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mlaM E AT & L I V E S TO C K A U S T R A L I A

Sugar

A Literature Review of Life Cycle Assessment in Agriculture

RIRDC Publication No. 09/029

RIRDCInnovation for rural Australia

A Literature Review of Life Cycle Assessment

in Agriculture

By Dr Steve Harris and Venky Narayanaswamy

March 2009

RIRDC Publication No 09/029 RIRDC Project No PRJ-002940

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2009 Rural Industries Research and Development Corporation. All rights reserved.

ISBN 1 74151 833 4 ISSN 1440-6845

A Literature Review of Life Cycle Assessment in Agriculture Publication No. 09/029 Project No. PRJ-002940

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.

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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

Steve Harris URS Level 3, 20 Terrace Road East Perth WA 6004 Australia

Phone: 08 9326 0224 Fax: 08 9326 0296 Email: [email protected]

Venky Narayanaswamy URS Level 3, 20 Terrace Road East Perth WA 6004 Australia

Phone: 08 9326 0209 Fax: 08 9326 0296 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: 02 6271 4100 Fax: 02 6271 4199 Email: [email protected]. Web: http://www.rirdc.gov.au

Printing by Union Offset Printing, Canberra Electronically published by RIRDC in March 2009

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Foreword Rural primary industries use water resources and energy, and emit greenhouse gases. Future Australian and international policy directions may require industries to account for their resource use and emissions. Life Cycle Assessment (LCA) is a method to analyse resource issues across the life cycle of a product. It can systematically identify key areas to improve environmental and economic performance, and can be applied to agricultural systems. A standardised LCA methodology for primary industries will help practitioners undertake LCA studies and greatly increase their value by providing results that are comparable between sectors and industries.

The reviewed literature was diverse in its goals, methodologies and coverage of agricultural issues. However, general consensus can be drawn in most areas of LCA. The lessons gathered by this review provide the foundation for the next stage of the project to develop a standard LCA methodology.

Transparency in the reporting of LCA studies, and the sharing of data and lessons, should be encouraged amongst primary industries in Australia.

The project was funded by RIRDC, Cotton Research and Development Corporation, Dairy Australia, Sugar Research and Development Corporation, Australian Pork Limited, the Australian, RIRDC Chicken Meat program and Meat and Livestock Australia.

This report, an addition to RIRDCs diverse range of over 1800 research publications, forms part of our Global Competitiveness R&D program, which aims to identify the impediments to the development of a globally competitive Australian agricultural sector and supports research investments on options and strategies for removing these impediments.

Most of our publications are available for viewing, downloading or purchasing online through our website: www.rirdc.gov.au.

Peter OBrien Managing Director Rural Industries Research and Development Corporation

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Acknowledgments We would like to thank and acknowledge the input of Steve Wiedemann (FSA Consulting) and Simon Winter (RIRDC). Thanks also to our colleagues at URS for their support and contributions in discussions.

This research project has been funded by RIRDC, Cotton Research and Development Corporation, Dairy Australia, Sugar Research and Development Corporation, Australian Pork Limited, the Australian, RIRDC Chicken Meat program and Meat and Livestock Australia.

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Abbreviations FPCM fat and protein corrected milk

FU functional unit

GHG greenhouse gas

LCA life cycle assessment

LCI life cycle inventory

LCIA life cycle impact assessment

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Contents

Foreword ............................................................................................................................................... iii

Acknowledgments................................................................................................................................. iv

Abbreviations......................................................................................................................................... v

Executive Summary............................................................................................................................viii

1. Introduction ....................................................................................................................................... 1 1.1 Objectives and Methodology ..................................................................................................... 1 1.2 LCA Principles........................................................................................................................... 2 1.3 Agricultural LCA ....................................................................................................................... 2

2. Review of LCA Literature ................................................................................................................ 3 2.1 Introduction................................................................................................................................ 3 2.2 Goal and purpose of agricultural LCA........................................................................................ 3 2.3 LCA system boundary ................................................................................................................ 5 2.4 Functional unit ............................................................................................................................ 6 2.5 Allocation methods for co-products............................................................................................ 7 2.6 Foreground and background data sources................................................................................... 8 2.7 Data quality and variability......................................................................................................... 9 2.8 LCA software............................................................................................................................ 10 2.9 Life cycle impact assessment method and impact categories ................................................... 10 2.10 LCA Evaluation ...................................................................................................................... 12 2.11 Compliance with international LCA standards ....................................................................... 12 2.12 Key findings and conclusions of the LCA studies. ................................................................. 12

3. Summary and next steps ................................................................................................................. 14 3.1 Summary and recommendations ............................................................................................... 14 3.2 Next steps.................................................................................................................................. 18

References ............................................................................................................................................ 23

Appendix 1 ........................................................................................................................................... 29 Minutes of Meeting with FSA Consulting 14/03/08....................................................................... 29

Appendix 2 ........................................................................................................................................... 31 Example of allocation avoidance by expanding the boundaries for comparison of systems with different outputs ...................................................................................................................... 31

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Tables Table 3-1 Summary of findings and consensus points of LCA studies, and comments .................. 19

Figures Figure 5-1 Example of material recycling and energy recovery ...................................................... 31 Figure 5-2 Example of an expansion of the system boundaries ....................................................... 32

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Executive Summary What the report is about

This document is the first part of a research project to develop a life cycle assessment (LCA) methodology for Australian primary industries. It presents the findings of a literature review that focuses on LCA agricultural literature relevant to the pork, poultry, cotton, sugar, red meat and livestock sectors.

Who is the report targeted at?

The report is aimed at the Australian primary industries.

Background

In general, all primary industries use energy and water resources and emit greenhouse gases. Future requirements (either Australian and/or internationally) may require agricultural industries to account for these uses and emissions. LCA is a method to report on and analyse these resource issues across the life cycle of agricultural products.

Australian primary industries have had mixed success individually developing and applying LCA. A standard methodology would focus resources and allow sharing of lessons between primary industries.

Aims/objectives

The literature review seeks to bring together lessons from Australian and international LCA studies in order to provide a foundation for the development of a standard methodology.

Methods used

Information and literature on agricultural LCA studies was gathered from the public domain, including international journals, the internet and industry reports.

Results/key findings and Recommendations

The literature was diverse in its goals, methodologies and coverage of agricultural issues. This diversity meant that in some aspects of LCA there was limited similarity in the coverage and therefore it was difficult to draw lessons in some areas. However, some general consensus can be drawn in most areas of LCA. Clusters of studies predominately exist in the sectors of pig production, grains, sugar and dairy. A summary of the findings and areas for discussion for each of the main categories of LCA are:

1) Goal and purpose of agricultural LCA

LCA studies have had a wide variety of goals and purposes but generally compared the environmental impact of farming practices or types of feed (e.g. for pigs).The intended future goal(s) of the LCAs for the Australian rural industries need to be established - to compare agricultural practices, feed choices or identify environmental improvements or help with reporting. For example, future requirements such as National Greenhouse and Energy Reporting (NGER) or similar. Public disclosure of LCA for comparability or marketing purposes would require a critical review according to ISO14040.

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2) LCA system boundary

The system boundary will largely depend on the goal of the study, for example is it for environmental improvement of the farm or the whole supply chain to consumer. This will also determine whether to include factors such as emissions associated with the production of medicines, insecticides, machines, buildings and roads.

3) Functional unit

The functional unit (FU) is dependent on the goal of the study and the system boundary, and are generally chosen to reflect the way each commodity is traded. The FU is typically one kilogram of product, or one hectare of land used. Consensus is needed on the functional unit for livestock e.g. one kilogram of bone free leaving the gate, one kilogram of live weight leaving the farm gate, or one tonne of carcass dead weight. The choice can help avoid allocation (see below).

4) Allocation methods for co-products

Economic allocation has in the past been utilised but studies of beef and dairy products have shown this to increase uncertainty. The order of preference is system expansion, physical relationships/causality, composition, and finally economic value. Allocation was not needed in one beef study because the choice of functional unit (live weight) and system boundary (birth to farm gate) meant by-products occurred outside the farm gate (by-products occur post-processing).

5) Foreground and background data sources

Foreground data refers to data that should be collected from the primary processes. This data needs to be collected directly, for accuracy, from the primary sources including input processes, farm processes, and production processing. Background data refers to data from secondary sources such as mining and extraction or grain production. Aspects such as transport, fertiliser, etc, may also be background but data should be collected for the main foreground processes. On-farm manure management is likely to be foreground and needs further consideration. Industry input is needed on whether to include (and if data exists or can be obtained) processes such as medicines, insecticides, machines, buildings and roads.

6) Data quality and variability

Uncertainty is rarely considered in LCA studies and even less so in LCAs of agricultural products. This hampers the ability to interpret the usefulness and legitimacy of results. Australian agricultural LCAs should therefore maintain transparency, and the data quality should comply with ISO14040 standards. The LCA's should include a description of data quality so that the audience can understand the reliability of a study results and properly interpret the outcome. LCA should perform uncertainty analysis to quantify the uncertainty of the results due to cumulative effects of model imprecision, input uncertainty and data variability.

Data variability across different years needs to be carefully considered, single year data may not be representative and single or rare events should be excluded. Therefore, consideration needs to be given as to whether the LCA utilises single year data or takes an average, or (for example) compares a drought year with a normal year.

7) LCA software

It is suggested that software specifically designed for LCA, is utilised to perform agricultural LCA, as this would help with tracking changes and updating data. LCA data management is generally too tedious and cumbersome for a spreadsheet model to be utilised. Therefore, the use of software is recommended as a necessary criterion to ensure robustness, uncertainty analysis, and comprehensive coverage of processes and data volumes in LCA modelling.

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8) Life cycle impact assessment method and impact categories

Industry may need to consider/ prepare going beyond water, GHG and energy issues to meet future requirements or comparisons with other countries. Eutrophication is widely noted as one of the major environmental impacts of agriculture, and has GHG implications, so its inclusion or exclusion needs to be considered carefully.

Toxicity has prominent consideration in Europe, and so if comparisons with other countries are likely this may need to be included. LCA involving water requires careful consideration on what types of water to include, e.g. surface, treated surface, groundwater, scheme water. Should infrastructure be included, in order to recognise the benefits of rainwater capture and water reuse? Energy can be measured per kilogram of product or per hectare. GHG can be measured per kilogram of product or per hectare. The greatest uncertainty is often noted as nitrous oxide emissions from crops.

9) LCA Evaluation

The vast majority of studies were contribution and hotspot analysis that compared various aspects of agriculture such as feed choice or farming practice. Contribution and comparative (e.g. comparing farming systems or feed choice) analysis will be appropriate for water, energy and GHG.

10) Compliance with international LCA standards

The study should comply with ISO14040, in order to be recognised as robust, and be comparable with other studies.

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1. Introduction This document is the first part of a research project to develop a life cycle assessment (LCA) methodology for Australian primary industries. URS was commissioned by the Rural Industries Research and Development Corporation (RIRDC) to undertake this task for a partnership involving Cotton Research and Development Corporation, Sugar Research and Development Corporation, Australian Pork Limited, the Australian Chicken Meat Federation, Meat and Livestock Australia and RIRDC. The first stage involves this literature review that focuses on LCA agricultural literature, in order to synthesise findings and consensus points.

1.1 Objectives and Methodology

This literature review focuses on agricultural LCA literature that deal specifically with meat and livestock, pigs, poultry, grains, cotton, and sugarcane production. Peer reviewed articles from international journals and publicly available agricultural LCA reports developed by rural industry bodies have been analysed to synthesise key consensus and divergent points on the following:

Goal and purpose of agricultural LCAs; LCA system boundary; Functional units; Allocation methods for co-products; Foreground and background data sources; Data quality and assessment; Data variability issues at the farm gate and at the industry sector level; LCA computations (what type of software and how LCA computations have been made using

spreadsheets or proprietary softwares such as Simapro or Gabi);

Life cycle impact assessment method and impact categories; Life cycle evaluation techniques, i.e. contribution analysis, perturbation analysis, significance

analysis, hotspot analysis, variability analysis;

Compliance with international LCA standards; and The key findings and conclusions of the LCA studies.

The key findings are summarised and recommendations are made on each of the above aspects. Areas are identified where input from the agricultural sectors are needed in order to reach a solid consensus and provide a solid foundation for development of the LCA methodology.

An initial discussion meeting was held with FSA Consulting on 14 March 2008 to discuss the project objectives and methods. FSA Consulting is providing expertise on agricultural production processes for this project. The broad content and conclusions from this discussion are presented as minutes in Appendix 1.

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1.2 LCA Principles

LCA was traditionally applied to analyse industrial production systems [59], but has been adapted within the last 15 years to assess the environmental affects of agriculture. The method analyses all inputs and outputs that cross a specified system boundary for a certain production system. The reference unit, that denotes the useful output, is known as the functional unit, and has a defined quantity and quality, for example 1 tonne of bread making wheat.

Agricultural LCA is often complex because in addition to the main product there are usually co-products, so that appropriate environmental impacts need to be assigned to each product, a process known as allocation. There may also be by-products or waste and emissions to the environment, for example nitrate (NO3) to water and nitrous oxide (N2O) to the air.

In LCA, all inputs are traced back to primary resources, for example electricity is generated from primary fuels like coal, oil and uranium [55]. Fertilisers that are based on ammonium use methane as a feedstock and source of energy. Other fertilisers, such as phosphate and potassium, also require energy for extraction from the ground, processing, packing and delivery. Machinery, including tractors and processing equipment, require steel, plastic, and other materials for their manufacture. This involves energy costs in addition to the direct diesel use.

1.3 Agricultural LCA

Agriculture does not consume resources in a linear sense, as for example many industrial processes and is not therefore a pure cradle-to-grave process [19]. Many central agricultural production resources, such as soil, fertility, seeds, cattle and manure are self produced:

For agricultural application, the term 'Life Cycle Assessment' could be misleading because the main agricultural life cycle, in mainstream mixed farming systems, is taking place within a farm and based on renewable resources using, enhancing and ensuring nature's processes. The term 'eco-balance' used for LCA in French or German is regarded to fit more accurately [19].

Agriculture has several other differences (complexities) from LCA of industrial processes. The principal feature is that agriculture utilises land and soil. The balances of soil nutrients such as nitrogen (N), phosphorus (P) and potassium (K), through fertiliser application and plant uptake, need careful consideration. Estimating long term balances requires the use of simulation modelling, which must be adapted to the local context, to take into account variations in soil texture, rainfall and altitude.

Many agricultural systems are interlinked and therefore changes to one system, for example arable crops used for animal feed, will have knock-on effects to other systems, i.e. the animal systems. Further complications occur with systems involving sheep meat and beef, because beef is partly derived from the dairy sector. Hence, there can be difficulties assigning environmental impacts between the beef and milk components. In addition, the animals may be reared in geographically diverse areas incorporating lowlands as well as highlands. Other complications exist through the various agricultural sectors and will be discussed in subsequent sections.

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2. Review of LCA Literature

2.1 Introduction

Over 70 articles, reports and journal papers were identified and reviewed as part of this literature review. The application of LCA, particularly for agriculture is still developing but has progressed significantly over the past five years to achieve a common understanding of the goals, structure, challenges, and procedural issues. This is demonstrated by ISO 14040: 2006 and ISO 14044: 2006 [1], [22], [23].

The majority of the most prominent LCA literature, especially with regard to agricultural production systems, is of European origin. They can generally be classified as having system boundaries defined as cradle-to-gate type and comparative LCA, in that they compare agricultural systems [61] (quoted in [57]). The target groups of LCAs are academia, LCA practitioners, food industries, regulators and consumers. Discussions on data quality, data variability at the farm gate, LCA computational tools used, and the degree of compliance with international standards were largely missing in the literature. The Swedish LCAs predominantly illustrate LCA as a tool to map technological advancement and operational practices to improve eco-efficiency performance of food supply chains. A similar such approach could be seen in an introductory broad-acre grains LCA paper [4], which illustrates the linkage between eco-efficiency (doing more with less) and LCA.

2.2 Goal and purpose of agricultural LCA

A wide variety of goals and purposes were found to exist across the literature.

2.2.1 Livestock and dairy

There are limited studies involving the analysis of beef meat as a product, with the majority of livestock reviews examining dairy products. However, Casey and Holden [62], examined Irish suckler beef units, comparing greenhouse gas (GHG) emissions of conventional, Irish agri-environmental scheme, and organic farms. They examined the kilogram of carbon dioxide equivalent (kgCO2-e) per kg of live weight (LW) leaving the farm gate per annum and also per hectare (per year) [62]. Hospido [16] sought to quantify the environmental impact of mastitis incidence, which is recognised as having a significant financial impact. Environmental impact was also significant due to discarded milk, the need for increased feed for calves and the affects on meat supply. Acidification, eutrophication and global warming were found to be the most significant impact categories.

A major study of the Australian dairy industry, commissioned by Dairy Australia, studied the environmental impacts of the dairy supply chain so that dairy companies can strategically focus their future efforts to improve environmental performance [79]. This study used the functional units of 1 tonne of product and on kilolitre of raw milk, and included the major dairy products such as milk, cheese, whey products milk powder, butter, yogurt and ice cream.

For the milk sector a prominent focus is on the comparison between organic and conventional milk production systems. Thomassen et al [12], compared the environmental impact of conventional and organic milk production systems, to identify hotspots in the conventional and organic milk production chains.

A study in New Zealand produced an updated reference for the eco-efficiency of New Zealand dairy farm systems in order to analyse the implications of intensification on their eco-efficiency [10]. The eco-efficiency in terms of milk production and land use was compared using Life Cycle Assessment

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(LCA) methodology [10]. Another Irish study focussed specifically on GHG emissions, defining the Irish dairy unit and assessing three scenarios being considered to reduce Irish GHG emissions [14]:

1) improved output from cows, requiring fewer animals to supply the national milk quota;

2) a slaughter scheme to remove as many non-milk producing animals from the system as possible and;

3) integration of scenarios 1 and 2.

The study used economic allocation to distinguish between the meat and dairy components. In a Swedish study, Cederberg and Mattson [13] compared conventional and organic milk production for a range of environmental impacts. This data was later used in a further study to compare different methods of handling allocation between the co-products milk, meat and surplus calves [9]. The study utilised system expansion to avoid allocation.

A German study assessed the environmental impacts of 18 grassland farms using three different farming intensity methods (intensive, extensive and organic) [19, 66]. The objective was to utilise LCA to assess impacts on a whole farm basis in order to compare different production systems. It compared impact categories such as energy and global warming potential, both per hectare and per tonne of milk.

2.2.2 Pig production

Several papers examined different aspects of feed for pigs such as:

the environmental impacts of the production and delivery of one kg of concentrated feed for finishing pigs [37];

the impact of feed choice on environmental performance pig production [34]; and the environmental impact reduction from enzyme supplemented feed [36]. Basset-Mens and van der Werf [64] evaluated and compared three different farming methods:

Good Agricultural Practice, organic and a French quality label called red label. Another study looked the implications of data variability in pig production [35].

2.2.3 Poultry

Only one study was found that addressed LCA for poultry (other literature was identified regarding eco-efficiency but did not address LCA per se), which compared the environmental impacts of poultry production with wild caught cod and farmed salmon. The analysis was partly quantitative and qualitative and focussed on energy use, antifouling and land use impacts.

2.2.4 Cotton /textiles

There were limited studies of significance for LCA of cotton and textiles. Woolridge et al. [52] used LCA to examine the energy difference between virgin textiles and recycled (donated at charity shop) textiles. Other studies examined the environmental impact from the washing frequency of cotton towels [53] and methodological problems and solutions for textile products [63].

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2.2.5 Sugar and Crops

In an Australian study, Narayanaswamy et al. [21,22] used LCA to compare life cycles of three grains, wheat, barley and canola. The entire life-cycle, cradle-to-consumption was considered as opposed to the majority of LCA studies that have only considered cradle-to-farm gate.

There has been a significant body of Australian research on various aspects of sugar cane production [75-78]. This has included:

a study of sugarcane production including growing, harvesting etc, through to raw sugar production;

a comparison of organic and conventional grown sugarcane; and a comparison of Australian sugarcane with US corn and UK sugar beet producers of sugars for

fermentation.

Renouf [77] notes that many of the dominant environmental impacts from cropping systems result from dynamic systems within agricultural soils. The use of agricultural modelling techniques to model these processes is noted as beneficial to LCAs cause.

Other research on sugar includes: environmental impacts of sugar production in Mauritius [46]; comparison of environmental benefits from bagasse-derived electricity and fuel ethanol [48]; and a comparison of sugar and corn for ethanol derived energy [47].

Water has received little attention in the LCA literature with the exception of Heuvelmans et al, who propose an indicator to integrate water quantity impacts of agricultural and silvicultural production with existing LCA methods [56].

2.2.6 Others

A major study in the UK aimed to quantify the resource use and environmental burdens arising from the production of ten key commodities and developed models that enable resource use and emissions arising from various production options in England and Wales to be examined in detail [55]. The commodities examined are: bread wheat, potatoes, oilseed rape, tomatoes, beef, pig meat, sheep meat, poultry meat, milk and eggs.

2.3 LCA system boundary

For agricultural LCAs the majority of studies used cradle-to-farm gate as the system boundary. For instance, Cederberg et al. [9] included production of all input goods to the farming system and all outflow emissions from the system. The geographic border is the farm gate.

In several studies, emissions associated with the production of medicines, insecticides, machines, buildings and roads are excluded because of lack of data [9, 13, 14]. Direct N2O emissions from cattle were also excluded as these are known to be negligible [14]. In addition, CO2 from enteric fermentation is generally regarded as recycling from plant matter, and thus makes no net addition to the atmosphere [14].

Depending on the goal of the study various inclusions and exclusions are observed. One study on the environmental impact of pig food included processes at a mixed arable-livestock farm, where both pig fattening and production of feed ingredients took place [34]. Production of soybean meal, rapeseed meal and synthetic amino acids was also included in the study, as was upstream production of mineral fertiliser, seed, diesel, fuel oil and electricity. Production of other feed additives such as premix, mono-calcium-phosphate and salt was not studied, since these products were added to the same extent (3.5%)

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in all scenarios, and were assumed to not markedly influence the final result. The focus was on feed choice, and processes not related to this were omitted e.g. production of buildings, machinery, infrastructure and veterinary medicines [34]. Others omitted the capital equipment and capital cost [46], whereas a study that sought to quantify impacts associated with the production and on-farm delivery of concentrated pig feed, only dealt with the processes up to and including the delivery of feed to the farm [37].

Haas et al. [19] focussed on the farm level and a purely agricultural LCA as opposed to an LCA of food products involving agricultural and food production processes. Input industry was only considered for energy and mineral fertiliser production, whereas the output industry (dairy) was not part of the assessment. The study compared three different farming methods (i.e. intensive, extensive and organic) and the primary energy needed for constructing farm buildings, stable and machinery was not included because no differences between farms and the farming intensity was expected. Single or rare events were excluded and considerations were restricted to the year of study (1997) [19].

2.4 Functional unit

The definition of the functional unit (FU) is described in ISO14040 [23]: LCA is a relative approach, which is structured around a functional unit. This functional unit defines what is being studied. All subsequent analyses are then relative to that functional unit, as all inputs and outputs in the LCI and consequently the LCIA profile are related to the functional unit.

The majority of studies use the functional unit 1 kg or tonne of product, but this is often combined or compared with the functional unit of 1 hectare. The functional unit can be used purely as a defined quantity such as 1 kg or may be corrected based on an attribute of the product or process. For instance, in studies of the milk production system the functional unit has been:

one kg of energy corrected milk (ECM) leaving the farm-gate [9] (the same study extended the system and used one kg of bone-free meat leaving the farm-gate for the beef production system);

the amount of milk produced and sold to the dairy per year by a standard Galician herd [16]; or 1 kg (or 1000 kg) of fat and protein corrected milk (FPCM), so that New Zealand production could

be compared to European studies [10] (one kg of average NZ milk corresponded to 1.09 kg FPCM).

Casey and Holden [14] defined the FU as: the production of 1 kg of energy corrected milk (ECM) over a time frame of one year. This FU encompasses the main product and the natural cycle, driven by lactation periods in one year. The authors argued that as a mass or volume measure the FU is appropriate for GHG emissions because it is applicable on a global scale.

Other studies simply used weight of product as the functional unit, for example: 1 kg of sugar exported from the island [46]; 1 average kg pig growth between 29 kg to 115 kg [34]; quantity of wheat containing 102kg protein to account for the differences in protein in a tonne of wheat from differing systems [59] (although the authors noted there could be problems with this definition if the downstream implications of blending grain with higher protein, are considered).

In a study that compared different agricultural practices, Bassett-Mens and van der Werf [64] noted that the results of LCA, in this case particularly organic farming, was very dependent on the choice of the FU. For a kilogram of pig, eutrophication and acidification were similar for organic and Good Agricultural Practice, whilst when expressed per ha, organic had less eutrophication and acidification than Good Agricultural Practice [64].

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2.5 Allocation methods for co-products

Allocation is described as partitioning the input or output flows of a process or a product system between the product system under study and one or more other product systems [23]. It is a complex issue, and particularly relevant in agricultural production because agricultural systems consist of closely inter-linking sub-systems of activities [59]. The International Organisation of Standardisation (ISO) [23] suggests that the following steps should be followed for allocation:

Step 1: Wherever possible, allocation should be avoided by:

a. dividing the unit process to be allocated into two or more sub-processes.

b. expanding the product system to include the additional functions related to the co-products.

Step 2: where allocation cannot be avoided, the inputs and outputs of the system should be partitioned between its different products or functions in a way which reflects the underlying physical relationships between them; i.e. they shall reflect the way in which the inputs and outputs are changed by quantitative changes in the products or functions delivered by the system.

Step 3: where physical relationship alone cannot be established or used as the basis for allocation, the inputs should be allocated between the products and functions in a way which reflects other relationships between them.

In a Swedish LCA study of milk, Cederberg et al. [9] avoided allocation by expanding the system boundary to include beef (see Appendix 2 for example of system expansion). Data was collected from a separate beef producing case study involving a beef cow that produces one calf per annum to be raised for one and a half years. The LCA of this beef production was included in the milk system. The comparison was based on a Swedish LCA case study of milk production where economic allocation between milk and meat was initially used.

Four different ways of handling the co-products surplus calves and meat from the milk system were analysed and examined [9]:

1) No allocation. This means that the product milk takes the whole environmental burden of the production system.

2) Economic allocation. This was based on average calculations from the Swedish Dairy Association of the yearly income per dairy cow in which the income of the products is divided as 92% for milk, 6% for meat from the culled cow and 2% for the surplus calf.

3) Cause-effect physical ('biological') allocation. The base for the so-called 'biological' allocation is the fact that there is a causal relationship between the dairy cow's feed mix and its production of milk, calves and meat. Calculation according to Swedish fodder tables for the supply of energy and protein to cover the dairy cow's milk production, maintenance and pregnancy give an overall allocation of 85% to the product milk and 15% to the meat and surplus calves. This division was based on the official Swedish feeding recommendations as to what proportion of the dairy cow's feed intake is needed for milk production.

4) System expansion. Allocation is avoided by expanding the milk system to include the alternative way of producing the co-products from milk production. The alternative way of producing calves for meat production is by beef cows producing one yearly calf and the alternative way of producing meat from the culled dairy cows is a beef production system.

The study showed that economic allocation between milk and beef actually favoured the position of the beef product. However, system expansion highlighted the environmental benefits of co-producing

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milk with its co-products of surplus calves and meat. This is because beef production in combination with milk can be carried out with fewer animals than in sole beef production systems.

Hospido [16] also avoided allocation through a system expansion to include milk, meat, skin and manure. Cederberg and Mattsson [13] allocated 15% of the impacts to the meat production on the basis of a biological causality, whilst Thomassen et al. [12] used an economic allocation between milk, meat and exported crops from the farm of 91%, 8.2% and 0.8%, respectively (quoted in [10]). In an Irish milk study, Casey et al. [14] compared three methods of allocation (no allocation, mass and economic allocation) and could not attempt systems expansion due to insufficient data.

In a comparative study of environmental benefits for sugar producers to improve energy efficiency through green energy, co-generation or fuel ethanol, Botha and von Blottnitz [48] avoided allocation by expanding the system to include augmented production of equivalent amounts of each product by the conventional route electricity from coal, gasoline from petroleum. The draft British greenhouse life cycle assessment standard stipulates economic value allocation method for allocating environmental burdens across co-products [72].

2.6 Foreground and background data sources

In general, existing data from literature and databases was utilised where deemed satisfactory, but where data was missing, surveys, onsite collection and consultation with experts (telephone conversations and meetings) were used. Foreground data was collected from farms for many of the dairy and livestock studies.

For a Swedish study, Cederberg and Mattson [13] collected data from two relatively large farms that were chosen because of the very high quality production data available (e.g. all fodder is weighed). They were specialised milk producers, meaning there are no co-products except meat from the farms. Two were chosen because there are no Swedish statistics available on average energy use and land use in different milk production systems. Hospido [15] collected real data from two farms and two fodder factories over two years. Other data was obtained from companies from other regions e.g. Tetra data obtained from literature.

Chen [67] utilised the Australian LCA Data Inventory which was initially generated in 1998 and updated in 2002. Narayanaswamy et al. [21] gathered foreground data from: suppliers, growers, transporters, processors, and consumers in Western Australia. Crop cultivation, crop storage, and end product production were also studied in local farms and companies. Background processes were those from which published or available input-output data were obtained from literature sources, electricity suppliers, transport companies, landfill facilities WA households and packaging.

A few studies involving cotton LCA used only literature [54, 53]. An extensive LCA study in the UK that examined 10 commodities including crops as well as livestock, gained much of the data from established inventories and factors [55]. Some values were described by constants but others needed to be described by functional relationships. For example, yields in response to N in synthetic fertiliser or manure; leaching from soil in response to N application rate, crop yield, soil type, and rainfall; milk yield and nutrients in diet [55].

In some cases data has been assumed, e.g. Basset and van der Werf [64] in a study on pig production, assumed distances between feed factory and farm. In others the lack of data has resulted in the exclusion of those emissions from the study, e.g, the production of medicines, insecticides, machines, buildings and roads [13, 14]. Machinery was excluded due to lack of data on machinery used in crop production for concentrate feed outside Sweden [13]. Data is also excluded where there are similarities between the factors being compared e.g. stables and farm buildings [13]. Hospido and Sonesson [16] excluded housing because data on certain aspects were unavailable and recent research had shown that there were only small differences in environmental impact of housing.

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Dahllof [63] developed a case study to compare three different fabrics for a sofa: cotton, wool with 15% polyamide, and Trevira CS. One of the key problems identified was to find inventory data in general for cotton cultivation. Irrigation water use varies greatly, as does the degree of sustainability of water withdrawal [63].

Specific software may also be utilised to calculate emissions, such as Basset-Mens et al. [10] who used the OVERSEER nutrient budget model to estimate:

Nitrogen excreted by cows The on-farm area used to apply the farm dairy effluent (FDE) lime maintenance requirement for the three intensification scenarios Nitrogen and Phosphorus emissions to waterways

2.7 Data quality and variability

Uncertainty is rarely considered in LCA studies and even less so in LCAs of agricultural products [35,70]. However, it was the focus of two recent papers that examined pig production in France [35]. The greatest uncertainties were found to be in the eutrophication and climate change impact categories. This is due to the uncertainty associated with field emissions and contributes more to the overall uncertainty than the uncertainty associated with emissions from livestock buildings, crop yield and feed efficiency. The main sources of uncertainty were found to be in the estimation of emission factors, due both to the variability of environmental conditions and to lack of knowledge (emissions of N2O at the field level) [35].

Williams et al. [55] agreed that the greatest uncertainty is nitrous emissions: N2O emissions from land are probably the least well understood agricultural emission and thus the prominence of N2O makes calculation of the Global Warming Potential (GWP) more uncertain than from other industries (especially those with easy to measure emission outlets). They are also more uncertain than the other environmental emissions from agriculture. Williams et al. [55] utilised the Intergovernmental Panel on Climate Change (IPCC) methodology but stated that there are other methods that could be justly used, for example the DNDC simulation model (DeNitrification-DeComposition a computer simulation model of carbon and nitrogen biogeochemistry in agro-ecosystems). They anticipated that agreement between practitioners could be reached, but that there could be substantial differences between particular features.

Measurements of individual emissions may have coefficients of variation, CV, (standard deviation divided by the mean) of as much as 70% (e.g. for N2O) [55]. Williams et al. [55] predicted that the errors in UK national inventories of gaseous emissions from agriculture, are typically about 30%. In another study, the errors in a whole farm model (which included field operations; profitability; emissions of ammonia, methane, nitrous oxide and nitrate; and soil P balance) were in the range of 10% to 34%, with most the emissions at about 32% [69].

Nunez et al. [68] in a comparative study using LCA to assess the environmental performance of beef, pork and ostrich meat, noted the lack of experience in how to solve the problems that arise when this methodology is applied to the environmental evaluation of the food chains. The authors noted the importance of having valid databases, mainly related to agricultural processes.

Botha et al. [48] made assumptions where no data or literature sources were available and documented them in order to complete the investigation. The assumptions were checked for consistency (mass and energy balance, carbon balance), and refined where necessary.

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The study of Western Australian grains by Narayanaswamy et al. [21, 22] has resulted in a compilation of data, for grains for WA. Some data also exists for Queensland sugar and Victorian corn, but for other agricultural sectors and states there is a great need to develop the required data and databases to facilitate LCA.

2.8 LCA software

SimaPro was utilised in several studies [6, 49, 64, 67], although many did not discuss the LCA software used. One notable exception was Williams et al. [55] who developed their own model using Excel as a platform, to compare numerous aspects of ten United Kingdom agricultural commodities. Other software mentioned was LCA Software package TEAM [48].

2.9 Life cycle impact assessment method and impact categories

Many LCA studies examined a range of environmental impact categories, the choice of which depended on the goals of the study. Particularly common categories in the agricultural LCA literature were acidification, eutrophication, energy use, land use, pesticide use, climate change/global warming potential, abiotic resources and ozone depletion. Williams et al. [55] consolidated abiotic resources onto one scale based on relative scarcity. Land use was split into different grades of land quality/type, abiotic resource use, pesticides used (dose/ha) i.e. impacts and resources used per tonne of product.

Haas et al. [19] argue that LCA in agriculture must cover all central environmental impacts and that the suggested impact categories of classical LCA's either must be adapted or cannot be applied. For example, in their study the term 'land use' was converted to 'landscape image', 'soil function/strain' and 'biodiversity' as separate impact categories [19]. A few studies however, have focussed on specific issues, such as Ramjeawon [46] who looked at energy use and climate change from sugar cane production in Mauritius.

With regard to GHG emissions, Williams et al. [55] interestingly point out: A potential credit to the burdens arises from the consumption of considerable quantities of carbon dioxide by crops (and emission of oxygen). However, when the crops are consumed the same quantity is released to the atmosphere and conventionally it is ignored. However, if one was to consider the nations net imports and exports of carbon dioxide, due to the consumption of food by the population, then this becomes an important benefit to agriculture compared with importing the same amount of food. Similarly, the collection of energy from the sun is ignored, although it varies with crop type and yield and the zero option of no agriculture collects no energy from the sun.

Water has not widely been considered as an impact category as many of the studies are located in Europe, and as Haas [19] observes: An impact category 'water use' meaning 'water consumption' creates similar definition problems and would be only appropriate in regions with a shortage of water, which in north-west Europe is usually not the case..

In general, two input related impact categories deal with water quantity: abiotic resource depletion and land use [56]. However, they but do not include water quantity impacts like flood and drought risk. Heuvelmans et al [56] developed a new impact category 'regional water balance' to address these risks. However, they noted that a drawback would be the increasing data requirements, which could hinder the models feasibility.

As previously discussed, the LCA of agriculture is more complex than LCA of industrial processes in several areas. Impact of water use is a particularly complex issue and depends on how the system boundaries are defined in time. The traditional approach evaluates the impact on water table height, for example, as the difference between the height at planting date and at harvesting date [56]. However with crop rotation systems a negative impact during one phase might be compensated later on. For example, lowering of the water table during a heavily irrigated phase can partly be compensated by

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lower water use during a fallow period. To account for such fluctuations of the environmental impact, it has been proposed to compare biological production systems in the perspective of one crop rotation [71].

Owen [74] proposes detailed indicators for water resources based on water quantity removed and returned to the environment, and the water quality returned to the environment. Water quantity indicators proposed are [74]:

1) In-stream water use indicator that includes such uses as hydroelectric or transport use;

2) In-stream water consumption indicator such as evaporative losses from reservoirs, in excess of unrestricted river loss;

3) Off-stream water use indicator including: i) sustainable surface withdrawals that are returned to usable surface water, ii) sustainable groundwater withdrawals and returned to usable surface waters;

4) Off-stream water consumption indicator comprising of: i) evaporative and other conveyance losses for surface and groundwater, and ii) transfers to a different water basin;

5) Off-stream water depletion indicator comprising primarily of withdrawals from overdrawn non-replenished groundwater.

Owen proposed a set of flexible water quality indicators, to be used in various combinations dependent on the LCAs goal and scope [74]. The choice of indicators would need careful consideration and explanation within the individual LCA study. Owen [74] listed the following, but noted that the list is not exhaustive: eutrophication, dissolved oxygen demand, thermal, pathogenic micro-organisms, colour and turbidity, suspended solids, toxic hazards (both humans and organisms).

Virtual water flow studies of products were also reviewed [5] for key shortcomings of weighting sources of fresh water resources and the associated substitutability and opportunity costs associated with value of alternative uses for water resources. The concept of virtual water does not provide a useful indication of relative environmental harm/damage, i.e. that water resources have been extracted and used within or beyond its sustainable levels. Although virtual water flow studies are the first of their kind to explicitly account for fresh water use in products, there are doubts on the suitability of basing the performance measure on weight of products rather than consumption value. In addition, the virtual water flows are highly aggregated numbers at the macroeconomic level and therefore, data quality issues and assumptions underpinning aggregation at various levels need to be considered. The virtual water studies do not embrace a life cycle concept as stipulated by the international LCA standards [23], [24]. Despite these reported shortcomings, the virtual water studies provide a bridge between LCA and resource efficiency and effectiveness frameworks, especially in accounting for fresh water flows associated with products and services. LCA does have a provision to apply fresh water use at the material, transport, energy, and waste disposal process levels provided data quality and variability issues of the primary production systems are systematically addressed.

Halberg et al. [25] argue that impact categories linked to environmental objectives with a local or regional geographical target should be area-based, whereas impact categories with a global focus should be product-based. It is argued that the choice of impact category should be linked to the definition of the system boundaries, in the sense that area-based indicators should include emissions on the farm only, whilst product-based indicators should preferably include emissions from production of farm inputs, as well as the inputs on the actual farm [25]. Greenhouse emissions release due to land use change has been discussed with prescriptive solutions in the draft British standard for greenhouse life cycle assessment [72].

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2.10 LCA Evaluation

The vast majority of studies were contribution and hotspot analysis that compared various aspects of agriculture such as feed choice or farming practice.

2.11 Compliance with international LCA standards

The majority of the studies recognise the ISO 14041 standards. Cederberg and Stadig [9] discussed the allocation suggestions of ISO 14041, and following these standards sought to avoid allocation in the LCA of milk by system expansion to include beef [9]. For some of the documents it is difficult to assess compliance with international LCA standards as some studies have used LCA data from other studies.

2.12 Key findings and conclusions of the LCA studies.

This section summaries the general findings and conclusions of the LCA studies. In terms of GHG, analysis Casey and Holden [14] suggest that having considered the larger contributors to GHG, it is important to examine the smaller contributors for amplifying and attenuating effects. These include concentrate feed, manure management and diesel consumption. A number of issues emerged for general application to livestock systems that would not be apparent if only the larger emission sources were considered. Other important issues that arise (and are applicable to both emission accounting type inventories and LCA type studies) which need to be considered in further work are [14]:

the necessity to properly quantify the age profile and enteric fermentation of all animals in more detail than is afforded by using just two categories i.e. dairy cows and all other cattle;

the nature of manure and the timing of spreading, because the results suggest that storage of manure contributed 3% of the total GWP while spreading was

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The function of foods is difficult to define. In terms of the overall assessment, Halberg et al. [25] suggest that: The choice between product-based and area-based assessment and evaluations of production systems is a political or normative one that has implications for which environmental issues will be most important, local or global, and which function of the production system should be highlighted. The choice is especially crucial when comparing systems with very different degrees of productivity (yields per hectare) because the extensive farming systems usually have lower yields and emissions per hectare, but above average emissions per kilogram of product compared with more intensive systems (defined in terms of input use per hectare or livestock unit) [25].

Halberg et al. [25] also suggest that: Indicators linked to environmental objectives with a local or regional geographical target should be area-basedwhile indicators with a global focus should be product-based. It is argued that the choice of indicators should be linked with the definition of the system boundaries, in the sense that area-based indicators should include emissions on the farm only, whereas product-based indicators should preferably include emissions from production of farm inputs, as well as the inputs on the actual farm.

The main findings from a major UK study by Williams et al [55] are summarised as follows:

1. Nitrous oxide (N2O) is the single largest contributor to global warming potential (GWP) for all commodities except tomatoes, exceeding 80% in some cases.

2. Organic field crops and animal products generally consume less primary energy than non-organic counterparts owing to the use of legumes to fix N rather than fuel to make synthetic fertilisers. Poultry meat and eggs are exceptions, resulting from the very high efficiency of feed conversion in the non-organic sector.

3. The relative burdens of GWP, acidification potential (AP) and eutrophication potential (EP) between organic and non-organic field-based commodities are more complex than energy and organic production often incurs greater burdens.

4. More land is required for organic production (65% to 200% extra).

5. All arable crops incur smaller burdens per tonne, than meats, but all commodities have different nutritional properties and energy requirements beyond the farm, so care must be taken in comparisons.

6. Ruminant meats incur more burdens than pig or poultry meats, but ruminants can derive nutrition from land that is unsuitable for the arable crops that pigs and poultry must eat.

7. Heating and lighting dominate the burdens of tomato production; but maximising the national use of combined heat and power (CHP) could reduce the primary energy consumption by about 70%.

8. Non-organic, loose classic tomatoes incur the least burdens and they increase progressively and definably towards organic, on-the-vine specialist types.

9. The model has been used to inform other research projects and is well placed to analyse variations in existing production systems as well as being readily developed for new systems or commodities.

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3. Summary and next steps

3.1 Summary and recommendations

In general the literature was patchy in its coverage of agricultural sectors and diverse in its goals and methodologies. However, some general consensus can be drawn in most areas of LCA. Clusters of studies predominately exist in the pig production, sugar and dairy sectors. As a young and growing field, LCA offers significant challenges in its application in any industrial sector. However, the challenges are probably even more profound in the agricultural sector because of the inherent complexities such as the inter-linkages between any co-products, and because the nutrient flows involved in land use.

Table 3-1 summarises the findings from each sector and highlights the consensus and divergent points. This table is intended as a basis to discuss with industry stakeholders the various aspects of LCA and when feedback is incorporated, form a template for LCA application in Australian agriculture.

A summary of the findings is presented below along with recommendations and areas where industry input and discussion is required (per Table 3-1).

3.1.1 Goal and purpose of agricultural LCAs

The LCA studies that were reviewed had a wide variety of goals and purposes. Many recent studies compared different farming practices (e.g. organic versus traditional) such as a beef study of GHG emission and an eco-efficiency study of milk production. LCAs of pig production often examined the environmental impact of different feed types. The only poultry study found, compared environmental impact of chicken with wild cod and farmed salmon. Cotton studies concerned cotton products and not actual on-farm studies and so there are limited useful findings from these. Sugar studies largely concerned the utilisation of sugar or bagasse for energy production. The majority of crop studies were carried out for single crops and cradle-to-farm-gate. Narayanaswamy et al. [21, 22] performed contribution and comparative analysis for three grains involving three different products, from cradle-to-fork. Several studies also compared actual farms, comparing farming practices for environmental hotspots.

Recommendations and areas where industry input is needed

The intended future goal(s) of LCAs for the Australian rural industries need to be established. A decision should be taken to as to whether the goal is:

compare agricultural practices; compare factors such as feed choices; identify environmental improvements; or all of the above potential goals. LCA could be utilised to help farmers and primary producers: market their products; identify environmental improvements; or help with reporting (e.g. future requirements such as NGER or similar).

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If they are intended to be disclosed to the public, for comparability or marketing purposes, they must undergo a critical review according to ISO14040.

3.1.2 LCA system boundary

Depending on the goal of the study various inclusions and exclusions were observed. Studies that compared farming practices (e.g. organic versus conventional) typically omitted factors such as machinery, that were deemed to be virtually the same for both types of practice. The majority of studies were cradle-to-farm-gate. In several studies, emissions associated with the production of medicines, insecticides, machines, buildings and roads were excluded because of lack of data [9, 13, 14]. However, where data did exist many of these aspects were included [55, 56]. The life cycle materiality threshold for greenhouse emissions is discussed in the draft British standard for greenhouse life cycle assessment [72]. It suggests that all emissions making a material contribution of more than 1%, to the overall GHG life cycle emissions, need to be included. In addition, at least 95% of the anticipated life cycle GHG emissions of the functional unit should be included. These threshold criteria need to be debated under Australian conditions as well as applicability of extending such criteria to water and energy use impact categories.


The system boundary will largely depend on the overarching purpose of applying LCA to agriculture. Consensus is needed on whether the focus should be on environmental improvement of only the farm (which therefore could lead to the system boundary being cradle-to-farm-gate) or the whole supply chain (in which case cradle-to-fork would be the system boundary). There could be opportunities to collaborate with others in the supply chain, e.g. retailers and transporters. Eco-labelling requires a full life-cycle assessment. There are other potential boundaries such as the wholesale production chain, i.e. livestock production through to the end of the abattoir. The FU for this could be 1 kg boneless, chilled beef. If the LCA is to be extended to the fork for food products, further research can extend the results from the wholesale point.

3.1.3 Functional unit

Functional units (FU) are generally chosen to reflect the way each commodity is traded. The FU was typically per 1kg of product, or 1 hectare of land used. No real consensus was observed on how to address the FU for livestock. The studies used various FUs such as:

Kg of bone free meat leaving the gate [9]; per 1 kg of live weight leaving the farm gate [62]; or 1 t of carcass dead weight [55]. Recommendations and areas where industry input is needed

The FU is dependent on the goal of the study, and the system boundary. There is a particular need for consensus of the FU for livestock, as this varied in the studies. FU choice can help avoid allocation (see 3.1.4). Other industries may could consider a quality component such as protein content for grains.

3.1.4 Allocation methods for co-products

Allocation was one area where the literature made reference to ISO 14040. Economic allocation has, in the past, been utilised but studies of beef and dairy products have shown this to increase uncertainty.

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The need for allocation can be dependent on choice of FU and system boundary. Allocation was not needed in one beef study because the functional unit choice (live weight) and system boundary (cradle to farm gate) meant that the by-products occurred outside the farm gate (by-products occur post-processing). Others based FU on the way the commodity was traded, for example a dead carcass.


The order of preference is:

system expansion (essentially including the co-products, see Section 2.5, or Appendix 2 for an example);

physical relationships/causality; composition; and economic value. With the development of databases and an increase in the availability of LCA data, system expansion is likely to be further utilised to increase accuracy. The current data availability and potential issues in obtaining the necessary data need careful consideration.

3.1.5 Foreground and background data sources

In general, existing data from literature and databases was utilised where deemed satisfactory, but where data was missing, surveys, onsite collection and consultation with experts (telephone conversations and meetings) were used. Foreground data was collected from farms for many of the dairy and livestock studies.


For accuracy, the following should be considered as foreground and where data should be collected: input processes, farm processes and production processing.

Background processes where data can be obtained from other sources include: transport, fertiliser, and potentially composting. However, composting (or more broadly, manure management) can make a significant contribution to GHG emissions (for intensive livestock it may be the largest on-farm GHG emitter). If the manure is managed on-farm the foreground data on this process would probably need to be collected including total manure generated (solid/liquid mass, VS and N), processes and end use. Manure may be classified as a co-product that needs to be included in the LCA as an energy / fertiliser resource. If manure management is offsite the emissions could be regarded as Scope 3 (under the GHG reporting protocols) and therefore background.

To avoid uncertainty medicines, insecticides, machines, buildings and roads are sometimes excluded where data is missing. Industry input is needed on whether to include these. This will to some extent be dependant on the study, e.g. if comparing two farming systems that operate similar machinery, then machinery can generally be excluded.

3.1.6 Data quality and variability

Uncertainty is rarely considered in LCA studies and even less so in LCAs of agricultural products, and hampers the readers ability to interpret the results. The greatest level of uncertainty is widely acknowledged as on-field emissions and, in particular, the N2O emissions, emission factors and the resultant contribution to climate change.

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Data quality should comply with ISO14040 standards. LCA's should be transparent and include a description of data quality, so that the audience can understand the reliability of the study results and properly interpret the outcome.

LCA should perform uncertainty analysis to quantify the uncertainty of the results due to cumulative effects of model imprecision, input uncertainty and data variability. Data variability across different years needs to be carefully considered, single year data may not be representative and single or rare events should be excluded. Therefore, consideration needs to be given as to whether the LCA utilises single year data or takes an average, or (for example) compares a drought year with a normal year.

Ideally, to reduce uncertainty the large sources of GHG (e.g. N2O field emissions) should be metered and monitored to reduce uncertainty and emission factors should be developed for specific regions/climatic conditions, seasonal variations.

3.1.7 LCA software

Only four papers specifically utilised Simapro for LCA analysis (although two papers utilised the Simapro database), and none were found to use GaBi software. One significant study developed its own models based on Excel spreadsheets [55].


It is suggested that software specifically designed for LCA, such as Simapro is utilised to perform agricultural LCA, this would help with tracking changes and updating data.

LCA data management is generally too tedious and cumbersome for a spreadsheet model to be utilised. Spreadsheets do not necessarily ensure life cycle tracing of hundred or so processes that might be involved in a supply chain. Therefore, the use of software is recommended as a necessary criterion to ensure robustness, uncertainty analysis, and comprehensive coverage of processes and data volumes in LCA modelling.

3.1.8 Life cycle impact assessment method and impact categories

A range of impact categories were used and some authors recommended that LCA in agriculture must cover all central environmental impacts. Others highlighted the risks of only reporting certain categories, for example application of nitrogen increases the GHG emissions but on the flip side increases productivity [55]. There are risks by focussing on only a few impact categories in LCA. The results may encourage action to reduce one impact which may actually result in a large increase in another impact category. E.g. nitrogen use has a large impact on eutrophication that may not be picked up. Also focussing on only a few categories may open the LCA study up to criticism depending on the goal, audience etc.

Energy use and GHG emissions are fairly common impact categories in LCA, but water use has not received much attention in agricultural LCA literature.


Australian industry should consider going beyond only the consideration of water, GHG and energy, to meet future requirements or comparisons with other countries. Eutrophication is widely noted as one of the major environmental impacts of agriculture. Eutrophication also has GHG implications, so its inclusion or exclusion needs to be considered carefully. Toxicity has prominent consideration in Europe and if comparisons with other countries are likely this may need to be included.

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LCA involving water requires careful consideration on what types of water to include, e.g. surface, treated surface, groundwater, scheme water. Careful consideration is needed on whether infrastructure should be included, in order to recognise the benefits of rainwater capture and reuse.

3.1.9 LCA Evaluation

The vast majority of studies were contribution and hotspot analysis that compared various aspects of agriculture such as feed choice or farming practice.


Contribution and comparative analysis (e.g. comparing farming systems or feed choice) are appropriate for water, energy and GHG.

3.1.10 Compliance with international LCA standards

Most of the literature appeared to comply with the ISO standards, although it was not discussed in many papers.


The study should comply with ISO14040, in order to be recognised as robust, and be comparable with other studies.

3.2 Next steps

The next steps of this research project were as follows:

1. Develop and distribute the draft methodology to agricultural stakeholders (representatives of Cotton Research and Development Corporation, Sugar Research and Development Corporation, Australian Pork Limited, the Australian Chicken Meat Federation, and Meat and Livestock Australia);

2. Present the draft methodology to stakeholders at a workshop to discuss the methodology and obtain feedback; and

3. Incorporate the workshop lessons and feedback into the draft methodology to produce a final methodology on LCA in Australian agriculture.

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Table 3-1 Summary of findings and consensus points of LCA studies, and comments Parameters/ Issues Poultry Piggeries Sugar Grains/ Feedlot

Red meat and Livestock Consensus Points Divergent Points

Comments & areas for industry input

LCA Goal

Assess env.impacts of seafood products and compare with chciken farming.

Compare environmental impacts of different feed types.

Compare environmental benefits of electricity from bagasse and bagasse derived ethanol,

Assess contribution of key stages in grain life cycle.

Compare three farming practices for GHG emissions

All compare environmental impacts of products or farming practices.

While most studies compare a range of environmental impacts, several compared only 1 or 2.

The likely goal(s) of the LCA's for the Australian rural industries need to be established. Is the goal to compare agricultural practices, feed choices or identify environmental improvements, or are all of these potential goals?

Overall Purpose

Find reference levels for env performance and identify hotspots for improvement.

Identify hotspots for each method

To inform sugar producers

To inform consumers and stakeholders

Assess whether moving toward more extensive methods of production could reduce GHG

Improve environmental performance

A range of purposes from informing stakeholders to academic endeavour.

The overall purpose of performing the Australian LCA's needs to be determined. Hence, would LCA be required to help farmers and primary producers: market their products, identify environmental improvements or help with reporting e.g. future requirements such as NGER or similar. If they are intended to be disclosed to the public for comparability or marketing purposes they must undergo a critical review according to ISO14040.

System Boundary Farm to fork

cradle-to-farm gate Cradle-to-gate Cradle-to-fork

Cradle to farm gate

Largely cradle to farm gate

A few consider up until the consumers fork

System boundary will depend on the goal of the study. E.g. is it for environmental improvement of the farm or the whole supply chain to fork. There could be opportunities to collaborate with others in the supply chain, e.g. retailers and transporters. If preparing for eco-labelling a full life-cycle may be required. For completeness and coverage, the life cycle materiality threshold discussed in the draft British standard for greenhouse life cycle assessment [72] could be considered as a starting point for a discussion.

Functional Unit Mass of fillet, Norwegian study

Kg of growth between 29-115 kgs and hectare of land use

Basket of products that can be derived from the sugarcane harvested from onehectare of land in one year.

1 loaf white bread, from wheat grown and consumed in WA. 1 hectolitre beer from barley, 1 litre cooking oil from canola

1 kg of liveweightduring 1 yr

1 kg of product or land use e.g. hectare depending on the goals and factors being compared

No agreed FU for livestock.

The FU is dependent on the goal of the study, and the system boundary. There is a particular need for consensus of the FU for livestock, as this varied in the studies. FU choice can help avoid allocation

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Parameters/ Issues Poultry Piggeries Sugar Grains/ Feedlot



Allocation methods Mass basis

Mass and price of products

System expansion to include alternative routes to produce products Economic

None needed, because choice of FU (live weight) meant co-products emerged outside farm gate .

System expansion is increasingly used as data availability increases

System expansion is not always possible, and can require extensive data.

The order of preference is: system expansion; physical relationships / causality; composition; and economic value. Current data availability and potential issues in obtaining the necessary data need careful consideration. Allocation was not needed in one beef study because of functional unit choice (live weight) and system boundary (cradle to farm gate) meant by-products occurred outside the farm gate (by-products occur post-processing). Allocation rules given in clause 8 of the draft British greenhouse life cycle assessment standard [72] could be considered as a starting point. However, it would require stakeholder inputs to extend these rules to water and energy use impact categories.

Foreground data Fishing data Simulated data

Sugarcane processing data was obtained from a local milling company Farm data

Farm data gathered, calculated (IPCC method) livestock emissions

Farm data gathered in most cases

Some studies are using only existing data

For accuracy, input processes, farm processes and production processing need to be foreground.

Background data

All other stages in fishing and poultry

Average data on prices and other grain production systems All data All other data All other data

All other stages. Medicines, insecticides, machines, buildings and roads are often excluded.

Some studies are using only existing data. Some studies do include fertilsers, pesticides, tractor fuel and machines.

Details such as transport, fertiliser, composting etc, may be background but main processes should be foreground (data should be collected). Medicines, insecticides, machines, buildings and roads are often excluded. Industry input is needed on whether to include these. This will to some extent be dependant on the study, e.g. whether comparing two farming systems with similar machinery.

Data Quality Assessment No No

All systems for which data was sourced from literature were checked for consistency, and were adjusted, where necessary. Yes Yes

Data quality is not always considered, but is considered in the most robust studies.

Some studies did not discuss quality.

The data quality should comply with ISO14040 standards and therefore LCA's should include a description of data quality so that the audience can understand the reliability of the study results and properly interpret the outcome.

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Data Variability

Average data, variability not considered

Uncertainty considered

Assumptions made where no data existed- Sensitivity analyses were conducted in orderto verify the effect of all major assumptions.

Yes consideration given to hotspot analysis and evaluation

Yes variation analysis GHG emissions on emission factors

Uncertainty is often considered, but full uncertainty analysis is rare.

Some studies did not discuss variability.

LCA should perform uncertainty analysis to quantify the uncertainty of the results due to cumulative effects of model imprecision, input uncertainty and data variability.

LCA Computation Simapro

proprietary, Simapro

LCA software Team Simapro Not discussed

Simapro used in four cases but surprisingly software received limited mention

Excel model was developed in one recent significant study of 10 commodities

Simapro is suggested as an appropriate platform to perform agricultural LCA, this would help with tracking changes and updating data.

Life cycle impact categories

Fossil fuels, acidification, carcinogens, climate change, respiratory inorganics

Energy, global warming, acidification, eutrophication

Acidification, non-renewable resources, eutrophication, human toxicity, terrestrial eco-toxicity, GWP, Eco-indicator 95

Resource energy, global warming, human toxicity, terrestrial ecotoxicty, acidification, eutrophication. GHG emissions

A wide range of impact categories are usually considered

A few studies concentrated on specific aspects, mostly GHG emissions.

Australian industry should consider going beyond, only the consideration of water, GHG and energy, to meet future requirements or comparisons with other countries. Eutrophication is widely noted as one of the major environmental impacts of agriculture. Eutrophication also has GHG implications, so its inclusion or exclusion needs to be considered carefully. Toxicity has prominent consideration in Europe and if comparisons with other countries are likely this may need to be included.

Treatment of land use change in the draft British greenhouse life cycle assessment standard [72] could be taken as a starting point to seek and consolidate industry inputs.

Water No No No Yes No Not considered in nearly all studies

The concept of virtual water has not received attention in the LCA literature, but may need close consideration

LCA involving water requires careful consideration on what types of water to include, e.g. surface, treated surface, groundwater, scheme water. Should infrastructure be included, in order to recognise rainwater capture and reuse?

Energy Yes, fossil fuels Yes No Yes No Considered in many Energy can be measured per kg of product or per hectare

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Greenhouse Emissions

Yes, global warming potential Yes Yes Yes Yes Considered in most

GHG can be measured per kg of product or per hectare. The greatest uncertainty is often noted as nitrous emissions.

Life cycle evaluation Contribution

Contribution and scenario analyses Comparative

Contribution and hotspot Contribution

Mostly contribution and hotspot analysis

Contribution and comparative analysis (e.g. comparing farming systems or feed choice) are appropriate for water, energy and GHG.

Compliance with ISO 14040 & 14044 Not stated Yes Not stated Yes Yes

Yes in the majority of cases

Full compliance with ISO14040 & 14044 is rarely discussed, and generally not a goal of most studies.

The study should comply with ISO14040, in order to be recognised as robust, and be comparable with other studies. The robustness must also be tested.

Key findings/ conclusions

Sea food products generally have greater environmental impacts as compared to land based products - chicken

Impact of feed choice on the environmental life cycle profile of pig production system - being significant

Electricity option preferred on energy, carbon, acidification & eutrophication whilst the liquid fuel option preferred in terms of resource depletion and toxicity concerns

Canola production and cooking oil most important for impact reduction, and thereafter wheat, and finally barley.

Extensive farming has lower emissions than conventional and organic even less GHG emissions, although production per hectare is much lower.

LCA is a useful and appropriate method to assess and compare environmental impact of products and practices. There are several additional complexities to LCA use in agriculture over its initial use in industry. Nitrous oxide emissions introduce the largest uncertainties.

LCA can be utilised to examine several aspects such as hotspots analysis, environmental performance improvement or as a reporting tool.

LCA studies should comply with ISO 14040 in order for LCA studies to be internationally acceptable. The comparison of LCA studies remains a difficult and uncertain exercise given the different methodologies and assumptions. Therefore the task to bring consensus and a standard methodology to LCA application in Australia is a commendable and worthwhile exercise.

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References 1. Rebitzer. G, Ekvall et al. Life cycle assessment Part 1: Framework, goal and scope definition,

inventory analysis, and applications, Environment International, Vol. 30, 2004, pp. 701-720.

2. Pennington. D.W, Potting et al. Life cycle assessment Part 2: Current impact assessment practice, Environment International, Vol. 30, 2004, pp. 721-739.

3. Sonesson. U. Life cycle based research in food and agriculture, a report produced by the Swedish Institute for food and biotechnology, Gteborg, Sweden, 2004.

4. van Berkel. R. The application of life cycle assessment for improving the eco-efficiency of supply chains, From Farm-to-Fork; linking producers to consumers through value chains: Proceedings of the Muresk 75th anniversary conference, Perth, Western Australia, 3-4 October 2002, pp. 1-16.

5. Frontier Econo

09-029 (1)avicultura

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