Environmental impacts of farm scenarios according to five assessment methods

Agriculture, Ecosystems and Environment 118 (2007) 327–338 www.elsevier.com/locate/agee

Environmental impacts of farm scenarios according to five assessment methods Hayo M.G. van der Werf a,*, John Tzilivakis b, Kathy Lewis b, Claudine Basset-Mens a a

INRA, UMR Sol Agronomie Spatialisation de Rennes-Quimper, 65, rue de Saint Brieuc CS 84215, 35 042 Rennes Cedex, France b Agriculture and Environment Research Unit (AERU), Science and Technology Research Institute, University of Hertfordshire, College Lane Campus, Hatfield, Hertfordshire AL10 9AB, United Kingdom Received 6 April 2005; received in revised form 31 May 2006; accepted 6 June 2006 Available online 4 August 2006

Abstract It is not known to what extent the outcome of studies assessing the environmental impacts of agricultural systems depends on the characteristics of the evaluation method used. The study reported here investigated five well-documented evaluation methods (DIALECTE, Ecological Footprint, Environmental Management for Agriculture, FarmSmart, Life Cycle Assessment) by applying them to a case study of three pig farm scenarios. These methods differ with respect to their global objective (evaluation of impact versus evaluation of adherence to good practice), the number and type of environmental issues they consider, the way they define the system to be analysed, the mode of expression of results (for the farm as a whole, per unit area or per unit product) and the type of indicators used (pressure, state or impact indicators). The pig farm scenarios compared were conventional good agricultural practice (GAP), a quality label scenario called red label (RL) and organic agriculture (OA). We used the methods to rank the three scenarios according to their environmental impacts. The relative ranking of the three scenarios varied considerably depending on characteristics of the evaluation method used and on the mode of expression of results. We recommend the use of evaluation methods that express results both per unit area and per unit product. Environmental evaluation methods should be used with great caution, users should carefully consider which method is most appropriate given their particular needs, taking into consideration the method’s characteristics. # 2006 Elsevier B.V. All rights reserved. Keywords: DIALECTE; Ecological footprint; Environmental management for agriculture; FarmSmart; Life cycle assessment; Organic agriculture; Label rouge; Pig production

1. Introduction Avariety of methods have been proposed for the evaluation of the environmental impacts of farms (Von Wire´n-Lehr, 2001; Van der Werf and Petit, 2002; Halberg et al., 2005). The development of such methods is essential, as they can serve as decision support tools for guiding the evolution towards more sustainable agricultural production systems (Hansen, 1996). * Corresponding author. Tel.: +33 2 23 48 57 09; fax: +33 2 23 48 54 30. E-mail address: [email protected] (H.M.G. van der Werf). 0167-8809/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2006.06.005

These tools are increasingly used by farmers (Goodlass et al., 2003), researchers (De Koeijer et al., 2002) and political decision makers (Schro¨der et al., 2004). The authors of studies using such methods to assess the environmental impacts of agricultural systems rarely acknowledge that the results obtained depend not only on the characteristics of the systems compared, but also on those of the evaluation method used. A methodological reflection on the structure of methods for the environmental evaluation of farms seems appropriate. Such methods generally present five major stages (adapted from Petit and van der Werf, 2003):

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1. Definition of the global objective of the method, e.g. The evaluation of environmental impact or the evaluation of adherence to good agricultural practice. This stage involves choices with respect to the intended user, the spatial scale for which the method is designed, and the consideration of economic and social dimensions, in addition to environmental impacts. 2. Definition of environmental objectives. Global objectives cannot be directly assessed or quantified, a set of more specific environmental objectives is required, which is at the heart of the evaluation method (Van der Werf and Petit, 2002). We define the term ‘‘environmental objective’’ as an environmental issue of concern and its associated desired trend. Other terms used for environmental issues (OECD, 1999; EEA, 2005) are environmental themes (Pointereau et al., 1999) and impact categories (Guine´e et al., 2002). Some examples of environmental objectives: reduction of energy use, reduction of emissions of nitrate or maintenance of soil quality. 3. Definition of the system to be analysed. Many methods are restricted to the evaluation of the direct impacts of a system, by considering only the impacts from operation of the system. Other methods also consider indirect impacts, resulting from the production of the inputs (fertilisers, feeds) to the system. 4. Construction or identification of indicators for each environmental objective. To quantify the degree to which the environmental objectives are attained, a set of indicators serving as evaluation criteria is required. The quality of an indicator will largely depend on the validity of its calculation algorithm. 5. Calculation of results. Indicator values are calculated for each of the production systems or scenarios to be compared. A partial or total aggregation of results may facilitate their interpretation. These stages involves choices, in particular with respect to the global objective of the method (stage 1), its environmental objectives (stage 2), the way in which the system is defined (stage 3), and concerning the indicators, since for each environmental objective one or several indicators are selected from many possible candidates (stage 4). Although the outcome of the assessment will obviously be affected by these choices, they are rarely discussed or justified by those proposing such methods. Methods show great variability with respect to the implementation of these choices. For instance, a review of 12 methods used for the evaluation of environmental impacts at the farm level revealed that the number of environmental objectives considered per method varied from 2 to 13 (Van der Werf and Petit, 2002). Of the total of 26 objectives, some were considered in six or seven methods, whereas others were considered in a single method only.

Although different methods for environmental evaluation have been compared on the basis of their published descriptions (Von Wire´n-Lehr, 2001; Van der Werf and Petit, 2002; Braband et al., 2003; Halberg et al., 2005), we did not find any comparative studies based on the actual application of different methods to a set of farms or farm scenarios. The study reported here investigated five well-documented evaluation methods by applying them to a case study of three farm scenarios. The objectives of the study were to examine to what extent the five methods produce different results and to investigate which characteristics of the methods affected the outcome. It should also allow us to propose recommendations for the selection of evaluation methods.

2. Materials and methods 2.1. Farm scenarios This study compared three contrasting scenarios of farms producing crops and pigs in Bretagne, western France, showing major differences with respect to crop production practices and input use, animal housing systems and crop and animal production levels (Table 1). Technical performance, input use and emissions to the environment used to construct the scenarios were based mainly on published data, complemented by real farm data supplied by a range of experts (Basset-Mens and van der Werf, 2005). The good agricultural practice (GAP) scenario corresponds to a current intensive (or ‘‘conventional’’) pig production farm, optimised in particular with respect to fertilisation practices, as specified in the French ‘‘Agriculture Raisonne´e’’ standards (Rosenberg and Gallot, 2002). In the GAP scenario, pigs are raised in a slatted-floor building. The organic agriculture (OA) farm scenario corresponds to organic agriculture according to the French version of the European rules for organic animal production (Ministe`re de l’Agriculture et de la Peˆche, 2000) and the European rules for organic crop production (CEE, 1991). The Red Label (RL) farm scenario corresponds to the ‘‘Porc Fermier Label Rouge’’ quality label (Groupement des Fermiers d’Argoat, 2000). In the OA and RL scenarios, pigs are born and raised outdoors until weaning, and in an open-front straw-litter building at low animal density after weaning. An inventory of some of the main emissions for the three farm types is presented in Table 2. Details on crop and feed production practices and on animal production practices can be found in Basset-Mens and van der Werf (2005). For the three scenarios we assumed that farmers adopt good agricultural practice, and respect current regulations. Some of the evaluation methods applied here, in particular EMA, but also FarmSmart, require extensive information with respect to the farm conservation practices (e.g. concerning hedgerows, field margins, and

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Table 1 Characteristics of the good agricultural practice (GAP), red label (RL) and organic agriculture (OA) farm scenarios

Crop production Farm size (ha) Annual crops Production of cereal straw

GAP

RL

OA

68 Pea, winter triticale, winter wheat 261 t, sold off farm

38.3 Grain maize, winter barley, winter triticale 67 t, used as animal bedding; an additional 62 t bought off-farm Ryegrass–clover paddock (10 ha)

40

40

52.8 Horse bean, grain maize, spring barley, winter oats, winter wheat 96, 65 t of which used for animal bedding, no straw sold Ryegrass–clover paddock (4.4 ha), lucerne (4.8 ha) 70

Slatted-floor 150 21.1 25.7 1313

Outdoor 67 18.9 28 1490

Outdoor 40 17.7 42 1695

Slatted floor 0.85 275 113 4857

Straw litter 2.6 312 115 3530

Straw litter 2.3 340 120 1480

Perennial crops % weight of pig feed produced on-farm Animal production: piglet production Housing Herd size (no. of sows) Weaned piglet/sow/year Weaning age (days) Feed/sow (boars incl.) (kg/year) Animal production: weaning to slaughtering Housing Surface per pig (m2) Feed consumed (kg) Slaughter weight (kg) Pig live weight produced/ha of farm surface/year

watercourses), in this respect the farm scenarios were assumed identical. For the three scenarios, all crops produced were used as components of the concentrate feed for the pigs. For GAP and RL, the crops produced on-farm contributed 40% to the weight of the concentrate feed used, for OA 70%. The three scenarios strongly differ in pig live weight production per ha of farm surface per year (Table 1): 4857 kg for GAP, 3530 kg for RL, and 1480 kg for OA. Input use (additional pig feed, fertiliser, electricity, diesel) per ha was largest for GAP and smallest for OA. 2.2. Evaluation methods In the recent past, farms had a single principal function: production of food and fibre for the market. Nowadays farms have a second main function, which becomes increasingly

important: production of non-market goods (e.g. environmental services). In the evaluation of the environmental impacts of farms, both functions should be considered. In this study we expressed farm impacts, when methods allowed, by two functional units: on the one hand per unit area, reflecting the farm’s function as a producer of nonmarket goods, and on the other hand per unit product, reflecting its function as a producer of market goods. 2.2.1. Life cycle assessment (LCA) LCA is a technique to evaluate the environmental burdens associated with a product, process, or activity. In the inventory analysis phase the resources consumed and the emissions to the environment, both on-farm and associated with the production and delivery of the inputs used on the farm, are listed. In the impact assessment phase, resources used and emissions are interpreted in terms of environmental

Table 2 Inventory of some of the main emissions according to Basset-Mens and van der Werf (2005), expressed per hectare of land used (both on and off farm) and per kg of pig, for the three farm scenarios Emission

Unit (kg)

Per hectare of land used GAP

Nitrate Oxides of nitrogen Ammonia Sulphur dioxide Methane Nitrous oxide Carbon dioxide Carbon monoxide a

NO3 NOx NH3 SO2 CH4 N2O CO2 CO

203 15.9 43.5 5.7 40.3 5.7 1625 5.5

RL 181 14.5 16.4 7.5 14.2 11.0 1783 4.9

For each substance, lowest value per ha of land used and per kg of pig in bold.

Per 1000 kg of pig OA a

127 14.6 17.7 4.8 12.4 7.6 1408 4.9

GAP

RL

OA

110 8.6 23.6 3.1 21.9 3.1 882 3.0

114 9.1 10.3 4.7 8.9 6.9 1120 3.1

125 14.4 17.4 4.7 12.2 7.5 1390 4.8

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impacts (Guine´e et al., 2002), by multiplying the aggregated resources used and the aggregated emissions of each individual substance with a characterisation factor for each impact category to which it may potentially contribute. The results presented here are based on a detailed LCA study of pig production (Basset-Mens and van der Werf, 2005), which expressed results per kg of pig live weight produced and per ha of land used (i.e. including land offfarm, used for the production of crop-based ingredients for concentrate feed). 2.2.2. Ecological footprint (EF) ‘‘The ecological footprint of a designated population is the area of productive land and water ecosystems required to produce the resources that the population consumes and to assimilate the wastes that the population produces, wherever on Earth the land and water is located’’ (Rees, 2000). The area of productive land and water ecosystems available per capita is designed as a fair Earth share, and constitutes a threshold value which can be used as a benchmark in an EF analysis (Wackernagel and Rees, 1996). We calculated the farm EF as the sum of four components. The first component is the land surface of the farm being assessed; the second is the land surface required to produce the ingredients of the concentrate feed which were not produced on-farm. The latter surface was calculated using average yields for 1996–2000 (FAO, 2002). The third component, ‘‘energy land’’, corresponds to the land required to produce the non-renewable energy used on the farm and for the production and delivery of the farm inputs. This was calculated assuming a net productivity of 80 GJ/ha (Wackernagel and Rees, 1996). The fourth component, ‘‘carbon-sink land’’, reflects the land area needed to sequester the CO2 corresponding to the greenhouse gasses emitted on the farm and for the production and delivery of the farm inputs. Greenhouse gasses resulting from the use of non-renewable energy were not included here, as these were taken care of in the ‘‘energy land’’ component. CO2-equivalents were calculated according to the GWP100 factors by IPCC (Houghton et al., 1996) in kg CO2 equiv.: N2O: 310, CH4: 21. An annual CO2 sequestration rate of 6.6 t ha1 was assumed (Wackernagel and Rees, 1996). 2.2.3. Environmental management for agriculture (EMA) Lewis and Bardon (1998) proposed EMA, ‘‘a computerbased informal environmental management system for agriculture’’. The core of the system is the performance assessment (PA) mode, which compares actual farmer production practices and site-specific details with regulatory compliance and what is perceived to be best practice for that site. EMA has been designed on a modular basis, with each module producing a report and an environmental performance index, known as an eco-rating, for a specific aspect of farming. Within each module, where appropriate, an

estimate of emissions is made, which, when collated, forms an emissions inventory (EI) for the farm being assessed (Lewis et al., 1999). EMA seeks to encourage continuous improvement in environmental performance, tackling issues and problems in small steps, that are practically manageable and financially affordable. Its technical system is a support mode, which incorporates modules to explore ‘‘What-If’’ scenarios, to identify site-specific solutions to environmental problems and so improve future eco-ratings. This mode helps the user identify solutions to problems spotted in the performance assessment mode. The second support mode is a hypertext advisory system. In this study the EMA 2004 version was used. 2.2.4. FarmSmart In 2000 the UK government launched a pilot set of agricultural sustainability indicators to provide a means of measuring the economic, social and environmental impacts of agriculture in Great Britain at national level (Tzilivakis and Lewis, 2004). It was hoped that stakeholders would find the indicators valuable for regional and local use. However, many of the indicators have been defined from the policy top-down perspective, some are not measurable directly on farm, and few have direct links with on-farm management decisions. Consequently, the key messages emerging from the indicators can easily be lost at farm level. In order to address these issues, FarmSmart, a simple tool for farmers, was developed (Tzilivakis and Lewis, 2004). It collates relevant information to identify appropriate indicator values for a specific farm and location and provide a management focus, such that farmers are provided with information to help them select indicators relevant to their situation, assess their performance, and take steps for improvements where required. The method yields 35 indicators referring to Economy, Management, Inputs, Resources and Conservation. In this study results are presented for those indicators showing differences for the farms compared. The FarmSmart Beta version 1.0.2 was used. 2.2.5. DIALECTE Solagro (2000) proposed DIALECTE for the evaluation of the environment at farm level by means of a comprehensive, simple and rapid approach. This method is an improved version of the ‘‘Solagro Diagnostic’’ proposed by Pointereau et al. (1999). The method yields 16 agro-environmental indicators (AEI) allowing a rapid and global evaluation of the environmental risks of the farm. It further produces a whole farm approach (WFA) consisting of an Energy analysis, of performance levels for Farm diversity and Management of inputs, and of an assessment of the potential impacts of the farm on water, soil, biodiversity and resource use. The method can be applied to all agricultural production systems in France. In this study DIALECTE version 4.0 (January, 2004) was used. No

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3.1.2. Environmental objectives Environmental objectives were grouped in four classes (Van der Werf and Petit, 2002): farming practice-related, input-related, emission-related and system state-related. The methods compared differ with respect to the number and type of environmental objectives taken into account (Table 3). LCA, EF and FarmSmart consider input-related and emission-related objectives. EMA considers farming practice-related and emission-related objectives, whereas DIALECTE considers farming practice-related, input-related and system state-related objectives. EF is narrow in focus, as only three objectives are considered. EMA (12 objectives) and DIALECTE (19 objectives) are wide ranging, and are the only methods taking into account objectives related to farming practices. FarmSmart and LCA (both six objectives)

results were presented for three of the 16 Agro-Environmental indicators (livestock units/ha of forage crop, irrigated surface, and length of the grazing season), as they were not relevant for the farms compared here.

3. Results 3.1. Characterisation of the methods 3.1.1. Global objective of the method LCA, EF, FarmSmart and DIALECTE share the same global objective: the evaluation of environmental impact, whereas EMA’s global objective is the assessment of adherence to best practice.

Table 3 Characterisation of the evaluation methods with respect to their environmental objectives, which were grouped as: farming practice-related, input-related, emission-related, related to the state of the system Environmental objectives

Methodsa LCA

EF

EMA PA

Farming practice related Fertiliser usage \ b Organic manure management \ Odour management \ Pesticide usage and general management \ Pesticide treatment frequency # Overall soil management \ Growing of legume crops and grass \ Crop diversity \ Soil cover by crops in winter " On-farm production of feed " Livestock husbandry \ Livestock diversity \ Energy and water efficiency \ Farmland conservation \ Input related Use of non-renewable energy # Land use # Water use # N fertiliser use # P fertiliser use # N balance (input–output) # P balance (input–output) # Pesticide use # Emission related Emission of greenhouse gases # Emission of acidifying gases # Emission of eutrophying substances # Emissions concerning terrestrial ecotoxicity # System state related Landscape quality Agricultural biodiversity Water quality Soil quality

FarmSmart EI

x x x x

DIALECTE AEI

WFA

x

x

x x x x x

x x x

x x x x x x

x

x x

x

x x x x

x x x x x x x x x

x

x x x x

x

x x

x x

x x x x

An x indicates that the objective is taken into account. a LCA, life cycle assessment; EF, ecological footprint, EMA, environmental management for agriculture; PA, performance assessment; EI, emissions inventory; AEI, agro-environmental indicators; WFA, whole farm approach. b #, objective to be minimised; \, objective to be optimised; ", objective to be maximised.

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Table 4 Indicator types for the evaluation methods Indicator type

Methods LCA

Pressure State Impact

x

EF

EMA

FarmSmart

PA

EI

x

x

x

DIALECTE AEI

WFA

x x

x x

x

to Impacts on human health and ecosystems, which may elicit a societal Response. None of the methods use Driving forces or Responses indicators (Table 4). LCA and EF use Impact indicators, EMA and FarmSmart use Pressure indicators, whereas DIALECTE uses both Pressure and State indicators. Thus, the methods compared here differ with respect to the types of indicators used.

For method acronyms see Table 3.

3.2. Evaluation of the farm scenarios are of intermediate scope, with FarmSmart relying mainly on input-related objectives, and LCA more on emissionrelated objectives. The methods compared thus are quite distinct with respect to the number and the type of environmental objectives considered. 3.1.3. Definition of the system For EMA and DIALECTE, the system evaluated consists of the farm only, whereas for LCA, EF and FarmSmart the system includes the production of inputs. 3.1.4. Indicators used Indicators serve as criteria to quantify the degree to which environmental objectives are attained. The indicators used by the five methods were categorised according to the Driving forces–Pressures–State–Impact–Responses (DPSIR) framework proposed by the European Environmental Agency (EEA, 1999). According to this view, social and economic Driving forces exert Pressure on the environment, as a consequence the State of the environment changes, this leads

The results for each method will be presented in this section, and the three scenarios will be ranked. We use ranking here as a tool to ‘‘condense’’ the extensive and diverse output produced by the different methods in order to be able to compare their results for the three scenarios. This does not imply that we consider ranking of farming systems to be the primary objective of these methods. A ranking of the three scenarios is only uncontroversial in the case where all indicators within a given evaluation method separately rank the three scenarios in the same order. This was, however, never the case. Our ranking was mainly based on the number of ‘‘best’’ and ‘‘worst’’ scores the scenarios obtained, details are given below. We implicitly considered thus that all indicators are equally important, which is a subjective choice and will not necessarily be true in ‘‘the real world’’. When ‘‘best’’ and ‘‘worst’’ scores were more or less evenly distributed among the scenarios, we attributed identical ranks. This procedure obviously is not 100% objective, but it is transparent (since the full information is available in Tables 5–11) and allows an

Table 5 The environmental impacts calculated according to Basset-Mens and van der Werf (2005) using life cycle assessment (LCA), expressed per ha of land used and per kg of pig produced for the three farm scenarios Impact category

Eutrophication Climate change Acidification Terrestrial toxicity Non-renewable energy use Land use a

Unit

Per hectare of land used

kg PO4-equiv. kg CO2-equiv. kg SO2-equiv. kg 1.4-DCB-equiv. MJ LHV m2 year

Per kg of pig

GAP

RL

OA

GAP

RL

OA

38.3 4236 80.1 30.4 29282 10000

26.4 5510 36.0 29.3 28503 10000

21.9a 4022 37.7 30.8 22492 10000

0.0208 2.30 0.0435 0.0165 15.9 5.43

0.0166 3.46 0.0226 0.0184 17.9 6.28

0.0216 3.97 0.0372 0.0304 22.2 9.87

For each impact category lowest value per ha of land used and per kg of pig in bold.

Table 6 The ecological footprint of the three farm scenarios, values in ha year per ha of farm surface and in ha year per 1000 kg of pig live weight Footprint component

Per hectare of farm surface

Per 1000 kg of pig

GAP

RL

OA

GAP

RL

OA

Farm Additional pig feed Non-renewable energy Carbon sink

1 1.63 0.96 1.05

1 0.96 0.79 1.25

1 0.32 0.41 0.58

0.21a 0.33 0.20 0.22

0.30 0.29 0.23 0.37

0.67 0.22 0.28 0.39

Total footprint

4.64

4.00

2.31

0.96

1.19

1.56

a

For each component lowest value per ha and per kg of pig in bold.

H.M.G. van der Werf et al. / Agriculture, Ecosystems and Environment 118 (2007) 327–338 Table 7 The eco-ratings calculated using the environmental management for agriculture (EMA) performance assessment mode for the three farm scenarios Eco-rating

Range

GAP

RL

OA

Fertiliser usage Organic manure Odour management Crop pesticide usage General pesticide management Overall soil management Indoor pigs Outdoor pigs Energy efficiency Water efficiency Farmland conservation Average eco-rating

+100/100 +100/100 0/100 0/100 0/100 +100/100 +100/100 +100/100 +100/100 +100/100 +100/100

19 S2 S20 11 S19 30 39 n.a. S44 40 24 4.2

28 3 31 S9 20 30 70 5 44 40 24 3.3

8a 3 31 n.a. n.a. 30 70 5 44 40 24 3.2

333

results were expressed per kg of pig produced, the picture was very different: OA did worst for all impacts with the exception of acidification, for which GAP had the highest impact. Overall GAP did best, as it had the lowest values for four impacts; RL had the lowest values for two impacts, here we rank scenarios GAP > RL > OA.

overall assessment of the three scenarios by the five methods (Section 3.3, Table 12).

3.2.2. Ecological footprint Expressed per ha of farm surface, OA had the smallest Ecological Footprint, and GAP the largest (Table 6). Footprint components (excluding farm surface, which – by definition – was identical across scenarios) were smallest for OA, RL had the largest value for carbon sink land and GAP for the other two components. Overall we rank OA > RL > GAP. Expressed per kg of pig, results were inverted, now GAP had the smallest footprint and OA the largest. GAP had the lowest values for all footprint components except land for additional pig feed, where OA had the smallest value. We rank GAP > RL > OA.

3.2.1. Life cycle assessment When LCA results were expressed per ha of land used, the OA scenario did best: it had lowest impact values for eutrophication, climate change and energy use. GAP had highest values for all impacts except climate change (Table 5), so we rank scenarios OA > RL > GAP. When

3.2.3. EMA The eco-ratings did not clearly differentiate the scenarios (Table 7). GAP did best for three eco-ratings, both RL and OA did best for two eco-ratings, differences were often minor. The average eco-rating was slightly better for OA than for the other two scenarios. RL and OA had worse eco-

a

For each eco-rating highest value in bold.

Table 8 The emissions inventory calculated using environmental management for agriculture (EMA), expressed per hectare of farm surface and per 1000 kg of pig produced, for the three farm scenarios Emission

Unit (kg)

Per hectare of farm surface GAP

Minimum potential nitrate leaching Oxides of nitrogen Ammonia Sulphur dioxide Methane Carbon dioxide Carbon monoxide a

NO3 NOx NH3 SO2 CH4 CO2 CO

104 9 61 13 22 1565 1

RL 150 14 41 0 18 421 0

Per 1000 kg of pig OA 0 6 14 0 8 337 0

a

GAP

RL

OA

21.4 1.8 12.5 2.7 4.5 322 0.21

42.5 3.9 11.7 0 5.2 119 0

0 3.8 9.4 0 5.1 228 0

For each emission lowest value per ha and per 1000 kg of pig in bold.

Table 9 Values of farm-level indicators, expressed per hectare of farm surface and per 1000 kg of pig produced, calculated using FarmSmart for the three farm scenarios Indicator

Unit

Per hectare of farm surface GAP

Pesticide active ingredient used Growth regulator active ingredient used N fertiliser use P fertiliser use Ammonia emission Methane emission Nitrous oxide emission Carbon dioxide emission Direct energy consumption Indirect energy consumption a

kg kg N (kg) P2O5 (kg) NH3 (kg) CH4 (kg) N2O (kg) CO2 (kg) GJ GJ

2.19 0.79 20.3 14.9 85.3 92.8 16.1 2485 21.5 21.1

For each indicator lowest value per ha and per 1000 kg of pig in bold.

RL 1.88 0.31 53.5 0 66.8 71.9 16.4 1830 6.7 19.7

Per 1000 kg of pig OA a

0 0 0 0 25.6 28.1 4.2 562 4.8 3.4

GAP

RL

OA

0.45 0.16 4.2 3.1 17.6 19.1 3.3 512 4.4 4.3

0.56 0.09 16.0 0 19.9 21.5 4.9 518 1.9 5.6

0 0 0 0 17.3 19.0 2.8 380 3.2 2.3

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Table 10 Values of agro-environmental indicators calculated using DIALECTE for the three farm scenarios Indicator Grass > 2 year N from manure on surfaces receiving manure N from manure/total N Surface receiving manure Length of hedges and woodland borders Direct energy use in diesel litre equivalents N balance (input–output) P2O5 balance (input–output) K2O balance (input–output) Number of species grown Pesticide treatment frequency indexc Surface without crop cover on December 31 Legume crop surface a b c

Unfavourable a

Unit % kg/ha % % m/ha l/ha kg/ha kg/ha kg/ha score ha/ha % %

0 340 0 0 0 300 100 100 100 1 4 100 0

Favourable

GAP

100 0 100 100 100 0 0 0 0 9 0 0 40

RL

OA b

0 231 89 68 70 499 69 44 78 3 9.2 16 32

26 194 74 74 70 178 103 39 64 5 5.5 17 13

17 73 100 80 70 120 1 3 S11 4 0 0 16

To guide interpretation, values considered unfavourable and favourable are indicated. For each indicator most favourable value in bold. Average number of standard pesticide treatments used by area and year. Standard treatment is the approved dosage for a certain crop.

ratings for odour management than GAP, which is surprising, as it is generally perceived that pig production on straw litter (as for OA and RL) causes less odour problems than pig production on slatted floors (as for GAP) (Paul Robin, pers. comm., 2004). We rank GAP  RL  OA. Expressed per ha of farm surface, the EMA emission inventory consistently yielded the lowest values for OA, whereas GAP had highest values for five of the seven substances considered (Table 8), we rank OA > RL > GAP. Per kg of pig produced, no scenario clearly stood out. OA showed the lowest values for nitrate loss and ammonia loss, for two other emissions it shared the lowest values with RL. RL had the lowest carbon dioxide emissions, while GAP emitted least oxides of nitrogen and methane. We rank GAP  RL  OA.

3.2.4. FarmSmart Expressed per ha of farm surface, OA showed the lowest values for all indicators, whereas GAP had the highest value for eight indicators out of ten (Table 9), we rank OA > RL > GAP. Per kg of pig, OA showed the lowest value for nine indicators out of ten, but here RL did rather worse than GAP, as it had the highest value for seven indicators, we therefore rank OA > GAP > RL. 3.2.5. DIALECTE For the agro-environmental indicators presented here, OA showed the most favourable value in eight out of thirteen cases, whereas both RL and GAP presented the most favourable value for two indicators each (Table 10). We rank OA > RL  GAP.

Table 11 Values calculated using the DIALECTE whole farm approach for energy efficiency, farm diversity, input management and potential farm impacts for the three farm scenarios Indicator

Unit

Maximum score

Energy efficiency, output (in meat)/input

1.17

Farm diversity Crop diversity and soil cover Livestock diversity, autonomy and fertility transfer Natural elements and space

Score Score Score

Input management Nitrogen Phosphorous Water Pesticides Energy

Score Score Score Score Score

Potential farm impact on: Water (quality and quantity) Soil (fertility, erosion control) Biodiversity (animal and plant) Resource use

Score Score Score Score

Total score a

For each indicator highest score in bold.

GAP

30 22 18 7.5 3 6 7.5 6

19 5 1

RL 1.20 17 5 2

OA 1.59a 19 6 5

0.5 0 6 0.1 3

0.8 0.8 6 1.6 3

7.5 2.9 6 7.5 5.3

20 20 20 20

8 9.4 0.8 9

9 11.7 0.8 15

16 12.6 5.8 17

180

61.8

72.7

110.6

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335

Table 12 Ranking of the three farm scenarios by the five evaluation methods Mode of expression

Methodsa

Scenario

LCA

EF

EMA

FarmSmart

PA Farm as a whole

GAP RL OA

Expressed per hab

GAP RL OA GAP RL OA

Expressed per kg of pig live weight

EI

1 1 1 3 2 1 1 2 3

3 2 1 1 2 3

3 2 1 1 1 1

DIALECTE AEI

WFA

2 2 1

3 2 1

3 2 1 2 3 1

a For EMA the performance assessment (PA) and emissions inventory (EI) modes are distinguished; for DIALECTE the agro-ecological indicators (AEI) and whole farm approach (WFA) are distinguished. For each mode of expression numbers within a column indicate ranks with 1, best; 3, worst. b Per ha of land used (including land off-farm) for LCA, per ha of farm surface for EF, EMA, and FarmSmart.

According to FarmSmart, OA did best, no matter whether results were expressed per ha or per kg of pig produced. When results were expressed per ha GAP did worse than RL, when results were expressed per kg of pig, RL did worse than GAP (Table 12). At the whole farm level, DIALECTE ranked OA first, both through its agro-environmental indicators and its whole farm approach. The agro-environmental indicators ranked GAP and RL similarly, whereas, in the whole farm approach, RL had better scores than GAP.

Within the whole farm approach, OA presented the highest and GAP the lowest energy output/input ratio (Table 11). For Farm diversity, Input management and potential farm impacts, OA showed the highest (i.e. best) scores for all indicators (Table 11). For seven indicators GAP had lowest scores and for one indicator RL had the lowest score. We rank OA > RL > GAP. 3.3. Ranking of the farm scenarios In order to obtain a view of the overall assessment of the three scenarios by the five methods, the rankings proposed in Section 3.2 (Tables 5–11) have been summarised in Table 12. Depending on the method, results were expressed for the farm as a whole, per ha, and per kg of pig live weight. LCA and EF produced identical rankings: when results were expressed per ha, OA did best and GAP worst, expressed per kg of product GAP did best and OA worst (Table 12). At the farm level, EMA established similar environmental performance for the three scenarios. In the EMA emissions inventory however, OA did best and GAP worst. When emissions were expressed per kg of pig produced, no clear differentiation emerged (Table 12).

3.4. Emissions inventories Three methods produced emissions inventories, which are summarised in Table 13. For GAP and LR, LCA and EMA produce values that are reasonably close, whereas FarmSmart values (when available) are consistently higher. For OA, EMA produces lower values than LCA and FarmSmart. These differences in levels of emissions per unit surface should be related to the way the three methods define the system under evaluation and to the extent to which they consider off-farm emissions. LCA expresses results per ha of land used (including land off the farm) and considers off-farm emissions associated with a wide range of inputs: the

Table 13 Emission inventories according to LCA (per ha land used), EMA-EI and FarmSmart (both per ha of farm surface), for the three farm scenarios Emission

Unit (kg)

LCA GAP

Nitrate Oxides of nitrogen Ammonia Sulphur dioxide Methane Nitrous oxide Carbon dioxide Carbon monoxide a

NO3 NOx NH3 SO2 CH4 N2O CO2 CO

203 15.9 43.5 5.7 40.3 5.7 1623 5.5

For each substance, lowest value in bold.

EMA RL 181 14.5 16.4 7.5 14.2 11.0 1783 4.9

OA a

127 14.6 17.7 4.8 12.4 7.6 1408 4.9

FarmSmart

GAP

RL

OA

GAP

RL

OA

104 9 61 13 22 – 1565 1

150 14 41 0 18 – 421 0

0 6 14 0 8 – 337 0

– – 85.3 – 92.8 16.1 2485 –

– – 66.8 – 71.9 16.4 1830 –

– – 25.6 – 28.1 4.2 562 –

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construction of pig housing, the production and delivery of concentrate pig feed (including growing of crops), and of fertilisers, pesticides, agricultural machines and energy carriers, including all sea and road transport involved. FarmSmart also considers a wide range of inputs, including concentrate feed, but emitted substances considered are limited to CO2 and N2O, while emissions are expressed per ha of farm surface. This means, for instance, that nitrate leaching associated with the crops for the concentrate pig feed was part of the system in the LCA approach, but not in FarmSmart. EMA finally does not include any off-farm emissions associated with inputs and expresses emissions per ha of farm surface. In addition to these differences in system definition, the use of different algorithms or emission factors has further contributed to differences among the inventories.

4. Discussion 4.1. Characterisation of scenarios and methods The three farm scenarios evaluated here differ strongly, both in input use (e.g. non renewable energy use was 33 GJ ha1 for OA and 77 GJ/ha1 for GAP (Table 5), purchase of concentrate feed was 1.7 t ha1 for OA and 9.0 t ha for GAP, data not shown), and in output (pig production per ha of farm surface was 1480 kg for OA and 4857 kg for GAP, Table 1). Thus, in relative terms, OA can be characterised as ‘‘low input–low output’’, GAP as ‘‘high input–high output’’, with RL being intermediate. The methods consider different environmental objectives and use different types of indicators. LCA and EF both consider input-related and emission-related objectives (Table 3), quantified by impact indicators (Table 4). EF (three objectives), has a more narrow focus than LCA (six objectives). EMA-PA deals uniquely with farming practice-related objectives, it is wide-ranging (eight objectives), whereas EMA-EI deals with four emission-related objectives. Both EMA-PA and EMA-EI use pressure indicators. FarmSmart, as LCA and EF, relies on input-related and emission-related objectives, which are quantified through pressure indicators. DIALECTE-AEI (11 objectives) and DIALECTE-WFA (13 objectives) are the only methods based on system staterelated objectives, in addition to farming practice and input use-related objectives. These methods use both pressure and state indicators. 4.2. Ranking of the scenarios We used the methods to rank the three scenarios according to their environmental impacts. Depending on the method used and on the way results were expressed (for the farm as a whole, per ha or per kg product), ranking from best to worst was OA > RL > GAP, or its inverse: GAP > RL > OA, or ranking proved inconclusive.

For three methods (EMA-PA, DIALECTE-AEI, DIALECTE-WFA) rankings were established at the scale of the farm as a whole (Table 12). EMA-PA did not differentiate among the three scenarios, whereas DIALECTE-AEI and DIALECTE-WFA both ranked OA best, with DIALECTEWFA ranking GAP worst, while DIALECTE-AEI attributed similar ratings to GAP and RL. EMA’s global objective (assessment of adherence to best practice) suffices to explain its lack of differentiation of the three scenarios, since all three confess adherence to good practice. Four methods (LCA, EF, EMA-EI, FarmSmart) allowed expression of results both per unit area and per unit product (Table 12). These methods are based on a limited number (three to six) of input-related and emission-related environmental objectives, several of which are shared (Table 3). The methods use impact and pressure indicators (Table 4). With results expressed per ha, all four methods ranked OA (low input–low output) best and GAP (high input–high output) worst (Table 12). However, with results expressed per kg of pig, rankings were not identical. LCA and EF ranked GAP best and OA worst, whereas FarmSmart ranked OA best and RL worst, while EMA-EI did not differentiate the scenarios. These results deserve a closer analysis. Increased use of inputs (e.g. fertilisers, concentrate feed) per unit area allows higher output of desired products, but also inevitably leads to more undesired outputs, i.e. emissions to the environment (e.g. De Koeijer et al., 2002; Schro¨der et al., 2003; Lewis et al., 2003). As a result, on a per area basis, impacts will logically tend to increase with increasing level of input use of the farm. This was confirmed here by the four methods producing identical rankings (OA > RL > GAP) when results were expressed per unit area. However, when impacts are expressed per kg of product, the correlation between input use per ha and impacts will depend on the ratio of undesired outputs (emissions to the environment) over desired outputs (products), both of which increase with input use, but not necessarily at the same rate. In the case-study presented here, the four methods did not produce identical rankings when impacts were expressed per unit product, illustrating greater sensitivity of this mode of expression to differences among the methods for environmental objectives considered and calculation algorithms used for the indicators. The results of the methods compared here clearly vary. Two main sources of difference can be distinguished (Table 12): (a) differences between modes of expression (e.g. LCA per ha versus LCA per kg) and (b) differences between methods within a mode of expression (e.g. EMAPA versus DIALECTE-AEI). 4.3. Differences between modes of expression The mode of expression of results had a major effect on rankings of the scenarios. The question whether impacts of agricultural production systems should be expressed per unit area or per unit product has been subject of considerable

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debate. From the LCA point of view (Guine´e et al., 2002), impacts should be expressed per unit product when the function of the system is the production of commodities, and per unit area for a non-market function (e.g. environmental services). Haas et al. (2000) have argued that for local/ regional impacts, such as eutrophication, expression per unit area is most appropriate, whereas for global impacts (e.g. climate change) impacts should be expressed per unit product. De Koeijer et al. (2002) prefer expression of impacts per unit area to take into account the carrying capacity of the environment. We feel that there is a strong case for expressing impacts of agricultural production systems both per unit area and per unit product. The impact/unit area ratio combines an environmental criterion and an area, the latter supplying a context, allowing the implementation of area-based threshold values founded on critical limits or ceilings, which can be derived from national or regional goals for emissions or impacts (IPCC, 2001; Erisman et al., 2003). The impact/unit product ratio combines an environmental criterion and a production criterion, and thus is a measure of environmental efficiency (Olsthoorn et al., 2001), rather than a measure of environmental impact. The two modes of expression are clearly complementary. Reliance on the sole impact/unit area ratio may well lead to a preference for low input–low output systems, which may decrease impacts at regional level, but may create a need for additional land use elsewhere, giving rise to additional impacts. On the other hand, reliance on the impact/unit product ratio only may well lead to a preference for high input–high output systems, which, when concentrated at regional scale, have been shown to cause major pollution problems (Tamminga, 2003). 4.4. Differences within modes of expression Within two of the three modes of expression major differences in the rankings were observed (Table 12). For the methods expressing results for the farm as a whole, EMA-PA differs from DIALECTE for its global objective (assessment of adherence to best practice for EMA, evaluation of environmental impact for DIALECTE), for the environmental objectives considered (Table 3) and the indicators used (Table 4). EMA-PA considers eight environmental objectives, and DIALECTE nineteen, but the methods have only two environmental objectives in common (Table 3). Thus here both the different global objectives and the nearly total disagreement with respect to the set of environmental objectives may have caused the different rankings. LCA, EF, EMA-EI and FarmSmart, which express results both per ha and per kg of pig produced, yielded identical rankings when results were expressed per ha. When results were expressed per kg of pig produced, contrasting rankings were obtained, or scenarios were not differentiated. These methods are based on a limited number of environmental objectives most of which are not shared (Table 3). So here

337

differences in the set of environmental objectives will probably have contributed to the different rankings. However, differences in calculation algorithms for the indicators used and in the way the boundaries of the system to be analysed were defined will also have played a major role, as can bee seen from the contrasting results obtained by the emissions inventories produced by three of these four methods (Table 13). As the four methods differ both for their set of objectives, indicators used and system definition, it is not possible to assess the relative contribution of each of these factors to the contrasting rankings obtained.

5. Conclusions This work has clearly demonstrated that the outcome of studies using indicator-based environmental evaluation methods to compare farming systems depends not only on the characteristics of the systems compared, but also to a large extent on those of the evaluation methods used. Five methods for evaluation of the environmental impacts of farms were used to rank three farm scenarios. Depending on the method, rankings obtained were similar, somewhat different or completely inverse, or ranking proved inconclusive. Outcomes differed due to differences in the evaluation methods concerning: (i) the global objective of the method, (ii) the set of environmental objectives considered, (iii) the definition of the boundaries of the system to be analysed, (iv) the calculation algorithms of the indicators used as evaluation criteria. As the methods compared differed for more than one of these characteristics, it was not possible to assess the relative importance of the contribution of each of these four factors. This study furthermore revealed the mode of expression of results (for the whole farm, per unit area, or per unit product) as a fifth factor strongly affecting the rankings obtained. Expression of impacts per unit area is complementary to expression per unit product. Reliance on the sole impact/unit area ratio may well lead to a preference for low input-low output systems, which may decrease impacts at regional level, but may create a need for additional land use elsewhere, giving rise to additional impacts. On the other hand, reliance on the impact/unit product ratio only may well lead to a preference for high input-high output systems, which, when concentrated at regional scale, have been shown to cause major pollution problems. We therefore recommend the use of evaluation methods that express their results both per unit area and per unit product. More generally we recommend that environmental evaluation methods be used with great caution. Users should carefully consider which method is most appropriate given their particular needs, taking into consideration the method’s global objective, its system definition, its set of environmental objectives, the quality of the indicators used and its mode of expression of results.

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Acknowledgements This research was funded by an OECD research fellowship within the OECD Co-operative Research Programme: Biological Resource Management for Sustainable Agriculture Systems. The authors are solely responsible for the data and opinion herein presented, that do not represent the opinion of OECD. This work is part of the research programme ‘‘Porcherie Verte’’ (Green Piggery) and was financially supported by ADEME (Agence de l’Environnement et de la Maıˆtrise de l’Energie) and OFIVAL (Office National Interprofessionnel des Viandes, de l’Elevage et de l’Aviculture).

References Basset-Mens, C., van der Werf, H.M.G., 2005. Scenario-based environmental assessment of farming systems: the case of pig production in France. Agric. Ecosyst. Environ. 105, 127–144. Braband, D., Geier, U., Ko¨pke, U., 2003. Bio-resource evaluation within agri-environmental assessment tools in different European countries. Agric. Ecosyst. Environ. 98, 423–434. CEE, 1991. Re`glement du conseil (CEE) no. 2092/9122, 1991 concernant le mode de production biologique de produits agricoles et sa pre´sentation sur les produits agricoles et les denre´es alimentaires. Journal Officiel de la Communaute´ Europe´enne, L198. De Koeijer, T.J., Wossink, G.A.A., Struik, P.C., Renkema, J.A., 2002. Measuring agricultural sustainability in terms of efficiency: the case of Dutch sugar beet growers. J. Environ. Manage. 66, 9–17. EEA, 1999. Environmental indicators: typology and overview. Technical Report No. 25. European Environmental Agency, Copenhagen, Denmark. EEA, 2005. EEA core set of indicators. Guide. EEA Technical Report. No. 1/2005. European Environmental Agency, Copenhagen, Denmark. Erisman, J.W., Grenfelt, P., Sutton, M., 2003. The European perspective on nitrogen emission and deposition. Environ. Int. 29, 311–325. FAO, 2002. FAOSTAT Agriculture Data. http://apps.fao.org/. Goodlass, G., Halberg, N., Verschuur, G., 2003. Input output accounting systems in the European community—an appraisal of their usefulness in raising awareness of environmental problems. Eur. J. Agron. 20, 17– 24. Groupement des Fermiers d’Argoat, 2000. Cahier des charges e´leveur— porcs fermiers e´leve´s en plein air. Homologation no. 20–88, SaintBrieuc, France. Guine´e, J.B., Gorre´e, M., Heijungs, R., Huppes, G., Kleijn, R., de Koning, A., van Oers, L., Wegener Sleeswijk, A., Suh, S., Udo de Haes, H.A., de Bruijn, H., van Duin, R., Huijbregts, M.A.J., 2002. Life cycle assessment. In: An Operational Guide to the ISO Standards, Centre of Environmental Science, Leiden University, Leiden, The Netherlands. Haas, G., Wetterich, F., Geier, U., 2000. Life cycle assessment framework in agriculture on the farm level. Int. J. Life Cycle Assess. 5 (6), 345– 348. Halberg, N., Verschuur, G., Goodlass, G., 2005. Farm level environmental indicators; are they useful? An overview of green accounting systems for European farms. Agric. Ecosyst. Environ. 105, 195–212. Hansen, J.W., 1996. Is agricultural sustainability a useful concept? Agric. Syst. 50, 117–143.

Houghton, J.T., Meira Filho, L.G., Callander, B.A., Harris, N., Kattenberg, A., Maskell, K., 1996. Climate Change 1995: The Science of Climate Change. Cambridge University Press, Cambridge, United Kingdom. IPCC, 2001. In: Mitigation, Metz, B. (Eds.), Climate Change 2001. IPCC Secretariat, Geneva, Switzerland. Lewis, K.A., Bardon, K.S., 1998. A computer-based informal environmental management system for agriculture. Environ. Modell. Software 13, 123–137. Lewis, K.A., Newbold, M.J., Tzilivakis, J., 1999. Developing an emissions inventory from farm data. J. Environ. Manage. 55, 183–197. Lewis, D.R., McGechan, M.B., McTaggart, I.P., 2003. Simulating fieldscale nitrogen management scenarios involving fertiliser and slurry applications. Agric. Syst. 76, 159–180. Ministe`re de l’Agriculture et de la Peˆche, 2000. Cahier des charges concernant le mode de production et de pre´paration biologique des animaux et des produits animaux de´finissant les modalite´s d’application du re`glement CEE no. 2092/91 modifie´ du conseil et/ou comple´tant les dispositions du re`glement CEE no. 2092/91. Homologue´ par l’arreˆte´ interministe´riel du 28 aouˆt 2000. Journal Officiel de la Re´publique Franc¸aise (30 August 2000). Paris, France. OECD, 1999. Environmental indicators for Agriculture. Concepts and Framework. Organisation for Economic Co-operation and Development, Paris, France. Olsthoorn, X., Tyteca, D., Wehrmeyer, W., Wagner, M., 2001. Environmental indicators for business: a review of the literature and standardisation methods. J. Cleaner Production 9, 453–463. Petit, J., van der Werf, H.M.G., 2003. Perception of the environmental impacts of current and alternative modes of pig production by stakeholder groups. J. Environ. Manage. 68, 377–386. Pointereau, P., Bochu, J.L., Doublet, S., Meiffren, I., Dimkic, C., Schumacher, W., Backhausen, J., Mayrhofer, P., 1999. Le diagnostic agrienvironnemental pour une agriculture respectueuse de l’environnement. Trois me´thodes passe´es a` la loupe. Travaux et Innovations. Socie´te´ Agricole et Rurale d’Edition et de Communication, Paris, France. Rees, W.E., 2000. Eco-footprint analysis: merits and brickbats. Ecol. Econ. 32 (3), 371–374. Rosenberg, P.E., Gallot, J., 2002. Re´fe´rentiel de l’agriculture raisonne´e. Arreˆte´ du 30 avril 2002 relatif au re´fe´rentiel de l’agriculture raisonne´e. Journal Officiel de la Re´publique Franc¸aise (Paris, France) 104 . Schro¨der, J.J., Aarts, H.F.M., ten Berge, H.F.M., van Keulen, H., Neeteson, J.J., 2003. An evaluation of whole-farm nitrogen balances and related indices for efficient nitrogen use. Eur. J. Agron. 20, 33–44. Schro¨der, J.J., Scholefield, D., Cabral, F., Hofman, 2004. The effects of nutrient losses from agriculture on ground and surface water quality: the position of science in developing indicators for regulation. Environ. Sci. Policy 7, 15–23. Solagro, 2000. DIALECTE, Diagnostic Liant Environnement et Contrat Territorial d’Exploitation. User Manual, First Version. Solagro, Toulouse, France. Tamminga, S., 2003. Pollution due to nutrient losses and its control in European animal production. Livestock Production Sci. 84, 101–111. Tzilivakis, J., Lewis, K.A., 2004. The development and use of farm-level indicators in England. Sustain. Dev. 12, 107–120. Van der Werf, H.M.G., Petit, J., 2002. Evaluation of the environmental impact of agriculture at the farm level: a comparison and analysis of 12 indicator-based methods. Agric. Ecosyst. Environ. 93, 131–145. Von Wire´n-Lehr, S., 2001. Sustainability in agriculture—an evaluation of principal goal-oriented concepts to close the gap between theory and practice. Agric. Ecosyst. Environ. 84, 115–129. Wackernagel, M., Rees, W.E., 1996. Our Ecological Footprint. Reducing Human Impact on the Earth. New Society Publishers, Gabriola Island, British Columbia, Canada.