storages. Consumptive or nonconsumptive embedded water flows from other countries. Consumptive Fresh Water Use L Consumptive uses from direct capture of runoff in on-farm storages, including storage evaporation. All consumptive water uses including embedded water flows from other countries. Withdrawals of water released again to sewer (specifically associated with meat processing). Blue Water Use L All consumptive uses, equivalent to Consumptive Fresh Water Use in this study Green Water Use L Plant uptake of soil stored moisture from rainfall Water Footprint L Blue Water Use + Green Water Use Foreground water use Water data were collected in the foreground system from farm and meat processor records. At the farms (breeder and grow-out farms), water was drawn from groundwater (ABS source), reticulated water supplies (ABS source) and direct capture of rainfall in small on-farm storages, including tanks and dams (non-abs source). The main water uses in the breeding and grow-out system were drinking, cooling and cleaning, all of which are consumptive uses. All meat processing plants used water from reticulated supplies, with some supplementing this with on-site groundwater extraction (alluvial bores). All but one processing plant released effluent water into the city sewerage treatment system. ABS water use was taken to be the total volume of water withdrawn, while consumptive water use was taken to be the difference between water withdrawals and releases to sewer (i.e. evaporative losses of water in the processing plant and addition of water to product). Water released to the city sewer entered a wastewater treatment process for either Brisbane or Adelaide. Water released to sewer was not considered a consumptive use because this water is treated and returned to the water system it was removed from, or is released for a secondary use. One QLD processing plant irrigated water to pasture on-site, which was considered a consumptive use attributable to the meat processing system. Aggregated water use inventory data are presented in Appendix 1. Background water use Blue water use data for upstream processes are not well documented within the AustLCI and EcoInvent databases. All upstream water within the databases were classified as consumptive uses, and were assumed to be from extracted water sources (as per the ABS water use definition). Grain production was assumed to be from dryland cropping regions and no water for irrigation was included. 23

40 A notable exception was irrigation water use from imported soybean meal (see Appendix 2). Blue water use data for US soybean production was taken from Aldaya et al. (2010), who reported 263 m 3 water / tonne soybeans. A detailed assessment of green water use in grain production was beyond the scope of this project. Green water use is not typically considered within LCA, because it does not directly contribute to environmental impacts or resource depletion. However, because the water footprint term has been widely used, an estimate of green water was made in order to determine the contribution of green water to the water footprint for chicken meat. Green water was predicted for the main crop species using the CropWat model (FAO 1998) for the northern cropping region in Australia (see Table 7). Results were reasonably similar to other published values for NSW wheat (Ridoutt & Poulton 2010) and Australian average wheat (Aldaya et al. 2010). For comparison, water footprint data previously reported by Hoekstra & Chapagain (2007) were adjusted to determine the green water contribution using the ratio of green:blue water for Australian wheat reported by Aldaya et al. (2010). Green water use data for soybean meal imported from the USA were based on Aldaya et al. (2010). Table 7 Green water use associated with Australian crop production Based on Hoekstra Crop Units This Study & Chapagain (2007) Ridoutt & Poulton (2010) Aldaya et al. (2010) Aust. Sorghum * Aust. Wheat m 3 / ** Aust. Barley metric * Aust. Soybean tonne * USA Soybean 1295 ** * Green water fraction of total water footprint disaggregated using a ratio of green water to virtual water of 0.73 for Australian wheat (after Aldaya et al. 2010). ** Data from Hoekstra & Chapagain (2007) and Aldaya et al. (2010) converted from US tons to metric tonnes. Grey Water Grey water use is a measure of the water required to dilute pollutants lost from the system. From agricultural systems, these losses are primarily nutrients (nitrogen and phosphorus). Grey water use was assumed to be a smaller contribution than green water and was beyond the scope of this study. Allocation Co-products were identified at three points in the foreground system. The first allocation point was the production of meat from spent breeding hens and eggs in the breeding system. Breeding birds are typically processed for human consumption or pet food at the end of life and have a small value. The second allocation point was the production of spent litter (manure and bedding) and meat chickens at the grow-out farm. Spent litter from breeder and grow-out farms is sold to nearby farmers for use as a fertiliser, with most of this product going to dairy farms or horticulture (QLD) or cereal cropping (SA). Demand for spent litter has created a strong market for this product. The third allocation point was between primary products (carcase weight) and secondary products at the point of slaughter (which are described further in Table 10). Allocation choices are described in Table 8. 24

41 Table 8. Allocation point Allocation methods applied throughout the supply chain Product and co-product (in brackets) Allocation procedure Explanation of Allocation choice Breeding farm Fertile eggs (spent breeding birds meat). System expansion A proportion of the meat from spent breeding birds enters the food supply chain and a proportion enters the pet food supply chain. Replacement product assumed to be prime chicken meat for the system expansion model. Grow-out farm Meat chicken live weight (nutrients contained in manure). System expansion System expansion used, accounting for both production and use of synthetic fertiliser compared to chicken meat litter. This was considered the best option because nutrients in spent litter are commonly used as a fertiliser replacement for pasture and crop production in chicken meat production regions. Meat Processing Chilled carcase weight CW (slaughter by-products and secondary by-products including: edible offal, bloodmeal, feathermeal, poultry oil, poultry meal and pet food). System expansion Allocation between slaughter products and co-products is the most sensitive allocation assumption in the study. Mass allocation and economic allocation methods were applied for comparison and results are reported in the discussion. Substitution products and allocation factors are reported in Table 10. The system expansion process for spent hens used purpose grown meat chickens as the main substitute product, i.e. spent hens and purpose grown chicken meat was considered equivalent. The reasoning for this was two-fold; firstly, despite the eating quality differences that may exist between meat from spent hens and purpose grown meat chickens, the nutritional function is essentially the same. Secondly, it is feasible for purpose grown chicken meat may be substituted in some instances with spent hens (i.e. for some processed product), and purpose grown chicken meat represents the most conservative meat product with which to substitute this product because of the low impacts of production. Allocation (system expansion) for manure nutrients accounted for both the manufacture and application of spent litter and synthetic fertiliser, and included the respective nitrogen emissions that arise from both systems. The chicken meat system was credited with both the avoided manufacturing cost of synthetic fertiliser and the emissions that would have arisen from the use of the fertiliser. Specific equivalence factors for substituting nutrients in spent litter are provided in Table 9. Table 9. Spent litter allocation (system expansion) assumptions applied throughout the supply chain Nutrient Substitution product Substitution value of nutrients in spent litter with synthetic fertiliser Nitrogen 1 kg of nitrogen as urea applied to land. 0.6 Phosphorus 1 kg of phosphorus as triple superphosphate 0.6 Potassium 1 kg of potassium as potassium chloride 1 As discussed in Wiedemann et al. (2010), nutrients in organic by-products may not be directly equivalent to synthetic fertiliser, mainly because of the lower levels of plant availability and greater risk of nutrient loss at the point of application for spent litter. This is reflected in the economic value and common practices for utilisation of spent litter in Australia (Dorahy & Dorahy 2008). 25

42 Specific assumptions regarding slaughter allocation are provided in Table 10. The range in values reflect small differences in the dressing percentage of whole birds and differences in apportioning byproducts to secondary products between processing plants. Table 10. Meat processing allocation assumptions applied throughout the supply chain Mass Allocation Factors Economic Allocation Factors Slaughter Products Carcase weight % % - Edible offal % % - Secondary Rendering Products System expansion substitution products Feathermeal % % Soymeal and sorghum on protein and energy equiv. basis Poultry meal % % Soymeal and sorghum on protein and energy equiv. basis Poultry oil % % Canola oil Bloodmeal % % Soymeal and sorghum on protein and energy equiv. basis Petfood slurry % 0-1.1% Soymeal and sorghum on protein and energy equiv. basis Petfood digest 0-1.1% 0-0.3% Soymeal and sorghum on protein and energy equiv. basis Alternative Housing Scenarios A scenario investigating the impact of housing ventilation and insulation was conducted in the QLD supply chain, based on data from one farm where natural ventilation was used. The scenario was based on changing the electricity and gas usage, without modifying any other assumptions. Bird performance was similar at the naturally ventilated farm compared to the average of the tunnel ventilated farms, though stocking density was considerably lower. Electricity use was 12 kwh / 1000 kg LWT, while gas use was 260 MJ / 1000 kg LWT. Energy use was cross-checked against an unpublished, disaggregated energy use dataset from chicken meat farms which showed that fan operation at tunnel ventilated farms accounted for ~80% of farm energy use. No adjustment was made to account for a higher proportion of downgrade birds from this system, or for mass mortality events that could occur in extreme weather events. Alternative Litter Utilisation Scenarios Two alternative scenarios were tested that investigate energy recovery from poultry litter. In these scenarios, litter was assumed to be taken directly from the grow-out sheds to either a combustion or central anaerobic digestion (CAD) plant. There are no examples in Australia where these systems are being used, though feasibility assessments have been completed to investigate expected energy yields and financial returns (see McGahan et al. 2010). In line with the scope of this project, the analysis in this covered GHG, energy and water use and did not consider economics. Assumptions were predominantly sourced from recent RIRDC projects on litter characterisation (Craddock & Hollitt 2010), biochar manufacture (Playsted et al. 2011) and waste to energy (McGahan et al. 2010). Assumptions regarding litter characteristics are shown in Table 11. Both plants were assumed to be within 75 km of the litter supply, with an average one-way transportation distance of 50 km for litter. Assumptions regarding the efficiency of energy recovery at the anaerobic digestion and combustion plants are shown in Table 12 and Table 13 respectively. 26

43 Table 11. Poultry litter characteristics for energy recovery scenarios Litter Characteristics Units Value Reference Bedding material softwood shavings Moisture % 26 Craddock & Hollitt (2010) Nitrogen % 3.9 Craddock & Hollitt (2010) Phosphorus % 1.3 Craddock & Hollitt (2010) Table 12. Production system assumptions for anaerobic digestion of poultry litter Litter Characteristics Units Value Range Reference Ratio of volatile solids (VS) to total solids (TS) McGahan et al. (2010) Ratio of COD to VS 1.5 McGahan et al. (2010) Litter degradability McGahan et al. (2010) Methane yield m 3 / kg COD 0.35 Density of methane kg/m DCCEE (2010) Energy density of methane MJ/kg DCCEE (2010) Electricity conversion efficiency 0.35 McGahan et al. (2010) Parasitic electricity demand 0.2 Nitrogen recovery as fertiliser fraction of N in litter 0.36 McGahan et al. (2010) Phosphorus recovery as fertiliser fraction of P in litter 0.72 McGahan et al. (2010) Table 13. Production system assumptions for combustion of poultry litter Litter Characteristics Units Value Range Reference Energy density MJ / kg TS McGahan et al. (2010), (Playsted et al. 2011) Electricity conversion efficiency Parasitic electricity demand Nitrogen recovery as fertiliser Phosphorus recovery as fertiliser* fraction of N in litter fraction of P in litter McGahan et al. (2010) 0.7 McGahan et al. (2010) * It is likely that ash would require post treatment to improve nutrient availability. Phosphorus was not substituted for synthetic fertiliser because of this requirement for further treatment that will involve additional inputs and energy. 27

44 Impact Assessment The impact assessment was done using SimaPro 7.3. This included a sensitivity analysis of model parameters and an uncertainty analysis. Uncertainty within the model relates to both natural variability in inventory data (i.e. differences in electricity use between two grow-out farms) and uncertainty related to assumptions made during the modelling process. Uncertainty data were estimated using a pedigree matrix, or were based on a range of possible values. Uncertainty was assessed using a Monte Carlo analysis in the LCA software program SimaPro. Monte Carlo analysis is a means of handling cumulative uncertainty within the system. Rather than estimating a theoretical minimum and maximum (i.e. the cumulative lowest and cumulative highest values), the analysis develops a distribution pattern from 1000 randomly selected scenarios, based on the possible range of values for each parameter. These results were used to provide the 95% confidence interval for the results. 28

45 Results Results are presented on the basis of 1 kilogram of chilled, whole chicken at the processor gate, abbreviated as carcase weight (CW). All results represent mean values from the uncertainty analysis. Comparison of results determined using an uncertainty analysis requires shared uncertainty between systems to be removed from the comparison. For example, where two systems (such as the QLD conventional and free range) share common feed inputs for production, the uncertainty related to feed production is shared between both systems. Where the 95% confidence intervals were shown to overlap, a comparative analysis was run to determine if the results differed when shared variability was removed. These results are reported where relevant. Energy Use Energy use (cumulative energy demand) results are presented in Table 14. Energy use differed significantly between QLD and SA, and was significantly lower for organic production. Table 14. Energy use for production of chicken meat in two state supply chains (QLD and SA) and two alternative grow-out systems (ree range and organic) Units QLD SA Free Range Organic MJ / kg CW 20.4 ± ± ± ± 1.0 Figure 4 shows a contribution analysis for energy use. Feed production represents the largest source of energy use within the supply chain, ranging from 52.4% (QLD) to 61.3% (SA). Energy use in feed production is primarily driven by diesel use for field operations and embedded energy from fertiliser production. The lower energy use for organic grain production reflects the use of natural sources of nitrogen rather than urea. Throughout the supply chain, transport contributed < 10% of overall energy use. The system expansion process for manure provided an energy offset close to 10% for all systems, mainly due to the offset provided by avoided urea. These data are also provided in Table

46 100% 90% 80% Contribution to Cumulative Energy Demand 70% 60% 50% 40% 30% 20% 10% Offsets - manure and slaughter by-products Meat processing Grow-out housing Grow-out feed Breeding 0% -10% -20% Queensland Conventional South Australia Conventional Queensland Free Range Queensland Organic Figure 4. Contribution of supply chain stages to energy use for chicken meat production in two state supply chains (QLD and SA) and two alternative grow-out systems (free range and organic) Table 15. Contribution of supply chain stages to energy use for chicken meat production in two state supply chains (QLD and SA) and two alternative grow-out systems (free range and organic) Queensland Conventional South Australia Conventional Queensland Free Range Queensland Organic Stage MJ / kg CW Breeding Grow-out feed Grow-out housing Meat processing Meat processing by-product offsets Grow-out manure offsets Total Total GHG Total GHG ranged from 1.89 ± 0.15 kg CO 2 -e / kg CW (SA) to 2.38 ± 0.16 kg CO 2 -e / kg CW (QLD) for conventional production. Total GHG from the free range system (2.19 ± 0.14 kg CO 2 -e / kg CW) was not significantly different from the average of the two conventional housing systems. However, the organic grow-out system generated significantly higher levels of GHG (2.86 ± 0.48 kg CO 2 -e / kg CW) than either the QLD, SA or free range systems. Results are presented in Figure 5. 30

47 kg CO2-e / kg whole carcase weight Methane Nitrous Oxide Carbon Dioxide Remaining Queensland Conventional South Australia Conventional Queensland Free Range Queensland Organic Figure 5. Total GHG emissions associated with production of chicken meat in two state supply chains (QLD and SA), and two alternative grow-out systems (free range and organic). Carbon dioxide (arising from fossil fuel energy use) was the dominant GHG for all supply chains, contributing from 48.4% (organic) to 63% (free range) of total GHG, while nitrous oxide (predominantly from grow-out manure handling and upstream grain production) contributed 28.9% (FR) to 44.7% (organic) of total GHG. Nitrous oxide emissions were elevated in the organic supply chain through the use of compost as a nutrient source for wheat production. The contribution of supply chain stages is shown in Figure 6 for each supply chain. Feed production was the single largest contributor to GHG, ranging from 45.6% (QLD) to 62.9% (Organic). This was followed by meat processing, which contributed between 11.1% (SA) and 18.0% (QLD) to total GHG. When feed, manure emissions and housing were aggregated for the grow-out stage this contributed 72.6% (QLD) to 82.1% (Organic) of total GHG. 31

48 100% 90% Contribution to total GHG 80% 70% 60% 50% 40% 30% 20% Meat processing Grow-out housing Grow-out feed Grow-out manure emissions Breeding 10% 0% Queensland Conventional South Australia Conventional Queensland Free Range Queensland Organic Figure 6. Contribution of supply chain stages to total GHG emissions for chicken meat production in two state supply chains (QLD and SA) and two alternative grow-out systems (free range and organic) Water Use Water use is reported here using two volumetric terms; Consumptive Fresh Water Use and ABS Equivalent Water Use (Table 16). For comparison, the contribution of green and blue water to the water footprint is shown in Figure 7. Table 16 highlights the different measures of water use depending on the system boundaries applied for inclusions and exclusions. The ABS equivalent water use indicator is a measure of transferrable water use in Australia. It excludes locally captured rainfall (used for drinking and cooling water at several grow-out farms) and embedded water from international sources. The most significant difference between the ABS and Consumptive Fresh Water Use data results from the exclusion of embedded irrigation water associated with imported US soybean meal in the ABS equivalent estimate. This single source contributed 71-85% of consumptive water use for the SA, free range and QLD systems respectively. Organic soybeans were assumed to be grown in Australia under dryland conditions. Table 16. Water use for production of chicken meat in two state supply chains (QLD and SA) and two alternative grow-out systems (free range and organic) ABS water use Consumptive fresh water use Units QLD SA Free Range Organic L ABS equiv / / / /- 3.5 / kg CW L / kg CW 127 +/ / / /

49 100% 90% 80% Litres / kg whole carcase weight 70% 60% 50% 40% 30% 20% Blue Water Use Green Water Use 10% 0% Queensland Conventional South Australia Conventional Queensland Free Range Queensland Organic Figure 7. Contribution of green and blue water to the water footprint of chicken meat production in two state supply chains (QLD and SA) and two alternative grow-out systems (free range and organic) 33

50 Discussion Farm Gate vs Processor Gate Several studies reviewed in the literature presented results per kilogram of carcase weight at the farm gate. This approach fails to take into account added emissions that would be generated to actually produce carcase weight (i.e. transport and slaughtering) and also tends to simplify or ignore allocation processes between primary product and co-products at the point of slaughter. In order to enable a high degree of comparability with the literature, results were also generated at the farm gate, using one kilogram of live weight (LW) as the functional unit. Results for energy, GHG and water are presented in Table 17. Table 17. Total GHG, energy and water use for chicken meat production at the farm-gate (live weight basis) in two Australian supply chains and two alternative grow-out systems Impact Category Units QLD SA Free Range Organic Total GHG kg CO 2 -e / kg / / / /- 0.3 LW Energy use MJ / kg LW / / / /- 0.5 ABS water use L ABS equiv. / / / / /- 1.5 kg LW Consumptive fresh water use L / kg LW / / / /- 1.5 Accounting for both emissions associated with meat processing and allocation between primary products and co-products at the point of slaughter, impacts increased on average by 62% (GHG) and 74% (energy). In general water increased by less (19%) for the SA, QLD and free range supply chains. The increase in water was less because of the substantial offsets received for protein meal by-products from slaughtering, which were substituted for water intensive soybean meal. In comparison, reporting results on a carcase weight basis from farm-gate results (i.e. Cederberg et al. 2009; Williams et al. 2006) by dividing impacts by dressing percentage (i.e. 70%, with no allocation to by-products) will increase impacts by 43%. i.e. 1.5 kg CO 2 -e / kg LW / 0.7 (dressing percentage) = 2.1 kg CO 2 -e / kg CW. The differences between live weight and carcase weight results were greater still where economic allocation or system expansion were applied to allocate between slaughter products and co-products. We contend that results should be presented using a functional unit appropriate to the determined endpoint in the supply chain. Following this approach, carcase weight results should only be presented where meat processing (and slaughter allocation processes) are included within the system boundary. 34

51 Allocation Methods Results were sensitive to the method of allocation between primary products and by-products at the point of slaughter. To investigate the sensitivity of this process, results were analysed using both economic allocation and system expansion for the QLD system. Results for total GHG and energy use showing the three methods are shown in Table 18. Table 18. Total GHG and Energy use for chicken meat from the QLD supply chain showing three alternative methods for allocating impacts Impact Category Units Mass Allocation Economic Allocation System Expansion Total GHG kg CO 2 -e / kg CW 2.2 +/ / / Energy Use MJ / kg CW / / /- 1.0 Table 18 shows a variation of 11% between the allocation methods at the point of slaughter. Few studies have investigated allocation processes at the point of slaughter in detail. The decision to use system expansion to account for slaughter by-products was partly in order to maintain consistency throughout the study. Several slaughter by-products (from numerous species) are important feed inputs for monogastrics (poultry and pigs). In the present study by-products to the diet were handled by substitution (mainly with soybean meal). Hence, at the point of slaughter it was consistent to use the same approach. Another significant substitution process was the handling of spent breeding hens. These were substituted for purpose grown chicken meat from young slaughter birds. An alternative to this would be to substitute another meat product (i.e. low grade pork or beef). This would have resulted in a large GHG offset (because both pork and beef tend to have higher emissions per kg of product). In reality, it is likely that low grade chicken meat from spent hens would be substituted for a range of products in the market for processing meat. To simplify the system, chicken meat was used as the substitute, which will have resulted in a smaller, more conservative offset than another meat product. Comparison of Supply Chains Of the conventional supply chain systems, South Australia produced slightly more efficient chicken meat than Queensland. For GHG, this was driven by lower emissions at every stage in the supply chain, with the greatest differences being from feed inputs (3.4% lower based on absolute values) and meat processing (21% lower). Lower feed GHG was related to the slightly better FCR for the SA supply chain, while the lower meat processing emissions were related to lower energy use in the SA processing plants and the more efficient waste treatment (release to sewer) compared to the average of the QLD supply chain. The South Australian electricity supply system generates lower GHG per unit of power supplied compared to QLD. This more efficient electricity resulted in ~5% lower GHG for SA chicken meat production. Chicken meat from free range production was quite similar to conventional production in terms of environmental efficiency. This was contrary to results presented by Williams et al. (2006), who showed 20% higher GHG from free range production compared to conventional, mainly because of the higher FCR for the free range production systems they studied. The similar results between conventional and free range production in the present study was not surprising, considering the similar level of production efficiency in the two systems, and similarities in the diets fed. Australian free range production was comparable to the international literature for conventional production (Table 23). 35

52 Organic production was found to generate 20% higher GHG emissions than either conventional or free range production. This difference between conventional and organic production was less than reported by Williams et al. (2006). The higher emissions for organic production were driven by the cumulative effects of higher FCR (2.3 vs 1.9) and higher GHG emissions from organic grain production. The higher FCR for organic chicken meat production is the result of growing older, heavier birds, combined with some dietary limitations that can restrict performance. Contributions to GHG are also the result of higher emissions from upstream grain production. This was partly related to higher field diesel use for tillage, and partly to the use of compost products for crop nutrition (particularly phosphorus). Of these, the use of compost (with associated nitrous oxide emissions during composting and application) had the greater effect on organic grain GHG. For comparison, the GHG, energy and consumptive water contribution for the organic diet and the average conventional QLD diet are shown in Table 19. Table 19. Total GHG and energy use for conventional QLD and organic meat chicken rations Impact Category Units Conventional Ration Organic Ration Total GHG kg CO 2 -e / kg Ration Energy use MJ / kg Ration * Consumptive Fresh Water Use L / kg Ration * Energy denotes embedded energy from fossil fuels, rather than the grain energy content of the ration. The higher energy use for the conventional ration is primarily related to energy associated with fertiliser manufacture, and this is reflected in the higher overall energy use for conventional chicken meat compared to organic chicken meat. Higher consumptive water use for the conventional ration is associated with embedded irrigation water in imported US soybean meal, compared to locally grown, dryland soybean for the organic system. Both free range and organic chicken meat production were considerably lower than previously reported for UK production systems (see Table 20). The greater differences observed by Williams et al. (2006) relate to the very poor FCR for UK organic production (2.7 vs 2.3 for this study). Table 20. Comparison of total GHG for free range and organic production systems Reference Study country Adjusted total GHG value kg CO 2 -e / kg CW Williams et al. (2006) UK/Wales - FR 6.0 UK/Wales - Organic 7.3 This Study Free Range / This Study Organic /

53 Scenario Analysis Manure GHG Emissions Manure management contributes a significant amount of the GHG for chicken meat production, and is one of the least understood emission sources in the supply chain. An alternate scenario was applied for comparison by using the Australian NGGI methodology (DCCEE 2010). When the QLD supply chain was modelled using the DCCEE (2010) method (with IPCC 2007 GWP values) the total GHG increased by 19%. The major driver of differences between the two methods was the higher nitrous oxide emission factor recommended by the DCCEE for the grow-out shed manure emissions. Differences between the two approaches are reported in Table 21. Table 21. Comparison of two methods (Mass Balance+IPCC and DCCEE default) for estimating manure emissions from chicken meat production in the QLD supply chain Mass Balance+IPCC 2006 DCCEE Default Input values N excretion Coverage of manure management system Based on data collected at farms for feed intake, feed nitrogen and digestibility Based on farm data. N intake N retention in live weight and mortalities (mass balance) Comprehensive, including shed, storage, land application and indirect emissions (from ammonia and leaching/runoff) Based on default values, approximately Based on estimated N in feed and N retention factor of 43% of N consumed; overestimated N excretion by ~ 10% Simplified; factors not supplied for spent litter storage. Shed N 2 O emissions EF = EF = 0.02; resulted in ~20 fold increase in GHG at this point Shed CH 4 emissions EF = 1.5% Factor of 2%; ~10% increase in GHG at this point Stockpile CH 4 emissions EF = 0.5%; resulted in an overall increase in methane of ~60% No factor provided Remaining N 2 O emissions MB+IPCC method resulted in 167% increase in remaining emissions because of more comprehensive coverage Overall result on GHG for Manure Lower overall GHG driven by lower shed nitrous oxide EF ~ 194% increase in overall GHG from manure management Based on studies reviewed in the literature and on the observed conditions of spent litter in Australian chicken meat grow-out units, the authors felt that the nitrous oxide emission factor for manure management recommended by the IPCC (Dong et al. 2006) was more likely to reflect actual emissions than the factor recommended by the DCCEE, which was drawn from an earlier IPCC manual. 37

54 Feed Production and Utilisation Feed Production the Potential Impact of Soil Carbon Flux The feed grain production system used in this study was based on the marginal grain production systems in Australia, which involve zero-tillage (which represents 70% of all grain farming systems in Australia). In reality, the chicken meat industry currently draws grain from a market that does not specify the production system. Within this market, some grain is likely to be produced using methods known to result in a loss of soil carbon, which would represent an additional GHG burden for grain users. To examine the potential impact of including losses from soil carbon in the chicken meat supply chain we ran a scenario for the QLD conventional system. The scenario used the soil carbon loss predictions published by Dalal & Chen (2001) for cereal grain (primarily sorghum and wheat). This resulted in a soil carbon loss for clay soils (45% clay) of kg C / ha.yr. Proportion of crop grown on soils experiencing carbon loss at the above rate was assumed to be 40%, with a uniform uncertainty range of 0-80%. This resulted in the total GHG for QLD conventional chicken meat rising by 18% to 2.8 ± 0.6 kg CO 2 -e / kg CW. Notably, the uncertainty in this estimate is quite high because of the lack of data identifying how much of the cropping zone is likely to be subject to soil carbon losses. It should also be noted that for other grain growing regions of Australia with lower soil clay contents, the carbon losses may be higher than those assumed for the northern grain growing region (Dalal & Chan 2001), possibly leading to higher emissions. Feed Conversion Ratio Feed conversion ratio is one of the most important production parameters driving the efficiency and economics of chicken meat production. As noted in the contribution analysis, feed inputs are the primary driver of total GHG, energy and water use. Consequently, feed conversion ratio is an important factor for environmental performance of chicken meat. For the QLD supply chain, the average uncorrected FCR was 1.9 (i.e. total feed intake / total live weight gain for the grow-out phase). Table 22 shows the effect of improving FCR from 1.9 to 1.7 in the QLD conventional supply chain, without any other changes to management or inputs. Table 22. FCR Sensitivity of GHG and energy use to changes in the FCR of meat chickens in the QLD supply chain Total GHG (kg CO 2 -e / kg CW) Energy use (MJ / kg CW) The industry has recorded consistent improvements in FCR over time as a result of improved breeding, diet formulation and management. According to McKay et al. (2000), the annual rate of FCR improvement is 0.02, which has resulted in a 0.6 kg reduction in feed use per kg chicken meat over the past 30 years. Assuming continued improvement, this may result in a trend towards lower GHG and energy use over time. This could be achieved through improved feed formulation and improved feed grain varieties. However, this scenario assumes that such improvements will be made without any other change to management that may impact energy use or GHG. 38

55 Alternative Housing Systems Conventional housing for meat chickens in Australia (as defined in this report) use tunnel ventilation to maintain a suitable environment for the growing birds. Ideally, these houses are insulated and well sealed to maximise efficiency. Prior to the widespread use of tunnel ventilation, housing was designed with natural ventilation. Naturally ventilated sheds use considerably less energy than tunnel ventilation (in the order of 87% less on-farm electricity). However, these sheds do not offer as effective a means to control climate extremes (particularly in summer) and may therefore be less productive. To investigate this, we ran two scenarios that simulated natural ventilation, with low farm electricity use. In the first scenario, electricity was reduced but productivity was maintained at the same level, while in the second scenario, productivity was reduced by 5% in the naturally ventilated system. Results show that if productivity can be maintained, natural ventilation may reduce GHG and energy by a modest amount (5% and 6% respectively on a CW basis). However, this improvement was eliminated when productivity was reduced. Alternative Spent Litter Utilisation Two alternative litter utilisation scenarios were investigated; anaerobic digestion (AD) and combustion. Of the two technologies, combustion is more established, with several such plants operating in the United Kingdom with similar levels of efficiency as modelled in this study. Anaerobic digestion is also a common method for treating manure, though few plants utilise poultry litter. The scenarios were based on the SA supply chain, and focussed on spent litter from the grow-out stage of production only. Results for both combustion and anaerobic digestion show a significant reduction in GHG and energy (~25-32%) per kilogram of chicken meat produced (see Figure 8) kg CO2-e / kg whole carcass weight Methane Nitrous Oxide Carbon Dioxide Remaining 0.00 SA Chicken meat - Spent litter combustion SA Chicken Meat - Spent litter AD Figure 8. Total GHG for chicken meat production with spent litter utilisation for energy generation (SA supply chain) Energy demand decreased for both scenarios by 20% (11.7 MJ / kg CW AD) and 27% (10.8 MJ / kg CW combustion). Water use did not change significantly. These results suggest that alternative litter utilisation may be the single most beneficial mitigation strategy available to the chicken meat industry. Two main factors contributed to lower GHG and energy use for the energy recovery scenarios; i) production of electricity to offset coal based electricity generation, and ii) reduction of emissions associated with litter application. There was a small credit in the AD scenario for the production of fertiliser. Fertiliser recovery was less significant than may have been expected, largely because most 39

56 nitrogen is lost (combustion) or is difficult to capture. Phosphorus is recoverable in both systems, but does not attract a large credit for GHG and energy because P fertilisers are subject to less energy intensive production than nitrogen fertilisers. The modelling was subject to a degree of uncertainty relating to litter characteristics (energy density, degradability) and system efficiency (digestion and combustion efficiency). Anaerobic digestion was subject to a slightly higher degree of uncertainty. In particular, the degradability of poultry litter is variable, and few data were available from Australian research (McGahan et al. 2010). Noting these uncertainties, the results are relatively conservative and should be representative of performance from commercial facilities within the error bars stated. The uptake of energy utilisation technology will be determined by the economics of the proposed system (Playsted et al. 2011). For some systems (i.e. combustion) this may be economically feasible in some situations based on current capital costs and returns. The economics of energy production may be altered by the proposed introduction of carbon taxes and credits, though the details for such legislation will be strongly debated and subject to a high degree of uncertainty for some time. Anaerobic digestion is widely used for dilute, liquid based waste systems. However, the need for a large amount of water to dilute the material and problems with methanogenesis because of high concentrations of ammonia have proven to be a barrier for using this technology with poultry litter. However, methods have been proposed to address these issues and a feasibility study for the Australian chicken meat industry has been completed (McGahan et al. 2010). This may be an option for some parts of the Australian industry. Efficiency Improvement Options Improvements in Feed Grain Production Feed was the largest contributor to environmental impacts for chicken meat in all impact areas investigated. In addition to improving FCR (discussed above), GHG emissions may also be reduced through the development of higher yielding grain varieties. To investigate the impact of using higher yielding wheat varieties, a scenario was modelled for the SA supply chain (grower ration > 50% wheat) where feed wheat was changed to a high-yield variety. The new variety was assumed to yield 0.5 t/ha higher than average (2.4 vs 1.9 t/ha or 26% higher), with no increase in inputs. This resulted in a modest reduction in GHG for chicken meat (~3%). Direct Supply Chain The chicken meat industry has direct control over feed milling, breeding, growing and meat processing. To enable closer scrutiny of this system, the QLD supply chain was re-analysed with the upstream impacts from grower feed removed. Results are shown in Figure 9. 40

57 100% 90% 80% 38% 70% 60% 50% 40% 30% 23% 23% 44% 48% 59% 34% Meat processing Grow-out housing Grow-out manure emissions Breeding 20% 10% 0% -10% 16% 18% 17% -10% -10% GHG Energy Water Figure 9. Contribution analysis for the QLD supply chain with grower feed removed For GHG, contributions are greatest from the meat processing sector, which is primarily the result of electricity use in the processing plant and energy requirements for rendering. Treatment of effluent was a significant contribution for the one plant that operated uncovered anaerobic lagoons. Not suprisingly, energy use was also high at the meat processing and rendering plants. Following meat processing, the equal second largest source of emissions were related to manure management emissions at the grow-out stage. Emissions arise from a number of points, with the greatest contributions coming from nitrous oxide emitted in the grow-out shed and from indirect emissions via ammonia volatilisation. Considering the lack of knowledge and large range in emission factors at critical points such as shed emissions of nitrous oxide, there are few opportunities to confidently mitigate these emissions. In theory, reducing nitrogen excretion via low N diets will reduce the substrate for nitrous oxide and ammonia emissions in the grow-out phase. However, the efficacy of this approach is determined by the magnitude of the nitrous oxide and ammonia emissions, which have not been studied under Australian conditions to date. Until this fundamental research is done, it is not practical to investigate low N diets or to investigate changes to litter management practices. The grow-out stage (excluding manure management) contributed 23% of emissions, which were mainly associated with energy use. As a fraction of the total, energy use at the grow-out stage contributed 12% of GHG emissions. The grow-out stage was the most energy intensive process in the supply chain. The two largest energy sources were electricity and gas, which amounted to >50% of energy use at this stage. It should be noted that energy use accounts for upstream factors and is therefore considerably more than may be assumed from the energy used at the farm. Inventory data will be of more value for benchmarking processes. Direct water use (ABS equiv. water use) was dominated by the meat processing stage. It is noted, however, that when releases of water back to the sewer were removed to calculate the consumptive water use, this figure was considerably lower. 41

58 Australian and International Studies Direct comparisons between LCA studies are difficult, if not impossible, because of differences in system boundaries, allocation methods and impact categories. However, results from other studies are useful for indicative purposes. Total GHG reported for Australian production systems was similar to most recent international chicken meat LCA studies (Table 23). Where slaughter processes were not included in the original studies, results were calculated from live weight values using the allocation methods and meat processing emissions from the present study. Four of the six literature studies compared in Table 23 show GHG results that fall within the 95% confidence interval of one or both of the conventional supply chain results. Total GHG in this study was lower than reported by Williams et al. (2006) for UK chicken meat production primarily because of the higher nitrous oxide emissions associated with feed production in the UK. Table 23. Total GHG for Australian and international chicken meat production from conventional production systems Reference Study country Adjusted total GHG value kg CO 2 -e / kg CW Verge et al. (2009) Canada 1.7 This Study SA / Katajajuuri et al. (2008) Finland 2.0 Cederberg et al. (2009) Sweden 2.2 Pelletier (2008) USA 2.3 This Study QLD / Prudêncio da Silva et al. (2008) Brazil Williams et al. (2006) UK/Wales 5.3 Most emissions throughout the Australian supply chains were in the form of carbon dioxide, reflecting the moderate-high levels of fossil fuel energy use and the high level of emissions associated with some forms of energy production (notably electricity) in Australia. Australian electricity is predominantly generated from coal-fired power stations, with only a small percentage of electricity supplied from renewable sources. Considering electricity emissions contributed 20-27% of GHG throughout the supply chains, this represents a considerable emission source. This places Australia at a disadvantage compared to some European countries were a higher proportion of electricity is generated from low emission sources such as nuclear, wind or anaerobic digestion. Energy use was difficult to compare with other published studies because of the lack of clarity in reporting energy use. However, the Australian data appear to be broadly comparable to most other published studies (Table 24). As with the GHG results, energy values were adjusted where necessary to account for slaughter processes using data from the present study. 42

59 Table 24. Energy use for Australian and International chicken meat production from conventional production systems Reference Study country Adjusted Energy Use (MJ / kg CW) This Study South Australia 14.7+/- 0.7 Williams et al. (2006) UK/Wales 14.8 Katajajuuri et al. (2008) Finland 17.3 This Study Queensland /- 1.0 Prudêncio da Silva et al. (2008) Brazil Pelletier (2008) USA 26.3 Few data were available for water use of chicken meat using the same categories applied here hence comparisons are not meaningful. Water footprint data reported by Hoekstra & Chapagain (2007) for chicken meat was similar to the combined blue and green water estimated in this study (unpublished data) though the contribution from blue water was only 3-4% of the overall water footprint. This report is one of a number of recently released LCAs for Australian livestock species and confirms the expectation that chicken meat production is a highly efficient meat producing industry when compared on the basis of GHG emissions. Results from this study are presented together with other studies on pork and eggs in Table 25. Table 25. Environmental impacts from the primary output product from four different livestock production systems in Australia Product Energy Use (MJ / kg product) Total GHG (kg CO 2 -e kg product) ABS Equiv. water Use (L / kg product) Reference Chicken meat (CW) This study Eggs Wiedemann & McGahan (2011) Pork (CW) a a a Wiedemann et al. (2010) a Results from this study are preliminary and may change when further research is released. Caution should be taken when assessing the results presented in Table 25. These studies are not directly comparable because of the differences that exist between functional units. Chicken meat and eggs are the only products that are sold at the retail level in the form in which these results are presented. Hence, from a market perspective, the products are not comparable. Nutritionally, these products are primarily sold for their protein content, however they do not share the same nutrition profile. Additionally, the studies presented in Table 25 were not conducted to compare the outcome of changing production and consumption patterns between these products. This question would require a dynamic model of marginal production systems for all meat products and predicted market responses to these shifts. Clearly, this is a much more complex process. Despite these difficulties in comparison, it is clear that a general trend exists between the species. Across the range of studies conducted in Australia (all with comparable methodologies), eggs are the most efficient wholesale product with respect to energy, GHG and water use. This is followed by chicken meat for GHG and water use. A current study is near to completion that will provide comparable results for beef production under Australian conditions, using the same LCA methodology 43

60 as applied in this study. Considering results from two other Australian beef LCAs (Eady et al. 2011b, Peters et al. 2010), beef is expected to result in higher GHG emissions than chicken meat production. Cederberg et al. (2009) report the same trend for Swedish production of eggs, chicken meat, pork and beef. Williams et al. (2006) reported a similar trend, though with higher emissions for eggs than chicken meat. The higher emissions for egg production relative to chicken meat in Williams et al. (2006) relate to low levels of productivity (particularly FCR) and high levels of manure emissions for egg production compared with Australian egg production (Wiedemann & McGahan 2011). As discussed previously, Williams et al. (2006) reported chicken meat as a farm-gate result, without accounting for meat processing, which under-estimated the emissions associated with chicken meat production. Differences in emissions between eggs and chicken meat are interesting to note, considering the same species (albeit with very different genetic strains) are used. Intuitively it may appear that egg production will result in higher impacts, because the product is harvested at a less developed stage in the life cycle. Consequently, Australian egg production (Wiedemann & McGahan 2011) has a slightly poorer FCR (1.95 kg feed / kg eggs) than chicken meat production. However, the differences between the two Australian studies were primarily driven by differences in post farm gate processing. Eggs require very little post processing (washing, grading and packing, with minor losses associated with broken eggs). Chicken meat by comparison, requires extensive processing (with associated impacts), and a considerable loss of mass between live weight and carcass weight as discussed previously. Conventional chicken meat production was found to be more efficient than Australian pork from two supply chains studied by Wiedemann et al. (2010). Primarily the differences relate to differences in the manure management system (particularly for conventionally housed pigs with effluent lagoons). Research in the Australian pork sector is ongoing and results may change when further studies have been completed. 44

61 Conclusions and Recommendations Implications for the Chicken Meat Industry This study represents the first full supply chain analysis of energy use, GHG and water use for the Australian chicken meat industry. The results indicate that Australian chicken meat is an environmentally efficient meat product. To the author s knowledge, the study also presents the first comprehensive analysis of water use for a chicken meat supply chain. The implications of this study can be viewed at two levels: 1. at the product level (market) where chicken meat competes for market share among meat and protein products, and 2. at the supply chain level (industry) where the focus is on system efficiency and mitigation of impacts. Product Level The study used a functional unit of 1 kilogram of carcase weight to represent the main output of the primary production system. This is similar to functional units used in other chicken meat studies. Interestingly, of the international chicken meat LCA studies, only two included slaughter processes in the impact assessment even though most studies presented results on a carcase weight basis. When results were compared by applying similar allocation methods (at the point of slaughter) and processing impacts, the environmental impacts of Australian chicken meat production were of a similar order to other studies. An exception to this was the widely published UK study by Williams et al. (2006) which reported much higher GHG emissions and a wider range of emissions between conventional, free range and organic supply chains compared with this study. Interestingly, Australian free range chicken meat production was found to have a similar level of efficiency to conventional production. This is important for the industry because of the steadily increasing demand for free range produce. However, it should be noted that some other measures of environmental performance (most notably nutrient losses to waterways, or eutrophication) should also be included in future research to provide a broader coverage of potential environmental impacts, as these will differ between management systems. Organic chicken meat was found to generate higher levels of GHG than conventional production (as also found by Williams et al. (2006)), mainly because of the poorer bird performance. As with other grain-reliant animal industries in Australia, the good environmental performance of chicken meat is related to efficiencies in grain production, and particularly to the low levels of nitrous oxide emissions known to arise from Australian cropping soils. However, impacts associated with soil carbon losses were not investigated in detail here, though these may represent an additional GHG contribution to chicken meat production. Australian chicken meat production was found to be slightly less efficient with respect to GHG than egg production (assessed in one Queensland supply chain), but more efficient than pork production from two Australian supply chains. The study was not extended beyond the processing plant to include distribution or retail. Katajajuuri et al. (2008) reported that retail and distribution may contribute an additional 20% to total GHG emissions for a cooked retail product. Further research is required to quantify these additional impacts under Australian conditions. However, it is interesting to note that the production phase of the chicken 45

62 meat life cycle would generate a similar level of emissions, per whole chicken, to a short car trip to the fast food retailer taken to collect it (assuming a 15 km round trip). Supply Chain Level The Impact of Feed Upstream feed production was found to be the single largest impact area of the chicken meat supply chain, contributing 46-63% of total GHG and 52-61% of energy use. The chicken meat industry can influence the impact of feed production in a number of ways. The most important for the industry is by improving FCR. By reducing FCR by 0.1, an improvement in GHG and energy use of ~2% was observed. A second scenario investigated the possibility of developing a high-yielding grain variety that could produce 0.5 t/ha higher yields without additional inputs. This scenario resulted in a modest (3%) reduction in GHG for chicken meat production. It is fair to assume that these scenarios could be additive. Another option that would significantly reduce GHG and energy use would be to reduce the use of imported soybean meal. In this study, it was assumed that 80% of soybean meal was imported. This resulted in additional transport, and also brought a large burden of irrigation water use associated with US soybean production. It is unlikely that ration formulations would be modified to address this unless the Australian soybean market was able to supply considerably more product at a competitive price however. The Direct Supply Chain An analysis of the direct supply chain (i.e. not including upstream grain production) was done to provide higher resolution data for the areas that are directly controlled by the chicken meat industry. This analysis showed that energy use was primarily driven by the grow-out stage (48%), while meat processing contributed 44%. Energy use was primarily associated with electricity and gas use at both stages. These results suggest that energy efficiency benchmarking and research will be an important priority for the industry at the grow-out (research currently underway) and processor stage, particularly if taxes on energy emissions are introduced. In the direct supply chain, total GHG emissions were greatest from meat processing (predominantly from energy use), manure management at the grow-out stage and energy use at the grow-out stage. Of these, manure management is the most poorly understood source of emissions and also may offer the greatest opportunity for mitigation. Quantification and mitigation of manure GHG are currently restricted by a lack of research into the most sensitive emissions from the grow-out shed (nitrous oxide and ammonia) and quantification of indirect emissions via ammonia deposition. This is seen in the large difference between emission estimation methods recommended by the IPCC and DCCEE. A more detailed understanding of manure emission processes is required to determine effective mitigation strategies. Water use for chicken meat was found to be comparable to other monogastric species. To provide a more detailed analysis, disaggregated water use results were generated using two water use categories. The ABS equivalent water use category can be considered comparable to water use in the Australian home. In fact, most of the water use in this category is drawn from the same source as used by local homes. Using this category, chicken meat production was found to use a comparatively modest amount of water ( L/kg CW). This means that the average roast chicken (1.7 kg) requires less water to produce throughout the whole supply chain than a 4 minute, water efficient shower (~40 L). Data were not reported for the water footprint of chicken meat, though an analysis of green water use showed that more than 96% of the water footprint for Australian chicken meat would be expected to come from rainfall used in dryland grain production. 46

63 Recommendations The results presented here represent a quite detailed study of two major chicken meat supply chains in Australia, and two additional grow out production systems. Being the first study of this kind in the Australian chicken meat industry, a number of recommendations exist for future research in this area. These relate primarily to improving efficiency and addressing uncertainty in the modelling. Efficiency improvement opportunities exist most strongly at the processing and grow out farm. For energy and GHG, the main opportunities relate to reducing electricity use and possibly utilising byproducts (such as spent litter) as an energy source. Grow-out housing was an important source of energy use, and to a lesser extent water use, for the direct supply chain. This suggests farm energy use would be a worthwhile area to investigate efficiency opportunities. It may also be beneficial for the industry to develop a broader set of data with which to benchmark farm energy use. Energy use in the processing sector is clearly important to the industry from a cost and environmental impact point of view. Further research and benchmarking of productivity may be beneficial in this sector also. Processing plants operating uncovered, anaerobic lagoons for waste treatment may also be in a position to reduce GHG emissions and offset energy use by installing anaerobic digestion systems. The investigation of spent litter utilisation presented in this report was quite limited, covering standard practice (sale of litter for use as a fertiliser), anaerobic digestion and combustion. Results suggest a substantial opportunity exists for reducing emissions and generating electricity, both of which can result in reduced costs and/or additional revenue. Considering the limited scope of the investigation presented here, a broader investigation may be warranted. It would be particularly interesting to compare a range of litter utilisation methods accounting for the additional impacts of transportation and soil carbon sequestration. Manure from the grow-out farm was shown to contribute substantially to the impacts from chicken meat production. Interestingly, the value of nutrients within spent litter also contributed a substantial offset to the production system. Limited research has been conducted on the emissions from Australian chicken meat production, resulting in a degree of uncertainty in these results. The differences between results from the IPCC / mass balance approach and the DCCEE (Australian inventory) approach highlight this. To accurately determine emissions for the industry and the national inventory, further research is warranted in this area. Considering manure management emissions could be mitigated using a number of different strategies, it is important to quantify actual emissions under Australian conditions. Two parts of the present study were based on desktop analyses: the feed grains and additives; and the organic supply chain grow-out farm. Considering the importance of feed grains data, further research may be warranted to quantify the impacts associated with grain production. In particular, soil carbon sequestration or losses could significantly influence the GHG emissions profile of chicken meat production. Further investigation of organic production systems and organic grain production would be valuable, particularly considering the apparent efficiency of Australian organic production. 47

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68 Appendix 1 Reporting Uncertainty All inventory data are reported with an indication of uncertainty. Uncertainty was accounted using two methods. Firstly, the pedigree matrix system from Weidema & Wesnaes (1996), was used for most inputs from the technosphere (i.e. electricity, fuel) and water inputs. The second approach used minimum and maximum values determined from the survey data, which were input using a triangular distribution in the modelling program SimaPro 7.3. This approach was taken for some flows between sub-systems (i.e. feed use) and for some important emission factors in the manure management system. These data are reported as a range (percentage +/- mean). Inventory Data Queensland Data were collected and aggregated at four different stages, the feed mill, the breeding unit (including breeder flocks, rearer flocks and hatchery), the grow-out farm and the meat processing plant. Data from each of these stages, aggregated for the QLD supply chain, are presented in the following section. Feed Milling Aggregated input data for the QLD feed mills are presented in Table 26. Table 26. Major inputs for feed milling (aggregated QLD supply chain) Inputs Data source description Units Energy Per tonne feed milled Uncertainty (SD) Electricity All data averaged from 3 feed mills kwh LPG L Diesel L Water Averaged from 3 feed mills, 100% reticulated supply L

69 Breeding Aggregated data for breeding and hatching are presented in Table 27 per 1,000 day-old chicks produced. Table 27. Aggregated inputs for chick production (breeding and hatching) (QLD supply chain) Inputs Data source description Units 1,000 day-old chicks Uncertainty (SD or range) Feed Ration Collected from farms kg /-15% Energy Electricity All data collected from farms kwh LPG L Diesel - farm L Petrol L Water Farm water use (drinking and cooling) Collected from breeding farms. Sourced from: Alluvial bores (68%),reticulated supply (32%). L 1, Outputs Manure N excreted IPCC/mass balance method kg 8.4 +/-15% Manure VS excreted IPCC/mass balance method kg /-10% 53

70 Grow-out Aggregated input data are presented on the basis of 1,000 kg of live weight produced in Table 28, and GHG emission data from manure management are reported in Table 29. Table 28. Aggregated inputs for the grow-out farm (QLD supply chain) Materials Data source description Units 1000 kg live weight Uncertaint y (SD or range) Feed ration Data collected from farm kg 1,886 +/-5% no. day-old Poultry Data collected from farm birds /-10% Energy Electricity All data collected from farms kwh LPG Litres Natural Gas m Diesel - farm L Petrol L Staff transport km Water Farm water use (drinking and cooling) Surface water (evaporation) Minor Inputs Collected from farms. Sourced from: surface diversions (27%), Alluvial bores (23%), reticulated supply (50%). Estimated from farm water storage surface areas L 3, L Bedding - Shavings/sawdust All data collected from farms m Chemicals Rodenticide kg Herbicide L Insecticide L Disinfectant L Construction Concrete (20 yr life) All construction data estimated from shed description and dimensions kg * Steel (20 yr life) kg Plastic (20 yr life) kg Panel (20 yr life) m Outputs Excreted manure N IPCC/mass balance method kg /-15% Excreted manure VS IPCC/mass balance method kg 348 +/-10% * Construction data were subject to a high degree of uncertainty, however, the overall contribution of these sources to all impact categories was less than 1%. 54

71 Table 29. Aggregated GHG emissions for the grow-out farm (QLD supply chain) Total manure GHG emissions Data source description Emissions per 1,000 kg live weight CH 4 shed emissions - IPCC (kg) MB+IPCC 1.26 CH 4 stockpile emission (kg) MB+IPCC 1.03 N 2 O-N shed emission (kg) MB+IPCC 0.03 N 2 O-N stockpile emission (kg) MB+IPCC 0.06 N 2 O-N application emission (kg) MB+IPCC 0.20 N 2 O-N indirect NH3 emissions (kg) MB+IPCC 0.16 N 2 O-N indirect L&R emission (kg) MB+IPCC 0.02 Total N2O-N Emissions MB+IPCC 0.46 CH 4 shed emissions - DCCEE (kg) DCCEE 1.54 CH 4 stockpile emission (kg) DCCEE n.a N 2 O-N shed emission (kg) DCCEE 0.69 N 2 O-N stockpile emission (kg) DCCEE n.a N 2 O-N application emission (kg) DCCEE 0.20 N 2 O-N indirect NH 3 emission (kg) DCCEE 0.14 N 2 O-N indirect L&R emission (kg) DCCEE 0.03 Total N 2 O-N Emissions DCCEE 1.05 Uncertainty data for manure emission estimates reported in Appendix 3. Meat Processing Aggregated data for meat processing were combined and presented on the basis of one tonne of chicken meat (dressed weight) processed. These data are provided in Table 30. Table 30. Aggregated inputs for the meat processing plant (QLD supply chain) Inputs Data source description Units Per t dressed weight Uncertainty (SD or range) Energy Electricity All data collected from processing plants kwh LPG L Gas consumption (Nat Gas) m Diesel L Petrol L Staff transport km 69.4 undefined Water Reticulated town - ABS All data collected from processing plants L 5, Reticulated town - Consumptive use L 2, Chemicals Cleaning chemicals Data collected from processing plants L

72 Inventory Data South Australia The South Australian supply chain was based on data from two supply chains. Aggregated data from facilities throughout these supply chains are provided bin Tables Feed Milling Table 31. Major inputs for feed milling (aggregated SA supply chain) Inputs Data source description Units Energy Per tonne feed milled Uncertainty (SD or range) Electricity All data averaged from 3 feed mills kwh LPG L Natural Gas m Water Averaged from 3 feed mills, 100% reticulated supply L Breeding Table 32. Aggregated inputs for chick production (breeding and hatching) (SA supply chain) Inputs Data source description Units 1000 day-old chicks Uncertainty (SD or range) Feed Ration Collected from breeding farms kg /-10% Energy Electricity All data collected from breeding farms kwh LPG L Diesel - farm L Petrol L Water Farm water use (drinking and cooling) Outputs Collected from breeding farms 100% reticulated supply L 2, Manure N excretion IPCC/mass balance method kg 6.9 +/-10% Manure VS excretion IPCC/mass balance method kg /-5% 56

73 Grow-out Table 33. Aggregated inputs for the grow-out farm (SA supply chain) Materials Data source description Units 1000 kg live weight produced Uncertainty (SD or range) Feed ration kg 1,853 +/-5% day old Poultry chicks /-10% Energy Electricity All data collected from farms kwh LPG Litres Diesel - farm L Petrol L Staff transport km 3.7 Undefined Water Farm water use (drinking and cooling) Minor Inputs Collected from farms. Sourced from: surface diversions (36%), reticulated supply (64%). L 5, Bedding - straw All data collected from farms kg Chemicals Rodenticides kg Herbicides L Insecticides L Disinfectant L Construction Concrete (20 yr life) All construction data estimated from shed description and dimensions kg * Steel (20 yr life) kg Plastic (20 yr life) kg Panel (20 yr life) m * Construction data were subject to a high degree of uncertainty, however, the overall contribution of these sources to all impact categories was less than 1%. 57

74 Table 34. Aggregated GHG emissions for the grow-out farm (SA supply chain) Emissions per 1,000 kg Total manure GHG emissions live weight CH 4 shed emissions - IPCC (kg) MB+IPCC 1.16 CH 4 stockpile emission (kg) MB+IPCC 1.18 N 2 O-N shed emissions MB+IPCC 0.03 N 2 O-N stockpile emissions MB+IPCC 0.05 N 2 O-N application emission (kg) MB+IPCC 0.19 N 2 O-N indirect (from NH3) emissions (kg) MB+IPCC 0.14 N 2 O-N indirect L&R emission (kg) MB+IPCC 0.00 Total N2O-N Emissions MB+IPCC 0.41 CH 4 shed emissions - DCCEE (kg) DCCEE 1.02 CH 4 stockpile emission (kg) DCCEE n.a N 2 O-N shed emissions DCCEE 0.60 N 2 O-N application emission (kg) DCCEE 0.19 N 2 O-N indirect (from NH 3 ) emissions (kg) DCCEE 0.12 N 2 O-N indirect L&R emission (kg) DCCEE 0.00 Total N 2 O-N Emissions DCCEE 0.91 Uncertainty data for manure emission estimates reported in Appendix 3 Meat Processing Table 35. Aggregated inputs for the meat processing plant (SA supply chain) Inputs Data source description Units Energy Electricity Per t dressed weight Uncertainty (SD or range) All data collected from processing plants kwh LPG L Gas consumption (Nat Gas) m Diesel - farm L Petrol L Staff transport km 35.5 Undefined Water ABS Reticulated supply L 6, Consumptive use Reticulated supply L 1, Chemicals Cleaning chemicals Total chemicals L

75 Inventory Data Free Range Free range data were collected from two companies (6 farms in total) and were modelled using averaged breeding and meat processing processes in each supply chain. Table 36. Aggregated inputs for the grow-out farm (free range supply chain) Materials Data source description Units 1,000kg live weight produced Uncertainty (SD or range) Feed ration Data collected from farms kg 1,886 +/-5% day old Poultry Data collected from farms chicks 448 +/-10% Energy 1.05 Electricity All data collected from farms kwh LPG Litres Diesel - farm L Petrol L Staff transport km Water Farm water use (drinking and cooling) Minor Inputs Data not available from all farms, average data from QLD and SA conventional used, Surface diversion (23%), Alluvial bore (18%), reticulated supply (59%). L 5, Bedding - straw Data collected from farms kg Construction Concrete (20 yr life) All construction data estimated from shed description and dimensions kg Steel (20 yr life) kg Plastic (20 yr life) kg Panel (20 yr life) m

76 Table 37. Aggregated GHG emissions for the grow-out farm (free range supply chain) Total manure GHG emissions Emissions per 1,000 kg live weight CH 4 shed emissions - IPCC (kg) MB+IPCC 1.04 CH 4 stockpile emission (kg) MB+IPCC 1.20 N 2 O-N shed emissions MB+IPCC N 2 O-N stockpile emissions MB+IPCC N 2 O-N application emission (kg) MB+IPCC N 2 O-N indirect (from NH3) emissions (kg) MB+IPCC N 2 O-N indirect L&R emission (kg) MB+IPCC Indirect N 2 O-N - range area (kg) MB+IPCC Indirect N 2 O-N L&R - range area (kg) MB+IPCC Total N2O-N Emissions MB+IPCC 0.45 CH 4 shed emissions - DCCEE (kg) DCCEE 1.25 CH 4 stockpile emission (kg) DCCEE n.a N 2 O-N shed emissions DCCEE 0.66 N 2 O-N application emission (kg) DCCEE N 2 O-N indirect (from NH 3 ) emissions (kg) DCCEE Indirect N 2 O-N L&R - spent litter (kg) DCCEE Indirect N 2 O-N - range area (kg) DCCEE Indirect N 2 O-N L&R - range area (kg) DCCEE 0.00 Total N 2 O-N Emissions DCCEE

77 Inventory Data Organic Table 38. Aggregated inputs for the grow-out farm (Organic supply chain) Materials Data source description Units Feed ration 1,000 kg live weight produced Uncertainty (SD or range) All production data based on average performance for Australian organic production determined by industry nutritionist kg 2,319.+/-10% Poultry day old chicks 359.+/-10% Energy Electricity Based on data collected from Queensland naturally ventilated growout housing kwh LPG Litres Diesel - farm L Petrol L Staff transport km 7.4 undefined Water Farm water use (drinking and cooling) Minor Inputs Bedding - straw Construction Based on average farm water use from conventional systems 100% bore water supply. L 3, Estimated based on minimum bedding depth requirements kg Concrete (20 yr life) All construction data estimated from minimum housing requirements kg Steel (20 yr life) kg Plastic (20 yr life) kg

78 Table 39. Aggregated GHG emissions for the grow-out farm (Organic supply chain) Total manure GHG emissions Emissions per 1,000 kg live weight CH 4 shed emissions - IPCC (kg) MB+IPCC 1.03 CH 4 stockpile emission (kg) MB+IPCC 0.98 N 2 O-N shed emissions MB+IPCC 0.05 N 2 O-N stockpile emissions MB+IPCC 0.08 N 2 O-N application emission (kg) MB+IPCC 0.30 N 2 O-N indirect (from NH 3 ) emissions (kg) MB+IPCC 0.26 N 2 O-N indirect L&R emission (kg) MB+IPCC 0.00 Indirect N2O-N - range area (kg) MB+IPCC 0.24 Total N2O-N Emissions MB+IPCC 0.93 CH 4 shed emissions - DCCEE (kg) DCCEE 2.16 CH 4 stockpile emission (kg) DCCEE 0.00 N 2 O-N shed emissions DCCEE 1.23 N 2 O-N stockpile emissions DCCEE 0.00 N 2 O-N application emission (kg) DCCEE 0.36 N 2 O-N indirect (from NH3) emissions (kg) DCCEE 0.25 Total N 2 O-N Emissions DCCEE

79 Appendix 2 Feed Inputs Feed inputs are the largest (and most expensive) input for chicken meat production, with the largest volume being fed to the slaughter birds. Meat chickens are fed on staged diets matched to the nutritional requirements of the growing birds. Rations are formulated on a least cost basis, resulting in variations to the input products throughout the year. For the purposes of the study, aggregated commodity inputs (aggregated for all diets for a 12-month period) were sufficient to accurately assess the upstream impacts associated with grain and feed input production. These data were aggregated across the companies to provide one diet for QLD and one diet for SA. Feed input data were also required for modelling manure GHG emissions (i.e. digestibility, ash and crude protein) and these data were generated based on the specific rations. Developing Simplified Rations Commodity inputs to the rations were simplified using a substitution process (Wiedemann et al. 2010, Wiedemann & McGahan 2011). In particular, substitution processes were used for protein by-product inputs such as meat meal because of the sensitivity of these products to allocation processes within the original supply chains for other livestock products (i.e. beef, pork or fish). Animal protein by-products were substituted for soybean meal (considered the marginal protein meal) on a kg of protein equivalent basis following Wiedemann et al. (2010) and were corrected for energy. Similarly, tallow was substituted for canola oil. Data were not available for a number of minor dietary inputs. These inputs fall into two categories: products that require a low level of manufacturing and are of low cost (i.e. salt) and products that are high cost such as vitamins, synthetic amino acids and some minerals. High cost inputs are more likely to be associated with high levels of manufacturing (and energy input) and may be transported globally. To address this, low cost inputs were substituted for lime (calcium carbonate), and high cost inputs were substituted for synthetic amino acids using economic value to inform the substitution ratio. Simplified meat chicken rations (aggregated for the whole grow-out period) are shown in Table 40 and Table 41. Table 40. Aggregated, simplified meat chicken ration for the QLD supply chain Commodities (protein content in brackets) kg / t Sorghum (10%) 428 Wheat (13%) 211 Soybean meal (45%) 167 Canola 24.5 Other protein meal (substituted for canola meal) 28 Other pulse grain (substituted for soybean) 26 Animal by-product meals (substituted for soybean meal on a protein equivalent basis) 61.3 Oil/Tallow 25.9 Feed additives ,000 63

80 Table 41 Simplified meat chicken ration for the SA supply chain Commodities (protein content in brackets) kg / t Barley (11%) 139 Wheat (13%) 455 Other cereals (substituted for wheat) 75 Soybean meal (45%) 158 Canola meal (36%) 35 Field Pea 50 Animal by-product meals 38.7 Oil/Tallow 23.2 Feed additives 26.3 Total 1000 Feed Grain Unit Processes Cereal Grains Cereal feed grains LCI data were collected from desktop assessments of QLD and SA grain production processes, based on regional statistics for grain yields, literature sources and expert knowledge of production inputs. The main literature source data for building the grain unit processes were from NSW Trade and Investment (formally DPI) crop gross margins (NSW DPI 2008) and the ABS (2009) Land Management Survey, which provided data on tillage frequency, stubble management and fertiliser inputs. While the NSW I&I gross margins offer a convenient source of data for production processes and inputs for grain farming, the predicted yields were considerably higher than average yields reported by the ABS (2009). The higher yields predicted for calculating the gross margins were associated with higher inputs of fertiliser than are expected to be found on average. To address this, fertiliser inputs were reduced to a level that balanced the nutrient removal rate with the crop. These data were previously reported for layer hens in Wiedemann & McGahan (2011). All cereal grain production was assumed to be no-till or minimum till, as these are the marginal technologies for grain production in Australia. The production, maintenance, repair and disposal of the agricultural vehicles were based on the EcoInvent process for tractor production. Because of a lack of process data, some minor pesticides were omitted. Nitrous oxide emissions in cropping arise from three sources: fertiliser application, indirect emissions associated with volatilisation losses during fertiliser application, and losses from nitrogen associated with crop residues. Emissions of nitrous oxide from all sources were based on the Australian tier 2 methodology (DCCEE 2010). These emission factors are reported in Table 42. Table 42. Nitrous oxide emission factors for field crops Source NH 3 -N loss factor N 2 O-N loss factor N fertiliser application Fertiliser volatilisation resulting in indirect N 2 O-N losses n/a 0.01 Crop residue nitrogen n/a Source: DCCEE (2010). 64

81 Soybean meal The majority of soybean meal used in stockfeed in Australia is imported (Ansell & McGinn 2009), with 67% of imports originating in the USA. For this study, an Australian market average soybean meal unit process was developed, based on a market mix of 80% soybean meal imported from the USA (including transportation via ship), and 20% soybean meal sourced from Australian grown soybeans. The Australian soybean unit process was based on a desktop assessment of Australian soybean production, based on regional statistics for grain yields, literature sources and expert knowledge of production inputs. The US soybean unit process was sourced from EcoInvent, with one modification; the nitrous oxide emissions were revised to arise only from nitrogen deposited in plant residue (as per the IPCC 2006 De Klein et al. 2006). This resulted in a substantial decrease in nitrous oxide from growing soybeans. Soybean milling was standardised for both the US and Australian sourced soybeans, and was based on an EcoInvent unit process. Allocation between meal and oil was done using an economic approach (62% to soybean meal, 38% to soybean oil). Though not ideal, the economic allocation approach was applied because of the difficulty in applying system expansion (most other oil substitutes are also coproducts from an oil seed, meaning a primary oil product is difficult to identify), and the difficulty in applying a biological allocation on the basis of mass, energy or protein. To check the sensitivity of this allocation process a system expansion approach was applied using Australian grown canola oil as the marginal oil, and this resulted in a comparable value to the economic allocation. Other ingredients Energy usage and GHG data for other feed ingredients were either based on literature or AustLCI unit processes. Data sources for relevant ingredients are presented in Table 43. Table 43. Energy and GHG emissions for minor inputs to the layer and pullet rations Ingredient Energy (MJ/kg) GHG (kg CO 2 -e/kg) Source Synthetic amino acids: Lysine, Methionine, Threonine Eriksson et al. (2005) Limestone, at mine/au U AustLCI Unit Process 65

82 Organic Supply Chain Ration A simplified organic ration was developed through consultation with a poultry nutritionist experienced in formulating organic rations. The ration was based on wheat and soybean, and is shown in Table 44. Table 44 Simplified ration for organic meat chickens Commodities (protein content in brackets) kg / t Wheat (12%) Soybean meal (45%) 245 Soybean (38%) 58.8 Limestone 18.2 Feed additives 15.6 Total 1000 Organic Wheat and Soybean Processes Organic grain production unit processes were developed based on data collected from the Australian organic industry (UNE 2008) and personal communications with QLD organic grain producers. Two production systems were modelled based on a wheat / soybean rotation; i) Low yield / low input system in low rainfall regions (< 550mm a.a.r) and return of poultry manure from the grow-out unit, and ii) High input / high yield system in higher rainfall zones (>700mm average annual rainfall) with nutrients supplied from compost applications every second year. Unit process input data are provided in Table

83 Table 45. Organic wheat / soybean rotations with high and low input and yield scenarios Low input poultry litter from grow-out High input compost Inputs Description / units Wheat Soybean Wheat Soybean Yield t / ha Diesel Compost (from animal manure) Poultry manure application Total L (all machinery operations) kg / ha 2.5 kg/ha 0.9 Transport of inputs to farm t.km (10 tonne truck) N Balance - wheat / soybean rotation N added kg N - fixation kg N - compost 0 44 kg N - poultry litter 20 0 N removed kg N - wheat 9 44 kg N - soybean N losses N 2 O-N Crop residue N 2 O-N Compost addition NH 3 -N Compost addition N 2 O-N Indirect from NH 3 -N N balance P Balance - wheat / soybean rotation P added Compost 0 21 Poultry manure 7 0 P removed wheat 2 9 soybean 5 12 P Balance 1 0 Total GHG kg CO 2 -e/t Grain delivered to local storage The primary difference between the high and low input / yield scenarios resulted from the source of additional nutrients (particularly phosphorus). The low input system utilised uncomposted poultry litter from the organic grow-out system. Emissions were accounted for under the poultry manure management system and hence are not attributed here to the grain. In contrast, the high input system utilised compost (using feedlot manure as a raw material). A composting unit process was developed based on raw feedlot manure, taking into consideration fuel inputs and emissions from the composting process. Emissions from the composting process are considerable, mainly because of the high nitrous oxide emission factor recommended by the IPCC 67

84 (Dong et al. 2006) for intensive composting (10% of N added with raw materials to the composting process). Wheat inputs to the organic ration were based on a market mix from the low and high input systems. The proportion of low and high input wheat was limited to 30% by the availability of poultry manure from the grow-out unit to meet the phosphorus requirements of this system. Soybeans from the low input system supplied the entire organic ration. 68

85 Appendix 3 Manure Production The first step to estimating manure GHG is to estimate manure excretion, and more specifically the mass of volatile solids (VS) and nitrogen (N) excreted in manure. Manure VS and N excretion were estimated with use of information collected from the supply chains (daily feed intake and the properties of the diet) or by applying the default data inputs for the alternative DCCEE method. Relevant data, along with the default DCCEE values are presented in Table 46. Table 46. Average feed intake and crude protein levels for meat chickens (as-fed basis) Daily feed intake (g/bird/d) Crude Protein of diet DCCEE default data a 21.1 a Farm data QLD Supply Chain a DCCEE (2010) report assumptions on a dry matter basis. To convert these to As-Fed (i.e. accounting for moisture in the grain) the intake and crude protein values were multiplied by 1/0.9 (the average dry matter fraction of diets). Volatile excretion was estimated for the IPCC method (Dong et al. 2006), using actual farm intake and feed properties determined from the ration data supplied by the companies. The Australian inventory method estimates VS from feed intake (Table 46), an assumed ration digestibility (80%) and an assumed manure ash content (8%) (DCCEE 2010). Excreted N was estimated using a mass balance equation based on nitrogen intake with feed (feed intake x crude protein) and nitrogen retention by the birds. The N retention rates were calculated using poultry composition data reported by Wiseman & Lewis (1998) and live weight data from the farms rather than the IPCC default values (Dong et al. 2006). This resulted in higher N retention rates (~50%) compared to 30% suggested by the IPCC. Similar N retention levels to those used in this study were found by two meat chicken mass balance studies (Coufal et al. 2006, Wiseman & Lewis 1998). Excreted N following the DCCEE (2010) method was based on default feed intake and crude protein (Table 46) and an N retention rate of 43%. 69

86 Spent Litter Mass Flow Manure emissions were estimated at each point in the manure management system following a theoretical mass balance approach (IPCC method) and a simplified mass balance approach for the DCCEE method. Emission sources for the theoretical mass balance are shown in Figure 10 for conventional systems. Emissions noted with bold text are those covered by the DCCEE. NH 3 indirect N 2 O NH 3, N 2 O, CH 4 NH 3, N 2 O and CH 4 NH 3 and N 2 O Meat chicken housing N Spent litter storage (50% stored prior to application) N Spent litter application Nitrogen transferred = initial N losses N leaching and run-off N 2 O Figure 10. Nitrogen mass flows from spent litter in the grow-out phase of meat chicken production Two factors relating to the flow of spent litter were required for the study, i) a partitioning factor between directly applied spent litter and stored spent litter, and ii) a partitioning factor between spent litter applied in regions susceptible to leaching and runoff. Some information has been documented regarding the handling of spent litter in Queensland, which enabled these factors to be estimated. Playsted & Wiedemann (2010) report that companies responsible for collecting spent litter and reselling this to end users did not store spent litter, preferring to remove the litter directly from the shed and transport it to the end user s farm. From a survey of spent litter users (predominantly horticulture, turf and dairy), some 60% applied spent litter directly, while the remaining proportion stored litter for up to 12 months prior to land application. Based on these data a conservative estimate of 50% was used to estimate the spent litter subject to storage emissions. Spent litter application was also partitioned between zones subject to leaching and run-off and those not subject to leaching and runoff. The DCCEE (2010) have classified different regions of Australia where leaching and runoff is expected to occur, based on the ratio of evapotranspiration to precipitation. Typically, coastal zones are expected to experience a degree of leaching and runoff, while inland zones are less likely to experience these conditions. It was therefore necessary to estimate the proportion of spent litter applied in regions likely to experience leaching and runoff and the proportion applied in regions where leaching and runoff infrequently occur. For the QLD supply chain an informal survey of spent litter re-sellers indicated that large volumes of litter are regularly transported to inland cropping zones, while the remaining litter is predominantly used by horticulture and dairy farms near the coast. For the purposes of this study it was assumed that 50% of litter from the QLD supply chain was applied in areas where leaching and runoff occur (to horticultural crops and dairy pastures) and 50% was applied to broad acre crops inland. Spent litter from the SA supply chain is applied in cropping districts which are not subject to leaching and runoff. Therefore leaching and runoff was not considered a source for this supply chain. 70

87 A third factor was required for the free range and organic supply chains to partition manure deposition between the shed and outdoor range areas. No studies were found in the literature that provided portioning factors for manure deposition between the shed and outdoor range. One animal behaviour study (Dawkins et al. 2003) indicated that only a small proportion of the flock utilise the range area (average of 8%) and it is not known if the proportion of time spent outdoors corresponds to the proportion of daily manure excretion deposited outdoors. In the present study we estimated that 10% of manure was deposited in the outdoor range for the free range supply chain. This was increased to 20% for the organic supply chain because the birds have access to the range area over a longer period of the grow-out phase than for free range birds. Emissions from Conventional and Free Range Grow-out Facilities Shed Emissions Methane Manure methane emissions were estimated using the following general formula: M ij = VS ij x B o x MCF x p Where: VS ij B o MCF volatile solids excretion. emissions potential - m 3 CH 4 /kg VS Integrated methane conversion factor. p density of methane (0.662 kg/m 3 ). The IPCC (Dong et al. 2006) and the DCCEE (2010) provide different values for B o and MCF for meat chickens. These are summarised in Table 47. Table 47 Methane potential and conversion factors from the DCCEE and IPCC used in this study IPCC a DCCEE B o MCF (poultry manure with litter) B o MCF % % b a IPCC (Dong et al. 2006) b MCF is 2% for QLD and 1.5% for SA. 71

88 Shed Emissions Nitrous Oxide Direct nitrous oxide emissions from manure management in the shed were calculated using the general formula reproduced below. E MMS = (NE x MMS x EF (MMS) x C g ) Where: E MMS NE MMS EF (MMS) Nitrous oxide emissions from manure management. Nitrogen Excretion. The fraction of birds that are managed in a specified manure management system. The emission factor for the relevant manure management system. C g The factor to convert mass of N 2 O-N to molecular mass (= 44/28). This formula is sensitive to the estimated nitrogen excretion and the emission factor applied. Recommended emission factors from the IPCC and DCCEE are reported in Table 48. Table 48. Manure management systems and emission factors for nitrous oxide from the DCCEE and IPCC used in this study MMS IPCC emission factor for nitrous oxide DCCEE (2010) emission factor for nitrous oxide Poultry manure with litter (bedding) a 0.02 Poultry manure deposited outdoors (free range and organic supply chains) 0.02 b No factor supplied for poultry, default for shed emissions used. a IPCC (Dong et al. 2006); b De Klein et al. (2006). Uncertainty in the shed litter nitrous oxide estimation was determined using a range of for the IPCC+MB method, and for the DCCEE (2010) method. Land Application Emissions Nitrous Oxide Manure N applied to fields is a source of nitrous oxide emissions. Nitrogen applied to fields is calculated as: N applic = N excreted N losses (N 2 O-N and NH 3 -N from sheds and storage) 72

89 The DCCEE and IPCC provide different emission factors for nitrous oxide and ammonia arising from manure application. These are summarised in Table 49. Table 49. Emission Manure application emission factors from the DCCEE and IPCC as used in this study IPCC a emission factors DCCEE emission factors Nitrous oxide Ammonia a IPCC (De Klein et al. 2006) Uncertainty in the field application nitrous oxide estimation was determined using a range of for both the IPCC+MB method and the DCCEE (2010) method. Indirect Nitrous Oxide Emissions Ammonia Indirect emissions of nitrous oxide occur as the result of ammonia volatilisation from the production system and from ammonia volatilisation during manure application. Ammonia emissions are deposited onto land where it contributes to a pool of soil nitrogen, some of which is re-emitted as nitrous oxide. Consequently, the emissions are attributed to the facility responsible for the ammonia emissions. Ammonia emission factors and total losses (as a percentage of excreted N) are shown in Table 50. Table 50. Emission source Aggregated ammonia emissions from egg production systems following the DCCEE and IPCC methods and emission factors Australian NPI review emission factors for Ammonia DCCEE emission factor for Ammonia Grow-out shed Manure storage 0.20 not identified as a source Land application 0.20 not identified as a source Total ammonia as a proportion of excreted N Uncertainty in the shed litter ammonia estimation was determined using a range of for both the IPCC+MB method and the DCCEE (2010) method. Of the nitrogen lost as ammonia (NH 3 -N), the DCCEE and IPCC apply an emission factor of 0.01 (1%) to calculate indirect nitrous oxide emissions. The uncertainty assessment used a range of for both methods. 73

90 Leaching and Runoff Indirect nitrous oxide emissions were estimated from nitrogen that is leached or lost from runoff. Nitrogen leaching and runoff was calculated from zones where leaching and runoff occurs (QLD only) using the following equation: M ijk = 2 = M ik x FracWET ik x FracLEACH j Where: M ijk = 1 = Mass of manure N lost through leaching and runoff M ik = mass applied in each production system FracWET ik = fraction of N available for leaching and runoff FracLEACH j = 0.3 (IPCC default fraction of N lost through leaching and runoff) Emissions of nitrous oxide were estimated using the DCCEE emission factor of , which is identical to the factor recommended by the IPCC (Dong et al. 2006). 74

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92 Using Life Cycle Assessment to Quantify the Environmental Impact of Chicken Meat Production by Stephen Wiedemann, Eugene McGahan and Glenn Poad Publication No. 12/029 In order to reduce environmental impacts while increasing food production, Australia s agricultural sectors need to dramatically reduce the resource use and emissions intensity of food production. The chicken meat industry has an important role to play in this effort, being the largest supplier of meat for domestic consumption in Australia. Food is an important part of the environmental impact of every Australian through the production, processing and consumption phase of the food supply chain. In order to contribute to knowledge of these impacts, and to meet the challenges for improved production efficiency, this report presents research on the environmental intensity of chicken meat production, focussing particularly on greenhouse gas emissions, energy and water use undertaken through a lifecycle assessment (LCA). The study was conducted with the Australian public, the research community and the chicken meat industry in mind, and represents an industry first for this information. RIRDC is a partnership between government and industry to invest in R&D for more productive and sustainable rural industries. We invest in new and emerging rural industries, a suite of established rural industries and national rural issues. Most of the information we produce can be downloaded for free or purchased from our website < RIRDC books can also be purchased by phoning for a local call fee. Most RIRDC publications can be viewed and purchased at our website: Contact RIRDC: Level 2 15 National Circuit Barton ACT 2600 PO Box 4776 Kingston ACT 2604 Ph: Fax: rirdc@rirdc.gov.au web: Bookshop: RIRDCInnovation for rural Australia