Environmental Assessment of Two Pork Supply Chains Using Life Cycle Assessment RIRDC Publication No. 09/176
RIRDC
Innovation for rural Australia
Environmental Assessment of Two Pork Supply Chains Using Life Cycle Assessment
By Stephen Wiedemann, Eugene McGahan, Shelly Grist and Tim Grant
January 2010 RIRDC Publication No 09/176 RIRDC Project No PRJ-3176 and PRJ-4519
© 2010 Rural Industries Research and Development Corporation. All rights reserved.
ISBN 1 74151 973 X ISSN 1440-6845 Environmental Assessment of Two Pork Supply Chains using Life Cycle Assessment Publication No. 09/176 Project No. PRJ-003176 and PRJ-004519 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 02 6271 4165.
Researcher Contact Details Mr Stephen Wiedemann FSA Consulting PO Box 2175, TOOWOOMBA, QLD 4350 Phone: 07-4632 8280 Fax: 07-4632 8057 Email:
[email protected] 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: Email: Web:
02 6271 4100 02 6271 4199
[email protected]. http://www.rirdc.gov.au
Electronically published by RIRDC in January 2010 Print-on-demand by Union Offset Printing, Canberra at www.rirdc.gov.au or phone 1300 634 313
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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 pork industry to the regulatory requirements of the future. Life cycle assessment (LCA) is a powerful tool for quantifying environmental impacts (i.e. global warming potential) and resource usage (i.e. water usage) throughout the whole pork supply chain. This report is a summary of the research and findings for the first pork LCA project conducted in Australia. The study covers two representative pork supply chains in eastern Australia, located in northern (southern QLD) and southern (southern NSW) regions. This project was funded by the RIRDC and Australian Pork Limited, from producer levies matched by Australian Government funds. This report, an addition to RIRDC’s diverse range of over 1900 research publications, contributes to Australia’s agricultural R&D body of knowledge that underpins continual improvement in the sector. In particular, this project makes significant contribution to our Climate Change and Variability R&D program, which is working with other research and development Corporation’s to develop an agreed methodology to undertake life cycle assessments for energy, greenhouse emissions and water use and assess the energy efficiency of Australian agricultural systems. Most of RIRDC’s publications are available for viewing, free downloading or purchasing online at www.rirdc.gov.au. Purchases can also be made by phoning 1300 634 313.
Peter O’Brien Managing Director Rural Industries Research and Development Corporation
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Contents Foreword ................................................................................................................................................ ii Executive Summary.............................................................................................................................. xi Introduction ........................................................................................................................................... 1 Literature Review.................................................................................................................................. 3 Carbon Accounting in Australian Agriculture......................................................................................... 3 Water Accounting in Australian Agriculture........................................................................................... 8 Life Cycle Assessment .......................................................................................................................... 12 Agricultural LCA Research ................................................................................................................... 17 Methodology......................................................................................................................................... 22 Overview ............................................................................................................................................... 22 Goal and Scope...................................................................................................................................... 23 Farm Mass Balance ............................................................................................................................... 26 Supply Chain Descriptions ................................................................................................................. 27 Northern Supply Chain.......................................................................................................................... 27 Southern Supply Chain.......................................................................................................................... 29 Meat Processing ....................................................................................................................... 30 Life Cycle Inventory............................................................................................................................ 31 General LCI Components...................................................................................................................... 31 Northern Supply Chain.......................................................................................................................... 32 Feed Milling ............................................................................................................................. 32 Breeder Unit Piggery................................................................................................................ 34 Breeder Unit Waste Treatment System .................................................................................... 38 Grower/Finisher Unit Piggery .................................................................................................. 39 Grower/Finisher Unit Waste Treatment System....................................................................... 41 Southern Supply Chain.......................................................................................................................... 42 Feed Milling ............................................................................................................................. 42 Breeder Unit Piggery................................................................................................................ 45 Breeder Unit Waste Treatment System .................................................................................... 47 Weaner Unit Piggery ................................................................................................................ 48 Weaner Unit Waste Management System ................................................................................ 50 Grower/Finisher Unit Piggery .................................................................................................. 51 Grower/Finisher Unit Waste Management System .................................................................. 52 Meat Processing Data ............................................................................................................................ 54 Upstream Processes ............................................................................................................................... 56 Development of a Simplified Diet............................................................................................ 56
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Cereal Crops ............................................................................................................................. 57 Other ingredients ...................................................................................................................... 60 Delimitations ............................................................................................................................ 60 Energy Supply and Transport................................................................................................... 60 Life Cycle Impact Assessment ............................................................................................................ 62 Dealing with Co-Production.................................................................................................................. 62 Northern Supply Chain Results ............................................................................................................. 63 Water Usage ............................................................................................................................. 63 Primary Energy......................................................................................................................... 64 Global Warming Potential........................................................................................................ 66 Southern Supply Chain Results ............................................................................................................. 66 Water Usage ............................................................................................................................. 66 Primary Energy......................................................................................................................... 68 Global Warming Potential........................................................................................................ 68 Discussion ............................................................................................................................................. 70 Water Usage .......................................................................................................................................... 70 Primary Energy...................................................................................................................................... 72 Global Warming Potential..................................................................................................................... 73 Scenario Testing .................................................................................................................................... 76 GHG Estimation Using DCC Estimates ................................................................................... 76 Mitigation of Pond Methane..................................................................................................... 77 Alternative Reuse of Piggery Nutrient By-Products ................................................................ 79 Comparison of Housing Systems for Finisher Pigs............................................................................... 81 Sensitivity Analysis............................................................................................................................... 82 Conclusions .......................................................................................................................................... 84 Recommendations................................................................................................................................ 86 References ............................................................................................................................................ 88 Appendix 1 ........................................................................................................................................... 97 Textbook Values of Excretion Rates........................................................................................ 97 Mass Balance Principles........................................................................................................... 98 Mass Balance Model – Volatile Solids .................................................................................. 100 Methane from Manure............................................................................................................ 101 Enteric Methane Emissions .................................................................................................... 103 Mass Balance Model – Nitrogen ............................................................................................ 103 Meat Processing Emission Estimation ................................................................................................ 113 Appendix 2 – Network Tree Diagram.............................................................................................. 115
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Tables Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 Table 11 Table 12 Table 13 Table 14 Table 15 Table 16 Table 17 Table 18 Table 19 Table 20 Table 21 Table 22 Table 23 Table 24 Table 25 Table 26 Table 27 Table 28 Table 29 Table 30 Table 31 Table 32 Table 33 Table 34
The global warming potential of the major greenhouse gases ..........................................................4 Virtual water use estimates for alternative protein sources.............................................................11 Comparison of GHG emissions from a range of beef and meat chickens LCA studies in the literature ..........................................................................................................................................18 Comparison of GHG emissions produced during the production of pork assessed using LCA .....20 Pig production parameters for the northern pork supply chain and typical ranges – breeding performance.....................................................................................................................................28 Pig production parameters for the northern pork supply chain and typical ranges – mortalities and culling .......................................................................................................................................28 Pig production parameters for the northern pork supply chain and typical ranges – growth and performance.....................................................................................................................................28 Diet characteristics for the northern pork supply chain...................................................................28 Pig production parameters for the southern pork supply chain and typical ranges – breeding performance.....................................................................................................................................30 Pig production parameters for the southern pork supply chain and typical ranges – mortalities and culling .......................................................................................................................................30 Pig production parameters for the southern pork supply chain and typical ranges – growth and performance ..............................................................................................................................30 Simplified commodity inputs for production of one tonne of breeder unit ration for the northern supply chain ......................................................................................................................32 Simplified commodity inputs for the production of one tonne of grower / finisher unit ration for the northern supply chain ..........................................................................................................33 Water requirements per tonne of grain used in the rations for the northern supply chain ..............33 Energy and water inputs for the production of one tonne of breeder ration for the northern supply chain (feed delivered to farm)..............................................................................................34 Energy and water inputs for the production of one tonne of grower / finisher ration for the northern supply chain (feed delivered to farm) ...............................................................................34 Inputs to the breeder unit to produce one weaner pig (6 weeks of age) in the northern supply chain ................................................................................................................................................35 Drinking water usage assumptions for various pig classes .............................................................35 Blue water inputs (L) for the breeder unit to produce one weaner pig (6 weeks of age) in the northern supply chain ......................................................................................................................36 Outputs from the breeder unit to produce one weaner pig (6 weeks of age) in the northern supply chain.....................................................................................................................................36 Water outputs (L) for the breeder unit to produce one weaner pig (6 weeks of age) in the northern supply chain ......................................................................................................................37 Northern supply chain piggery breeder unit nitrogen balance ........................................................38 Manure methane production............................................................................................................38 Inputs to the grower/finisher unit to produce one finished pig and one tonne of liveweight gain (6 to 21.3 weeks of age) for the northern supply chain ...........................................................39 Blue water inputs (L) for the grower/finisher unit to produce one finished pig and one tonne of liveweight gain (6 to 21.3 weeks of age) for the northern supply chain .....................................39 Outputs from the grower/finisher unit to produce one finished pig and one tonne of liveweight gain (6 to 21.3 weeks of age) for the northern supply chain ...........................................................40 Water outputs (L) from the grower/finisher unit to produce one finished pig and one tonne of liveweight gain (6 to 21.3 weeks of age) for the northern supply chain .........................................40 Northern supply chain piggery grower / finisher unit nitrogen balance..........................................41 Manure methane production from the northern supply chain grower / finisher unit ......................41 Simplified commodity inputs for production of one tonne of breeder unit ration for the southern supply chain......................................................................................................................42 Simplified commodity inputs for production of one tonne of weaner unit ration for the southern supply chain......................................................................................................................42 Simplified commodity inputs for production of one tonne of finisher unit ration for the southern supply chain.....................................................................................................................................43 Water requirements per tonne of commodity used in the rations for the southern supply chain ....43 Energy and water inputs for the production of one tonne of breeder ration for the southern supply chain (feed delivered to farm)..............................................................................................43
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Table 35 Table 36 Table 37 Table 38 Table 39 Table 40 Table 41 Table 42 Table 43 Table 44 Table 45 Table 46 Table 47 Table 48 Table 49 Table 50 Table 51 Table 52 Table 53 Table 54 Table 55 Table 56 Table 57 Table 58 Table 59 Table 60 Table 61 Table 62 Table 63 Table 64 Table 65 Table 66 Table 67 Table 68 Table 69 Table 70 Table 71 Table 72 Table 73 Table 74
Energy and water inputs for the production of one tonne of weaner ration for the southern supply chain (feed delivered to farm)..............................................................................................44 Energy and water inputs for the production of one tonne of finisher ration for the southern supply chain (feed delivered to farm)..............................................................................................44 Inputs to the breeder unit to produce one piglet (3 weeks) for the southern supply chain..............45 Blue water inputs to the breeder unit to produce one piglet (3 weeks) for the southern supply chain ................................................................................................................................................46 Outputs from the breeder unit piggery to produce one piglet (3 weeks of age) in the southern supply chain.....................................................................................................................................46 Blue water outputs (L) for the breeder unit piggery to produce one piglet (3 weeks of age) in the southern supply chain ................................................................................................................47 Southern Supply Chain Piggery Breeder Unit Nitrogen Balance ...................................................47 Methane production from manure management at the breeder unit for southern supply chain ......48 Inputs to the weaner unit to produce one weaner pig from 3-8 weeks for the southern supply chain ................................................................................................................................................48 Blue water inputs for the weaner unit to produce one weaner pig from 3-8 weeks for the southern supply chain......................................................................................................................49 Outputs from the weaner unit to produce one weaner pig from 3-8 weeks for the southern supply chain.....................................................................................................................................49 Water outputs for the weaner unit to produce one weaner pig from 3-8 weeks for the southern supply chain.....................................................................................................................................49 Southern Supply Chain Piggery Weaner Unit Nitrogen Balance....................................................50 Methane production from manure management at the weaner unit for southern supply chain.......50 Inputs to the grower/finisher unit to produce one finisher pig from 8-23 weeks for the southern supply chain.....................................................................................................................................51 Blue water inputs (L) to the grower/finisher unit to produce one finisher pig from 8-23 weeks for the southern supply chain ..........................................................................................................51 Outputs from the grower/finisher unit to produce one finisher pig from 8-23 weeks for the southern supply chain......................................................................................................................52 Water outputs (L) for the grower/finisher unit to produce one finisher pig from 8-23 weeks for the southern supply chain ..........................................................................................................52 Southern Supply Chain Piggery Grower/Finisher Unit Nitrogen Balance......................................52 Methane production from manure management at the weaner unit for southern supply chain.......53 Inputs to the meat processing plant to process one finisher pig or one tonne of HSCW for the southern supply chain......................................................................................................................54 Blue water Inputs to the meat processing plant to process one finisher pig or one tonne of HSCW for both supply chains.........................................................................................................54 Outputs from the meat processing plant to process one finisher pig or one tonne of HSCW for the southern supply chain ..........................................................................................................55 Substitution ratios used for pig rations in the northern and southern supply chain diets ................56 Australian production and usage statistics for fertiliser products ...................................................57 Transport per tonne of urea supplied to south east QLD ................................................................58 Farm process data for grain production used for modelling feed grain inputs................................58 Soymeal co-product management using system expansion.............................................................59 Soymeal co-product management using economic allocation.........................................................59 Transport per tonne of soybean meal supplied to south east QLD..................................................60 Energy and GHG emissions for minor inputs to the pig rations for Australian pork production....60 Total water inputs (L / kg HSCW) for pork production in the northern supply chain ....................63 Primary energy usage for pork production in the northern supply chain ........................................64 GWP for pork production in the northern supply chain with three methods for managing coproducts ...........................................................................................................................................66 Total water inputs (L / kg HSCW) for pork production in the southern supply chain....................67 Primary energy usage for pork production in the southern supply chain........................................68 GWP for pork production in the southern supply chain with three methods for managing co-products ......................................................................................................................................69 Virtual water use estimates for various meat production systems from the literature.....................70 Water usage estimates for beef production in Australia using ABS water definitions ...................71 Water usage estimates on a kilogram of liveweight gain basis for pork and beef using ABS water definitions ..............................................................................................................................71
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Table 75 Table 76 Table 77 Table 78 Table 79 Table 80 Table 81 Table 82 Table 83 Table 84 Table 85 Table 86 Table 87 Table 88 Table 89 Table 90 Table 91 Table 92 Table 93 Table 94 Table 95 Table 96 Table 97 Table 98 Table 99 Table 100 Table 101 Table 102
Comparison of Primary Energy (PE) from Australian and international pork LCA studies...........72 Comparison of GWP from Australian and international pork production using LCA....................73 GWP contribution by supply chain stage for the northern supply chain.........................................74 GWP contribution by supply chain stage for the southern supply chain ........................................74 Sensitivity analysis for the effect of N2O emission factors on GWP in grain production for pork production in the southern supply chain .................................................................................75 Input data for Scenario 1 GWP in southern supply chain using DCC standard reference values...76 Methane and GWP estimates for pork production in the southern supply chain using DCC standard reference values for estimation of methane and nitrous oxide emissions .........................76 GWP for one kilogram of prime pork from the northern and southern supply chains with pond covering and methane flaring for primary piggery ponds ......................................................77 Sensitivity analysis for contribution of nitrous oxide with grain production to GWP for the northern supply chain with pond covering and methane flaring .....................................................78 Piggery nutrient by-product utilisation under current management and alternative scenario for the northern and southern supply chain piggeries .....................................................................80 Piggery nutrient by-product substitution ratios with fertiliser products..........................................80 GWP for pork production in the northern and southern piggery supply chains with system expansion to account for nutrient by-product utilisation.................................................................81 Sensitivity analysis of GWP for methane emission calculations in northern supply chain.............82 Sensitivity analysis of GWP for methane emission calculations in southern supply chain ............83 Sensitivity analysis of GWP for nitrous oxide emission calculations in southern supply chain .....83 Predicted Nitrogen Excretion per Pig (by Class) per Year (kg)......................................................97 Predicted Volatile Solids Excretion per Pig (by Class) per Year (kg) ............................................97 Predicted Volatile Solids and Nitrogen Excretion per Pig Class Using PIGBAL.........................100 Maximum methane-producing capacity of the manure (Bo) (IPCC 1997) ...................................102 Measured maximum methane-producing capacity of the manure (Bo) (Vedrenne et al. 2008)....102 Maximum CH4-Producing Capacity for U.S. Livestock Manure (EPA 1999)..............................102 Nitrogen Losses from Conventional Piggery Sheds as reported in the Literature ........................105 DCC (2007) Estimates on Nitrogen to Manure Management System (kg/yr) ..............................105 Total Nitrogen Effluent to Manure Treatment System from Different Calculation Techniques Presented in the Literature .........................................................................................106 Concentration of TKN in the Sludge of Anaerobic Ponds (From Casey 1992). ...........................106 DCC Estimates on Fraction Nitrogen Lost as Nitrous Oxide from Various Manure Management Systems....................................................................................................................108 Comparison of Composition of Fresh and Stockpiled Spent Litter Australian Piggeries (Adapted from Nicholas et al. 2006) .............................................................................................110 DCC (2007a) Estimates on Fraction of Nitrogen Volatilised from Manure Management Systems.... .......................................................................................................................................................111
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Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16
GHG Emissions from Agriculture in Australia (AGO 2007)..............................................................5 Components of All Agricultural Emissions (AGO 2006) ..................................................................6 Manure Management Emissions by Livestock Class (AGO 2006).....................................................6 Examples of Scope 1, Scope 2 and Scope 3 Emissions (WRI 2004) ..................................................7 Main impact pathways related to freshwater use (Mila i Canals et al. 2009). ...................................14 Life Cycle Assessment Framework - ISO 14040 (2006)...................................................................22 Simplified northern supply chain system boundary showing where foreground and background data were used ...................................................................................................................................24 Simplified southern supply chain system boundary showing where foreground and background data were used ...................................................................................................................................25 Blue water usage for the northern supply chain ................................................................................64 Primary Energy contribution by supply chain sector for the northern supply chain. ........................65 Blue water usage for the southern supply chain ................................................................................67 Primary Energy contribution by supply chain sector for the southern supply chain. ........................68 GWP contribution graph for the northern supply chain with and without pond covering ................79 GWP contribution graph for the southern supply chain with and without pond covering ................79 Simple Mass Balance Diagram for a Conventional Piggery .............................................................99 Nitrogen Loss Pathways from the Deep Litter Shed .......................................................................108
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Abbreviations ABS
Australian Bureau of Statistics
APL
Australian Pork Limited
ADG
Average Daily Gain
CH4
Methane
CPRS
Carbon Pollution Reduction Scheme
CO2-e
Carbon Dioxide Equivalent
DCC
Department of Climate Change
FS
Fixed Solids (Ash)
FCR
Feed Conversion Ratio
GHG
Green House Gases
GWP
Global Warming Potential
HSCW
Hot Standard Carcase Weight
LCA
Life Cycle Assessment
ML
Mega litre
N
Nitrogen
N2O
Nitrous Oxide
NGERS
National Greenhouse and Energy Reporting System
NGGI
National Greenhouse Gas Inventory
P
Phosphorus
PE
Primary Energy
QDPI&F
Queensland Department of Primary Industry and Fisheries
RIRDC
Rural Industries Research and Development Corporation
TS
Total Solids
VS
Volatile Solids
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Executive Summary What the report is about The pork industry, together with many other agricultural industries, has come under increasing pressure to quantify important environmental impacts and resource usage from the production of pork. Life cycle assessment (LCA) is an ideal tool for quantifying these impacts and resource usage for a whole supply chain. This report presents LCA results for two Australian pork supply chains for global warming potential (GWP), water usage and primary energy usage, and covers a range of management options. The report summarises the data collected as part of the life cycle inventory (LCI) stage of the project which was previously presented in milestone reports for this project and incorporates the project conclusions and recommendations. Who is the report targeted at? This report has been developed for Australian Pork Limited and the Climate Change and Variability R&D program of the RIRDC. Dissemination of the results to secondary target audiences (i.e. pork producers or the general public) will be at the discretion of the funding bodies. Background The Australian pork industry has invested significant funding into research and extension to improve the environmental management of piggeries in Australia, with an aim to reduce adverse environmental impacts from pork production. However, the industry has not conducted a comprehensive review of the environmental performance of piggeries with respect to greenhouse gas emissions, energy usage or water usage prior to this project. Recently these impact and resource usage issues have become the main focus for the environmental performance of an industry or a product both in Australia and worldwide, with increasing pressure from governments and the public for access to such data. The pork industry identified this as a research priority and identified two supply chains (located southern Queensland and southern NSW) for conducting a LCA. Pork has been the subject of several international LCA projects, which have consistently identified pork as a ‘low environmental burden’ meat product for global warming potential (GWP). However, pork production in these countries is significantly different to Australian conditions, both from a production perspective and when considering climatic conditions. Prior to this study the performance of Australian pork was approximated from European studies. This is clearly inadequate considering the different drivers of greenhouse gas production likely to occur in Australia compared to Europe. Moreover, few European agricultural LCA’s have investigated the water usage from meat production, as this has not traditionally been considered of relevance in most regions of Europe. However, in the Australian context water usage is of great importance at the farm and industry level, and is a major element in the LCA study. At the farm level, this information is required to improve efficiency of costly inputs such as energy and water. Likewise, sources of greenhouse gas emissions need to be quantified to determine the most effective mitigation measures that are available to the industry, particularly with the prospect of taxing on carbon emissions in the future.
Aims/objectives The project aimed to assess the environmental impacts and resource usage from pork production with respect to energy usage, water usage and greenhouse gas emissions (global warming potential – GWP). The assessment compared alternate management systems and geographical regions to provide an indication of performance for the industry. The detailed objectives were:
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1. To undertake a life cycle assessment of a farrow-to-finish piggery through to meat processing using piggeries with conventional housing and effluent treatment (case study 1). 2. To undertake a life cycle assessment of a farrow-to-finish piggery through to meat processing using piggeries where the grower/finisher pigs are housed in deep-litter sheds (case study 2). 3. To undertake scenario testing of waste management and by-product handling alternatives within these two supply chains. 4. To identify the major sources of water and energy usage, and GHG emission in each supply chain. 5. To identify major data gaps in the analysis and make recommendations for future research and development. This report addresses all the objectives of the project and presents findings for future research in the related fields. Methods used The general LCA process is outlined in a series of Australian and international standards which have been adhered to in the goal and scope development process. In addition to this, the Methodology for Agricultural Life Cycle Assessment in Australia developed for the RIRDC by Harris & Narayanaswamy (2009) has been reviewed and tested in this project. The study investigated primary pork production through to the point of wholesale distribution of carcasses (after meat processing). The main foreground supply chain stages were therefore identified as: •
Feed milling,
•
On-farm pork production at the piggery, which can be further broken down to:
•
o
Piglet / weaner production,
o
finisher production,
Meat processing.
At the farm level, pig production and waste management systems were modelled using the publically available nutrient mass balance program PIGBAL. This program, combined with detailed piggery production data ensured a reasonable degree of accuracy for key processes that determine GHG production. Methods for determining GHG emissions were sourced from the relevant DCC estimation manual for agriculture, developed for calculating the national greenhouse gas inventory (DCC 2007a). The piggeries were further broken down to differentiate emissions from the production sheds and the waste treatment and by-product utilisation systems (effluent treatment ponds). This improved the identification of important points of GHG emissions in the waste management system and allowed scenario testing based on alternative management practices. Farm production data were collected over a one year period (2007-08). These data extended to construction materials and consumable inputs that contain embedded water, energy and GHG. These upstream processes were modelled using unit processes available from AUSLCI, and international databases such as EcoInvent. Particular attention was directed to grain production, as a major component of the burden associated with pork production comes from the production of grain used to feed pigs. Grains data were modelled independently by FSA Consulting in an earlier project (unpublished) and were cross-checked with other Australian grain LCA research such as Narayanaswamy et al. (2004). The environmental burdens associated with some grain inputs (notably
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sorghum) have not been previously reported for Australia in the publically available literature despite the importance of this grain for the intensive livestock industries. The meat processing stage was assessed using limited foreground data collected from four pork processing plants in eastern Australia. These data were aggregated to provide ‘average’ values for meat processing, thereby removing the influence of this stage of the supply chain between the two case studies without compromising the accuracy of the final result. This was in line with the focus of the study (on-farm pork production) as identified by the funding body at the beginning of the project. Results/key findings Water Blue water usage ranged from 41-49 L / kg HSCW for the southern and northern supply chains respectively. Blue water usage (also comparable with the ABS water use definition) was generally lower than data reported in the literature for other species. The water footprint estimates ranged from 2,753 – 3,020 L / kg HSCW for the southern and northern supply chains respectively. For both supply chains this was lower than estimates for Australian and international pork production presented in the literature. Primary Energy Primary energy usage in the two supply chains varied from 20.3 to 24.5 MJ / kg HSCW for the southern and northern supply chains respectively. Primary energy usage was lower for the southern supply chain (deep litter housing for weaner / finisher pigs) suggesting that deep litter housing is less intense than conventional housing in this comparison. For the two supply chains, primary energy was 10-54% higher than most other studies presented in the literature. This is likely to be in response to greater energy losses associated with electricity supply and greater transport distances in the Australian pork supply chains. Global Warming Potential GWP for the two supply chains measured 3.1 – 5.5 kg CO2-e / kg HSCW for the southern and northern supply chains respectively. GHG emissions were primarily sourced from waste stream methane and nitrous oxide, though nitrous oxide from upstream grain production was also significant in the southern supply chain. The lower emissions from the southern supply chain were mainly the result of lower shed emissions during the finishing phase because of deep litter housing in the southern supply chain. A sensitivity analysis of emission factors for methane and nitrous oxide showed a cumulative range from -28% to + 59% for GWP in the southern supply chain, and -29% to + 11% for the northern supply chain, highlighting the sensitivity of the Department of Climate Change emission factors (which have not been validated under Australian conditions). A scenario was modelled in which effluent treatment ponds were covered and methane burned by flaring, resulting in a 90% reduction in methane emissions. This resulted in almost 50% lower emissions from the northern piggery (to 2.3 kg CO2-e / kg HSCW). The results were less pronounced for the southern supply chain, with GWP falling to 2.7 kg CO2-e / kg HSCW. Interestingly this showed that conventional pork housing and manure treatment systems may be the better performers when methane mitigation techniques are applied. It needs to be clearly acknowledged that the functional unit used for this study is primarily a supply driven unit which represents the end of the ‘primary production’ supply chain for pork. Hot standard carcass weight is significantly different to the pork that consumers have in their mind. Moreover, direct comparison with other species may not be valid because of the differences in carcass designation
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(i.e. head on vs head off). For comparison across species it would be more valid to extend the system until a more comparable product is reached, such as lean meat (bone out) or a pork loin chop for example. Implications for relevant stakeholders: Water usage (and water footprint) data presented in this report are lower than presented for pork and other species in the international literature. This highlights the water efficient nature of the Australian pork industry, despite the inclusion of evaporation from effluent storages and water storages used for fresh water containment. Pigs utilise less water than extensive or intensive beef production in Australia. This may favour the species in an environment where water availability and cost are increasing, allowing pigs a competitive advantage over some other forms of intensive livestock. Global Warming Potential (GWP) for Australian pork is slightly higher than several international studies, though the drivers of GWP are quite different for Australian piggeries and European studies. Importantly, there may be simple options for Australian producers to greatly reduce emissions through management changes. Considering the Australian pork industry is under constant pressure from imported pork, these results open the possibility for the Australian industry to promote its product as ‘low environmental burden’ compared with imported product for local markets. This represents a key marketing opportunity provided further research using different piggery case studies support the results from this study. The GWP figures presented in this report are not equivalent to the requirements of other greenhouse reporting frameworks such as the NGERS or proposed CPRS. LCA covers significantly more emission sources (particularly from upstream processes) than are required under these legislative frameworks, with or without agricultural emissions included. To present data equivalent to the requirements of the NGERS or proposed CPRS for pork production would require re-analysis of the data. It is important to note that greenhouse gas emission estimates were highly sensitive to changes in the emission factors used for calculation. None of the key greenhouse gas emission factors for the pork industry presented in the DCC (2007a) have been derived from Australian research, and the effects of even small changes to the emission factors for methane and nitrous oxide may change these results considerably. This underlines the need for research to validate these emission factors or propose changes to the Australian greenhouse gas calculation methodology. A scenario investigating low input pond covering and methane flaring showed that pork producers have viable options to reduce the most significant emission from conventional piggeries, pond methane. Considering this is the most simple form of methane mitigation where the energy is not harnessed, further emission reductions would be possible if methane were used in the shed for heating, or if electricity was generated from the waste stream. The comparison of deep litter and conventional housing showed that, for the current management systems, deep litter housing required lower water and energy inputs, and resulted in a lower GWP than conventional housing. The result for GWP was reversed overall however for the covered pond scenario. Because of the effect of pond covering on these results, the greenhouse gas performance of deep litter housing systems compared to conventional housing for future developments will be contingent on the availability of efficient pond methane mitigation technology. If this is available, it appears that conventional housing will be superior, particularly if the gas could be captured and used to reduce on-site energy requirements.
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Recommendations While promising, the results presented in this report are very limited, representing only two supply chains in the eastern states of Australia. Considering the diversity of farming practices the results could not be accurately extrapolated for the whole industry, as noted in the goal and scope of this project. Consequently, the results are better used to inform industry and guide strategic research than for wide-scale reporting in the general public. Considering the promising nature of the results however, further case study supply chains are recommended to improve the robustness of the data presented. While the study does cover water usage and present the ‘water footprint’ of Australian pork, it must be noted that these data are a poor representation of the environmental impact caused by water use. It is recommended that the results of this study for water usage be handled carefully, with full explanation of the methodology and implications of the results. Moreover, we recommend extending the study to investigate the environmental impacts of water use in the pork supply chains under one or more of the state-of-the-art impact assessment methodologies (i.e. Mila i Canals et al. 2009; Pfister et al. 2009) which have been developed since the initiation of this project. Three recommendations emerge from the scenario and sensitivity analysis regarding greenhouse gas emissions from Australian pork production. Firstly, the pond covering and methane flaring scenario highlighted this option for significantly reducing GHG emissions from the industry. This suggests that on-going research attention should be directed at low input options for reducing pond methane, and quantifying these reductions so that they can be factored into the DCC emission methodology. Additionally, further LCA scenario modelling is recommended to quantify the effect of high input methane mitigation options such as anaerobic digestion, which will have the added benefit of offsetting energy usage in the piggery, thereby reducing greenhouse gas emissions further still. This could be done as a relatively simple task based on the findings to date. The second recommendation relates to the sensitivity of the DCC greenhouse gas emission factors for the pork industry. The sensitivity analysis identified how changes to these factors (using a range of values available in the scientific literature) may alter the GWP performance of the supply chains by close to 60%. The DCC emission factors are based on international studies and have not been validated by Australian research. Considering this, the results presented from this report may vary by some 60% when the cumulative effect of both methane and nitrous oxide sensitivity analyses are considered. With the prospects of an emissions trading scheme including agriculture within 6 years, fundamental, Australian research to validate these emission factors or propose new factors is strongly recommended. PIGBAL was used for estimating the nitrogen balance and volatile solids data used in this project and is the basis for the DCC calculation methodology. It is recommended that research is directed towards validating the nitrogen balance in PIGBAL, and that on-going research to improve the volatile solids estimations is carried out when this can be done in conjunction with other projects. The GWP figures presented in this report are not equivalent to the requirements of other greenhouse reporting frameworks such as the NGERS or proposed CPRS. However these data could be presented by re-analysing the data collected for the project. Considering the importance of feed grain in the emission profile of pork production (as with other intensive livestock products), a joint project to develop more robust LCI data for feed grain production in eastern Australia is recommended.
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Life cycle assessment is able to quantify multiple environmental factors simultaneously. A limitation in this study was the focus on only three impact categories. This is particularly apparent when considering nitrous oxide, as other environmentally relevant forms of nitrogen are emitted from the same cycle. It is recommended that this study re-analyse the data to present results for an additional two categories relevant to the pig industry, acidification (from ammonia – NH3), and eutrophication (from nitrate – NO3 and phosphate). This will avoid promoting technologies or practices that ‘burden shift’ environmental problems from one area to another, and will provide a more holistic assessment of the pork industry. Because of the supply driven functional unit used throughout this study, the results must be very carefully presented to the general public, if they are to be presented at all. The emission profile of pork as a sale product will be considerably higher than the data presented here. If the goal is to present data such as the ‘carbon footprint’ of fresh pork to the Australian public, it is recommended that the supply chains are extended to the supermarket shelf for a range of products. If results are to be used in comparison with other meat species, it would be more valid to compare retail meat yield or comparison of similar products at the retail level, such as pork loin chops and lamb cutlets. Similarly, if the results are to be used to compare with imported pork products, it is recommended that a parallel study is established of the most relevant competitor to ensure a similar analysis is conducted.
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Introduction Background Australia’s primary industries are under increasing environmental, social and economic pressure to measure and reduce resource usage and environmental impacts. For the pork industry, major resource and environmental issues are related to water usage, energy usage and greenhouse gas (GHG) emissions. However, to date there has been no assessment of resource usage or GHG emissions from the whole Australian pork supply chain. A great many resources are used in the production of pork at many different points in the supply chain, however the greatest intensity of resource usage is generally required for on-farm production of the pigs. GHG emissions occur from a range of sources including the burning of fossil fuels (coal for electricity generation, liquid fuels, gas) and from livestock related emissions (i.e. methane and nitrous oxide from piggery waste streams). As with resource use, overseas research suggests the majority of GHG emissions are likely to occur at the farm level rather than from more obvious sources such as truck transport. Life cycle assessment (LCA) is a framework for assessing the resource usage and environmental impacts of the production of a product or the administration of a service. A LCA study takes into account the ‘whole of life’ impact of the product or service, effectively closing the gaps in the supply chain and identifying where the most significant sources, or ‘hotspots’, of resource usage and environmental impacts occur. LCA is a rigorous and effective tool for informing industry on their resource usage and emissions profile. This information can lead to more efficient and sustainable production, and will begin to help the industry adapt to new regulations that may be imposed to manage GHG emissions. Likewise, LCA is a robust tool for presenting valid information to the wider public on the environmental performance of a product such as Australian pork. The presentation of robust data based on a rigorous assessment framework to substantiate or challenge the claims of the popular media cannot be understated. Terms such as ‘food miles’ and ‘carbon footprint’ are becoming well known among consumers, and are being used to promote competing products in overseas markets. However, these terms are rarely understood and do not always reflect the true environmental impact of a product because of the variability in methodology and system boundaries applied. Water usage is also under scrutiny in many regions of Australia, and in some cases consumers are being encouraged to reduce meat consumption to reduce the pressure on water resources. These claims are rarely substantiated by scientific research or are subject to ambiguity, hence the need for industry to focus research on generating these data. LCA is a relatively new research area with an ongoing development and refinement of the methodology, particularly with respect to agriculture. Agriculture is a highly dynamic and variable production system compared to many other industry sectors, particularly with respect to GHG emissions. This LCA project relies on the established methodologies for estimating GHG determined by the National Greenhouse Gas Inventory Committee (NGGIC) (DCC 2007a, b). These methodologies are largely based on the IPCC (1997) guidelines which have not been widely validated under Australian conditions in the pig industry. Consequently, the GHG emission component of this study is only as reliable as the fundamental research and validation of emission factors relating to onfarm emissions. This being said, they represent the ‘state-of-the-art’ for GHG estimation in Australia at present. The refinement of these emission factors is a key research need for the pork industry in order to improve calculation of GHG emissions from Australian pork production. This is the first LCA study conducted on Australian pork, and has been limited to a study of two supply chains in eastern Australia. The study cannot be considered representative of the whole
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industry at this stage, though it is a comprehensive study based on Australian production from two ‘representative’ supply chains.
Objectives of the Research The principle objective of the project is to assess the environmental impacts and resource usage from pig farming with respect to energy usage, water usage and greenhouse gas emissions. The assessment will compare alternate management systems and geographical regions to provide an indication of variability within the industry, although this will not be a comprehensive assessment due to the limited piggery operations involved in the project. To achieve this overall objective, the project had three main components: Part A Goal and Scope development. This part establishes the fundamental elements of the study and was carried out with reference to the RIRDC report Life Cycle Assessment Methodology for Australian Rural Industries (Harris & Narayanaswamy 2009). Part B Inventory development. This component assessed the material and energy flows in different pig production systems including conventional and deep-litter housing systems. The inventory assessment was limited to water use, energy use and greenhouse gas emissions. Part C Life Cycle Impact Assessment (LCIA) and scenario analysis. The LCIA used the classifications, characterisation, normalisation and data inventory determined in Parts A and B and incorporated sensitivity analysis to determine the key assumptions and areas of uncertainty requiring further research. Following discussions with APL and the progression of other projects (i.e. “Estimates of manure production from animals for methane generation” and “Life Cycle Assessment Methodology for Australian Rural Industries”), the objectives for this project were redefined and made more specific. The revised objectives were to: 1. Undertake a life cycle assessment of a farrow-to-finish piggery and meat processing plant in northern Australia using tunnel ventilated, flushing sheds (Case Study 1). 2. Undertake a life cycle assessment of a farrow-to-finish piggery and meat processing plant in southern Australia where the grow-out pigs are housed in deep-litter sheds (Case Study 2). 3. Undertake scenario testing of alternatives within these two supply chains, namely: covering effluent ponds and flaring excess methane and reuse of by-product nutrients as a fertiliser replacement. 4. Identify the major sources (hot spots) of water and energy usage, and GHG emission in each supply chain. 5. Identify major data gaps in the analysis and make recommendations for future research and development.
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Literature Review Carbon Accounting in Australian Agriculture GHG Emissions in Agriculture
The greenhouse effect comes from molecules that are complex and less common in the atmosphere than the dominant gases nitrogen and oxygen (Le Treut et al. 2007). The gases with the greatest influence on global warming are water vapour (H2O), carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4) and ozone (O3). In addition, there is a range of human-made halocarbons (such as perfluorocarbons (PFCs), hydrofluorocarbons (HFCs), chlorofluorocarbons (CFCs) and sulphur hexafluoride (SF6) that exist in small amounts but are very potent and contribute to the total warming (Garnaut 2008). Compared to nitrogen and oxygen, which collectively comprise 99 per cent of the volume of the atmosphere, greenhouse gases occur only at trace levels, making up just 0.1 per cent of the atmosphere by volume (IPCC 2001a). Despite the low concentration of greenhouse gases in the earth’s atmosphere, their presence means that the earth has an average global surface temperature of about 14ºC—about 33ºC warmer than if there were no greenhouse gases at all (IPCC 2007a). Only some of these gases are directly emitted by human activities. Humans have less direct control over gases such as water vapour and ozone, although concentrations of these gases can be affected by human emissions of other reactive gases (Garnaut 2008). After water vapour, carbon dioxide is the most abundant greenhouse gas in the atmosphere. Most gases are removed from the atmosphere by chemical reaction or are destroyed by ultraviolet radiation. Carbon dioxide, however, is very stable in the atmosphere. Hence, this leads to the whole discussion about “carbon”. However, there are many other GHG’s and some of these do not include any carbon, e.g. N2O and SF6 hence carbon is somewhat of a misnomer. The warming of the atmosphere by different greenhouse gases is compared using the global warming potential (GWP). This compares the radiative forcing from a given mass of 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 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 will vary depending on the time period used in the calculation (Garnaut 2008). If a molecule has a high GWP on a short time scale (say 20 years) but has only a short lifetime, it will have a large GWP on a 20-year scale but a small one on a 100-year scale. Conversely, if a molecule has a longer atmospheric lifetime than CO2, its GWP will increase with time. For example, sulphur hexafluoride has the highest global warming potential of all gases at 22,800 times that of carbon dioxide because it has a long atmospheric lifetime of 3200 years, but has a low impact on overall warming due to its low concentrations. 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 four greenhouse gases and two groups of gases (HFCs and PFCs) is shown in Table 1. The GWP of each greenhouse gas is expressed on a carbon dioxide equivalency (CO2-e) basis. Contributing greenhouse gases are multiplied by their GWP to determine an equivalent amount of emitted CO2. Carbon dioxide equivalency is a quantity that describes, for a given mixture and amount of greenhouse gas, the amount of CO2 that would have the same GWP, when measured over a specified timescale (generally 100 years).
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Table 1 – The global warming potential of the major greenhouse gases Greenhouse Gas
Lifetime in the atmosphere (years)
100 year global warming potential
Carbon Dioxide
Variable
1
Methane
12
25
Nitrous Oxide
114
298
Sulphur hexafluoride
3,200
22,800
HFCs
1.4 - 270
124 – 14,800
PFCs
740 – 50,000
7,400 – 17,700
Source: Garnaut (2008)
Two compounds of particular importance to the carbon emissions from pork production are methane and nitrous oxide. Methane (CH4) has a GWP 25 times that of CO2 while nitrous oxide (N2O) has a GWP 298 times that of CO2 when measured on a 100 year timescale. This introduces a degree of confusion however, as the Australian inventory (discussed in the following section) uses GWP factors of 21 for methane and 310 for nitrous oxide based on earlier IPCC factors.
GHG Emission Accounting
Many terms are now prevalent in the area of estimating GHG emissions for enterprises or products. Common examples include: •
National inventories (such as the Australian National Greenhouse Gas Inventory – the NGGI),
•
‘Carbon accounting’ which is most commonly a business accounting tool that follows a framework established by international protocols (i.e. WRI 2004). and Australian legislation (i.e. the National Greenhouse and Energy Reporting Act),
•
‘Carbon Footprinting’ which is a frequently used but poorly defined term that generally relates to the GHG emissions from production of a product, though the boundaries for this assessment and the GHG gases included have not been agreed upon.
•
‘Global Warming Potential’ which is an LCA indicator for the GHG emissions over the whole production life cycle of a product, and is generally the most comprehensive of all the approaches at the product level.
The importance of greenhouse gas accounting and mitigation at the national level has been established by Australia’s commitments under the Kyoto Protocol. The Kyoto Protocol is an agreement made under the United Nations Framework Convention on Climate Change (UNFCCC). Countries that ratify this protocol commit to reducing their emissions of CO2 and the five other GHG’s, or to engage in emissions trading if they maintain or increase emissions. The Kyoto Protocol now covers 181 countries globally but only represents around 60% of global GHG emissions. As of December 2007, the USA and Kazakhstan are the only signatory nations to have signed but not ratified the act. The first commitment period of the Kyoto Protocol ends on December 31, 2012, and international talks began
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in May 2007 on a subsequent commitment period. Under the Kyoto Protocol, negotiations occurred that allowed different countries to have different reductions (or increases) in GHG emissions. National limitations range from 8% reductions for the European Union and some others to 7% for the US, 6% for Japan, 0% for Russia, and permitted increases of 8% for Australia and 10% for Iceland. For the Kyoto Protocol to be monitored, it is necessary to calculate the GHG emissions for individual countries for individual years from 1990 onwards. Hence, a National Greenhouse Gas Inventory (NGGI) is required to estimate the total GHG emissions from a country over a year. It is immediately evident that a standard GHG accounting procedure must be developed so that all countries report their emissions fairly and equitably, and this has been done worldwide by the IPCC (1996). When calculating a NGGI, Australia uses both the IPCC default methods and country-specific methods for some factors (typically for larger, more important emissions). The Department of Climate Change (DCC) (formerly the Australian Greenhouse Office, AGO) provides methodologies for the calculation of GHG emissions for each sector (http://climatechange.gov.au/inventory/methodology/index.html). The most recent methodology for agriculture was published in 2006 and is quoted as DCC (2007a) in this report. Factors and methods for the estimation of individual emissions (i.e. enteric methane) can be drawn from the NGGI methodology for use at an industry or individual enterprise level. In 2005, it was calculated that agriculture contributed 16.8% of Australia’s GHG emissions, making it the second largest emitting sector behind stationary energy (Figure 1). This contribution rises to 23% when the energy and transport used by the agricultural sector is included. Figure 2 shows that enteric emissions are the largest component of agriculture’s emissions followed by agricultural soils (mainly N2O emissions from fertiliser usage). Manure management (4 %) is the estimation of GHG emissions from manure primarily in the intensive livestock industries (lot feeding, pigs, poultry and dairy) as shown in Figure 3.
Figure 1 – GHG Emissions from Agriculture in Australia (AGO 2007)
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Figure 2 – Components of All Agricultural Emissions (AGO 2006)
Figure 3 – Manure Management Emissions by Livestock Class (AGO 2006)
The impact of methane emissions in Australia is understated because the DCC uses a global warming potential (GWP) for methane of 21 and not 25 for their greenhouse gas inventory calculations. This is due to the UNFCCC having agreed that the revised figures of GWP for different gases will not apply to greenhouse gas reporting until the second commitment period (2013-2017). This has serious implications for livestock methane emissions. Similarly, for N2O emissions the DCC uses a GWP of 310 and not 298 for their greenhouse gas inventory calculations (DCC 2007a, b). Because the NGGI relies on an industry-by-industry approach to calculate emissions, it is not comparable to other forms of accounting such as carbon footprinting or LCA. Carbon accounting is a term often used to describe business level GHG emissions accounting. A framework and tools for assessment have been 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 its 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. The protocol recommends that a consistent approach for setting an organisational boundary must be used for accounting and reporting on GHG emissions. These are categorised into three broad scopes:
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• • •
Scope 1: all direct GHG emissions Scope 2: indirect GHG emissions from consumption of purchased electricity, heat or steam 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
Figure 4 illustrates examples of scope 1, 2 and 3 emissions from business. This framework has been adopted by the Australian National Greenhouse and Energy Reporting System (NGERS). Essentially scope 1 and 2 emissions must be reported if a facility emits more than 25kt of CO2-e in a year.
Figure 4 – Examples of Scope 1, Scope 2 and Scope 3 Emissions (WRI 2004)
The Carbon Footprint concept has gained increased popularity in recent years and is now widely used in government, business and the media. However, the definition of “carbon footprint” is surprisingly vague given the growth in the term’s use in recent years (East 2008). Carbon footprinting has not been driven by research but rather has been promoted by nongovernmental organisations, companies, and various private initiatives as a tool for the measurement of GHG emissions associated with consumer products (goods and services) (Weidema et al. 2008b) This has resulted in many definitions and suggestions as to how the carbon footprint should be calculated. In Australia, the weight of evidence suggests that most carbon footprints include Scope 1 and 2 emissions as mandatory, with some including scope 3 emissions with the measurement being expressed in CO2 equivalents. This ensures that the activity being “footprinted” is consistent with the corporate reporting requirements under the NGERS. Carbon footprints may be a good entry point for increasing consumer awareness and fostering discussions about the environmental impacts of products. However, the most significant issue with the variability in the definition of carbon footprint is that it makes fair comparisons between products impossible if a standard and rigorous definition is not used. This being said, LCA indicator ‘global warming potential’ offers a close parallel the carbon footprint term, and in some cases LCA studies are being reported as a ‘carbon footprint’ using an LCA method. Because of the relatively greater
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development of LCA methodology and the comprehensive nature of ‘life cycle’ thinking, this is a more useful approach in the author’s mind. Hence this is the approach taken for the assessment of pork industry GHG emissions.
Water Accounting in Australian Agriculture
Water usage in the general literature is calculated using one of two methods and definitions, water engineering (water balance principles) or virtual water / water footprint. More recently, methodology development in LCA has advanced, largely as an integration of the above two approaches.
Water Engineering Water engineering is the traditional approach to water use assessment adopted by private enterprises and governments to define the quantity of water used in a particular locality (i.e. a farm, catchment, state). In Australia the Bureau of Statistics (ABS) provide definitions for the consideration of water use, and engineers apply water balances to determine water use within a given system.
ABS Water Definitions The ABS adopt an approach that is aligned with the engineering approach, though it does not represent a full water balance. The ABS defines water use as the sum of distributed water use, self-extracted 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. Essentially this definition corresponds to “Blue” water and does not include rain falling on properties. For water to be considered “used”, it has either been transferred from its natural watercourse or extracted from groundwater. Hence, small overland flow dams used for watering livestock are not considered as water use. “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. Delineation is also made between the terms consumption and use. Water consumption differs from water use in the sense that it represents the net water balance for an activity less the amount of water passed on for other uses. For example; a hydroelectric power station has a high water use - accounting for all of the water which enters the facility - but a very low water consumption, since almost all of the water ‘used’ is discharged downstream for other uses. 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.
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Farm Water Balance Definitions The engineering approach to system water accounting describes water movements associated with the system in the context of inputs and outputs. 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 the definition of a water use, delineation can be made between beneficial uses of water and non-beneficial uses, or losses. With this clarification made, a more representative working definition of water use is the sum of beneficial uses. However, it is also understood that there are non-beneficial uses (losses) associated with beneficial uses, and these should also be included in the total water use value. The water engineering approach quantifies water use for a physical system through construction of a water balance. The technique is based on accounting for system inputs and outputs, with imbalances resulting in changes to system storage under the assumption that there are no net gains or losses (i.e. no water is generated or destroyed). The strength of this approach – when used for water accounting – 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. The factors involved in this balance are a mix of physical processes and farm operations. The major components of a livestock facility water balance are as follows: Inflows – water may enter the system from many sources, which include: • •
•
Rainfall – describes water entering the system through rainfall. This can be accounted for as direct input to storages or cropping areas, but can also indirectly account for the generation of surface runoff. Pumped water – describes water brought into the system via some form of pumping. Common water sources are rivers, creeks and bores. This can also account for water delivered to the property by some form of distribution network or water supply scheme. Measurements of these inputs are generally quite simple with the use of flow meters in pipelines and channels. Surface runoff – describes water entering the system while flowing over the ground surface. Surface runoff inputs are usually very difficult to quantify, except where they are transferred into storages or for direct use by pumps, pipes or channels.
Outflows – depending on the level of detail, the water balance will define outputs by measurement and deduction. Outputs include: •
•
•
Transpiration – describes the process of plants removing water from the ground to support life and growth, and the eventual release of that water as vapour to the atmosphere. Transpiration rates vary considerably between plants of different types and species, and also vary according to climate and environmental conditions. Evaporation – describes the loss of water from open water surfaces through vaporisation. The driving factors of the evaporation process are solar radiation, temperature, wind speed and humidity. Evaporation can be inferred from measured rates using a standardised pan, or calculated from measures of climatic conditions. Evaporation losses can be reduced by a number of approaches from engineering solutions (such as designing storages to minimise open water surface areas for the volume of water stored, covers for open water surfaces etc.) to management strategies (such as...). In many locations across Australia, annual potential evaporation losses exceed annual rainfall inputs. It follows that evaporation is a major loss component for many farm water balances. Respiration/Perspiration – describes the water losses associated with animal processes.
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•
•
•
Seepage – describes outflow from storages and distribution channels by percolation through the base and/or walls. In engineered earthen storages or channels, seepage can be minimised through careful compaction of the lining material. The target minimum seepage rate from compacted earthen structures is approximately 0.000001 mm/day. Alternatively, seepage losses can be eliminated by the provision of a physical barrier, such as a plastic or concrete lining layer. Deep drainage – describes the infiltration of water into the ground beyond the root zone of plants. In the case of effluent irrigation, deep drainage is usually triggered by applications of water in excess of what is required to fill the root zone of the planted crop to maximum water holding capacity. Deep drainage can also be induced through rainfall onto a recently irrigated field. Surface runoff – describes water draining off the farm on the ground surface, usually triggered by rainfall. Surface runoff may be captured in storages, and can also include excess irrigation water draining off irrigated fields.
Water balances must also be applied to the individual components of a system to represent the behaviour of the components and describe the interactions between components. The generation of surface runoff requires analysis of a soil moisture balance, while water storages are also subject to a separate balance analysis to quantify fluctuations in storage volume. If water use is to be attributed to production (i.e. L per kg of pork) the general approach would be to account for all ‘system’ water inputs (from watercourses, storages, groundwater etc) which are directly related to production. In this manner, rainfall is included in the balance, but is generally excluded from the calculations of ‘water use’ since it does not exist because of the production operation. An exception to this is where rainfall water quality is affected, as when the water is captured on an effluent pond and is restricted from use by the surrounding environment. In this case, the rain fall may be considered a ‘use’.
Virtual Water and Water Footprinting The Virtual Water (VW) concept was first proposed by Allan (1998) to describe the water required to produce tradable commodities (particularly food) in water stressed economies. The concept was proposed as an explanation of water stress alleviation in the Middle East / North Africa region. These regions have low volumes of water available for food production and manage this scarce resource by importing considerable quantities of food commodities rather than producing this food locally. This reduces the competition that agriculture may otherwise place on water resources, allowing greater supply for human (drinking, sanitation) and industrial purposes. The concept is based on the assumption that irrigation water is saved in the focus country through the importation of food. In the Middle East and North Africa this assumption is valid, because additional crop production could only be achieved through irrigation. Hoekstra (2003) identifies two definitions of VW, i) the volume of water that was required to produce a product in reality (i.e. if wheat is produced in Australia and exported to the middle east, the VW by this definition is the water required to produce the crop in Australia in the year of production), and ii) the volume of water that would have been required to produce the product in the country of interest (i.e. for the above example, this would represent the volume of water that would have been required to produce the same amount of wheat in the Middle East where the wheat is imported to). The lack of consensus in definitions for VW contributes to variable figures within the literature depending on the approach adopted. This concept expands on a traditional understanding of water use which is more commonly based on ‘extracted water use’ as is defined by the ABS for example. To further improve the understanding of virtual water, Falkenmark describes water in terms of ‘blue’ water (which represents our general understanding of liquid water that may be sourced from surface or
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groundwater supplies) and ‘green’ water, which may be classed as evapotranspiration water (i.e. Falkenmark 2003, Falkenmark & Rockstrom 2006) or ‘soil stored moisture from rainfall’. These authors argue that a new paradigm is required in the description and consideration of water use and water use efficiency that incorporates green water into assessments of water resources and efficiency. The perspective taken by these authors is directed towards management of water scarcity in the face of growing demands for food production. Falkenmark (2003) contends that the food production needs of the world will increasingly require the optimisation of green water usage, because of the diminished and highly allocated reserves of blue water available world-wide. This distinction between blue and green water is very useful when considering water resources and water scarcity, and offers a clear way to interpret the variance in ‘water usage’ figures presented in the literature for meat production based on the inclusion or exclusion of green water. This being said, few authors have made this distinction when presenting VW or water footprint data for meat production to date. Virtual water estimates are generally made retrospectively, based on the water requirements (evapotranspiration) of crop production and animal requirements in specific regions. As noted, this may represent an estimate of the water it actually required to grow the given product, or an estimate of the avoided water, the water that would have been required to grow the crop in the country of interest. This latter method is somewhat analogous to the concept of system expansion in life cycle assessment. Methodologies for the calculation of virtual water using both approaches have been reported by Hoekstra (Hoekstra & Hung 2002, 2005, Chapagain & Hoekstra (2003) and Renault (Renault 2003; Zimmer & Renault 2003). Hoekstra (Hoekstra & Hung 2002) introduced the term ‘water footprint’ to refine their assessments of virtual water. These authors present their data interchangeably under the headings ‘virtual water’ and ‘water footprint’. However, the ‘water footprint’ term is a useful distinction for describing the methodology presented by these authors, and relates to the virtual water use of a specific product from a specific country. The virtual water use/water footprint of a range of agricultural products has been compiled by Hoekstra and Chapagain (2007). Results from these authors are presented in Table 2. Table 2 – Virtual water use estimates for alternative protein sources Species
L / kg (Australian estimates)
L / kg (World average)
Pork
5,909
4,856
Chicken meat
2,914
3,918
Eggs
1,844
3,340
Sheep meat
6,947
6,143
Beef
17,112
15,497
Soybeans
2,106
1,789
Source: Hoekstra & Chapagain (2007).
Table 2 shows the range in virtual water use from alternative protein sources. As an alternative plant protein, soybeans are not significantly superior to the more efficient meat products, particularly if the protein content were taken into account. The virtual water and water footprint tools may be useful for minimising local water impacts through the trade of food commodities between regions with differing levels of water scarcity, however these concepts are misleading when used to comment on the resource usage or environmental impacts of a
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production system. For example, a hydro-electric power plant may ‘use’ more water than a coal burning power plant, but the impact of this water usage may be quite different. For example, the hydro-electricity plant may not reduce the quantity or quality of water from other users downstream, while the coal burning power plant may evaporate water, thereby removing it from the immediate water stream.
Life Cycle Assessment Methodology development for LCA in Australian agriculture was enhanced by the funding of a LCA methodology project by the RIRDC (Harris & Narayanaswamy 2009). This project focussed on GHG, energy and water assessment in LCA. In general this document represents a honing of the ISO standards for LCA (ISO 14040-14044) with some specification with regards to on-farm data collection and the handling of water. The methodology for handling water from Harris & Narayanaswamy (2009) is summarised here, along with advancements to this approach that have been published in the literature since the RIRDC method was developed.
LCA GHG and Energy
LCA has a well developed method for accounting GHG emissions and energy usage (i.e. ISO 14040 2006a, b). These will not be explained in detail here. GHG emissions have been determined following the NGGI emission factor methods (DCC 2007a), using mass balance principles to generate volatile solids and nitrogen excretion rates at the piggery sites. These calculations have been done in accordance with Harris & Narayanaswamy (2009). Assessment of energy usage was based on total energy consumed on each site in the supply chain, transport between sites and upstream processes. Energy was primarily consumed in the form of electricity, gas, petrol and diesel and has been accounted for in accordance with the Australian agricultural LCA methodology Harris & Narayanaswamy (2009). Considering the ambiguity regarding the term ‘carbon footprint’ we suggest using the term ‘the carbon footprint of pork production using life cycle assessment’ if the carbon footprint term is desired.
LCA Water Usage and Impact Categories Current and Proposed Approaches to Water Usage in LCA Water usage in agricultural LCA is the focus of on-going debate over methodology both in Australia and internationally. To date, there is no established methodology or suite of methodologies that have been established for agricultural LCA, though several have been proposed in Australia (Harris & Narayanaswamy 2009), Peters et al. (unpublished) and internationally (Owens 2002; Mila i Canals et al. 2008; Pfister et al. 2009; Ridoutt et al. 2009). Life cycle assessment has not, as a rule, included water use within its framework of assessment. Historically this may be related to the low levels of water stress in countries where LCA has developed (primarily Europe) and its application to industrial processes that utilise comparatively low volumes of water (Mila I Canals et al. 2008). This being said, a number of methods for the assessment of freshwater use have now been proposed, and several options are sufficiently developed to be compared with alternative methods for water accounting.
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LCA has a strong methodological basis from which to incorporate water usage estimates. LCA is used for assessing resource usage and impacts to humans or the environment, both of which are relevant to water usage. The approaches discussed will present definitions for both assessment of resource usage and, where relevant, impacts from water usage. 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), funds may be characterised as sub-artesian groundwater supplies or dams (exhaustible resources), while flows refer to streams and rivers (non-exhaustible in principle). Water use and depletion indicator approach – Owens Owens (2002) further defined water in terms of in-stream uses (i.e. hydroelectric generation) and offstream withdrawal, and suggests 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 then released to the original river basin (downstream users are not deprived of any water volume) • Water consumption of consumptive use. Off-stream water use where water release or return does not occur (i.e. evaporation from a storage, transpiration from crop production), • Water depletion. Withdrawal from a water source that is not replenished or recharged (i.e. a water deposit). 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 & 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, un replenished groundwater sources.
For agriculture, most extracted water represents a consumptive use, as it will be either evaporated, transpired, lost in conveyance or incorporated into a product and removed from the catchment. Water depletion may also be relevant for agricultural systems that withdraw water from the Great Artesian Basin (GAB), which may be classified as an un-replenished source. The methodology presented by Owens is considered foundational in the field of LCA. Owens (2002) also presents a range of potential indicators for water quality, but does not detail impact categories for human health or ecosystems. Freshwater ecosystem impact approach – Mila I Canals Mila i Canals et al. (2009) have expanded and modified the approach provided by Owens (2002) to provide water characterisation factors for freshwater use. Mila i Canals et al. (2009) integrate the blue and green water terms drawn from the virtual water framework, and propose accounting for these water sources as separate inputs to the life cycle inventory. Water outputs are simplified into two paths, namely non-evaporative uses (‘water use’ under Owens’ definition) and evaporative uses (‘water consumption’ under Owens’ definition). Mila i Canals et al. (2009) do not consider inter basin
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transfers as a consumptive use but rather consider this as a change in resource availability between the source and the receiving water basin. Mila i Canals et al. (2009) identify two main aspects of water that need to be considered, i) water as a resource for humans as competing users, and ii) water as a habitat. Related to these, four impact pathways are identified: 1. Direct water use leading to changes in freshwater availability for humans, leading to changes in human health, 2. Direct water use leading to changes in freshwater availability for ecosystems, leading to effects on ecosystem quality (freshwater ecosystem impact, FEI), 3. Direct groundwater use causing reduced long-term freshwater availability (freshwater depletion, FD), 4. Land use changes leading to changes in the water cycle (infiltration and runoff) leading to changes in freshwater availability for ecosystems, leading to effects on ecosystem quality (FEI). The association between water use and changes to human health is not straight forward. Other authors have noted that freshwater availability per se is not commonly cited as a concern, but access to clean water is (Rijsberman 2006). This author goes on to identify economic status as the primary threat to clean water availability. For these reasons Mila i Canals (2009) suggest omitting this aspect from LCA.
Figure 5 – Main impact pathways related to freshwater use (Mila i Canals et al. 2009).
Treatment of green water. 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. To date, their approach has not been demonstrated with case studies in the literature, though it does have potential for integrating concepts from VW and LCA into a robust method of assessment. Water footprinting and water scarcity approach – Pfister et al. and Ridoutt et al.
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Ridoutt et al. (2009a, 2009b) have worked towards an integration of the water footprinting approach with LCA principles to enable determination of water use on water scarcity, with a demonstration of this approach for complex food products produced in Australia such as peanut M&M’s and Dolmio pasta sauce. The authors state the value of such an assessment is in providing information to companies and the general public to identify water-use reduction opportunities and inform consumers of the environmental performance of products. The authors have integrated standard water footprinting methods with a life cycle assessment approach to system boundaries, functional units and treatment of co-products. Their approach (presented in Ridoutt et al. 2009a) utilised a mix of actual water usage data throughout the supply chains of relevance, and literature data where real measurements were not available. Water usage is categorised as blue, green and dilution water, and these are measured separately. As a hybrid approach, this study is not consistent throughout. For example, water used for irrigation is taken from real field application data for some crops, while for others a retrospective estimate is made from crop models of evapotranspiration. Moreover, the ‘water use’ estimates for irrigation that utilise real data represent ‘irrigation volume applied to the field’ with no consideration of whether the water was used by the plants for transpiration or was lost as seepage. Where irrigation water volumes were believed to have exceeded water requirements, green water was assumed to be zero. While this approach may be suitable for broad scale assessments, it will not identify opportunities to improve water management at the farm level for two reasons, i) water loss pathways have not been quantified, and ii) green water has been excluded even though it plays an important role in the water balance and represents a possible source of renewable water for the agricultural system. Ridoutt et al. (2009a) did not attempt to define the actual human or ecosystem health impacts of water use, and acknowledge the following limitations of the water footprinting approach at the product level: •
There is a lack of correspondence between water footprints and the availability of water for alternative uses in the absence of production
•
There is difficulty in relating water footprints to potential social and environmental harm.
These issues are particularly compounded by the addition of all water types (blue, green and dilution) into a single figure that represents the ‘water use’ of a product. To extend this approach, Ridoutt et al. (2009b) identify that the main concern relating to water consumption in agri-food production is the potential to damage freshwater ecosystem health. Therefore, they identify blue water abstractions and changes in blue water availability from land use change as the two related focus areas. Ridoutt et al. (2009b) do not consider the estimation of green water as being necessary for the goals identified. They consider green water to be ideally handled under the category of land use in LCA. This approach appears very similar to Mila I Canals et al. (2009) described previously and has the associated strengths. However, Ridoutt et al. (2009b) proposes excluding green water from the inventory where Mila i Canals et al. (2009) do not. Ridoutt et al. (2009b) concedes the following limitations to their approach with relation to the exclusion of green water: •
It is not able to identify changes in water productivity in rain-fed production systems
•
It is not able to maximise calorific or nutritive value per unit of water consumed.
Considering the importance of dryland agriculture (based on green water) in Australia and the key role it plays in the future of food production, it is a notable limitation to remove this from an assessment methodology. This is a case of moving towards an environmental impact assessment tool at the expense of a ‘resource quantification’ tool.
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A similar though more detailed approach has been presented in Pfister et al. (2009). This study again integrates virtual water measures with LCA, with the attention focussed solely on blue water use. However, they add to this a regionalised water stress measure, proposing a new midpoint category ‘water deprivation’. Water deprivation is a measure of the water use (abstracted and evaporative water use, or ‘water consumption’) related to the degree of water stress within a catchment. The water stress index (WSI) is a measure of the balance of freshwater withdrawals to hydrological availability. Moderate and severe water stress occurs above a threshold of 20 and 40% respectively. As an example of this methodology, Pfister et al. (2009) present a case study of global cotton production. They show, for example, that although consumptive water use for cotton in Australia (3.92 m3/kg) is lower than water use in Mali (4.07 m3/kg), the water deprivation in Australia (1.42 m3/kg) is higher than Mali (0.99 m3/kg). This shows the ability of the method to provide information on catchment specific impacts as opposed to simply estimating total volumes of water used. As such this is a major advancement in freshwater impact categories. Pfister et al. (2009) identify the need for further development of indicators that are able to assess changes in green water flows from production systems. Progress in this area of research is moving towards development of a ‘stress weighted water volume’ for a product (Ridoutt pers. comm.). This approach holds merit, as it will result in a single, comparable number that is understandable to the general public and has already taken into account the likely environmental impact of the water use. Australian LCA methodology development The Australian methodology for agricultural LCA (Harris & Narayanaswamy 2009) provides another alternative to defining and measuring water use. The methodology identifies the following water usage elements in the inventory phase: • • • • • • • •
collected rainwater (treated and untreated); collected surface water (treated and untreated); ground water (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, 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; heating and cooling (e.g. evaporative losses); transport; and evaporation, seepage, drainage etc.
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The methodology proposes presenting water use under two definitions, i) the ABS water use definition reported previously in this document (which is roughly equivalent to Blue water), 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.
Preferred Approach to Water Usage Estimation of water use in this project follows an inventory approach that is broadly consistent with Owens (2002) and Harris & Narayanaswamy (2009). Additionally, ‘green’ water has been assessed for the crop growth phase, based on literature values for crop water use and yield. This was done by constructing a water balance for each farm in the supply chain. For the feed mills and meat processing plants, water usage data were supplied by on-site managers, and are assumed to represent ‘water pumped’ as per the ABS definition. The intent was to develop an inventory that is flexible enough to be assessed under a range of impact categories which were not available at the time of initiating this project, and have been deemed outside the scope of the assessment for this reason. We suggest that the water data are analysed at a later date using one or more of the methods recently published by Harris & Narayanaswamy (2009), Mila I Canals et al. (2009) or Pfister et al. (2009).
Agricultural LCA Research Life cycle assessment research in Australian agricultural industries have been limited in number to date, however major studies have now been completed for the dairy industry (Lundie et al. 2003), red meat industries (Peters et al. submitted, a) and the grains industry (wheat, barley, canola – Narayanaswamy et al. 2004 and Maize – Beer et al. 2005). Ridoutt et al. (2009a, b) have published work on water footprinting for the Mars group in Australia, covering several agri-food supply chains. Other private work has been carried out for some industries but these are not available in the public literature at this time. Internationally, many studies have been completed on pork and other meat production systems. These include: beef (Subak 1999; Haas et al. 2001; Nemry et al. 2001; Ogino et al. 2004; Chassot et al. 2005; Casey & Holden 2006; Barber et al. 2007; Verge et al. 2008), milk and dairy products (Cederberg and Mattsson 2000; Haas et al. 2001; Berlin 2002; Eide 2002; de Boer 2003; Hospido et al. 2003 and Casey and Holden 2005) and meat chickens (Nemry et al. 2001; Bennett et al. 2006 and Pelletier
17
2008). Multi-species comparisons covering beef, pork and meat chickens have been made by Williams et al. (2006), Baumgartner et al. (2008) and Wiedema et al. (2008). For comparison, the range of data available in the literature for two other meat species is presented in Table 3. Pork specific studies are covered in depth in the next section. Table 3 – Comparison of GHG emissions from a range of beef and meat chickens LCA studies in the literature
1
Species
kg CO2-e/kg CW1
Beef
8.4 – 32.3
Meat Chickens
2 – 5.1
References Subak (1999) – Ogino et al. 2004 – high
low
Pelletier (2008) – low, Weidema et al. (2008) – high
where necessary, data were transformed to a carcass weight basis (unallocated) for comparison purposes.
International Pork LCA Research
Intensive pig production is often associated with adverse environmental impacts related to production at the farm level. Several LCA studies have been done for various systems of pork production, primarily in Europe. A total of seven European studies were reviewed, most of which presented data for global warming potential (GWP) and primary energy (PE) – see Table 4. A further two partial LCA studies (Eriksson et al. 2005; Pelletier et al. 2007) covered the grower phase only. None of these studies assessed water use. Dalgaard et al. (2008) has conducted detailed research on main feed ingredients used in pork production such as soybean meal, and Lopez-Ridaura et al. (2009) studied effluent handling of piggery slurry specifically. Two detailed studies (Basset-Mens & van der Werf 2005 and Dalgaard et al. 2007) focused on identifying the environmental ‘hot spots’ in the pork production chain, although the functional units and the goals for each study were usually quite different. Dalgaard et al. (2007) assessed the environmental impacts for 1kg of Danish pork (carcass weight) delivered to the Port of Harwich, England. The goal of the report was to compare the environmental impacts of 1 kg of Danish pork delivered to Port Harwich to an equivalent kg of pork produced in Sweden, France and Great Britain. The pork production systems used in the study varied between conventional housing and deep litter based on the proportion of these facilities in the whole Danish pork industry. The highest contributions to global warming for their study arose from the production of feed and the handling of manure. Interestingly, the greenhouse gas contribution of transport (i.e. the ‘food miles’) was minimal largely because the mode of transport was by ship which has a low level of CO2 intensity per tonne km (tkm) travelled. A limitation with the concept of ‘Food miles’ is that the mode of transport is often overlooked and there are considerable differences in CO2 emissions between transport by ship or by truck. Dalgaard et al. (2007) noted that GHG emissions could be reduced for pork production through lowering feed (and protein) consumption and improving the handling of manure/slurry. In particular, greenhouse gas emissions can be reduced if the manure/slurry is treated in an anaerobic digester, with the biogas used for heat and power production. Basset-Mens and van der Werf (2005) evaluated the environmental impacts of three contrasting pig production systems. The scenarios compared were conventional good agricultural practice (GAP) according to French production rules, a French quality label scenario called red label (RL) and a French organic scenario called organic agriculture (OA). Of the three French production systems studied, the French GAP production system is most similar to the two Australian case studies that are
18
part of this report. The GAP results were used by Dalgaard et al. (2007) in their comparison to other European pork production systems. In the GAP production system pigs are raised at high density in a conventional slatted-floor building. The primary grain ingredients (wheat, barley, triticale, oats, maize and pea) were grown locally to both the piggery and the feed mill in Bretagne. The assumed distances from where the crop was grown to the feed mill and from the feed mill to the pig farm were both 100km. Additional crop-based feed ingredients were imported from overseas, soy from Brazil and sunflower from Argentina. All crops produced in France were based on best practice advice from interviews with local experts with fertilization according to anticipated crop needs and included integrated pest management. In addition for the GAP scenario it was assumed that some of the fertilizer requirements for the crops grown in Bretagne were met by the spreading of liquid manure. The overall amount of manure used for the crop-based feed ingredients was adjusted to correspond to the amount of manure produced by feeding the feed of which these ingredients were part of. Basset-Mens and van der Werf (2005) distinguished two stages in the GAP pig production: piglet production and weaning to slaughtering. The data on technical performance was based on published French statistics. The number of weaned piglets/productive sow/year was 25.5. The weaning age was 25.7 days. For weaning to slaughtering the feed to gain ratio was 2.7, the slaughter age was 175 days and the slaughter weight was 113 kg. NH3 and N2O emissions from slurry in pig buildings were calculated according to IPCC (1996) and UNECE (1999) methodologies. Methane (CH4) emissions due to enteric fermentation and housing type were calculated according to the IPCC (1996) methodology. The production of excreta for each system was quantified based on the feed to gain ration and the protein content of the feed. Environmental impacts of global warming, eutrophication, and acidification as well as energy use, land use and pesticide use were evaluated for two functional units: kg of pig produced (live weight at slaughter), and ha of land use. A summary of the literature review is compiled in Table 4.
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Table 4 – Comparison of GHG emissions produced during the production of pork assessed using LCA Dalgaard (2007)
al.
Basset-Mens & van der Werf (2005)
Cederberg Flysjo (2004)
FU
1 kg of Danish pork (carcass weight) at farm gate
kg of pig produced (LW at slaughter)
kg of bone and fat free meat
Tonne (reported here on kg) Carcass Weight
kg Carcass weight
GWP
3.3 kg CO2-e1
2.3 kg CO2-e1
4.4 kg CO2-e
6.4 kg CO2-e
11.2 kg CO2-e
Breakdown of GHG emissions by stage
61% crop and feed production
73% crop and feed production 20% weaning to slaughter stage
1.22 1.12 2.09
NR
NR
Energy Use
NR
15.9 MJ eq
18.4 MJ eq
17 MJ eq
193 MJ eq
Breakdown of energy usage by stage
-
74% crop and feed production
NR
NR
NR
Production System
Conventional slatted-floor (high density)
Conventional controlled environment sheds
25% fully slatted 25% partially slatted 50% DL
Conventional ‘intensive’ pig production and lower intensity scenario weighted by % of industry
Stages assessed
Piglet production 7kg Weaner – 7-30 kg Finisher – 30 – 105 kg
NR
Weaners to 30kg 30kg-slaughter at 76-109kg
NR
weaned piglet/sow/yr
26.09
25.5
25 slaughter pigs/yr
NR
NR
Finishing feed to gain ratio
2.67
2.7
4.2*
2.74
3.03
Local grains
Barley (local),
wheat, barley, triticale, oats, maize and pea [trucked ~ 100km]
All cereals
Wheat, maize,
Imported grains
Soy (Argentina)
meal
Soy – Brazil, Sunflower - Argentina
All sources
Manure Management
Manure/slurry stored and then applied to fields replacing some fertilizer
Liquid manure spread on crops replacing some fertilizer
Emission
IPCC (2006)
Morvan & Leterme (2001) IPCC (1996) Economic
rates
Co-product handling
et
System expansion
Conventional slattedfloor (high density)
Piglet production 25.7 days Weaner to slaughter 175 days, 113 kg
&
kg CO2 kg CH4 kg N2O
protein
Williams (2006)
et
al.
Barley,
Weidema (2008)
et
al.
Cereals
Soy
Imported soy
Slurry
Slurry and deep litter applied to crops replacing fertiliser
Conventional
IPCC 1997
National methods
IPCC 2000
Economic
Economic
System expansion
1 where necessary, data were transformed to a carcass weight basis (unallocated) for comparison purposes. * Calculated from slaughter weight of pigs and total feed use.
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The results from Weidema et al. (2008) clearly represent the highest GWP and primary energy values for pork production reported in the literature. This study incorporates economic input-output data which reportedly includes a far greater proportion of the upstream impacts of the production system. This is the likely cause of the higher emission and energy usage data compared to other studies. Cederberg & Flysjo (2004) studied three ‘future’ pig production scenarios based on differing objectives (welfare focus, environmental focus, low cost / max production focus). The data quoted in Table 4 are for the ‘future’ low cost / maximum production focus study. This study also cites an earlier study (Cederberg & Darelius – in Swedish) that reports 5.5 kg CO2-e / kg pork for current production in Sweden as of 2001. Cederberg & Flysjo (2004) identified that production systems with the lowest greenhouse gas emissions had manure handled as slurry instead of deep litter. Partial or streamlined LCA studies for pork production have been carried out by Eriksson et al. (2005) and Pelletier et al. (2007). Eriksson studied the performance of Swedish grower pigs from 29kg115kg in conventional pig housing with three different feed scenarios, i) Soy based feeding with imported soy (SOY), a domestically produced low CP diet – balanced with amino acids (SAA) and a feed based on organic inputs (PEA). The results for GWP, per kilogram of liveweight gain (liveweight), were 1.5 kg CO2-e for the SOY diet, 1.4 kg CO2-e for the SAA diet and 1.3 kg CO2-e for the PEA diet respectively. Pelletier et al. (2007) conducted a more limited analysis of pig production in Canada. Again, this study focussed on the grower pig with a number of scenarios for manure management. The system boundaries were set to consider only on-site emissions, excluding feed production and all other upstream emissions. As such the study is not comparable with other LCA studies. However, for interest the results showed that the emissions range from 0.54 kg CO2-e to 1.2 kg CO2-e per kilogram of pig (presumably liveweight).
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Methodology Overview The goal and scope of a LCA is the essential first step to identify fundamental elements to the project. Definition of the goal and scope for the pork industry LCA was done in consultation with key industry stakeholders (Australian Pork Limited) to clarify the intended purpose of the project and outcomes that could be achieved. In general, the structure of an LCA project can be described by Figure 6 below, in which goal and scope definition is the first stage. Goal and scope definition is influenced by the available data from case study supply chains.
Figure 6 – Life Cycle Assessment Framework - ISO 14040 (2006)
Figure 6 shows the interaction of the interpretation stage between all other stages of the LCA. This indicates that goal and scope definition, while being the first stage of the project, interacts with later stages and may consequently change.
Impact categories assessed LCA can be used to assess a wide range of environmental impacts including resource usage (energy, water, land resources), air emissions (greenhouse gasses, acidifying gases), emissions to water (phosphorus, nitrogen) and release of toxins to the environment. These are assessed under a wide range of impact categories. The impact categories covered by in this LCA include global warming potential, primary energy use and water use. These are the most common and important impact categories covered in Australian agricultural LCA (as defined by Harris & Narayanaswamy 2009).
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Goal and Scope Goal Goal definition covers the intended application and target audience for the LCA study. Through consultation with the industry, the following applications were identified: 1. To provide information to industry on the environmental impacts of producing pork 2. To identify and validate environmental research priorities in the pork production supply chain and to inform industry and government research investment 3. To identify the environmental impacts of different production systems (i.e. deep litter compared to conventional production systems) 4. To identify the likely environmental impacts associated with changing waste stream management (i.e. the environmental benefit associated with covering piggery ponds and flaring methane).
The following target audiences were initially identified for the pork LCA: Primary target audience 1. Australian Pork Limited (Industry research funding body), 2. Rural Industries Research and Development Corporation (RIRDC) – funding body. Secondary target audience 3. The general public, 4. Australian pork producers, 5. Government (research funding providers), 6. Government (environmental regulation bodies – EPA’s). The authors wish to clearly identify some issues that need to be considered when presenting the research findings to secondary target groups: 1. Only two specific (but representative) supply chains were modelled. This project was not intended to provide data that would be regarded as generically representative of the whole Australian pork sector and should not be represented in this way. 2. The selected supply chains end at the meat processing plant, hence this is not a “cradle-tograve” LCA. The project cannot inform supermarkets and consumers of the full impacts of fresh pork sold in Australia. 3. The report must be cleared with the pork producers involved to ensure confidentiality is maintained. The boundary of the LCA may be expanded to include retail distribution through to purchase at a later date if this is required.
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Scope The scope of the LCA covers the definition of the functional unit, the proposed system boundary, data quality requirements and critical review of the LCA.
Functional Unit The functional unit was developed in alignment with the goal and scope of the project and in conjunction with the general agricultural LCA methodology (Harris & Narayanaswamy 2009). The following functional unit has been selected for the pork supply chain: 1 kg (or 1 t) of hot standard carcass weight (HSCW) pork at the abattoir docking gate. Additionally, for comparison of the two supply chains against one another, it was useful to disaggregate data within the farm production system. This gave a further two mid-point functional units that could be used within the study. •
1 live piglet / weaner pig at the farm gate, and
•
1 live slaughter pig at the farm gate.
Data can also be extracted at the ‘farm-gate’ for the live pork finishing phase (1 kilogram of pork liveweight gain from weaning to finishing). Attention for the study was focussed at the farm level, as this stage of the supply chain will typically have the greatest share of environmental impacts according to the literature (i.e. Dalgaard et al. 2007). Farm level production is also expected to be the focus for research outcomes from the project. The selection of a functional unit post processing (1 kg of HSCW at the meat processing load out gate – point of retail distribution) was done to enable comparison with other Australian meat LCA’s and international pork LCAs. System Boundary In line with the goal and scope of the project, the system boundary has been developed to include the production system, extending to the abattoir docking gate (point of distribution). Extension of this system boundary to include distribution and retail is recommended at a later stage for completeness; however this was beyond the scope of the current project. The system boundaries are shown in Figure 7 and Figure 8 for the northern and southern supply chains respectively.
Figure 7 – Simplified northern supply chain system boundary showing where foreground and background data were used
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Figure 8 – Simplified southern supply chain system boundary showing where foreground and background data were used
Data Requirements The data requirements are determined by the goal and scope of the project. In order to provide some comparison between different Australian pork production systems, different systems and geographical areas needed to be assessed. For this reason the case studies selected included a northern supply chain (farrow-to-finish conventional piggery) and a southern supply chain (multi site production that includes pigs finished on deep litter). Foreground data including water usage, energy usage, major inputs and outputs were collected over one whole year to take any seasonal effects into account. The foreground data from each supply chain are considered confidential, and data presented in this report are standardised on a per-unit basis so as not to identify the data providers. Background data for upstream processes have been sourced from Australian LCI database released in SimaPro LCA Software Centre for Design at RMIT and Life Cycle Strategies Pty Ltd (2009) and from European databases such as EcoInvent, where Australian data were lacking.
Critical Review Considering the goal of the LCA is to present information to the public, the results must be reviewed by an independent expert to meet the ISO Standard 14040 (2006a). The critical review has been carried out throughout the LCA modelling process by an external expert (Mr Tim Grant – Life Cycle Strategies). Comments and recommendations from the external review have been incorporated into this final report.
LCA Modelling The LCA was modelled using two programs, i) PIGBAL was used to model on-farm processes and develop the piggery mass balance (described in Appendix 1), and ii) Simapro™ was used to model the whole supply chain LCA. The modelling was essentially done using current production data and considers the impacts of a ‘static’ production system for a determined timeframe (2007/08). This was considered appropriate for an ‘industry first’ study to characterise the status quo. The case study
25
supply chains are large, progressive piggeries that operate using the best management practices for Australian pork production with peak performance, hence they may not accurately represent all production systems in Australia. However, piggery performance data were checked against industry averages and modified to reflect representative performance for Australian piggeries where necessary. This also protects the sensitive nature of performance data for the piggeries involved.
Farm Mass Balance Life cycle assessment traditionally considers a supply chain from the perspective of inputs and outputs. This is appropriate when considering a factory, which has an easily defined boundary (i.e. the walls of the factory), limited numbers of products entering or leaving the system and a limited number of emission pathways. However, agricultural enterprises are complex open systems with many inputs and outputs, many of which are difficult to measure. This is particularly apparent with greenhouse gases, which may be emitted from several points on a pig farm (the piggery shed, effluent treatment pond and soils in litter reuse areas). It is likely that these emission sources will dominate the greenhouse gas profile of the pork supply chain, hence a method is required to determine these emissions as accurately as possible. In this project, emissions were calculated by conducting a mass balance of the piggery system. The mass balance is focused on carbon and nitrogen, and considers all the inputs to the piggery (primarily feed) and the measured outputs (sale pigs, mortalities). From these known inputs and outputs, excreted carbon (in the form of undigested feed or volatile solids) and nutrients can be estimated. The mass balance then models losses that occur within the system based on literature research and data collected on farm where available. An alternative to the mass balance approach is to base emissions directly on literature estimates (“textbook” values) of outputs from a piggery regardless of the specifics of the system being considered. The DCC (2007a) suggest using the mass balance approach (with PIGBAL) for determining volatile solids and nitrogen flows. Once these are determined, methane and nitrous oxide emissions can be calculated using the emission factors provided. The DCC (2007a) also provides fixed volatile solids and nitrogen flow data that represent ‘textbook’ values. A scenario was modelled based on these fixed numbers for comparison with the mass balance approach. Appendix 1 provides a considerable amount of detail on the principles of the ‘text book’ and mass balance approach to farm scale emission estimation.
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Supply Chain Descriptions Northern Supply Chain The northern pork supply chain consists of a conventional farrow-to-finish operation, with feed supplied by two off-site mills and sale pigs marketed to several meat processing plants. The piggery is a closed production system, with all pigs bred on-farm. This piggery has three distinct production units on the one farm, including a multiplier facility, a breeding facility and a finishing facility. •
The multiplier unit produces replacement breeding stock for the breeding unit. Excess pigs (mostly males) from this section of the piggery are sold off at finishing age.
•
The breeding unit produces weaner pigs that are transferred to the finishing unit. At this unit the pigs are weaned at approximately 3 weeks of age and are then transferred into nursery housing for a further 3 weeks, leaving the breeder unit at 6 weeks of age.
•
The finishing unit receives pigs at 6 weeks of age from the breeding unit and houses pigs until finishing age (21.3 weeks of age).
The farm has detailed monthly records of energy use and water use for each of these production units. Data were not available for individual sheds within these units (e.g dry sow, farrowing, nursery), thus data could be extracted at the following mid-points (representing mid-point FU’s for the northern supply chain): •
1 weaner pig (6 weeks of age)
•
1 finisher pig (21.3 weeks of age)
•
1 tonne of pig liveweight gain from 6-21.3 weeks.
LCI data are presented for these units, however overall results are only presented for the standard FU ‘1 kg of hot standard carcass weight (HSCW) of pork at the meat processor docking gate’. This was done to allow some comparison of the two supply chains at the finishing stage. Each component of the supply chain was treated as a separate unit in the LCI for modelling purposes. Feed Mill Two commercial feedmills supply feed to the northern piggery. Feedmill #1 is located 150 km from the piggery and supplies all piglet mash and nursery mash diets, as well as approximately 50% of the pelleted weaner diet. Feedmill #2 is located 120 km from the piggery and supplies the remainder of the piggeries’ feed requirements as a pelleted diet. Both feedmills were able to supply primary data for the production of 1 tonne of feed delivered to the piggery. Pig Production Data Specific production data for the piggery were supplied by the piggery operators during the data collection year (Table 5, Table 6 and Table 7). Also included in these tables are some ranges of pig production data that are likely to represent the performance of piggeries in the northern regions of Australia. These data were based on industry averages (Collman & Spencer 2005), Pigstats 99 (Meo & Cleary 2000) and expert opinion.
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Table 5 – Pig production parameters for the northern pork supply chain and typical ranges – breeding performance Farrowing Index (Litter / sow / yr)
Farrowing Rate
Range
Range
%
Value Used
2.0 – 2.45
Live Piglets / Farrowing
2.45
80 – 88
Value Used
Range
86.3
9 – 11
Pre-weaning mortality (%)
Value
Weaned / Sow / Yr
Range
Value Used
Range
Value Used
12 – 15
14.13
17 - 24
22.2
Used
10.64
Table 6 – Pig production parameters for the northern pork supply chain and typical ranges – mortalities and culling Sow Mortalities (%)
Sow Culling rate (%)
Sucker Mortality (%)
Weaner Mortality (%)
Grower/Finisher Mortality (%)
Range
Value Used
Range
Value Used
Range
Value Used
Range
Value Used
Range
Value Used
8 - 15
10.8
35 – 60
47.34
8 - 10
14.13
0.8 - 4
1.13
0.8 – 3
1.95
Table 7 – Pig production parameters for the northern pork supply chain and typical ranges – growth and performance Feed Conversion Rate (FCR) dressed weight- whole herd
Average Daily Gain (ADG) dressed weight - birth to finisher (g/d)
Carcass yield dressing percentage) (%)
Average Sale Weight (kg)
Average Sale Age (days)
Range
Value Used
Range
Value Used
Range
Value Used
Value used
Value used
4.7 – 3.8
3.87
420 - 555
483
72 - 80
76
96.9
151.5
Relevant dietary details for the various diets fed at the piggery, including digestibility, crude protein, amount ingested and estimated wasted feed are reported in Table 8. The piggery feeds two weaner, two grower, two ‘finisher 1’ and two ‘finisher 2’ diets, however these were combined into average single weaner, grower, ‘finisher 1’ and ‘finisher 2’ diets. Table 8 – Diet characteristics for the northern pork supply chain Diet
Digestibility (%)
Crude Protein (% - as fed)
Ingested (kg/pig/d)
Estimated Wastage (kg/pig/d)
Total Offered (kg/pig/d)
Boar
78.9
13.60
2.89
0.15
3.04
Gilt
81.9
15.91
2.89
0.32
3.21
Dry sow
78.9
13.60
2.89
0.15
3.04
Lactating sow
83.8
17.81
5.40
0.28
5.68
Piglet
90.9
19.85
0.13
0.02
0.15
Nursery
88.7
19.94
0.53
0.09
0.62
Weaner
86.7
18.00
1.08
0.12
1.20
Grower
83.5
15.04
1.70
0.19
1.89
Finisher 1
81.2
13.60
2.28
0.12
2.40
Finisher 2
81.5
14.16
2.40
0.13
2.53
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Southern Supply Chain The southern supply chain piggery consists of a conventional farrowing unit producing weaners (3 weeks of age), followed by deep litter grow out units (where pigs are housed on litter rather than slatted floors) that house pigs through to sale. These grow out units are managed and owned by separate entities under a contracting arrangement, where the ownership of the pigs is maintained by the company that breeds the pigs. Feed for each enterprise is supplied from an off-site feed mill owned by the pig breeding company. Sale pigs are marketed through a single meat processing plant. Pigs are reared to weaning age (3 weeks) in conventional concrete slatted floor housing where effluent is flushed into an effluent treatment system. The weaned piglets are transported to a deep litter weaner facility where they are housed until 8 weeks of age. From this facility they are transported 240 km to a deep litter grow-out facility, where they are housed until finishing weight (95 kg). From there they are transported 175 km to the meat processing plant. The breeder and grow-out facility have detailed records of energy and water usage. Detailed data were not obtained for the weaner facility however, and the information from the grow-out facility was extrapolated to obtain the relevant data. Both the weaner and grow-out facility operate similar deep litter housing systems and are expected to have similar environmental profiles, allowing the data to be extrapolated with some degree of accuracy and consistency. This would allow the following midpoint and end-point functional units to be calculated for this supply chain: •
1 weaned pig (3 weeks of age) – conventional housing
•
1 pig grown from wean (3 weeks) to 8 weeks - deep litter housing.
•
1 pig grown from 8 weeks to 23 weeks - deep litter housing.
•
1 tonne of pig liveweight gain from 8 – 23 weeks – deep litter housing.
As with the northern supply chain, LCI data are presented for these units, however overall results are only presented for the standard FU ‘1 kg of hot standard carcass weight (HSCW) of pork at the meat processor docking gate’. This was done to allow some comparison of the two supply chains at the finishing stage. Each component of the supply chain was treated as a separate unit in the LCI for modelling purposes.
Feed Mill All piggeries in the southern supply chain were supplied from one feed mill operated by the company that owns the pig breeding operation. This feed mill is located 28 km from the breeder unit, 60 km from the weaner unit and 175 km from the grow-out unit. The feed mill supplied primary data for the production of 1 tonne of feed.
Pig Production Data Specific production data for the southern pork supply chain were supplied by the piggery operators (Table 9, Table 10 and Table 11). Also included in these tables are some ranges of pig production data that are likely to represent the performance of piggeries in southern Australia. These data were based on industry averages, Pigstats 99 (Meo and Cleary 2000) and expert opinion.
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Table 9 – Pig production parameters for the southern pork supply chain and typical ranges – breeding performance Farrowing Index (Litter / sow / yr)
Farrowing Rate
Range
Range
%
Value Used
2.0 – 2.45
2.25
Live Piglets / Farrowing
80 – 88
Value Used
Range
80.0
9 – 11
Pre-weaning mortality (%)
Value
Weaned / Sow / Yr
Range
Value Used
Range
Value Used
12 – 15
12.0
17 - 24
20.0
Used
10.11
Table 10 – Pig production parameters for the southern pork supply chain and typical ranges – mortalities and culling Sow Mortalities (%)
Sow Culling rate (%)
Sucker Mortality (%)
Weaner Mortality (%)
Grower/Finisher Mortality (%)
Range
Value Used
Range
Value Used
Range
Value Used
Range
Value Used
Range
Value Used
5 - 14
5
35 – 60
46.0
8 - 10
12.0
0.8 - 4
2.5
0.8 – 3
1.95
Table 11 – Pig production parameters for the southern pork supply chain and typical ranges – growth and performance Feed Conversion Rate (FCR) dressed weight- whole herd
Average Daily Gain (ADG) dressed weight - birth to finisher (g/d)
Carcass yield dressing percentage) (%)
Average Sale Weight (kg)
Average Sale Age (days)
Range
Value Used
Range
Value Used
Range
Value Used
Value used
Value used
4.7 – 3.8
NC
420 - 555
452
72 - 80
76
95
157.2
Meat Processing In line with the focus of this study at the farm level, meat processing data were collated and aggregated for the two supply chains to streamline the LCA. Hence, differences in the final results will be in response to pig production rather than slaughtering, and the functional unit ‘1 kg of HSCW’ is the primary unit of consideration. Aggregated meat processing data are presented in the LCI based on primary data collected from 3 processing plants (northern supply chain) and one processing plant for the southern supply chain. Primary data included energy usage, water usage, waste management practices and production data to link resource usage and emissions to the functional unit (1 kg of HSCW). While the functional unit at the meat processing plant is a kilogram of ‘hot’ standard carcass weight, the actual output from the processing plant has been chilled (generally for 24+ hours) and the energy usage data incorporating the refrigeration process. The end point of the meat processing facility and the primary supply chain under consideration in this LCA is the plant docking gate (the point of distribution for carcasses to wholesalers, exporters or further processing facilities). Transport to the meat processing plant was calculated separately for each supply chain based on the location of the meat processing plants actually used.
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Life Cycle Inventory General LCI Components Foreground data were collected for the production farms, feed mills and abattoirs in each supply chain. Data collected from the production farm included: •
Farm layout (shed types and sizes, waste management system, machinery usage etc)
•
Production system details (including herd structure and total annual pork production)
•
Feed usage (including amounts of individual ingredients)
•
Clean water usage (drinking, cleaning, flushing, cooling and general farm)
•
Electricity usage (pumping, cooling, cleaning, lighting, heating, maintenance, office)
•
Gas consumption (heating)
•
Fuel consumption – both diesel and petrol (pumping, vehicles, cleaning, maintenance)
Electricity, gas, fuel consumption and water usage data were also collected from the feed mills and meat processing plants. Transport distances and vehicle type (fuel usage) between each system unit were collected from recorded data or operator estimates. To protect the anonymity of the supply chains (production farm, feed mill and meat processing plants) all data are reported on a unit of production basis (per pig or per kg HSCW produced). The LCA used background data for crop production based on previous LCA research and unpublished grains LCA data developed by the authors. Foreground data were collected during 2007-08. However, in some cases partial datasets of 3-4 months duration were the only available information. The LCI data and calculations have been reported previously in milestone reports for the northern and southern supply chains. These reports have been summarised here with documentation of data sources and emission factors used.
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Northern Supply Chain Feed Milling Inputs Feed inputs form one of the most significant upstream environmental impacts for pig production. Data were collected for the total feed intake over 12 months for each class of pig, by ration type. Commodity inputs for the pig rations were obtained from the feed mill and from the piggery nutritionist. There are many rations fed throughout the year with a different formulation based on the nutritional requirements of the pigs and the cost of inputs. To simplify these numerous rations, 2 representative rations were developed for the breeder unit and the finisher unit respectively. These were developed by taking the total commodity inputs for these units over a 12 month period (data obtained from the nutritionist). Pig diets rely on considerable by-product inputs from other industries for feed formulation, these by-products were accounted for by substitution with the first primary product that could be considered nutritionally equivalent in terms of energy and protein. In some cases two commodities were balanced to provide a similar energy and protein profile. Further explanation of the simplified diet and substitution ratios used for by-products is presented in the section on upstream processes. Table 12 and Table 13 show the simplified rations for the breeder and grower / finisher units in the northern supply chain. Table 12 – Simplified commodity inputs for production of one tonne of breeder unit ration for the northern supply chain Inputs Materials
Type
Description
Barley 12% Maize 8% Sorghum 10% Wheat 12% Soybean meal (45%) Tallow Bentonite Limestone Lysine - HCL Methionine Threonine Other feed additives
Sorghum + sorghum equiv. Soybean meal equiv.
various Total
* Note 0.01% rounding error.
32
Amount (kg) 149 13 405 178 203 8 16 6 6 1 1 20 1000*
Table 13 – Simplified commodity inputs for the production of one tonne of grower / finisher unit ration for the northern supply chain Inputs Materials
Type
Description
Barley 12% Sorghum 10% Wheat 12% Soybean meal (45%) Tallow Bentonite Limestone Lysine - HCL Methionine Threonine Other feed additives
Sorghum + sorghum equiv. Soybean meal equiv.
Various Total
Amount (kg) 157 414 205 188 7 16 4 3 1 0.2 5 1000
These data informed upstream grain production calculations. For the northern supply chain, energy and water usage data were collected from two feed mills that supply the piggery. These data were collated to generate simplified input data for the breeder and grower / finisher unit (Table 15 and Table 16). These include diesel used to transport the feed to the piggery. Water is used at the feed mills to produce pelletised feed (steam generation), with no water reported for mashed feed. Feed mill #1 reported 10 litres of water per tonne of feed produced and feedmill #2 reports 132 litres of water per tonne of feed produced. It was assumed this water was drawn from reticulated supply. Upstream blue water usage was estimated using data from Narayanaswamy et al. (2004) who reported 6000 L/t of process water used per tonne of grain produced. In lieu of species specific data, this figure was used for all grain commodities in the diet to obtain values on a tonne of feed produced. Upstream green water usage in mega litre (ML) per tonne of each major crop produced was estimated from various literature sources on water requirements and yield. These estimates are provided in Table 14 for the grain commodities used in the northern supply chain diets. Table 14 – Water requirements per tonne of grain used in the rations for the northern supply chain
Sorghum – QLD (1) Wheat – QLD (1) Maize – QLD (1) Barley – QLD (2) Soybean – USA (3)
Typical Yield (t/ha) 4.0 1.2 3.0 1.8 1.75
(1) –DEEDI (2009) (2) Sturgess et al. (2004) (3) University of Arkansas (2009)
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Water Use (ML/ha) 2.45 0.80 6.00 1.32 2.54
Water Use (ML/t) 0.61 0.67 2.00 0.72 1.45
Table 15 – Energy and water inputs for the production of one tonne of breeder ration for the northern supply chain (feed delivered to farm) Energy
Type Electricity LPG - Litres Diesel
Description Data collected by feedmills Data collected by feedmills Data collected by feedmills
Units kWH L L
Amount* 32.5 3.3 5.8
Resources
Type Water Water Water Upstream Blue Water Upstream Green Water
Description Reticulated supply Groundwater Surface Unknown Rainfall
Units L L L L L
Amount* 105 0 0 569 793,562
Table 16 – Energy and water inputs for the production of one tonne of grower / finisher ration for the northern supply chain (feed delivered to farm) Energy
Type
Description
Electricity LPG – Litres
Resources
Units
Amount*
kWH L
36.6 4.4
Diesel
Data collected by feedmills
L
5.3
Type Water Water Water Upstream Blue Water Upstream Green Water
Description Reticulated supply Groundwater Surface Unknown Rainfall
Units L L L L L
Amount 121 0 0 578 774,701
Breeder Unit Piggery Inputs Piggeries use a range of inputs for the production of weaners. For example, energy is used for heating/cooling, feed management, transport of commodities to/from the piggery and internally, lighting, water pumping and general maintenance. Typically this energy is sourced from electricity, LPG gas, diesel and petrol. Diesel is also used to transport pigs to the meat processing plant and feed to the piggery. Energy usage data were collected from records kept by the piggeries on a monthly basis. Piggeries require a significant amount of infrastructure. To account for this in the LCI, construction details were sourced from the structural engineering company that designed the piggery. These data indicated a total concrete volume of 0.29 m3/m2 of floor area. Steel used in construction was 31 kg/m2 of floor area. By assuming a working life of the piggery of 25 years, the amount of concrete and steel required per weaner produced (6 weeks) was determined.
34
Fuel usage for the transport of pigs to the meat processor was determined by calculating the overall distance by road and through discussion with livestock carriers to determine the average fuel usage for the truck types used. Major inputs to the breeder unit piggery are presented in Table 17. Table 17 – Inputs to the breeder unit to produce one weaner pig (6 weeks of age) in the northern supply chain Inputs
Type
Description
Materials Feed Ration
Breeder ration
Combined for breeder unit
Construction
Steel Concrete
Supplied by Structural engineers Supplied by Structural engineers
Electricity LPG - Litres Diesel - farm Diesel - to abattoir (backfatters) Petrol
Data collected from farm Data collected from farm Data collected from farm Data collected from farm Data collected from farm
Units
Weaner Produced
kg
81.7
kg
0.2
m3
0.01
kWH L L L L
18.0 0.4 0.2 0.03 0.1
Energy*
* Does not include upstream energy usage.
Water is used at the piggery for several purposes, with the dominant uses being drinking and cooling. The piggery utilises surface water for evaporative cooling and groundwater for drinking and cleaning. The piggery was able to supply detailed monthly metered data from April 2007 to March 2008 on surface water usage (cooling) and groundwater usage (drinking, cleaning and farm maintenance). The farm maintenance water is metered separately. Drinking water usage is not metered separately but was calculated using drinking water assumptions from PIGBAL. The remaining portion of the metered groundwater was assumed to be used for cleaning and maintenance. Table 18 – Drinking water usage assumptions for various pig classes Pig Class
Water Ingested L/hd/d
Water Usage* L/hd/d
Boars
13.0
15.6
Gilts
13.0
15.6
Dry sows
13.0
15.6
Lactating sows
30.0
36.0
Suckers (at Sow)
0.5
0.6
Weaner
3.6
4.3
Growers
4.0
4.8
Finisher
9.0
10.8
Source: PIGBAL (Casey et al. 2000)* Assumes 20% water wastage
In addition to abstracted water, the piggery also inadvertently collects rainfall on open storages used for fresh water and effluent. This water enters the piggery water balance as fresh water or cleaning water. Water inputs are shown in Table 19, while outputs are shown in Table 20.
35
Table 19 – Blue water inputs (L) for the breeder unit to produce one weaner pig (6 weeks of age) in the northern supply chain Type
Description
L / Weaner Produced
Evaporation - Upstream Reticulated supply Groundwater Groundwater Groundwater Surface Rainfall
Clean surface water storage Drinking Cleaning Maintenance/ office Cooling water Effluent pond catchment Total
113 0 460 255 7 232 222 1291
Outputs The piggery produces a range of outputs and emissions to the environment from the use of resources (energy, water) and from the pigs (enteric methane, undigested feed, manure components). Emissions were estimated using Simapro, published data and calculations based on the farm mass balance and application of the DCC (2007a) methodology (enteric methane). Based on the feed intakes and text values of individual feed dry matter digestibility, dry matter and ash content, the amount total solids (TS), volatile solids (VS) and ash excreted from the pigs in the northern supply chain was calculated. This is an important mid step in the calculation of a volatile solids production and hence predicted methane emissions. VS excretion has been modelled using PIGBAL (Casey et al. 2000). Outputs are presented in Table 20. Table 20 – Outputs from the breeder unit to produce one weaner pig (6 weeks of age) in the northern supply chain Outputs
Type
Description
Units
Weaner Produced
DCC 2007a
kg CH4
0.2
Emissions to Air Enteric Methane Materials Nitrogen Phosphorus Potassium Salt Total Solids Volatile Solids Ash
To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS)
PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance
kg kg kg kg kg
1.4 0.4 0.4 1.8 18.0
To manure management system (MMS) To manure management system (MMS)
PIGBAL Mass Balance PIGBAL Mass Balance
kg kg
13.3 4.7
Weaners kg Liveweight
1.0 3.1
Pork Weaners Backfatters
Water that enters the piggery is either lost due to evaporative losses (pig respiration/perspiration and shed evaporative losses) and liveweight gain, or exits the piggery in the effluent waste stream. Pig respiration losses were estimated from National Research Council – NRC (1998) where they reported work by Holmes and Mount (1967) that respiration losses range from 0.2 to 0.58 L /pig/d for
36
pigs ranging in weight from 20 to 60 kg. The average pig weight of pigs in the northern supply chain breeder unit is 58 kg. Sweating losses were estimated from NRC (1998) and amount to losses of 20 g/m2 of pig/d. Shed evaporation losses were estimated as 20% of pan evaporation for the location. All shed cooling water was assumed to be lost to evaporation. These losses were added to give a shed evaporative loss for the system. Water is also lost from the effluent ponds as evaporation. This loss was estimated as 80% of pan evaporation. Any remaining water is available for reuse (effluent irrigation) after the effluent ponds have filled. The water outputs for the breeder unit for the northern supply chain are shown in Table 21. Table 21 – Water outputs (L) for the breeder unit to produce one weaner pig (6 weeks of age) in the northern supply chain Type
Description
Evaporation Exported off-site Effluent Evaporation
Respiration, sweating and shed evaporation Removed in liveweight gain To effluent treatment system Effluent ponds Estimated average remaining for reuse after ponds fill
Irrigation
L / Weaner Produced
37
349 9 589 526 286
Breeder Unit Waste Treatment System Nitrogen Balance Nitrogen losses represent a significant source of environmental impact from pork production. In the absence of real time measurement of nitrogen losses from the systems, flows and losses were estimated using the PIGBAL program. Additional data collected from a range of literature sources have been used to generate ranges of nitrogen losses for use in the sensitivity analysis. Table 22 – Northern supply chain piggery breeder unit nitrogen balance Functional Unit
kg / weaner produced (6 weeks)
N Inputs (grain)
2.1
N retained (pigs)a
NH3-N Volatilisation (shed)
N Partitioning to Sludge
NH3-N Volatilisation (pond)
N2O-N (pond)
N2O-N (deposition)
N -retained liquid
Value
Value and Range
Value and Range
Value and Range
Value and Range
Value and Range
Value and Range
0.5
0.13
0.33
0.61
0.00
0.01b
0.50
Alternative values a
0.1
0.1
0.2
0.5
0.2
0.8
0.0
0.03
0.01
0.02
0.2
0.7
Includes mortalities bN2O-N deposition is accounted in the N balance as N volatilised as NH3-N.
Methane from Manure Methane emissions were estimated using the PIGBAL volatile solids (VS) estimates and the DCC (2007a) estimation factors (Table 23). An alternative estimate was generated using the fixed estimate for VS from DCC (2007a) for use in the DCC scenario. Table 23 – Manure methane production Manure Methane (kg) Value and Alternative kg weaner produced (6 wks)
3.5
Alternative value
5.9
38
Grower/Finisher Unit Piggery Inputs The same assumptions were made for the calculation of inputs and outputs for the grower/finisher unit in the northern supply chain piggery. These data are summarised in Table 24 and Table 25. Table 24 – Inputs to the grower/finisher unit to produce one finished pig and one tonne of liveweight gain (6 to 21.3 weeks of age) for the northern supply chain
Units
Finisher Produced
Tonne Liveweight Gain (621.3wks)
kg
197.2
2405
kg
0.3562
4.3
m3
0.0070
0.085
Data collected from farm Data collected from farm Data collected from farm
kWH L L
11.782 4.208 0.298
144 51 4
Data collected from farm Data collected from farm
L L
0.917 0.149
11 2
1.028
12.5
Inputs
Type
Description
Materials Feed Ration
Breeder ration
Combined for finisher unit
Construction
Steel Concrete
Supplied by Structural engineers Supplied by Structural engineers
Energy Electricity LPG - Litres Diesel - farm Diesel - farm to abattoir Petrol Pork Weaners
Weaners
Table 25 – Blue water inputs (L) for the grower/finisher unit to produce one finished pig and one tonne of liveweight gain (6 to 21.3 weeks of age) for the northern supply chain Type
Description
Finisher Produced (L)
Upstream - Evaporation Reticulated supply Groundwater Groundwater Groundwater Surface Rainfall
Clean surface water storage
106 0 603 419 8 218 243 1,597
Drinking Cleaning Maintenance/ office Cooling water Effluent pond catchment TOTAL
39
Tonne Liveweight Gain (6-21.3wks) (L) 1,297 0 2,353 5,110 93 2,653 2,963 14,469
Outputs Outputs from the grower / finisher unit piggery cover the same elements as described for the breeder piggery, these are summarised in Table 26 and Table 27. Table 26 – Outputs from the grower/finisher unit to produce one finished pig and one tonne of liveweight gain (6 to 21.3 weeks of age) for the northern supply chain
Outputs
Type
Units
Finisher Produce d
Tonne Liveweight Gain (621.3wks)
kg CH4
0.426
5.2
PIGBAL Mass Balance
kg
2.45
29.8
PIGBAL Mass Balance
kg
0.65
8.0
PIGBAL Mass Balance
kg
0.82
10.0
PIGBAL Mass Balance
kg
2.34
28.5
PIGBAL Mass Balance
kg
41.55
506.6
PIGBAL Mass Balance
kg
33.46
408.0
PIGBAL Mass Balance
kg
8.09
98.6
kg LW Gain kg LW
82.0 97.2
1000 NR
Description
Emissions to Air DCC 2007
Enteric Methane Materials To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS)
Nitrogen Phosphorus Potassium Salt Total Solids Volatile Solids Ash Pigs OR
Finisher Gain Finishers
Table 27 – Water outputs (L) from the grower/finisher unit to produce one finished pig and one tonne of liveweight gain (6 to 21.3 weeks of age) for the northern supply chain Finisher Produced (L)
Type
Description
Evaporation Exported off-site Effluent Evaporation Available for reuse
Respiration, sweating and shed evaporation Removed in liveweight gain To effluent treatment system Effluent ponds Estimated average remaining for reuse after ponds fill Total
40
385 8 848 538 553 2,332
Tonne Liveweight Gain (621.3wks) (L) 4,691 98 10,341 6,565 6,739 28,434
Grower/Finisher Unit Waste Treatment System Grower/Finisher Nitrogen Balance The grower / finisher unit nitrogen balance was calculated using the same assumptions as the breeder unit. Table 28 presents the N balance results, with alternative values from the literature presented as a range to indicate the variability in nitrogen loss pathways. Table 28 – Northern supply chain piggery grower / finisher unit nitrogen balance N Inputs (grain)
N Inputs (pigs)
N retained (pigs)
N loss (mort.)
NH3-N Volatilisatio n (shed)
N Partitioning to Sludge
NH3-N Volatilisatio n (pond)
N2O-N (pond)
N2O-N (deposition)
N -retained liquid
Value
Value
Value
Value
Value and Range
Value and Range
Value and Range
Value and Range
Value and Range
Value and Range
kg / tonne liveweight gain
58.6
4.7
30.3
0.54
2.6 (2.1 – 3.0)
6.9 (5.5 – 9.0)
12.6 (7.3 - 18.7)
0.06 (0.05 – 0.92)
0.25 (0.19 – 0.33)
10.3 (5.5 – 16.0)
kg / finisher produced
4.8
0.39
2.5
0.04
0.21 (0.17 - 0.25)
0.56 (0.45 – 0.74)
1.0 (0.61 – 1.5)
0.01 (0.0 – 0.8)
.02 (0.02 –0-.03)
0.85 (0.45 – 1.3)
Functional Unit
* kg / tonne of liveweight gain (6 – 21.3 weeks). a N2O-N deposition is accounted in the N balance as N volatilised as NH3N.
Grower/Finisher Volatile Solids and Methane Production Volatile solids and methane production from the finisher system were calculated using the same approach applied to the breeder unit. Table 29 reports volatile solids and methane production estimates together with alternative values calculated with text book values. Table 29 – Manure methane production from the northern supply chain grower / finisher unit Manure Methane (kg) Value and Range 1 finisher grown from 6-21.3 weeks Alternative values
9.0 7.5 – 10.7
One tonne finisher growth from 621.3 weeks Alternative values
109 93 – 134
41
Southern Supply Chain Feed Milling Inputs As with the northern supply chain, feed data were collected from the feed mill that supplies feed to the piggeries, and from the piggery nutritionists responsible for formulating rations. These data do not represent a single ration fed throughout the year, as the rations change constantly based on available commodities, commodity price and the requirements of the growing pigs. Hence, feed input data reported in Table 30, Table 31and Table 32 have been averaged over a 12 month period. Table 30 – Simplified commodity inputs for production of one tonne of breeder unit ration for the southern supply chain Type Barley 10% Wheat 11% Wheat 13% Soymeal 45% Tallow Lysine - HCL Methionine Threonine Other feed additives
Description
Soymeal + soymeal equiv.
Total
Amount (kg) 631 218 24 134 4.9 0.1 0.2 0.2 5.9 1,018*
* Note 1.8% error arising primarily from the substitution of plant and animal protein with soymeal.
Table 31 – Simplified commodity inputs for production of one tonne of weaner unit ration for the southern supply chain Type
Description
Wheat 13% Soymeal 45% Tallow Lysine - HCL Other feed additives Total
Amount (kg) 767 249 14 1.9 2.5 1,034*
* Note 3.4% error arising from the substitution of plant and animal protein meals with soymeal.
42
Table 32 – Simplified commodity inputs for production of one tonne of finisher unit ration for the southern supply chain Type Barley 12% Sorghum 10% Wheat 12% Lupins Soymeal 45% Tallow Limestone Lysine - HCL Methionine Threonine Other feed additives
Description
Amount (kg)
Sorghum + sorghum equiv.
Soymeal + soymeal equiv.
Total
83 562 114 8 165 17 12.7 4.6 0.3 1.1 6.8 975
* Note 2.5% error arising from the substitution of animal and plant protein meals with soymeal.
As in the northern supply chain, upstream blue water usage in grain production was estimated using data from Narayanaswamy et al. (2004). Upstream green water usage in ML per tonne of commodity produced was estimated from various literature sources on water requirements and yield. These estimates are provided in Table 33 for the grain commodities used in the southern supply chain diets. Table 33 – Water requirements per tonne of commodity used in the rations for the southern supply chain
Sorghum – QLD (1) Wheat – NSW (2) Barley – NSW (2) Soybean – USA (3)
Typical Yield (t/ha) 4.0 1.0 1.0 1.75
Water Use (ML/ha) 2.45 0.56 0.56 2.54
Water Use (ML/t) 0.61 0.56 0.56 1.45
(1) –DEEDI (2009) (2) Milgate et al. (2009) (3) University of Arkansas (2009)
Energy and water usage at the feed mill was determined for each aggregated ration from data collected from the mill. These data are presented in Table 34, Table 35 and Table 36 for one tonne of ration delivered to the piggery. Table 34 – Energy and water inputs for the production of one tonne of breeder ration for the southern supply chain (feed delivered to farm) Energy
Type Electricity LPG – Litres Diesel
Description Data collected by feedmills Data collected by feedmills Data collected by feedmills
Water Water Water Upstream Blue Water Upstream Green Water
Reticulated supply Groundwater Surface Unknown Rainfall
Units kWH L L
Amount 26.9 0.2 0.6
L L L L L
655 0.0 0.0 604 683,047
Resources
43
Table 35 – Energy and water inputs for the production of one tonne of weaner ration for the southern supply chain (feed delivered to farm) Energy
Type Electricity LPG - Litres Diesel
Description Data collected by feedmill Data collected by feedmill Data collected by feedmill
Water Upstream Blue Water Upstream Green Water
Reticulated supply Unknown Rainfall
Units kWH L L
Amount 26.9 0.2 2.3
L L L
655 619 790,688
Resources
Table 36 – Energy and water inputs for the production of one tonne of finisher ration for the southern supply chain (feed delivered to farm) Energy
Type
Description
Units
Amount
Electricity LPG - Litres Diesel
Data collected by feedmill Data collected by feedmill Data collected by feedmill
kWH L L
26.9 0.2 6.7
Water Upstream Blue Water Upstream Green Water
Reticulated supply Unknown Rainfall
L L L
655 560 704,561
Resources
44
Breeder Unit Piggery Inputs Input data were collected from records kept by the piggeries on a monthly basis and have been aggregated to report on a functional unit basis. Total water usage was collected from metered data, though uses at the farm are not metered separately and were estimated from using standard inputs reported in PIGBAL. Cleaning water was estimated by deduction. In this supply chain, gilts are grown out at the grower site and brought back to the piggery. For this reason, gilts are an input for the production of weaners. Construction data were not readily available for the breeder piggery in this supply chain, thus the northern supply chain data were assumed to be a reasonable approximation, based on the similar shed design and basic construction. Considering the small contribution construction materials are expected to make to the overall result, it is not considered a significant source of error. Data were collected for car movements at all facilities for relevant services provided by contractors, and company vehicle movements. Employee travel was not included. Cars were assumed to be petrol, consuming an average of 10L/100km. Minor commodities used by the piggeries (such as veterinary chemicals) were assumed to be transported with these services (collated in the LCI processes). Table 37 – Inputs to the breeder unit to produce one piglet (3 weeks) for the southern supply chain Units
Piglet Produce d
Combined for breeder unit Supplied by Structural engineers
kg kg
56.3 0.251
Concrete
Supplied by Structural engineers
m3
0.002
Gilts
From deep litter production
Gilt
0.027
Electricity
Data collected from farm
kWH
16
LPG - Litres Diesel - farm Diesel - to abattoir (backfatters) Petrol
Data collected from farm Data collected from farm Data collected from farm
L L L
2.0 0.05 0.05
Data collected from farm
L
0.04
Inputs
Type
Description
Materials Feed Ration Constructio n
Breeder ration Steel
Pigs Energy*
* Does not include upstream energy usage.
In addition to abstracted water, the piggery also inadvertently collects rainfall on open storages used for effluent treatment. This water enters the piggery water balance as fresh water. Water inputs are shown in Table 38.
45
Table 38 – Blue water inputs to the breeder unit to produce one piglet (3 weeks) for the southern supply chain Type
Description
Reticulated supply Groundwater Groundwater Groundwater Surface water Rainfall
L / Piglet Produced
Drinking Cleaning Maintenance/ office Effluent pond catchment Total
0 362 246 4 0 119 731
Outputs The piggery produces a range of outputs and emissions to the environment from the use of resources (energy, water) and from the pigs (enteric methane, undigested feed, manure components). Emissions were estimated using Simapro, published data and calculations based on the farm mass balance and application of the DCC (2007a) methodology (enteric methane). Based on the feed intakes and text values of individual feed dry matter digestibility, dry matter and ash content, the amount TS, VS and FS excreted from the pigs in the southern supply chain was calculated. This is an important mid step in the calculation of a volatile solids production and hence predicted methane emissions. VS excretion has been modelled using the PIGBAL (Casey et al. 2000) model (described previously). These outputs are presented in Table 39. Table 39 – Outputs from the breeder unit piggery to produce one piglet (3 weeks of age) in the southern supply chain Outputs
Type
Units
Piglet Produce d
kg CH4
0.12
PIGBAL Mass Balance
kg
0.9
PIGBAL Mass Balance
kg
0.3
PIGBAL Mass Balance
kg
0.2
PIGBAL Mass Balance
kg
1.1
PIGBAL Mass Balance
kg
11.5
PIGBAL Mass Balance
kg
9.3
PIGBAL Mass Balance
kg
2.2
kg LW kg LW
6.1 5.1
Description
Emissions to Air Enteric Methane Materials To manure management system Nitrogen (MMS) To manure management system Phosphorus (MMS) To manure management system Potassium (MMS) To manure management system Salt (MMS) To manure management system Total Solids (MMS) To manure management system Volatile Solids (MMS) To manure management system Ash (MMS) Weaners Pigs Backfatters
DCC 2007
Water that enters the piggery is either lost due to evaporative losses (pig respiration, perspiration and shed evaporative losses) and liveweight gain, or exits the piggery in the effluent waste stream.
46
The water balance of water outputs was calculated using the same methodology as for the northern supply chain (Table 40). Table 40 – Blue water outputs (L) for the breeder unit piggery to produce one piglet (3 weeks of age) in the southern supply chain Type
Description
L / Piglet Produced
Evaporation Exported off-site Effluent Evaporation Irrigation
Respiration, sweating and shed evaporation Removed in liveweight gain To effluent treatment system Effluent ponds Estimated average remaining for reuse Total
114 6 489 232 376 1,217
Breeder Unit Waste Treatment System Nitrogen Balance Nitrogen losses represent a significant source of environmental impact from pork production. In the absence of real time measurement of nitrogen losses from the systems, flows and losses were estimated using the PIGBAL program. This is the basis for nitrous oxide emission estimates from the piggeries. Based on the nitrogen intake (as feed) and nitrogen removal as mortalities and live pigs, the total nitrogen excreted from the pigs can be estimated. This is an important mid step in the calculation of a nitrogen balance. Nitrogen excretion has been modelled using the PIGBAL (see Appendix 1) and is summarised in Table 41. Table 41 – Southern Supply Chain Piggery Breeder Unit Nitrogen Balance NH3-N Volatilisatio n (shed)
N Partitioning to Sludge
NH3-N Volatilisatio n (pond)
N2O-N (pond)
N2O-N (deposition )
N -retained liquid
Value and Range
Value and Range
Value
Value and Range
Value
Value
0.095
0.20
0.262 - 0.549
0.003
0.011
0.197 - 0.393
Range - low
0.066
0.14
0.18
0.001
0.005
0.136
Range - high
0.096
0.40
0.46
0.018
0.011
0.398
Functional Unit
kg / piglet produced (3 weeks)
N Inputs (grain)
1.2
N inputs (pigs)
0.09
N output (pigs - inc. mortalities)
0.32
Methane from Manure Table 42 provides estimates of piggery waste stream methane production for the southern supply chain from various methods described in the methodology.
47
Table 42 – Methane production from manure management at the breeder unit for southern supply chain
Manure Methane (kg) Value and Alternative kg piglet produced (3 wks)
2.5
Alternative value
4.2
For the LCA modelling, the predicted PIGBAL estimates of methane lost from the manure treatment system were used with a range of data from the literature used in the sensitivity analysis. The DCC (2007a) textbook value was used in a separate scenario.
Weaner Unit Piggery Inputs Data for the weaner unit were interpolated from the finisher unit on a functional unit basis. The piggery buildings used to rear weaners and grow pigs to finish weight are ‘Eco-shelters’. These have very simple construction, with a partial concrete floor, a steel frame and canvas roofing. Subsequently they have a shorter life span. Construction data were not readily available for the weaner piggery in the southern supply chain, thus data were based on materials used in typical ‘Eco-shelter’ construction. Bedding usage for the southern supply chain was supplied by the operators of the grower/finisher unit. Estimated usage is 160 g/day per pig place. This figure is lower than that recommended by the NEGP, which suggests ranges between 500 and 1000g/day per pig place. Table 43 – Inputs to the weaner unit to produce one weaner pig from 3-8 weeks for the southern supply chain Inputs
Type
Description
Units
Materials Feed Ration Construction
Weaner ration Steel
Combined for finisher unit
kg kg
Weaner Produce d 22.9 0.085
Concrete
m3
0.001
Canvass
m2
0.089
kWH L L L Piglet (3 wks)
0.80 0.10 0.18 0.03
kg
0.96*
Energy Electricity Diesel - farm Diesel - farm to farm Petrol Pigs
Piglets weaned from sow
Straw
Into sheds for bedding
Data collected from farm Data collected from farm Data collected from farm Data collected from farm
Supplied by farm
1.026
* Straw estimate based on data provided for grow out facility.
In addition to abstracted water, the piggery also collects rainfall in stormwater control ponds. Water inputs are shown in Table 44.
48
Table 44 – Blue water inputs for the weaner unit to produce one weaner pig from 3-8 weeks for the southern supply chain Type
Description
Groundwater Groundwater Rainfall
Drinking Cooling/Cleaning Stormwater pond catchment Total
Weaner Produced (L) 141 4 74 219
Outputs The outputs for the deep litter weaner unit are summarised in Table 45. Table 45 – Outputs from the weaner unit to produce one weaner pig from 3-8 weeks for the southern supply chain Outputs
Units
Weaner Produced
DCC 2007
kg CH4
0.044
Only for deep litter sheds
DCC 2007
kg N2O
0.017
To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) Weaners
PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance
kg kg kg kg kg kg kg kg LW Gain
0.3 0.1 0.1 7.7 4.8 4.2 0.6 9.4
Type
Description
Emissions to Air Enteric Methane Shed Nitrous Oxide Materials Nitrogen Phosphorus Potassium Salt Total Solids Volatile Solids Ash Pigs
Water that enters the piggery is either lost due to evaporative losses (pig respiration, perspiration and shed evaporative losses) and liveweight gain or exits the piggery in the effluent waste stream. The water balance of water outputs was calculated using the same methodology as for the northern supply chain (Table 46).
Table 46 – Water outputs for the weaner unit to produce one weaner pig from 3-8 weeks for the southern supply chain Type Evaporation Exported off-site Exported off-site
Description Respiration, perspiration and shed evaporation Removed in liveweight gain To deep litter
49
Weaner Produced (L) 85 5 57
Weaner Unit Waste Management System Nitrogen Balance Nitrogen losses represent a significant source of environmental impact from pork production. In the absence of real time measurement of nitrogen losses from the systems, flows and losses were estimated using the PIGBAL program. This is the basis for nitrous oxide emission estimates from the piggeries. Based on the nitrogen intake (as feed) and nitrogen removal as mortalities and live pigs, the total nitrogen excreted from the pigs can be estimated. This is an important mid step in the calculation of a nitrogen balance. Nitrogen excretion has been modelled using the PIGBAL (see Appendix 1) and is summarised in Table 47. Table 47 – Southern Supply Chain Piggery Weaner Unit Nitrogen Balance Functional Unit
kg / weaner produced (3 8 weeks) kg / t pig growth (3 - 8 weeks)
N Inputs (grain)
N inputs (pigs and straw)
N output (pigs - inc. mortalities)
NH3-N Volatilisatio n (shed)
N2O-N Volatilisation (shed)
N to manure Stockpile
NH3-N Volatilisatio n (stockpile)
N -retained stockpile
Value
Value
Value
Value and Range
Value and Range
Value and Range
Value and Range
Value
0.64
0.17
0.41
0.07 (0.04 – 0.07)
0.02 (0.01 – 0.05)
0.33 (0.19-1.07)
0.07 (0.04 – 0.21)
0.21 (0.12 – 0.21)
67.6
18.0
43.3
7.18 (4.11 - 7.18)
1.79 (1.03 - 4.84)
35.0 (20.1–114.0)
7.01 (4.01 – 22.79
22.4 (12.8 – 22.4)
Methane from Manure Table 48 provides estimates of piggery waste stream methane production for the southern supply chain from various methods described in the methodology. Table 48 – Methane production from manure management at the weaner unit for southern supply chain
Manure Methane (kg) Value and Alternative kg weaner produced (3 – 8 wks)
2.0
Alternative value
4.7
For the LCA modelling, the predicted PIGBAL estimates of methane lost from the manure treatment system were used with a range of data from the literature used in the sensitivity analysis. The DCC (2007a) textbook value was used in a separate scenario.
50
Grower/Finisher Unit Piggery Inputs Input data were obtained from the operator of the grower finisher piggery for all major inputs. The amount of bedding usage for the southern supply chain was supplied by the operators of the grower/finisher unit. Estimated usage is 160 g/day per pig place. This figure is lower than that recommended by the NEGP, which suggests ranges between 500 and 1000g/day per pig place. Table 49 – Inputs to the grower/finisher unit to produce one finisher pig from 8-23 weeks for the southern supply chain Inputs
Type
Description
Materials Feed Ration Construction
Finisher ration Steel
Combined for finisher unit
Concrete Energy
Straw
Canvass Electricity Diesel – farm Diesel - farm to abattoir Petrol Into sheds for bedding
Finisher Produce d
Tonne Finisher Gain
kg kg
203 0.17
2,546 2.15
m3
0.002
0.03
2
0.177 0.79 0.09 0.53 0.03 23
2.25 10.1 1.2 6.7 0.4 291
Units
Data collected from farm Data collected from farm Data collected from farm Data collected from farm Supplied by farm
m kWH L L L L
In addition to abstracted water, the piggery also inadvertently collects rainfall on open storages used for environmental management at the site (capture of site runoff). Water inputs are shown in Table 50.
Table 50 – Blue water inputs (L) to the grower/finisher unit to produce one finisher pig from 8-23 weeks for the southern supply chain Type
Description
Groundwater Groundwater Rainfall
Drinking Cooling/Cleaning Stormwater pond catchment TOTAL
51
Finisher Produced (L) 639 28 482 1,149
Tonne Finisher Gain 8091 352 6,105 14,548
Outputs Outputs from the grower / finisher unit are reported in Table 51 below. Table 51 – Outputs from the grower/finisher unit to produce one finisher pig from 8-23 weeks for the southern supply chain Outputs
Type
Finisher Produced
Tonne Finishe r Gain
kg CH4
0.5
5.8
DCC 2007
kg N2O
0.1
1.7
PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance PIGBAL Mass Balance
kg kg kg kg kg kg kg kg LW Gain kg LW
2.1 0.8 0.7 8.7 34.0 26.5 7.5 79 95
27.1 9.5 8.5 110.6 430.9 335.7 95.2 1000 NR
Description
Units
DCC 2007 Only for deep litter sheds Nitrogen Phosphorus Potassium Salt Total Solids Vol. Solids Ash
To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS) To manure management system (MMS)
Pork
Finisher Gain Or Finishers
Emissions to Air Enteric Methane Shed Nitrous Oxide Materials
Table 52 – Water outputs (L) for the grower/finisher unit to produce one finisher pig from 8-23 weeks for the southern supply chain Type
Description
Evaporation Exported off-site Exported off-site
Respiration, sweating and shed evaporation Removed in liveweight gain To deep litter
Finisher Produced (L) 259 8 398
Tonne Finisher Gain (L)
3280 107 5040
Grower/Finisher Unit Waste Management System Nitrogen Balance Nitrogen losses represent a significant source of environmental impact from pork production. In the absence of real time measurement of nitrogen losses from the systems, flows and losses were estimated using the PIGBAL program. This is the basis for nitrous oxide emission estimates from the piggeries. Based on the nitrogen intake (as feed) and nitrogen removal as mortalities and live pigs, the total nitrogen excreted from the pigs can be estimated. This is an important mid step in the calculation of a nitrogen balance. Nitrogen excretion has been modelled using the PIGBAL (see Appendix 1) and is summarised in Table 53.
Table 53 – Southern Supply Chain Piggery Grower/Finisher Unit Nitrogen Balance
52
Functional Unit
kg / finisher produced (8 23 weeks) kg / t pig growth (8 23 weeks)
N Inputs (grain)
N inputs (pigs and straw)
N output (pigs - inc. mortalities)
NH3-N Volatilisatio n (shed)
N2O-N Volatilisation (shed)
N to manure Stockpile
NH3-N Volatilisatio n (stockpile)
N -retained stockpile
Value
Value
Value
Value and Range
Value and Range
Value and Range
Value and Range
Value
4.6
0.44
2.5
0.44 (0.34 – 0.57)
0.11 (0.09 – 0.14)
2.2 (1.7-3.1)
0.43 (0.33 – 0.62)
1.7 (1.3 – 2.5)
58.2
5.5
31.1
5.6 (4.3 - 7.1)
1.4 (1.1 – 1.8)
27.1 (20.9–39.2)
5.4 (4.2 – 7.8)
21.7 (16.7 – 31.3)
Methane from Manure Table 54 provides estimates of piggery waste stream methane production for the southern supply chain from various methods described in the methodology. Table 54 – Methane production from manure management at the weaner unit for southern supply chain
Manure Methane (kg) Value and Alternative kg finisher produced (8 – 23 wks)
1.5
Alternative value
1.6
For the LCA modelling, the predicted PIGBAL estimates of methane lost from the manure treatment system were used with a range of data from the literature used in the sensitivity analysis. The DCC (2007a) textbook value was used in a separate scenario.
53
Meat Processing Data Inputs Meat processing plants use large quantities energy and clean water in the slaughtering process. Energy is used for machine operation and cooling of the slaughtered carcass. Water is used to maintain high food hygiene standards. Water is used for watering and washing livestock, cleaning process equipment and work areas, and washing carcasses. Cleaning makes up a large proportion (around 50%) of water use. While the functional unit selected implies a freshly slaughtered animal (‘Hot’ carcass weight) the energy figures presented include the cooling process. Hot Standard Carcass Weight is a common descriptor in the processing industry and was selected accordingly. To convert these data to ‘cold’ carcass weight, a shrinkage factor of 0.98 may be applied. Table 55 provides example input data for the meat processing plant in the southern supply chains. The northern supply chain data only vary based on the slaughter weight of pigs (2kg heavier). Table 56 provides water input data for both supply chains. Table 55 – Inputs to the meat processing plant to process one finisher pig or one tonne of HSCW for the southern supply chain Inputs
Type
Pork
Finishers
Energy
Electricity
Units
Finisher Processe d
Tonne HSCW Finisher
kg Liveweigh t
95
1,316
kWH
14.8
205
L
0.9
13
cubic metres
0.3
4.7
L
0.03
0.44
L
0.014
0.19
kg
0.67
9.2
Description
LPG - Litres Natural Gas - cubic metres Diesel Petrol Coal
Data collected from 4 abattoirs Data collected from 4 abattoirs Data collected from 4 abattoirs Data collected from 4 abattoirs Data collected from 4 abattoirs Data collected from 4 abattoirs
Table 56 – Blue water Inputs to the meat processing plant to process one finisher pig or one tonne of HSCW for both supply chains Inputs
Type
Water
Reticulated supply
Finisher Processed (L)
Tonne HSCW Finisher (L)
473
6,553
Outputs Table 57 provides output and emission data for the meat processing plant. The primary emissions are from energy consumption, with considerably smaller emissions from methane and nitrous oxide in the waste stream and effluent irrigation. Methane emissions from the meat processing waste stream were
54
calculated using the DCC (2007b) method for industrial waste water (described previously). GHG emissions from N2O loss during waste water treatment are not covered by the DCC (2007b) for industrial waste water. Effluent irrigation losses are similarly not covered by the DCC (2007b). For this assessment, estimations of N2O will be made using the assumptions for animal effluent (DCC 2007a). The nitrogen content of irrigated wastewater has been estimated from unpublished data accessed from the processing plants in the study and from data collected previously by FSA Consulting from pork processing plants and multi-species processing plants killing pigs. These data were used in the LCA modelling for both supply chains. Table 57 provides data for the southern supply chain. Data for the northern supply chain differ only with respect to slaughter weights. Table 57 – Outputs from the meat processing plant to process one finisher pig or one tonne of HSCW for the southern supply chain Outputs
Type
Description
Units
Finisher Produced
Tonne HSCW Finisher
Pork
Finishers
Meat By-products
kg HSCW kg
72.2 22.8
1000 316
Emissions to Air Methane Pond
Methane
From DCC COD Estimation
kg CH4
0.46
6.4
Nitrous Oxide – Pond
N2O
From DCC - DCC N Estimation
kg N2O
0.0002
0.003
Nitrous Oxide Irrigation
N2O
From DCC - DCC N Estimation
kg N2O
0.00008
0.0011
55
Upstream Processes Development of a Simplified Diet Commodities used for the production of pig feed for both supply chains vary throughout the year depending on the availability of grain commodities, by-products and variability in commodity prices. Diets may use large amounts of by-products from the food processing sector to reduce the costs of diet formulation. Diets are formulated for a set ratio of energy, protein, amino acids and minerals, together with additives that improve feed properties such as digestion. Consequently, many input commodities are naturally substituted in the diet throughout the year. In order to determine the environmental burden associated with the pig feed, a simplified diet was constructed based on the annual usage of commodities throughout one year for each piggery. This provided the tonnes of grain actually used by the piggeries. By-products within the diet were modelled by substituting these products with the most likely “purpose grown” alternative commodity, which is considered the “marginal” replacement following the consequential modelling approach. The two selected marginal commodities were sorghum (cereal, energy grain) and soybean meal (protein). By-products were substituted based on their energy and protein content to maintain the diet to the original specifications. It is acknowledged that a degree of inaccuracy is introduced by using this approach, as not all protein sources have a similar amino acid profile or energy content to soybean meal. However, the error associated with this assumption was not expected to be large. Table 58 – Substitution ratios used for pig rations in the northern and southern supply chain diets Inputs
Type
Substitution ratio (kg)
Substituted product (kg)
Biscuit Meal Millrun Molasses Corn Extruded
1 1 1 1
Sorghum Sorghum Sorghum Sorghum
Palm KM Bloodmeal 85 Canola 36 Fish 56% Meat Meal (50%) Skim Milk Powder Soybean - fullfat Soymeal 48% Sunflower 30 Tallow Whey Powder
0.377 2.08 0.847 1.39 1.23 0.813 0.847 1.06 0.709 0.309
Soymeal (45%) Soymeal (45%) Soymeal (45%) Soymeal (45%) Soymeal (45%) Soymeal (45%) Soymeal (45%) Soymeal (45%) Soymeal (45%)
Bentonite
1
Lime
Materials Cereal by-products
Meal Supplements
Soymeal (45%)
Other additives
56
Cereal Crops The major cereal crops that contribute to the piggery ration are sorghum, wheat and barley with small contributions of maize. Process data for some of these crops were not available in the literature for grain production in the eastern states, however these grains have previously been modelled by FSA Consulting (unpublished) and were used in this study. Sorghum is a major feed grain crop in Australia, which is reflected in the proportion of sorghum in both the northern and southern supply chain diets. The proportion of sorghum in the southern supply chain diet was unexpected, as sorghum must be transported about 800 km from the closest major sorghum production regions (i.e. Gunnedah) to the southern supply chain feed mill. This is the result of ongoing drought conditions in the southern wheat belt leading to shortages of supply of traditional feed cereals (wheat, barley). Considering the on-going nature of these drought conditions and the possible effects of climate change in the long term, it is not unreasonable to anticipate the ongoing use of sorghum in these regions. This may lead to expansion of sorghum production towards central NSW. Considering that sorghum has the lowest gross margin of the feed grains used, it was selected as the marginal cereal (energy) grain for both supply chain diets following the rationale of Weidema (2003). Sorghum, wheat and barley were modelled using production data based on local knowledge of grain production for the northern grains region (Nth NSW and Sth QLD) and from NSW DPI’s Farmer Enterprise Budget Series for summer and winter crops in 2008-2009. From these sources, LCI data (inputs – i.e. fertiliser, herbicides) and processes (i.e. tractor hours and cultivation rate) were determined. All cereal grain production was assumed to be no-till, as this is considered the marginal technology 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, aerial spraying operations were modelled as tractor operations, and some minor pesticides were omitted. Sequestration of CO2 in the crops was not included in the crop unit processes. The transport distance for both crop inputs from a local rural store to the farm, and of grain to the local storage facility was assumed to be 100km. Emissions of nitrous oxide from fields were based on the updated emission rate of 0.3% of nitrogen added to the system (DCC 2007a).
Fertilisers Fertiliser used for grain production in Australia is sourced from domestic and international manufacturers. In order to represent average Australian figures, the transport emissions and energy requirements are based on the percentage of fertiliser that is imported, relative to that which is produced in Australia (after Beer et al. 2005; Cottrell et al. 2008). Table 59 shows the relative differences between production and use tonnes for urea and di-ammonium phosphate. Table 59 – Australian production and usage statistics for fertiliser products Production (tonnes)
Use (tonnes)
Local production (%)
Urea
214,00
1,032,066
20.7
Di-ammonium Phosphate
342,985
579,171
59.2
Source: Fertiliser Industry Federation of Australia Inc (2006)
The major international urea producers are USA, Russia and China (Beer et al. 2005). For simplicity, it was assumed that 20% of the urea used in Australian grain production is sourced from Western
57
Australia and 80% is sourced from the USA. DAP, like urea is also imported to meet total consumption in Australia. According to Beer et al. (2005) the Western Mining Corporation facility at Phosphate Hill QLD is a fully integrated fertiliser production facility. This facility is assumed to be the domestic source for DAP in SE QLD and NE NSW. The remaining 40% of the DAP is assumed to be imported from the US. Based on these assumptions, the transport distances for Urea and DAP are shown in Table 60. Table 60 – Transport per tonne of urea supplied to south east QLD Product
Mode
Distance km
% of Supply
Transport (km)
Urea
Domestic shipping
6000
20
1200
International shipping
15000
80
12,000
Road Transport (articulated) to local rural store
200
100
200
Road Transport (articulated) [from Phosphate Hill]
1500
60
900
International shipping
15000
40
6000
Road Transport (articulated) to local rural store
200
100
200
DAP
tonnes
Table 61 shows data for grains modelled for the simplified pig feeds for both the northern and southern supply chains. The inputs relate to the production of an average yield for each crop (row 1) rather than per tonne. Table 61 – Farm process data for grain production used for modelling feed grain inputs Inputs
Wheat
Barley
Sorghum
Canola
Soybean
Lupins
Yield (t)
Description
3
3.2
4.5
1.6
1.5
2
Glyphosate (kg)
2.8
2.8
5.5
2.8
5.7
5
Atrazine
3.2
2,4 D
0.8
Urea (kg)
46% N
200
178
150
267
-
DAP (kg)
18 % N, 20% P
-
40
-
-
-
Starter 12Z (kg)
12% N, 20% P
40
60
50
110
99
90
77
130
6
13
25.1
24.7
23.8
27.4
28.4
42.5
Rigid truck to Farm [100km] (tkm)
41.4
26.8
19.3
33
7
23.5
Artic. truck to grain storage [100km] (tkm – based on yield)
300
320
450
160
150
200
Total CO2-e/t at local storage
219
158
108
364
167
181
Total Nitrogen added (kg N) Diesel (L)
Tractor Operations
60
58
Soybean meal The majority of soybean meal used in stockfeed in Australia in the 2006-07 marketing year was imported (Ansell & McGinn 2009), with some 67% of imports originating in the USA. Soybean was modelled as an imported and locally produced input for pig rations, assuming that 80% of the Soymeal used was grown in the USA. Soybean meal was modelled using two approaches to handle co-products (soy oil) arising during manufacture. The first approach uses system expansion to account for the co-product of soybean meal production (soy oil) based on canola production (after Dalgaard et al. 2008). This is the preferred approach used for the pork LCA, however an economic allocation process has also been included for comparison. Applying the system expansion approach, the production of an equivalent quantity of canola oil is removed from the production system of soy meal. However, the production of canola oil also results in two co-products: canola oil and canola meal. The approach taken to account for the canola meal follows a similar method to that proposed by Dalgaard et al. (2008), where canola meal was substituted for a combination of soy meal and sorghum based on protein and energy content. One kg of canola meal substitutes 0.95 kg of marginal meal, which is equivalent to 0.66 kg of soybean meal and 0.29 kg of sorghum (the appropriate marginal cereal grain for Australia). Table 62 – Soymeal co-product management using system expansion Soybean meal
Soybean meal
Soybean
[imported from US] g CO2-e/kg
150
Canola
Canola meal
Sorghum
57.2
108
oil 44.6
167
1,110
Economic allocation values for soybean meal are from Dalgaard et al. (2008). The economic allocation values for canola meal are based on price values posted at the Canola Council of Canada (2009). Table 63 – Soymeal co-product management using economic allocation
g CO2-e/kg
Soybean meal (imported from US - 69%)
Soybean meal
Soybean oil
Canola oil
Canola meal
(69%)
(31%)
(72%)
(28%)l
282
177
416
863
199
Energy inputs for milling of soybeans and canola (rapeseed) were based on Dalgaard et al. (2008). The transport distances for the soybean meal are shown in Table 64.
59
Table 64 – Transport per tonne of soybean meal supplied to south east QLD Mode
Distance (km)
% of Supply
Transport (tkm)
Domestic shipping
6000
20
1200
International shipping
15000
80
12,000
200km
100
200
Road Transport [to local rural store]
(articulated)
Other ingredients Energy usage and GHG data for other feed ingredients were either based on literature or appropriate Ecoinvent unit processes. Data sources for relevant ingredients are presented in Table 65. Table 65 – Energy and GHG emissions for minor inputs to the pig rations for Australian pork production Ingredient
Energy (MJ/kg)
GHG (kg CO2-e/kg)
Source
Synthetic amino acids: Lysine, Methionine, Threonine
86
3.6
Eriksson et al. (2005)
Tallow, at plant/CH U
0.305
0.669
Ecoinvent
Lime, calcined, at regional store/AU U
0.0183
1.11
Australian Unit Process LCI
Delimitations In modelling the feed some ingredients were omitted due to lack of available data. Most of these contributed < 0.3% by mass to the feed mix.
Energy Supply and Transport Electricity data are from the Australian LCI database (2009). This model is based on data from Electricity Supply Association of Australia, the National Greenhouse Gas Inventory Project and the National Pollutant Inventory (NPI). The data include fuel capital, production (pre-combustion), electricity generation, transmission and distribution. The grid mix for electricity in Queensland is based on ESAA 2007. Fuel production from refineries is developed from ABARE 2007 and National Pollutant Inventory data for 2006. Other refinery emissions are also taken from APPEA 2007. Where local measurements of combustion heat emissions are available these will have been used, while default emission values for combustion heat will come from the National Greenhouse Gas Inventory (2006) and the NPI workbook emission factors. Transport data for trucks and ships are based on the data from Applebaum Consulting (2003) and NGGI for 2007 (National Greenhouse Gas Inventory Committee 2007). Where specific information
60
on load factors and backhaul ratios is available, transport inventories have been measured in kilometres travelled. In the absence of specific information, national average transport data are based on tonne kilometre inventories.
61
Life Cycle Impact Assessment Dealing with Co-Production The handling of co-products has a considerable effect on the results for pork production in both supply chains. The co-products from the system are: cull sow meat, slaughter by-products and excreted nitrogen, phosphorus and potassium in the waste stream. In line with ISO 14044, system expansion was used to avoid allocation of these co-products. The system expansion concept can be explained as follows: To manage co-products, the assumption is made that the system is dynamic and that by-products are replaceable in the marketplace. Hence, if more pork is produced, this will result in more cull sow meat which will displace another product in the manufacturing meat industry. System expansion is the term given for incorporating the ‘avoided’ products that are displaced in the market because of the presence of cull sow meat. This process has been used to handle co-products throughout the supply chain. For cull sow meat and slaughter by-products two ‘avoided’ products were considered; i) slaughter pigs (baconers), and ii) grass fed beef. If it is assumed that cull sows compete with slaughter pigs (baconers), then the system is expanded to produce more of the major product. This ends with the same result as conducting a mass allocation between cull sows and baconers (slaughter pigs), cull sows and slaughter by-products (i.e. allocating environmental burdens based on the mass of the products). If beef was assumed as the substitution product, this will result in a considerable avoided environmental burden for global warming potential, equivalent to the GWP of the beef that is effectively avoided. For this option, a conservative estimate of GWP for Australian beef production of 9.2 kg CO2-e / kg HSCW was used. This figure was calculated using the DCC (2007a) methodology for enteric methane emissions (only) from beef cattle in Australia, based on the performance of a standard beef herd. Data assumptions were based a southern NSW beef herd. This is considerably lower than most of the international LCA literature available for beef, which average about 19.5 kg CO2-e / kg CW. To present a balanced assessment, both substitution products are shown, with the mass allocation (substitution of cull sows and edible offal with pork from slaughter pigs) used as a default. Another common way of handling co-products is to allocate environmental burdens based on the relative economic value of the primary product and co-products. Allocation using this process has also been included for comparison. For nutrient by-products, the substituted products considered were synthetic fertilisers (urea, triple superphosphate and potassium chloride). Direct comparison of nutrients from the piggery waste stream and these synthetic fertilisers was not possible because of the differences in nutrient availability however. To manage this, equivalence factors were developed to balance the waste stream nutrient value with the synthetic fertiliser used in the system expansion (see Table 85).
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Northern Supply Chain Results Water Usage Water usage was calculated using a broad inventory approach to allow water usage impacts to be modelled at a later stage. The impact of water usage is not reported here, however total water ‘use’ classified as ‘green water’ (soil moisture derived from rainfall) and ‘blue water’ (water taken from surface, groundwater, reticulated or direct rainfall capture sources). The combination green and blue water usage estimates may be considered the water footprint of pork and has been calculated using a similar methodology to that applied by Hoekstra & Chapagain (2007). The blue water usage results presented here are equivalent with the ABS definition of water use. Water usage was estimated using a water balance for the piggery and accounting for green water usage with crop production. ‘Process’ water, or water used during manufacture, was included for upstream feed production after Narayanaswamy et al. (2004) but not for many other upstream processes because of a lack of available data. For this reason the upstream water uses are incomplete in this study, and blue water usage may be higher than calculated. However, on-site water usage for the feed mill, the piggery and the meat processing plants were all taken from foreground processes, with rainfall included where it was captured on-site in effluent holding ponds or runoff catchment ponds. Water captured in this way is withheld from watercourses for environmental quality reasons because of the potential risk of nutrient transport if this water leaves the site contaminated. This water may or may not enter the piggery water balance (for example, the northern piggery withdraws water from the effluent pond to flush manure from the sheds) and is all assumed to evaporate, hence its calculation as a use. The quantity of evaporation sourced from rainfall included as a ‘blue water use’ from effluent pond catchment is 6.4 L / kg of HSCW for the northern supply chain. Table 66 – Total water inputs (L / kg HSCW) for pork production in the northern supply chain Description
Source
L /kg HSCW
Green water usage - Feed ingredients
Rainfall
2,971
Blue water – Feed milling Blue water - Upstream feed ingredient process water
Reticulated Unknown
0.4 2.2
Blue water – All on-site piggery uses1
Multiple2
39.6
Blue water – Meat processing water
Reticulated
6.8
Total Blue Water
49
1
On site uses were reported by sector in the LCI. 2 Multiple sources include surface abstractions, reticulated supply abstractions, groundwater abstractions and direct rainfall capture. Further detail provided in the LCI.
The blue water used on a liveweight basis at the farm level only without evaporation from effluent storage amounts to approximately 25 L / kg liveweight. This figure includes some evaporation from the surface water storage. Water outputs were also modelled and these are more accurately described as uses, however because of the quality of the data, inputs were taken as the primary data for water use, assuming that all water inputs are evaporative uses. On-farm water usage Water usage at the ‘on-farm’ level is of interest to pork producers, hence, blue water usage data have been broken down into contributing components and are presented for the northern supply chain in Figure 9.
63
Figure 9 – Blue water usage for the northern supply chain
Blue water use is dominated by drinking water requirements, followed by cooling and cleaning. Rainwater catchment at the pond represents a significant proportion of total water use (the total volume of rainfall is assumed to evaporate). Note that upstream water for feed represents process water only (rainfall excluded).
Primary Energy Primary energy usage was determined from the sum of all fossil fuel energy usage in the pork supply chains. Total primary energy usage for the northern supply chain, per kilogram of pork HSCW is presented in Table 67. Table 67 – Primary energy usage for pork production in the northern supply chain Impact Category Primary Energy
Unit
Mass allocation
MJ
24.5*
* Numbers presented are the Higher Heating Value
Energy intensive stages in the supply chain include crop production and on-site usage at the piggery (see Figure 10).
64
Figure 10 – Primary Energy contribution by supply chain sector for the northern supply chain.
65
Global Warming Potential Global warming potential (GWP) is a mid-point indicator, based on the contribution to global warming based on a 100 year time scale. Primary gas species from pig production are methane (CH4) from waste stream emissions and direct livestock emissions, carbon dioxide (CO2) from fossil fuel usage and nitrous oxide (N2O) from upstream grain production and from the reuse of piggery by-product nutrients. The northern supply chain piggery primarily produces greenhouse gases from energy usage at the piggery (electricity, gas, diesel etc) and livestock emissions, dominated by manure methane and nitrous oxide. Of these, the greatest contributor is methane generation from the effluent treatment ponds. For the northern pork supply chain, GWP measured 5.5 kg CO2-e / kg of pork HSCW produced. The handling of co-products, as described, can cause variability in results. For this reason, results are also presented using economic allocation between co-products, and system expansion using low grade beef. These data are presented in Table 68. Table 68 – GWP for pork production in the northern supply chain with three methods for managing coproducts Impact Category
GWP for 1 kg pork HSCW
Unit
Mass allocation
Economic allocation
System Expansion (low grade beef)
kg CO2-e
5.5
5.6
5.0
As expected, the alternative methods show slightly higher impacts for pork using economic allocation, because the value of these by-products is lower than pork from the slaughter pig. When the system is expanded to offset co-products with low grade beef, the emissions are reduced considerably. A series of sensitivity tests and scenarios were conducted to test these data and compare with a modified system (pond covering and methane flaring). These are reported in the discussion section.
Southern Supply Chain Results Water Usage As with the northern supply chain, water usage was calculated using a broad inventory approach to allow water usage impacts to be modelled at a later stage. As with the northern supply chain, ‘process water’ usage only represents the proportion used in grain production and is therefore incomplete. Onsite water usage data were collected for the feed mill, the piggeries and the meat processing plants, representing total water inputs (as per the ABS definition) with rainfall included where it was captured on-site in effluent holding ponds or runoff containment dams. Water captured in this way is withheld from watercourses for environmental quality reasons because of the significant quality change that occurs when this water is mixed with effluent. It is assumed all this water evaporates (hence included as a use). At the breeder piggery site, this water may enter the piggery as flushing water. The quantity of evaporative water (from rainfall) included as a ‘blue water use’ from effluent pond catchment is 9.2 L / kg of HSCW.
66
Table 69 – Total water inputs (L / kg HSCW) for pork production in the southern supply chain Description
Source
Green water usage - Feed ingredients
Rainfall – soil moisture
Blue water usage Blue water – Feed milling Blue water - Upstream feed ingredient process water
Reticulated Unknown
2.6 2.2
Blue water – All on-site piggery uses
Multiple1
29.6
Blue water – Meat processing water
Reticulated
6.8
Total Blue Water
41
1
L /kg HSCW 2,712
Multiple sources include surface abstractions, reticulated supply abstractions and direct rainfall capture. Further detail provided in the LCI.
The blue water used on a liveweight basis at the farm level only without evaporation from effluent storage amounts to approximately 15.5 L / kg liveweight. Water outputs were also modelled and these are more accurately described as uses, however because of the quality of the data, inputs were taken as the primary data for water use, assuming that all water inputs are evaporative uses. On-farm water usage Water usage at the ‘on-farm’ level is of interest to pork producers, hence, blue water usage data have been broken down into contributing components and are presented for the southern supply chain in Figure 11.
Figure 11 – Blue water usage for the southern supply chain
Water use is dominated by drinking water requirements. Interestingly, the second largest contributor to water use for the southern supply chain was rainwater water catchment on the effluent ponds. The cleaning and cooling component of water use for this supply chain is considerably lower than for the northern supply chain because of the different management systems applied (conventional shedding vs deep litter) and possibly climate effects.
67
Primary Energy Primary energy usage was determined from the sum of all fossil fuel energy usage in the pork supply chains. Total primary energy usage for the southern supply chain, per kilogram of pork HSCW is presented in Table 67. Table 70 – Primary energy usage for pork production in the southern supply chain Impact Category Primary Energy
Unit
Mass allocation
MJ
20.3*
* Numbers presented are the Higher Heating Value
Energy intensive stages in the supply chain include crop production and milling and on-site energy usage at the piggery (see Figure 12).
Figure 12 – Primary Energy contribution by supply chain sector for the southern supply chain.
Figure 12 shows the contribution of energy from the avoided fertiliser replaced by compost from the finisher and weaner systems.
Global Warming Potential For the southern pork supply chain, GWP measured 3.1 kg CO2-e / kg of pork HSCW produced. The handling of co-products, as described, can cause variability in results. For this reason, results are also presented using economic allocation between co-products, and system expansion using low grade beef. These data are presented in Table 71 .
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Table 71 – GWP for pork production in the southern supply chain with three methods for managing coproducts Impact Category
GWP for 1 kg pork HSCW
Unit
Mass Allocation
Economic allocation
System expansion (low grade beef)
kg CO2-e
3.1
3.6
2.3
As with the northern supply chain, the economic allocation method applies a larger proportion of the GWP burden on pork from the slaughter pigs following the value of this as the primary product. When the system is expanded to offset co-products with low grade beef, the emissions are reduced considerably. A series of sensitivity tests and scenarios were conducted to test these data and compare with a modified system (pond covering and methane flaring). These are reported in the discussion section.
69
Discussion Water Usage No pork LCA studies reviewed in the literature presented water usage results. The most prolific form of water usage estimates available in the literature are water footprint estimates, such as those compiled by Hoekstra and Chapagain (Hoekstra & Chapagain 2007) and Pimentel et al. (2004). Water use estimates from these authors are presented in Table 72 together with results from this study (green and blue water combined to provide a ‘water footprint/virtual water estimate’). It must be noted that comparisons across species create difficulties because of differences in the handling of carcasses at the point of slaughter. For example, the dressing percentage of beef is lower than pork partly because the head is removed. Consequently comparisons need to take these differences into account. Table 72 – Virtual water use estimates for various meat production systems from the literature Species
L / kg (Australian estimate)
L / kg (World average)
Reference
Beef 17,112 15,497 Hoekstra & Chapagain (2007) Sheep meat 6,947 6,143 Hoekstra & Chapagain (2007) Pork 5,909 4,856 Hoekstra & Chapagain (2007) Southern supply chain 2,753 This study Northern supply chain 3,020 This study Pork 6,000* Pimentel et al. 2004 Chicken meat 2,914 3,918 Hoekstra & Chapagain (2007) * This represents an estimate for pork production in the USA. Not clear if this represents live or carcass weight.
The data presented in this report show significantly lower water usage than estimated for Australian pork production by Hoekstra & Chapagain (2007). Hoekstra & Chapagain (2007) have generated data using a desktop study, relying on the CROPWAT model to generate water usage data for grain production (the primary source of water inputs). Our study relied on water usage estimates from Australian research for water use efficiency in dryland cropping, which were most likely well below the estimates used by Hoekstra & Chapagain, though they do not break down water usage into the relative contribution from each source. Water footprint and virtual water usage figures have limited value. We have presented data here for comparative purposes, but we do not endorse the use of water footprint data for assessing the environmental impacts of water usage in the pork industry. Water footprints are a way of considering the volume of embedded water that a country trades with agricultural products, and the data presented from this study may be useful for such a defined purpose. Further analysis of these water data to determine the likely impacts of water use on the environment are recommended to provide a more insightful result. No studies were found that assessed the ‘blue’ water usage of pork production on a functional unit basis. However, two other studies (Foran et al. 2005; Peters et al. in press) present water data for Australian beef production using a similar definition to the one used in this study. These data are presented in Table 73.
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Table 73 – Water usage estimates for beef production in Australia using ABS water definitions Species
Estimate (L / kg HSCW)
Literature source
Beef
209
Adapted from Foran et al. (2005)
Beef
18
Peters et al. (in press) low
Beef
540
Peters et al. (in press) high
Pork – Northern supply chain
49
this study
Pork – Southern supply chain
41
this study
On this basis, water usage for pork production is generally lower than for beef production. This is not surprising considering the primary driver of water use is for drinking water, and the requirements for beef production across a whole breeding herd will by necessity be greater than pork because of the production efficiency differences. Peters et al. did not include overland flow (captured in farm dams) in their study, accounting for the lower bound value. Water usage for feedlot beef production in Australian feedlots has also been reported by Davis et al. (2008). These data were calculated on a ‘kilogram of HSCW gain’ basis, reflecting the ‘gate-to-gate’ nature of a feedlot. Davis et al. (2008) calculate water as the sum of pumped water at the feedlot from all sources, used for drinking, cleaning, maintenance and sundry purposes. Evaporation losses from open water storages are also included. This study did not include any upstream process water use or water captured in effluent holding ponds, hence for comparison we re-analysed our data to reflect the same system boundary they applied, i.e. the ‘on-farm water use’ without rainfall (see Table 73). Table 74 – Water usage estimates on a kilogram of liveweight gain basis for pork and beef using ABS water definitions Species
Water Usage L/kg HSCW gain*
Reference
30-104
Davis et al. (2008)
Pork – Northern supply chain
16.8
this study
Pork – Southern supply chain
10.1
this study
Beef
* L / kg HSCW gain represents water used on-farm without including water used for breeding, presented on a carcass weight basis.
The water used in pork production, based on these supply chains, is considerably less than used for beef cattle production, and differences would be much greater on a water footprint basis where larger green water inputs would be seen for beef production (having a lower feed conversion efficiency compared to finisher pigs). The differences between the two pork supply chains highlights the contribution of cleaning and cooling water use in conventional shed system (northern) compared with deep litter systems (southern). In deep litter systems the vast majority of water use is drinking water with very little additional water for cleaning or cooling.
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Primary Energy Primary energy in pork production varies in the literature. Most studies reviewed indicated energy use in the order of 15-18 MJ / kg carcass weight (CW, approximately similar to HSCW), though one study (Weidema et al. 2008) was an order of magnitude higher than this at 193 MJ / kg CW. This is most likely the result of the hybrid modelling technique applied by this author, which results in a far greater contribution of energy from services that may not be considered in other LCA studies, this one included. Primary energy estimates for both supply chains were 10-54% higher than most estimates presented in the literature (see Table 75). Table 75 – Comparison of Primary Energy (PE) from Australian and international pork LCA studies Country
PE MJ/kg CW1
France
15.9
UK
17
Sweden
18.4
Southern Supply Chain
Australia
20.3
Northern Supply Chain
Australia
24.5
EU average
193
Reference Basset-Mens & van der Werf (2005) Williams et al. (2006) Cederberg & Flysjo (2004)
Weidema et al. (2008)
1 CW is carcass weight, measured as Hot Standard Carcass Weight in this study. Where necessary, data were transformed to a carcass weight basis (unallocated) for comparison purposes.
Energy usage was lower in the southern supply chain because of lower electricity, gas and liquid fuel usage per kilogram of pork produced. This relates to lower energy usage for pigs during the finishing stage in the southern supply chain (deep litter) compared to the conventional housing with controlled temperature used in the northern supply chain. Primary energy usage for the Australian production systems may be higher than the European systems studied in response to the inefficiency of electricity production and possibly the larger distances required for transport. Transport energy contributed 18-25% of PE for the northern and southern supply chains respectively. The transport distances within the Australian supply chain are considerably higher than those reported for European production (i.e. Dalgaard et al. 2007). High energy conversion and transmission losses in electricity production (from coal) may also contribute to higher energy uses for Australian pork production. Energy use for feed production (Figure 10 and Figure 12) was similar to that reported by Basset-Mens & van der Werf (2005) per kilogram of feed. The primary energy usage for feed delivered to the farms ranged from 3.4-4.6 MJ/kg feed in this study, which was similar to that reported by van der Werf et al. 2005.
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Global Warming Potential Pork from the two supply chains studied is produced with fairly low greenhouse emissions when compared to the literature for global pork production (see Table 76). For the southern system, where pigs are raised on deep litter from 3-23 weeks, the GWP was comparable to the lowest emissions reported in the literature (Table 76). Pork produced in the northern supply chain showed a higher level of emissions, because of the contribution of methane from effluent treatment ponds. However, performance was still comparable to other international studies. Table 76 – Comparison of GWP from Australian and international pork production using LCA Country
GWP kg CO2-e/kg CW1
Main contribution to burden
France
3.0
73% crop / feed production
Southern Supply Chain
Australia
3.1
27% crop / feed production 25 % waste stream emissions
Dalgaard et al. (2007)
Denmark
3.3
61% crop / feed production
Cederberg & Flysjo (2004)
Sweden
4.4
NR
Cederberg & Darelius, in Cederberg & Flysjo (2004)
Sweden
5.5
NR
Australia
5.5
66% Methane from pond
Williams et al. (2006)
UK
6.4
NR
Weidema et al. (2008)
EU average
11.2
NR
Reference
Basset-Mens & van der Werf (2005)
Northern Supply Chain
1 CW is carcass weight, measured as Hot Standard Carcass Weight in this study. Where necessary, data were transformed to a carcass weight basis (unallocated) for comparison purposes.
The factors contributing to GWP in Australian piggeries are very different from international studies however. GWP from the northern Australian supply chain was dominated by methane emissions from the effluent treatment ponds (66% of GWP) highlighting the importance of the waste management system. This is not surprising, as primary treatment ponds in Australia are designed to treat volatile solids with an anaerobic treatment process which produce large volumes of methane as a by-product (APL 2004). A similar trend was apparent for the southern supply chain, though to a lesser extent (14% of GWP from methane from the breeder effluent treatment, 2.1% of GWP from weaner and 9.1% from the finisher – primarily N2O). Little data are available in the literature on actual methane emissions from Australian piggery ponds, despite the contribution of this emission source to the GWP emission profile from pig production. A sensitivity analysis was used to check the emission factors used to calculate both methane and nitrous oxide, and these are presented in the following sections. Methane from ponds can also be mitigated by covering ponds and flaring. This was investigated as a scenario and is discussed in the following section. Basset-Mens & van der Werf (2005) and Dalgaard et al. (2007) report crop emissions / feed production as the primary source of greenhouse emissions in the pork supply chain. This is primarily driven by the high levels of nitrous oxide emissions from grain production in Europe. In comparison, grain production (and specifically nitrous oxide from grain production) played a far less significant
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role in the Australian pork supply chains. This is the result of fundamental climate and management differences for grain production in Australia. Australian grain crops are grown with low inputs of nitrogen (see Table 61) and typically experience few saturation / drying events during the growing season, leading to low supply of nitrogen available for denitrification to N2O, and less favourable conditions for these emissions. Hence the total supply of nitrogen (the source) and the factors that promote emissions are both lower. Comparisons have not been drawn with other meat products because of the difficulties in comparing the standard carcass of pork to other species which use different slaughter techniques. A breakdown of GWP by supply chain stage based on collected values relating to each stage is presented for the northern supply chain (Table 77) and the southern supply chain (Table 78) respectively. Table 77 – GWP contribution by supply chain stage for the northern supply chain Supply chain unit Crops and milling Breeding and Weaner (on-farm) Breeding and Weaner Production (manure management) Finisher (on-farm) Finisher (manure management) Meat Processing Total
Contribution (%) 15% 5% 17% 8% 49% 6% 100%
kg CO2-e / kg HSCW 0.83 0.28 0.94 0.44 2.70 0.33 5.5
Table 78 – GWP contribution by supply chain stage for the southern supply chain Supply chain unit Crops and milling Breeder (on-farm) Breeder (manure management) Weaner Production (on-farm) Weaner Production (manure management and reuse) Finisher (on-farm) – Energy and construction Finisher (N2O in shed) Finisher (CH4 in shed) Finisher (manure management and reuse) Meat Processing Total
Contribution (%) 28% 7% 15% 4% 2% 2% 13% 4% 10% 15% 100%
kg CO2-e / kg HSCW 0.87 0.22 0.47 0.12 0.06 0.06 0.41 0.12 0.31 0.47 3.10
GWP is primarily driven by the waste management systems (primarily pond methane and smaller contributions from nitrous oxide) for both systems, though this is more apparent for the northern system where all effluent is treated in anaerobic pond systems. The dominance of contributions from manure management show the opportunity to reduce overall emissions by targeting this source is considerable. The contribution from crop production is relatively lower than reported by Dalgaard et al. (2007) and Basset-Mens & van der Werf 2005. This is likely to be in response to the lower nitrous oxide emissions from grain production in the Australian system. These differences were not observed to the same extent for primary energy usage. The contribution of energy usage to GWP at the farm level is relatively small (approximately 13% for the northern supply chain). This is largely because GWP is driven so strongly by methane and nitrous oxide from the manure management system. This was slightly lower for the southern supply chain. Australia uses a Tier 2 approach for estimating nitrous
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oxide emissions from grain production, based on local research into nitrous oxide emissions. This has led to the emission factor being revised from 1.25% of applied nitrogen to 0.3%. We conducted a sensitivity analysis on the emission factor for nitrous oxide emissions during grain production to investigate the effect of this change, with results shown in Table 79. Table 79 – Sensitivity analysis for the effect of N2O emission factors on GWP in grain production for pork production in the southern supply chain Supply Chain
Nitrous oxide emission rate in grain production
GWP (kg CO2-e)
Crop & Feed Production Contribution
0.3%
3.1
27 %
1.2%
3.3
32 %
0.3%
5.5
14.6 %
1.2%
5.6
15.8 %
Southern
Northern
The rate of nitrous oxide emission contributed between 1.2 and 5% variation in GWP results for the northern and southern supply chains respectively, indicating that this is not a highly sensitive input. This is because of the dominance of piggery emissions from the supply chains. Nitrous oxide emission rates are considerably higher for most European studies (i.e. 1.1 – 2.8 kg N2O-N / ha – Cederberg & Flysjo 2004) compared to 0.2 – 0.3 kg N2O-N / ha for cereal grains modelled in this study. When the yield difference is taken into account, this amounts to 0.15 – 0.4 kg N2O-N / t for the Swedish study compared to 0.05 – 0.1 kg N2O-N / t for cereal grains in this study. The dominant feed grain in the Australian system (sorghum) had the lowest N2O-N emission rate (0.05 kg / t) of all the Australian grains, contributing to the difference in results. In addition to field emissions during grain production, emissions from the application of slurry to crop land are typically high in European LCA studies (i.e. Dalgaard et al. 2007; Cederberg & Flysjo 2004). In the Australian case studies, effluent is stored in ponds and is not currently irrigated onto land. Rather, excess water is evaporated and the ponds are emptied at intervals of 10-15 years, after which the sludge may or may not be utilised. The standard scenario modelled assumed that sludge was not utilised. When ponds are emptied it is likely that some nitrous oxide emissions will occur, however there were no data to determine these emissions and they were not included in the inventory. For the deep litter piggeries where litter is composted and then applied to crop land, nitrous oxide emissions were calculated following the DCC (2007a) method. Handling of nutrient by-products was further investigated using a scenario where the system was expanded to include avoided fertiliser products that would occur if these nutrients were utilised, and this is reported in a later section.
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Scenario Testing GHG Estimation Using DCC Estimates The DCC (2007a) provide two methods for estimating greenhouse gas emissions from piggeries. Method 1 is to use the mass balance program PIGBAL (Casey et al. 2000) to estimate volatile solids and nitrogen excretion (the approach followed for this study). The second method is to use ‘text book’ reference values for volatile solids and nitrogen emissions, provided in the manual. These reference numbers are likely to be more conservative than the modelling approach, so a scenario was modelled for the southern supply chain to compare the two approaches. The data used are presented in Table 80, and the results are presented in Table 81 for the southern supply chain. Table 80 – Input data for Scenario 1 GWP in southern supply chain using DCC standard reference values MMS - Emissions to Air (CH4)
MMS - Emissions to Air (N2O)
Application Emissions to Air (N2O)
0.00
2.48
0.0013
0.00
Weaner produced
0.017
0.019
0.0104
0.0069
Finisher Produced
0.11
0.118
0.0673
0.0238
0.00
4.23
0.0027
0.00
Weaner produced
0.046
0.044
0.0388
0.0180
Finisher Produced
0.132
0.128
0.0973
0.0507
Shed (N2O) PIGBAL estimates – default model run Breeder Produced)
(Piglet
DCC ‘Standard’ reference values – Scenario 1 Breeder Produced)
(Piglet
Table 81 – Methane and GWP estimates for pork production in the southern supply chain using DCC standard reference values for estimation of methane and nitrous oxide emissions Estimation Method
Weaner CH4 emissions (kg)
N2O Emissions (kg)
GWP kg CO2-e
DCC reference values
4.23
0.39
3.9
PIGBAL & DCC (default)
2.48
0.24
3.1
The DCC (2007a) standard reference values are conservative and therefore provide higher estimates of methane from the breeder unit (conventional flushing system with effluent ponds) and higher estimates of nitrous oxide emissions from the weaner and finisher piggeries resulting in higher GWP estimates when this scenario is compared with the mass balance approach. This highlights the importance of a validated mass balance program that can be used to model specific farm level emissions rather than relying on text book values alone.
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Considering the variability in methane and nitrous oxide emissions using different techniques, a sensitivity analysis was conducted to assess a range of possible emission factors for both gases. This is presented in a following section.
Mitigation of Pond Methane Pond methane at the farm level was identified as the largest source of GHG emissions from the northern supply chain, and was a considerable contributor in the southern supply chain. There are several options available to the industry to mitigate pond methane with varying degrees of efficacy. One option is to cover effluent ponds and capture the resulting gas, then flare this gas. Flaring converts methane to biogenic carbon dioxide which has no net global warming potential. This is a minimum input option to reduce global warming potential from piggeries. A simple scenario was run for each supply chain where primary ponds at the piggeries were covered and a simple flaring device fitted. The additional capital inputs for this management system were estimated and divided by the life span of the materials. It was assumed that no on-going inputs are required for the flaring system. A correctly designed primary pond is able to treat 90% of volatile solid inputs, producing methane as the primary by-product. A similar level of volatile solid treatment was assumed with the pond covering scenario, leading to a 90% reduction in methane emissions from the covered pond / flaring system. For the northern supply chain, pond methane is generated at both the breeder and finisher units, contributing 66% of the overall GHG emissions for the production of pork. The breeder unit has a primary pond surface area of approximately 3 m2 (0.3 kg/yr) / weaner produced, while the finisher unit had a primary pond surface area of approximately 1 m2 (0.1 kg/yr) / finisher produced. The same assumptions were used as with the southern supply chain for modelling the capital requirements of pond covering. For the southern supply chain, pond methane is generated at the breeder unit contributing 14.1 % of the overall GHG emissions for the production of pork. The breeder unit piggery has a primary pond surface area of approximately 2 m2 (0.2 kg) / piglet produced. It was assumed that the pond cover will be constructed using a supported high density polypropylene cover with a lifespan of 10 years. This volume of covering material was modelled using unit processes from the Ecoinvent database. The results of this scenario at both the northern and southern supply chains for the global warming indicator are presented in Table 82.
Table 82 – GWP for one kilogram of prime pork from the northern and southern supply chains with pond covering and methane flaring for primary piggery ponds Impact Category
Global Warming Potential for 1 kg of Pork HSCW Production
Northern Supply Chain
Southern Supply Chain
Current system – no mitigation of pond methane (kg CO2-e)
Pond covering and flaring (kg CO2-e)
Current system – no mitigation of pond methane (kg CO2-e)
Pond covering and flaring (kg CO2-e)
5.5
2.3
3.1
2.7
Pond covering and flaring of methane has a dramatic effect on the GWP from pork production in the two supply chains, with the greatest effect at the northern supply chain where all volatile solids are
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treated in a pond system. The southern supply chain shows a less dramatic change because emissions from the breeder piggery only are mitigated. This reverses the results for the two supply chains, showing that while the southern supply chain performs better under the current management scenario, the northern supply chain (with conventional management) would perform better overall with respect to GWP if pond covers were installed. Global warming potential from pork produced in both supply chains is lower than any data reported in the literature. While LCA studies have inherent variability because of methodological differences, the underlying contribution to GWP from efficient Australian grain production may allow a lower GWP than European competitors. To investigate the effect of N2O variations after the main source of GHG emissions were removed, a sensitivity analysis was conducted on the N2O emission rate for feed grain production using two emission rates for the northern supply chain (where the masking effect of pond emissions were greatest). The results are shown in Table 83. Table 83 – Sensitivity analysis for contribution of nitrous oxide with grain production to GWP for the northern supply chain with pond covering and methane flaring
Northern chain
supply
Nitrous oxide emission rate in grain production
GWP (kg CO2-e)
Weaner Feed %
Finisher Feed %
0.3%
2.27
9.18
26.6
1.2%
2.36
9.48
27.9
It is interesting to note that the contribution of nitrous oxide to the GWP of pork was not highly sensitive to changes in the emission rate (overall change of 1.7%) for the northern supply chain. This is partially because of the low nitrogen input rates used with Australian grain production which lead to a smaller pool of soil nitrogen from which nitrous oxide emissions are released. Contribution graphs for GWP with and without pond covering is presented in Figure 13 for the northern and Figure 14 for the southern supply chain respectively.
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Figure 13 – GWP contribution graph for the northern supply chain with and without pond covering
Figure 14 – GWP contribution graph for the southern supply chain with and without pond covering
On the , the ‘breeder’, ‘weaner’ and ‘finisher’ labels relate to energy use on farm, while the ‘MMS’ relates to the ‘manure management systems’ for both systems and MSE relates to ‘manure system expansion’. When pond methane is removed from the northern supply chain piggery, the contribution from upstream crop production increases to make up almost half of the GWP. This value is still lower than observed in the literature. For the GWP contribution from the ‘finisher’ phase at the southern piggery, this is primarily contributed by nitrous oxide (13%) and methane (4%) for the uncovered scenario (see Table 78). Interestingly, the meat processing plant (abattoir) becomes a significant contributor at this point. Meat processing emissions are derived from both energy (minor) and waste stream methane and nitrous oxide. The contribution is considerably greater than the 8% contribution observed by Peters et al. (in press) for red meat processing.
Alternative Reuse of Piggery Nutrient By-Products Pork production has a relatively low utilisation rate of valuable nutrients such as nitrogen, phosphorus and potassium that enter the system with pig feed. Nutrients that are not incorporated into the animal’s body are excreted in urine or faeces. These excreted nutrients are either lost to the atmosphere (in the case of nitrogen only) and / or are transferred to the piggery pond in conventional flushing systems, where they remain in the pond supernatant or sludge. For deep litter systems, the same loss pathways exist for nitrogen, and remaining nutrients are combined with the litter and removed from the shed at the end of each batch of pigs. The degree of nutrient by-product utilisation in the supply chains vary. It has been assumed that for the current system, nutrients are not utilised from the pond systems. In practice, water from the effluent system evaporates and remaining nutrients are concentrated in the sludge. This is cleaned every 10-15 years but the sludge is not utilised to its nutrient potential. For the deep litter grower and finisher systems in the southern supply chain, spent litter (including excreted nutrients) are currently
79
composted and sold to farmers for land application. Hence, system expansion has been used to account for the nutrient by-products under the current management scenario and no change has been made to this approach. An alternative to this is to irrigate effluent from the northern (and southern breeder) systems for crop or pasture production, and utilise nutrients in the sludge when ponds are cleaned by drying the material and spreading on land at sustainable rates. The scenarios are summarised in Table 84. Table 84 – Piggery nutrient by-product utilisation under current management and alternative scenario for the northern and southern supply chain piggeries Supply Chain
Northern
Southern
Waste stream system
Current Utilisation scenario
Alternative utilisation scenario
Breeder unit
pond system – supernatant and sludge
Pond water balance maintained through evaporation of excess water, sludge removed every 10-15 years (assumed nutrients not utilised).
Effluent irrigation and sludge reuse with full nutrient utilisation.
Finisher unit
pond system – supernatant and sludge
Pond water balance maintained through evaporation of excess water, sludge removed every 10-15 years (assumed nutrients not utilised).
Effluent irrigation and sludge reuse with full nutrient utilisation.
Breeder unit
pond system – supernatant and sludge
Pond water balance maintained through evaporation of excess water, sludge removed every 10-15 years (assumed nutrients not utilised).
Effluent irrigation and sludge reuse with full nutrient utilisation.
Grower/Finisher unit
Deep litter
Composted and applied to crop land – Nutrients Utilised.
No change
Nutrients present in the sludge, effluent and deep litter cannot be considered directly transferable on a kilogram basis with nitrogen or phosphorus fertilisers. This is because of the form nutrients take within the manure (a balance of organic and inorganic forms) which alter the plant availability of nutrients compared to chemical fertilisers. To account of this, substitution ratios were developed for the different materials, based on expert knowledge of the nutrient composition in these products. The substitution ratios are conservative, and represent the total amount of nutrient expected to be released for plant growth over 3 seasons after losses are accounted for (see Table 85). Table 85 – Piggery nutrient by-product substitution ratios with fertiliser products Nutrient
Substitution ratios
Substitution product Piggery Sludge
Piggery Effluent
Piggery Spent Litter
0.5
0.6
0.5
Nitrogen
1 kg of Urea N
Phosphorus
1 kg of Superphosphate P
Triple
0.7
0.7
0.7
Potassium
1 kg of Potassium Chloride K
0.9
0.9
0.9
80
Global warming potential results for the northern and southern supply chain with and without system expansion to account for nutrient by-products are presented in Table 86. The values for each system show a minor reduction in the environmental burden from applying system expansion for the nutrient by-products (less than 1%). Table 86 – GWP for pork production in the northern and southern piggery supply chains with system expansion to account for nutrient by-product utilisation Impact Category
Unit
Northern supply chain
Southern supply chain
Current management System expansion Current management System expansion (credited value of (no mitigation of pond (credited value of (no mitigation of nutrient bynutrient bymethane) pond methane) products) products) GWP for 1 kg HSCW pork
kg CO2-e
5.52
5.47
3.1
3.0
The result of using system expansion to account for the nutrient value of piggery nutrient by-products was minimal. This is because of the minimal energy offsetting value provided to expand the system to account for avoided chemical fertilisers was offset by the additional nitrous oxide emissions that occur when effluent irrigation was carried out. A more significant result would be expected if anaerobic digestion and fertiliser manufacture was modelled as a treatment method for effluent.
Comparison of Housing Systems for Finisher Pigs Finishing pigs in conventional sheds (partially slatted floors, flushing effluent system and controlled temperature) or deep litter sheds are options available to pig farmers in Australia. Generally, deep litter sheds require lower infrastructure for construction and lower energy inputs, but result in poorer pig performance compared to conventional pig sheds. This study covered finishing in conventional sheds (northern) and finishing on deep litter (southern). Direct comparisons are not possible because of the differences in climate and ages of pigs in the different stages of the system, however some indications can be drawn about the relative performance of these systems. For water use, pigs finished on deep litter used on average 6.7 L less water per kilogram of HSCW produced. This is because of the lower water requirements for cleaning and cooling in these systems. This saving may be significant in water constrained environments. For GWP, the southern supply chain with deep litter finishing showed an overall reduction in GHG emissions of 2.4 kg CO2-e / kg HSCW. Of the total emission profile for both systems, 0.268 kg of CO2-e were emitted from the waste stream during finishing on deep litter, compared to 2.71 kg of CO2-e from the northern supply chain finisher unit effluent ponds. These systems both housed pigs for approximately 15 weeks during the finishing phase. The greenhouse emissions for the manure system alone in the northern supply chain are greater than those reported by Eriksson et al. (2005) or Pelletier et al. (2007), who both studied piggery finisher systems. Eriksson reports GWP for grower pigs (29kg-115kg) in conventional pig housing ranging from 1.3 kg CO2-e to 1.5 kg CO2-e per kg of liveweight gain. Pelletier et al. (2007) reports a range from 0.54 kg CO2-e to 1.2 kg CO2-e per kilogram of pig (presumably liveweight), though this study only considered on-site emissions, excluding feed production and all other upstream emissions.
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Considering the results for GWP are dominated by the pond methane emissions, the results changed considerably when pond covering and methane flaring was modelled in the northern supply chain. Pond covering reduced waste stream emissions to 0.27 kg CO2-e making this system comparable to the southern system on the basis of manure management emissions. Considering the likely performance gains from conventional housing, the performance is likely to be superior provided pond covers and methane flaring is installed. It should be noted however, that the emission factors used may be subject to considerable variability and results need to be considered cautiously. An analysis of the sensitivity of these emission factors is discussed in the following section.
Sensitivity Analysis Considering the dominant role of pond methane and to a lesser extent nitrous oxide emissions in the overall global warming impacts from pork production, the sensitivity analysis focussed on the variable factors driving these emissions from the waste stream. The emission calculations for each stage in the waste stream have been described in Appendix 1, which also reports on the findings of a literature review on methane and nitrous oxide emission rates. There are two factors in the calculation of methane emissions that were varied using the range presented in the literature, these are the emissions potential factor (Bo), and the methane conversion factor (MCF). The DCC manual (2007a) provides a Bo value of 0.45m3 CH4/kg VS as per the IPCC (1997) and a MCF for lagoons of 90%. Table 87 shows the result of the sensitivity analysis on methane emissions from the breeder and finisher systems, and the overall effect on GWP. Table 87 – Sensitivity analysis of GWP for methane emission calculations in northern supply chain Breeder unit CH4 emissions (kg)
Finisher unit CH4 emissions (kg)
GWP kg CO2-e
% change
Bo (Low)
1.93
4.9
3.88
- 29%
Bo (High)
4.11
10.4
6.08
+ 11%
MCF (Low)
2.77
7.0
4.72
- 14%
GWP for the northern supply chain is highly sensitive to changes in the emissions potential factor (Bo), with low and high range scenarios resulting in an overall 40% variance in GWP for pork. Modelling a lower MCF (70% compared to 90%) resulted in a 14% reduction in GWP. This variance highlights the need for robust, Australian research to validate these emission factors under a range of climatic and pond management systems used in Australia. For the southern supply chain, a sensitivity analysis of both methane and nitrous oxide emission factors was conducted. Methane emissions from the pond were investigated using the same literature data for Bo and MCF as the northern supply chain. The results are presented in Table 88.
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Table 88 – Sensitivity analysis of GWP for methane emission calculations in southern supply chain Breeder unit CH4 emissions (kg)
GWP kg CO2-e
% change
Bo (Low)
1.35
2.8
- 7%
Bo (High)
2.87
3.1
+ 2%
MCF (Low)
2.23
3.0
- 2.8%
The southern supply chain is not highly sensitive to Bo or MCF because the relative contribution of pond methane (only emitted from the breeder unit) is only 14.1% of GWP. Considering nitrous oxide, there are a number of emission factors for losses throughout the system in the deep litter sheds. These include losses in the deep litter shed, losses during storage / composting of deep litter and losses during application. A similar approach was used to determine the bounds of the sensitivity analysis by using a range of values from the literature. It is noted that no references for Australian N2O emissions from deep litter sheds could be sourced because of the lack of research in this area. Results from the scenarios modelled are shown in Table 89. Standard values for methane emissions have been used in all scenarios. Table 89 – Sensitivity analysis of GWP for nitrous oxide emission calculations in southern supply chain Emission source
Shed N2O emissions
Stockpile / Compost N2O emissions
N2O Emission rate
GWP kg CO2-e / kg HSCW
% change
Low
2.7
-12%
High
4.4
43%
Low
2.8
-9%
High
3.5
14%
There is a considerable variation in nitrous oxide emissions from deep litter sheds in the literature. In the scenarios, a low emission rate of 0.1% was used as a practical minimum, and a high rate of 4.25% (of excreted N) was used. This was considered a practical maximum where sufficient bedding is supplied, though values in the literature range up to 12% of excreted N where insufficient bedding quantities are used (Hassouna et al. 2005). This range in the sensitivity analysis resulted in a 55% variation from minimum to maximum, highlighting the effect this factor may have on GWP for pork production on deep litter. The effect of changes in emission rate during stockpiling / composting was lower, resulting in a 23% variation in the overall result for GWP. If these data were considered for the finisher system only, N2O emissions could dominate the emission profile for the whole enterprise depending on the emission factor used. These factors need to be validated for Australian deep litter systems to improve the robustness of this research.
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Conclusions The results of the first life cycle assessment of pork production in Australia suggest that environmental performance (measured by water usage, energy usage and GWP) is similar to other studies presented in the literature and is in some cases superior. While LCA studies are inherently variable, the approach in this study has been conservative with respect to sensitive procedures such as the handling of co-products. It needs to be clearly acknowledged however, that the functional unit used for this study is primarily a supply driven unit which represents the end of the ‘primary production’ supply chain for pork. Hot standard carcass weight is significantly different to the pork that most consumers have in their mind. Moreover, direct comparison with other species may not be valid because of the differences in carcass designation (i.e. head on vs head off). For comparison across species it would be more valid to extend the system until a more comparable product is reached, such as a pork loin chop (which may be compared with a lamb cutlet or beef sirloin for example). At the industry level, this is a very important outcome for Australian pork. The Australian industry is under constant pressure from imported pork. However, if the results from this study were representative of the industry as a whole, importing pork into Australia may be a poor environmental outcome for the world, without even taking into account the long distance refrigerated transport required to land pork from Denmark or Canada to Australia (which would add perhaps 5% more to the GWP emission profile of pork from these countries). The pork industry therefore has a key opportunity to present its product as ‘clean, green’ provided further studies of different case studies can validate the results from this study. Water usage (and water footprint) data presented in this report are lower than presented for pork and other species in the international literature. This highlights the water efficient nature of the Australian pork industry, despite the inclusion of evaporation from effluent storages and water storages used for fresh water containment. It is noted that while water usage data (measured according to the ABS definition and comparable with the blue water terminology) and water footprint data (green and blue water combined) may be useful from a resource usage perspective, they are not particularly useful for indicating the environmental impacts of pork production on Australia’s scarce water resources. Considering this, care must be taken when interpreting these data and further analysis is required to indicate the impacts of water use from pork production. Primary energy usage was lower for the southern supply chain, most likely because of lower energy usage during the pig finishing phase. For the two supply chains, primary energy was 10-54% higher than most other studies presented in the literature. This is likely to be in response to greater energy losses associated with electricity supply and greater transport distances in the Australian pork supply chains. There are underlying factors that contribute to better global warming performance for Australian production compared to Europe, particularly Australia’s dry climatic conditions that tend to reduce key emissions such as nitrous oxide. It is important to note however that few of the key emission factors presented in the DCC (2007a) have been derived from Australian research, and the effects of even small changes to the emission factors for methane and nitrous oxide may change these results by up to 70% if a cumulative ‘worst case’ scenario for methane and nitrous oxide emissions was calculated. This underlines the great need for research to validate these emission factors or propose changes to the method used for the pig industry in Australia. The GWP figures presented in this report are not equivalent to the requirements of other greenhouse reporting frameworks such as the NGERS or proposed CPRS. LCA covers significantly more
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emission sources (particularly from upstream processes) than are required under these legislative frameworks, with or without agricultural emissions included. To present data equivalent to the requirements of the NGERS or proposed CPRS for pork production would require re-analysis of the data. While these results cannot be extrapolated for all of the Australian pork industry, it does show the potential for a very high standard of environmental performance when measured by these indicators only. One great attribute of the life cycle assessment tool is the ability to assess the whole of a supply chain for multiple environmental factors simultaneously. This can avoid promoting technologies or practices that ‘burden shift’. This is particularly relevant for the nitrogen cycle, where two other emission sources (ammonia – NH3 and nitrate – NO3) are environmentally important emissions that the pig industry must manage to ensure ongoing high performance standards. Because of the interrelated nature of the nitrogen cycle in particular, it would be highly valuable to re-analyse the data collected to provide information on ammonia and nitrous oxide emissions under a range of possible scenarios. The scenario investigating low input pond covering and methane flaring showed that, unlike other meat products, pork producers have viable options to reduce the most significant emission from conventional piggeries, pond methane. This provides a further option to improve the GWP of Australian pork production and position the product as ‘carbon friendly’. Considering this is the most simple form of methane mitigation where the energy is not harnessed, greater savings would be possible again if methane were used in the shed for heating, or if electricity was generated using anaerobic digestion. The modelling of further scenarios with such modifications would demonstrate these savings. The comparison of deep litter and conventional housing showed that, for the current management systems, deep litter housing required lower water and energy inputs, and resulted in a lower GWP than conventional housing. The result for GWP was reversed overall however, when pond covering was used in the supply chains (for the breeder unit in the southern supply chain and the breeder and finisher unit in the northern supply chain.
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Recommendations While promising, the results presented in this report are very limited, representing only two supply chains in the eastern states of Australia. Considering the diversity of farming practices the results could not be accurately extrapolated for the whole industry, as noted in the goal and scope of this project. Consequently, the results are better used to inform industry and guide strategic research than for wide-scale reporting in the general public. Considering the promising nature of the results however, further case study supply chains are recommended to improve the robustness of the data presented. While the study does cover water usage and present the ‘water footprint’ of Australian pork, it must be noted that these data are a poor representation of the environmental impact caused by water use. It is recommended that the study be extended to investigate the environmental impacts of water use in the pork supply chains under one or more of the state-of-the-art methods (i.e. Mila i Canals et al. 2009; Pfister et al. 2009) which have been developed since the initiation of this project. Three recommendations emerge from the scenario and sensitivity analysis regarding greenhouse gas emissions from Australian pork production. Firstly, the pond covering and methane flaring scenario highlighted this option for significantly reducing GHG emissions from the industry. This suggests that on-going research attention should be directed at low input options for reducing pond methane, and quantifying these reductions so that they can be factored into the DCC emission methodology. This will require further research. Additionally, further LCA scenario modelling is recommended to quantify the effect of high input methane mitigation options such as anaerobic digestion, which will have the added benefit of offsetting energy usage in the piggery, thereby reducing greenhouse gas emissions further still. This could be done as a relatively simple task based on the findings to date. The second recommendation relates to the sensitivity of the DCC greenhouse gas emission factors for the pork industry. The sensitivity analysis identified how changes to these factors (using a range of values available in the scientific literature) may alter the GWP performance of the supply chains by close to 60% when the cumulative effect of both methane and nitrous oxide sensitivity analyses are considered. The DCC emission factors are based on international studies and have not been validated by Australian research. Considering this, the accuracy of results presented from this report is limited by the emission factors available. With the prospects of an emissions trading scheme including agriculture within 6 years, fundamental, Australian research to validate these emission factors or propose new factors is strongly recommended. PIGBAL was used for estimating the nitrogen balance and volatile solids data used in this project and is the basis for the DCC calculation methodology. It is recommended that research is directed towards validating the nitrogen balance in PIGBAL, and that on-going research to improve the volatile solids estimations is carried out when this can be done in conjunction with other projects. The GWP figures presented in this report are not equivalent to the requirements of other greenhouse reporting frameworks such as the NGERS or proposed CPRS. However these data could be presented by re-analysing the data collected for the project. Considering the importance of feed grain in the emission profile of pork production (as with other intensive livestock products), a joint project to develop more robust LCI data for feed grain production in eastern Australia is recommended. This is needed for sorghum in particular, and should cover the range of practices used for production and average performance over several years. This project could be considered by the feed grains partnership R&D. Life cycle assessment is able to quantify multiple environmental factors simultaneously. A limitation in this study was the focus on only three impact categories. This is particularly apparent when considering nitrous oxide, as other environmentally relevant forms of nitrogen are emitted from the
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same cycle. It is recommended that this study re-analyse the data to present results for an additional two categories relevant to the pig industry, acidification (from ammonia – NH3), and eutrophication (from nitrate – NO3 and phosphate). This will avoid promoting technologies or practices that ‘burden shift’ environmental problems from one area to another, and will provide a more holistic assessment of the pork industry. Because of the supply driven functional unit used throughout this study, the results must be very carefully presented to the general public, if they are to be presented at all. The emission profile of pork as a sale product will be considerably higher than the data presented here. If the goal is to present data such as the ‘carbon footprint’ of fresh pork to the Australian public, it is recommended that the supply chains are extended to the supermarket shelf for a range of products. If results are to be used in comparison with other meat species, it would be more valid to compare similar products at the retail level, such as pork loin chops and lamb cutlets to avoid differences in carcass estimates. Similarly, if the results are to be used to compare with imported pork products, it is recommended that a parallel study is established of the most relevant competitor to ensure a similar analysis is conducted.
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Pfister, S, Koehler, A, Hellweg, S 2009, Assessing the Environmental Impacts of Freshwater Consumption in LCA, Environmental Science and Technology, in press. Pimentel, D, Berger, B, Filiberto, D, Newton, M, Wolfe, B, Karabinakis, E, Clark, S, Poon, E, Abbett, E & Nandagopal, S 2004, Water resources: Agricultural and Environmental; Issues, BioScience, 54, 909-918. Pimentel, D, Houser, J, Preiss, E, White, O, Fang, H, Mesnick, L, Barsky, T, Tariche, S, Schreck, J & Alpert, S 1997, 'Water resources: agriculture, the environment, and society: an assessment of the status of water resources', BioScience, 47, 2 97-108. Pimentel, D & Pimentel, M 2003, Sustainability of meat-based and plant-based diets and the environment, American Journal of Clinical Nutrition, 78, 660-663. Renault, D 2003, Value of virtual water in food: Principles and virtues, in: Virtual water trade: Proceedings of the International Expert Meeting on Virtual Water Trade, Value of Water Research Report Series No. 5 12, edited by: Hoekstra, A. Y., UNESCO-IHE, Delft, The Netherlands. Ridoutt, BG, Eady, SJ, Sellahewa, J, Simons, L & Bektash, R 2009a, Water footprinting at the product brand level: case study and future challenges, Journal of Cleaner Production, 2009, doi:10.1016/j.jclepro.2009.03.002 Ridoutt, BG, Eady, SJ, Sellahewa, J, Simons, L & Bektash, R 2009b, Product water footprinting: how transferable are the concepts from carbon footprinitng? Australian Life Cycle Assessment Conference Proceedings, Melbourne. Rijsberman, FR 2006, Water scarcity: Fact or fiction? Agricultural water management, 80, 5-22. Canola Council of Canada 2009, Seed, Oil and Meal Prices 2009, Canola Council of Canada, . Sharpe, RR & Harper, LA 2002, Nitrous Oxide and Ammonia Fluxes in a Soybean Field Irrigated with Swine Effluent, Journal of Environmental Quality, 31, 524-532. Smith, CJ, Snow, VO, Leuning, R & Bond, WJ 2001, "Salt Dynamics in Effluent Irrigated Soils”. How much nitrogen is lost from crops irrigated with piggery effluent? Final Report to the Pig Research and Development Corporation, Canberra, ACT. Sturgess, J, Butler, G & McIntyre, K 2004, Agronomic responses of six barley varieties to different plant populations - an old question revisited , New directions for a diverse planet: Proceedings of the 4th International Crop Science Congress, Brisbane, Australia. Subak, S 1999, Global environmental costs of beef production, Environmental Economics, 30, 79-91. UNECE 1999, Control options/techniques for preventing and abating emissions of reduced nitrogen compounds., United Nations Economic and Social Council, Economic Commission for Europe University of Arkansas 2009, Soil Water Management Soybeans - Crop Irrigation, Accessed 3rd June 2009. http://www.aragriculture.org/soil_water/irrigation/crop/soybeans.htm USDA 2006, Soybean Production Costs and Returns from the 2006 ARMS, USDA, 17 May 2009, .
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van der Werf, HMG, Petit, J & Sanders, J 2005, The environmental impacts of the production of the concentrated feed: the case of pig feed in Bretagne, Agricultural Systems, 83, 153-177. Vanderholm, DH 1975, Nutrient Losses from Livestock Waste During Storage, Treatment and Handling. In: Managing Livestock Wastes, Proceedings of the Third International Symposium on Livestock Wastes. pp. 282-285. American Society of Agricultural Engineers, St. Joseph, Michigan. Verge, XPC, Dyer, JA, Desjardins, RL & Worth, D 2008, Greenhouse gas emissions from the Canadian beef industry, Agricultural Systems, 98, 126-134. Williams, AG, Audsley, E & Sanders, DL 2006, Determining the environmental burdens and resource use in the production of agricultural and horticultural commodities, Main Report, Defra Research Project IS0205, Bedford: Cranbrook University and Defra. Available from: www.silsoe.cranford,ac,uk, and www.defra.gov.au. Weidema, B 2003, Market information in Life Cycle assessment, Danish Ministry of the Environment. Environmental Protection Agency, . Weidema, BP, Thrane, M, Christensen, P, Schmidt, J & Løkke, S 2008b, Carbon Footprint: A catalyst for Life Cycle Assessment, Journal of Industrial Ecology, 12, 3-9. Weidema, BP, Wesnæs, M, Hermansen, J, Kristensen, I & Halberg, N 2008, Environmental improvement potentials of meat and dairy products, Institute for Prospective Technological Studies, JRC European Comission, 196 pp. WRI 2004, The Greenhouse Gas Protocol – A Corporate Accounting and Reporting Standard, Revised Edition, World Resources Institute, Washington, DC. http://www.wri.org/publication/greenhouse-gas-protocol-corporate-accounting-and-reportingstandard-revised-edition Zimmer, D & Renault, D 2003, Virtual water in food production and global trade: Review of methodological issues and preliminary results, in: Virtual water trade: Proceedings of the International Expert Meeting on Virtual Water Trade, Value of Water Research Report Series No. 12, edited by: Hoekstra, A. Y., UNESCO-IHE, Delft, The Netherlands.
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Appendix 1 Textbook Values of Excretion Rates Text book values are a typical method used for estimating manure production from piggeries. Such publications include: Effluent at Work (Kruger et al. 1995); the American Society of Agricultural and Biological Engineers Standards - ASABE (2005), Midwest Plan Service-MWPS (1993) and the National Environmental Guidelines for Piggeries - NEPG (APL 2004). Table 90 and Table 91 below demonstrate the large differences in reported values for nitrogen and volatile solids excretion from these various texts. Table 90 – Predicted Nitrogen Excretion per Pig (by Class) per Year (kg) Weight Range (kg)
Average Weight (kg)
Effluent at Work
ASABE 2005
MWPS 1993
NEPG
Gilt
100 – 160
130
11.7
-
15.9
12
Boar
100 – 250
175
15.0
10.2
11.7
15
Gestating Sow
160 – 230
195
11.7
11.7
12.9
13.9
Lactating Sow
230 – 160
195
-
-
10.4
27.1
-
-
16.4
31.0
-
50
Piglet
1.4 – 8
4.7
-
-
1.0
2.3
Weaner
8 – 25
16.5
2.9
2.3
3.4
3.9
Grower
25 – 55
40
7.7
10.4
4.9
9.2
Finisher
55 – 100
77.5
14.2
17.6
9.4
15.8
Pig Class
Sow Litter
and
Table 91 – Predicted Volatile Solids Excretion per Pig (by Class) per Year (kg)
97
Weight Range (kg)
Average Weight (kg)
Effluent at Work
ASABE 2005
1993
National Piggery Guidelines
Gilt
100 – 160
130
110
-
-
162
Boar
100 – 250
175
139
124
140
151
Gestating Sow
160 – 230
195
110
164
110
151
Lactating Sow
230 – 160
195
-
365
-
215
-
-
274
-
272
325
Piglet
1.4 – 8
4.7
-
-
-
11
Weaner
8 – 25
16.5
51
41
50
47
Grower
25 – 55
40
124
-
91
90
Finisher
55 – 100
77.5
234
-
213
149
20-120
70
-
137
-
-
MWPS Pig Class
Sow + Litter
Grower/Finisher
Textbook values are variable and non-dynamic, i.e. they do not take into account the known conditions at a case study piggery such as the feed conversion ratio or the type of feed being fed, which will have a significant effect on excretion rates. For this reason it was not the preferred approach for the LCA project.
Mass Balance Principles
The amount of waste produced from a piggery can be predicted using mass balance principles by knowing the nutrient content and digestibility of the diet. The mass balance spreadsheet model (PIGBAL - developed by the Department of Primary Industries and Fisheries) can be used to predict the amount of nutrients excreted from intensive piggeries. This section explains the PIGBAL model used to generate outputs from the case study piggeries in the LCA project. Literature values used to validate the model are also reported. The characteristics of effluent or spent litter exiting or removed from a piggery depends on the nutrient composition of the diet being fed, the age and weight of the pigs, the amount of feed and water wastage, the chemical analysis of the input water and bedding material, and the quantity of nitrogen lost in the shed via volatilisation. The system is represented below in Figure 15. The amount of any particular nutrient (e.g. nitrogen) that is excreted in the manure (urine and faeces) can be determined from the amount that is fed, minus the amount retained by the animal as live-weight gain. The theory behind the mass balance approach for predicting waste output from piggeries is that by knowing the amount of a particular component of feed (ash, nitrogen, phosphorus, etc) offered to a pig and by subtracting the amount retained by the animal, the amount excreted can be determined. The critical factor in being able to use this mass balance approach to predict the output of a particular component is to know the retention of that component by the pig.
98
The PIGBAL spreadsheet model uses the body composition of pigs, DMDAMP (Dry Matter Digestibility Approximation of Manure Production) and mass balance theory to predict the amount of total solids (TS), volatile solids (VS), nitrogen (N), phosphorus (P) and potassium (K) produced by a piggery. PIGBAL allows different diets to be entered for each class of pig. The feed intake and percentage of feed wastage can be adjusted for each pig class. Modifications can also be made to the percentage of nitrogen lost during other stages of waste handling and treatment.
GASEOUS LOSSES
PIGS
WATER
PIGGERY PIGS (where applicable)
SHED
FEED
MORTALITY
EFFLUENT
Figure 15 – Simple Mass Balance Diagram for a Conventional Piggery
The PIGBAL model was used in the National Environmental Guidelines for Piggeries (APL 2004) to estimate the solids and nutrient (nitrogen) excretion of different classes of pig. The ASABE (2005) also uses similar theory with feed nitrogen intake and retention in the body to predict excretion rates. These estimations will give a superior prediction of nutrient excretion than text book values that are based on body weight of the animal.
99
Table 92 – Predicted Volatile Solids and Nitrogen Excretion per Pig Class Using PIGBAL Weight Range (kg)
Age range (weeks)
VS Excretion (kg/yr)
Nitrogen Excretion (kg/yr)
Gilt
100 – 160
24 – 30
162
12
Boar
100 – 300
24 – 128
151
15
Gestating Sow
160 – 230
151
13.9
Lactating Sow
230 – 160
215
27.1
Pig Class
Sucker
1.4 – 8
0–4
11
2.3
Weaner
8 – 25
4 – 10
47
3.9
Grower
25 – 55
10 – 16
90
9.2
Finisher
55 – 100
16 – 24
149
15.8
PIGBAL (Casey et al. 2000).
The PIGBAL model was used to generate a piggery mass balance for the supply chain piggeries based on herd data collected from the piggeries. The key outputs from the mass balance are excreted volatile solids (primary determinant for estimation of manure methane production) and excreted nitrogen (the primary determinant for the estimation of nitrous oxide).
Mass Balance Model – Volatile Solids
The PIGBAL spreadsheet model uses the body composition of pigs, DMDAMP (Dry Matter Digestibility Approximation of Manure Production) theory (McGahan et al. 1998) to predict the amount of total solids (TS), volatile solids (VS) and fixed solids/ash (FS) produced by a piggery. PIGBAL allows different diets to be entered for each class of pig. The feed intake and percentage of feed wastage can be adjusted for each pig class. Equations 4 to 7 are the basis of the PIGBAL model: TS excreted = 1 – (DMI x DMD of the ration)
Equation 4
where: DMI is the dry matter intake (kg/head/day) DMD is feed dry matter digestibility The amount of FS excreted is calculated by mass balance as the difference between the amount in the diet and the amount retained by the animal as liveweight gain. FS excreted = FS fed – FS retained
Equation 5
DMD is a coefficient or percent of the fed dry matter that is digestible. In Equation 6, DMD prediction requires laboratory analysis (peptin cellulose technique), a digestion trial or it is publicly available for many feed ingredients in Australia. DMD = (Feed DM – Faeces DM) / Feed DM
Equation 6
100
VS is calculated with Equation 7: VS excreted = TS – FS
Equation 7
Volatile solids production for the supply chains was estimated with PIGBAL using these predictions, with the standard DMD values as reported in PIGBAL.
Methane from Manure
Methane is formed from the organic fraction (volatile solids) of the manure and waste feed. Methane emissions (M) from manure were estimated using the DCC (2007a) as follows: M = VS x Bo x MCF x p
Equation 8
Where: VS = Volatile Solids Production Bo = Emissions potential - 0.45m3 CH4/kg VS (IPCC 1997) MCF = Methane Conversion factor (for lagoons = 90%, for ‘dry lots’ (assumed to cover deep litter systems) the value is 1.5%) P = Density of methane (0.662 kg/m3) LCA modelling of methane emissions from the manure management system (lagoons and deep litter composting/stockpiling) was based on estimates of VS production from the PIGBAL model. A separate scenario was also conducted using predicted VS production using DCC (2007) estimates and the above equation. To conduct a sensitivity analysis on the effect of varying methane emissions a literature review was first conducted on likely ranges of methane emission potential (Bo) and methane conversion factor (MCF) for piggery manure. From the above equation it is seen that the IPCC (1997) method to estimate methane emission from volatile solids requires the constant Bo, with the maximum methane producing capacity for manure produced by livestock category. Table 93 shows the maximum methane-producing capacity of the manure for pigs and cattle. Table 94 presents data from experiments in France with a maximum and minimum Bo value for pig and dairy cattle slurry. The pig value from France (Vedrenne et al. 2008) is lower than the value from IPCC, perhaps due to temperature and/or the use of a slurry component. For dairy cattle, the IPCC value is about the average of the France values. EPA (1999) also provides Bo values for beef, dairy and swine for various diets in Table 95. This table shows the variability of the data. At present there is no Australia specific value of Bo and this information would be helpful to provide more accurate estimation of methane production for pigs and other intensive animal industries (beef feedlots and dairies) under Australian conditions.
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Table 93 – Maximum methane-producing capacity of the manure (Bo) (IPCC 1997) Animal
Bo m3 CH4/ kg VS
Pigs
0.45
Dairy cattle
0.24
Non-dairy cattle
0.17
Table 94 – Measured maximum methane-producing capacity of the manure (Bo) (Vedrenne et al. 2008) Bo
Slurry Min m3 CH4/ kg VS
Max m3 CH4/ kg VS
0.244 0.204
0.343 0.296
Swine Dairy cattle
Table 95 – Maximum CH4-Producing Capacity for U.S. Livestock Manure (EPA 1999) Animal Type Beef
Dairy
Swine
Diet 7% corn silage, 87.6% corn Corn-based high energy 91.5% corn silage, 0% corn
Converted Bo (m3CH4/kgVS) 0.29 0.33 0.17 0.23
References cited Hashimoto et al. (1981) Hashimoto et al. (1981) Hashimoto et al. (1981) Hill (1984)
0.33 0.24 0.17 0.14
Chen, et al. (1980) Morris (1976) Bryant et al. (1976) Hill (1984)
Corn-based high energy Corn-based high energy Corn-based high energy Corn-based high energy
0.10 0.36 0.48 0.32 0.52 0.48 0.47 0.44
Chen, et al. (1988) Summers & Bousfield (1980) Hashimoto (1984) Hill (1984) Kroeker et al. (1984) Stevens & Schulte (1979) Chen (1983) Lannotti et al. (1979)
Corn-based high energy
0.45
Fischer et al. (1975)
58-68% silage 72% roughage Roughage, poor quality Barley-based ration Corn-based high energy
Cragg et al. (2008) found methane production based on organic loading for a piggery with a covered anaerobic lagoon in New Zealand to be 0.279m3 CH4/kg VS added. Very little research appears to be done on research on variation in methane conversion factors (MCF) for piggery manure. Zeeman and Gerbans (n.d.) found with low hydraulic retention times, sludge retention times also decrease, resulting in a lower MCF factor as compared to the factor of 90 percent as proposed by the IPCC Guidelines, especially when low temperature conditions prevail for pig manure. They reported a MCF value of only 50 percent, when a temperature of 10-15oC and a storage time (hydraulic and sludge) as long as 360 days prevails, with a value of 70% used in their modelling.
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For the sensitivity analysis methane potential (Bo) values were modelled between a low of 0.244 and a high of 0.52 m3 CH4/ kg VS produced. Methane conversion factors (MCF) values were modeled between a low of 70% and a high of 90% – the default value.
Enteric Methane Emissions Pigs are a non-ruminant animal and convert a smaller proportion of feed energy intake into methane than do ruminants (DCC 2007a). DCC (2007a) provides an equation for the estimation of enteric methane (ME) from pigs, which has been used in the LCI assessment: ME = I x 18.6 x 0.007 / F
Equation 9
Where: I = intake (kg of dry matter) F = 55.22 MJ/kg CH4 18.6 = MJ GE/kg feed dry matter
Mass Balance Model – Nitrogen
The initial step required to develop the mass balance is to collect background data related to the inputs and outputs indicated in Figure 15. The following sections elaborate on the data collection requirements for each piggery. Incoming Pigs Both piggery supply chains operate a closed production system with no importation of pigs, all genetics are sourced as semen and have not been accounted for in the LCA. Within the supply chain there is a net transfer of pigs relevant to the estimation of some of the mid-point functional units (i.e. the 1 t pig growth (6 to 21.3 wks) and these have been accounted for where relevant.
Incoming Feed Incoming feed is the primary source of nitrogen entering the piggeries. Nitrogen within the feed is determined through assessment of dietary components (crude protein) and total quantities of feed brought on-farm. These data were supplied by the feedmills and piggery nutritionists. Outgoing Pigs and Mortalities The number of pigs leaving each piggery per year was calculated from herd performance modelling and data supplied by the piggeries. From these data, the total nitrogen leaving the farm in live pigs can be calculated using Equation 1: PNout = Pout x Wout x CarcN where
Equation 1
PNout
= Mass of Nitrogen taken out of the piggery with removed pigs
Pout
= Number of pigs taken out of the piggery
103
Wout
= Weight of pigs taken out of the piggery
CarcN = Nitrogen content of pig carcasses (2.56%)
The number of mortalities, by class, was determined from herd performance and data supplied by the piggeries. Based on the number of mortalities and the estimated weight of carcasses from each class of pig, the total mass of mortalities removed from sheds during the 12 month period can be calculated. From these data, the total nitrogen leaving the farm in pig mortalities can be calculated using Equation 2: PNmort = Wmort x CarcN where:
Equation 2
PNmort = Mass of Nitrogen taken out of the piggery with removed pigs Wmort = Weight of pigs taken out of the piggery CarcN = Nitrogen content of pig carcasses (2.56%)
Excretion Based on the nitrogen intake (as feed) and nitrogen removal as mortalities and live pigs, the total nitrogen excreted from the pigs can be estimated. This is an important mid step in the calculation of a nitrogen balance. Loss estimates after excretion rely on literature values and validation using known concentrations of pond nitrogen (where available).
Effluent to Manure Treatment/Management System Significant nitrogen losses by ammonia volatilisation inside a piggery shed are likely as ammonia is rapidly released from piggery effluent in the shed when urinary urea is hydrolysed by faecal microbial ureases and is volatilised to the atmosphere (Panetta et al 2006). Vanderholm (1975) reported that up to 50% or more of the nitrogen in fresh manure may be in the ammonia form or may be converted to the ammonia form very soon after excretion. Values for nitrogen loss through ammonia volatilisation in pig sheds is limited because of incomplete documentation of the total amount defecated, the severe restrictions of sample storage and analysis and the variability of field-testing (Overcash et al. 1983). Values vary greatly depending upon the effluent collection system (flushing or static pits), and other factors such as pH, temperature and litter moisture content (Elliott and Collins 1982). Overcash et al. (1983) showed that the ammonia (NH3-N) fraction of Total Kjeldahl Nitrogen (TKN) in piggery effluent varied considerably. They adopted an average value of 0.50 as the ratio of NH3-N/TKN. Thus a considerable proportion of the total-N is available for loss immediately after excretion by pigs. TKN comprises the organic plus the ammonium nitrogen content. It is a more useful nitrogen measure compared to ammonia because even when nitrogen converts between the ammonia and organic forms the TKN remains constant (Overcash et al. 1983). In a study of room temperature storage losses, Moore et al. (1983) found that TKN losses from manure pits were less than 10% after four weeks. A number of studies have been conducted in Europe on predicting the amount of nitrogen lost in the shed after excretion. Groot Koerkamp et al. (1998) found ammonia emissions of 7200 mg/d per pig. This translates to between 2.0 kg ammonia-N per pig per year emission from fully slatted finisher pig sheds in Holland. Pedersen et al. (2004) found ammonia emissions ranging between 7100 and 20000 mg/d per pig. This translates to between 2.0 and 5.7 kg ammonia-N per year emission from fully slatted fattening sheds in Europe. However, these European studies are likely to overestimate nitrogen loss in Australian piggery sheds, as the manure is often held in deep pits for long periods (over winter) in Europe.
104
Of more relevance is a study by Harper et al. (2004b) which measured ammonia emissions of 7.3% of feed input in a farrow to finish flushing system in the southeastern United States. Using mass balance measurements of inputs and outputs from a section of a conventional, daily flush finisher shed, McGahan et al. (2001) predicted shed losses of approximately 10% of excreted nitrogen. Table 96 – Nitrogen Losses from Conventional Piggery Sheds as reported in the Literature Country Reference
Units
Reported losses
Estimated Nitrogen lost as a % of N feda
Losses as a % of excreted N
Moore et al. (1983)
-
TKN
< 10%
-
