Alexander Wezel

Carlo Grignani

Michel Marie

Multi-criteria agro-environmental evaluation of low input dairy farming systems

List of figures

List of tables

Introduction

The concept of sustainable development has taken a growing place in national and international policies since the statement of the Brundtland Commission on the impoverishment of people and environment due to the actual development model (WCED, 1987). In its report, the Commission defined as sustainable the "development that meets the needs of the present without compromising the ability of future generations to meet their own needs". This general concept has been applied to many human activities in order to define the best actions to undertake for the preservation of resources, environment and people.

In the last fifty years also agriculture has been questioned about the sustainability of its practices: high pesticides use and its effects on people's health and environment, land degradation and deforestation, biodiversity losses, food safety issues (f.i. BSE-mad cow, dioxin chicken, GMOs) and animal welfare, use of non-renewable energy and greenhouse gases emission, natural resources exploitation and landscape modification [START_REF] Meynard | Produire autrement: réinventer les systèmes de culture[END_REF]. These problems are the consequences of a global strategy to increase agriculture productivity in order to feed a growing world population. The new technologies discovered in the developed countries have been transferred to some developing countries through what is called the "Green revolution": chemical fertilizers, pesticides and new selected varieties to be highly productive in the new cropping conditions. Also livestock production has seen a strong intensification, in particular for dairy farming with the selection of high milk yielding cows (Nardone and Solms-Lich, 1999).

After five decades of implementation, global agriculture has witnessed the failure of this strategy, with declining marginal yields for input use and the environmental damages mentioned above. The aim of producing enough food for a still growing population, expected to reach 9 billion in 2050, has not been met. Global competition and low prices for commodities have caused the impoverishment of many farmers around the world or obliged them to leave their land. Today of the 2,5 billion people living of agriculture, 600 million cannot make a living out of it and 1,3 billion practice a subsistence agriculture (Griffon, 2007a). Furthermore in the recent years a new tendency stood out: the search for foreign arable land by net-importer or highly populated countries (such as Saudi Arabia, Unite Emirates, China and India) in order to assure food for their domestic population in the event of a new food crisis [START_REF] Cotula | Land grab or development opportunity? Agricultural investment and international land deals in Africa[END_REF].

On its side, livestock farming, with particular focus here on dairy systems, has been pointed out for its impact on the environment, for example due to nutrient leaching and eutrophication, as well for its social acceptability, in terms of ecological and ethical production (Gibon et al., 1999a). The possibility for these hazards to become important impacts has drawn the attention of the public opinion and opened a new perspective for environmental and agriculture legislation.

How is agriculture supposed to face and answer these issues? Is sustainable agriculture the solution to those problems? If yes, which practices should be implemented to achieve sustainability and how to evaluate their effectiveness?

The idea of sustainability has far evolved from the catch phrase of WCED mentioned above and many definitions have been proposed [START_REF] Mebratu | Sustainability and sustainable development: historical and conceptual review[END_REF]. Agriculture could be defined sustainable if it is able to maintain in the long term its productivity as well as its benefits to society [START_REF] Ikerd | The need for a system approach to sustainable agriculture[END_REF]. This means that not only ecological but also economic and social aspects should be considered in the development of practices that could assure high production levels and at the same time preserve ecosystems functionality. The need here is to find a technological alternative that draws its knowledge from natural ecosystems interactions and services, coupling them with modern technologies to assure a sufficient food production today and in the future both at local and global scale.

This concept of 'ecological intensification' of agriculture has been given different names:

agroecology, conservation farming, eco-agriculture, ever-green agriculture etc. (Griffon, 2007 b).

The practices promoted are the same: crop rotation and association, introduction of leguminous plants to fix nitrogen from the atmosphere, use of local varieties adapted to the climate, cover crops, planting of hedges and grass strip, agroforestry, coupling animal breeding to crop production, use of animal manure and compost, minimum tillage, use of biological pest control methods, etc. The goals of these techniques are often multiple and synergic: soil fertility and soil structure improvement, erosion prevention, leaching of nutrients reduction to avoid eutrophication, closing of the nutrients cycle, biodiversity conservation, development of natural antagonists populations, soil, water and air quality preservation, fossil energy use limitation.

Most of these practices are already implemented in organic farming, but this does not exclude a technological transfer to other types of agriculture that cannot face the obligations of organic standards, in order to reduce chemical inputs and pesticides use and promote an environmentally friendly intensification.

The need to test the effectiveness of agroecological practices at farm system and local level, has led to the establishment of long term farm trials (f.i. Rodale Institute 1 , Fibl for organic farming 2 ), but more might be started to adapt those techniques to different local environments and social contexts, in order to obtain a sort of library where field actors could compare and learn from different experiments and results [START_REF] Meynard | Produire autrement: réinventer les systèmes de culture[END_REF].

However, experimental farms have to be evaluated in order to assess which practices best fit sustainability requirements for a studied situations. Many methods and tools have been proposed

to approach sustainability evaluation (Van der Werf and Petit, 2003;[START_REF] Ness | Categorizing tools for sustainability assessment[END_REF] that could be applied to farming practices. Sustainability assessment can be carried out either ex ante, in order to visualize the potential results of different decision rules and practices management, or ex post to establish if and at which degree the set objectives have been met. Furthermore, an important step of the evaluation process is the validation of the method with regards to its usefulness and consistency towards the evaluated system.

This work aims at contributing to the evaluation of the agro-environmental sustainability of the experimental farm of INRA-Mirecourt, where two low-input organic dairy farming systems have been designed, one based on grassland (SH) and the other based on mixed crops-pasture management (SPCE) [START_REF] Mignolet | Concevoir des systèmes de production économes et autonomes et modéliser leur organisation au sein de territoires. Le projet d'une équipe de recherche pour contribuer au développement durable[END_REF]. The objective is to validate a method to evaluate the agro-environmental sustainability of the dairy farm. The method proposed for this assessment is a multi-criteria analysis of different aspects involved in the promotion of sustainable farm management which are evaluated through a tree of aggregated indicators.

The next chapter will propose an overview on the methods that have been used to assess dairy farming sustainability, in order to underline the difference between them and reasons why multi-criteria analysis has been chosen for the evaluation of INRA-Mirecourt dairy systems.

Sustainability of dairy farming systems

Dairy cattle farming, as any animal husbandry activity in general, presents many constraints that could affect its sustainability. Certainly, the final effect depends on the farming management: manure availability could be a strength in a mixed cropping-breeding system, on the contrary it would rise disposal problems in an intensive indoor system. Whilst the first could valorize the self-sufficiency of its nutrients cycle, the latter would produce a surplus of nutrients that needs to be discharged somewhere else. Furthermore, the use of concentrates and dry fodder for feeding results in high land use devoted to cereals and proteaginous crops, with consequent application of fertilizers and pesticides, high consumption of direct and indirect fossil energy and water consumption for irrigation, animal drinking, cleaning of milking materials and stables.

High density husbandry conditions could enhance disease spreading and animal stress, thus increasing the use of allopathic treatments.

These issues have emerged for most of the times sustainability of farming activities has been considered as a matter of "resource sufficiency" rather than as system's reproduction capacity, a concept expressed as "functional integrity" [START_REF] Thompson | Sustainable livestock production: methodological and ethical challenges[END_REF].

Thus the wrong approach to farming system management could transform these constraints into threats to agro-environmental sustainability in the form of greenhouse gases emissions (CH 4 , N 2 O, CO 2 ), water pollution due to pesticides and nutrients leaching, air pollution (NH3, NOx, SO 2 ), soil erosion, diseases resistance to treatments, deterioration/worsening of animal health and well-being and appearance of food safety issues (BSE, dioxin chicken, etc.). For instance, genetic variability could be threaten by the preference choice of high productive races at dispenses of local breeds. On the other side biodiversity of flora and fauna could be enhanced by the implementation of grazing and the maintenance of natural grassland, or could be lost with fodder crops specialization for indoor husbandry. Grazing could also contribute to soil erosion reduction, as well as the valorization of marginal areas and the maintenance of open spaces [START_REF] Gibon | Managing grassland for production, the environment and the landscape. Challenges at the farm and the landscape level[END_REF]. However, not only environmental but also economic and social aspects must be remembered: for example, the unpredictability of input market prices linked to the dependence on off-farm fodders threatens economic viability of farms, or the choice of local breeds could valorize local economy and keep alive local traditions.

These general concerns about dairy farming are the starting point of sustainability assessment, which is perceived as a hierarchical framework (Van Cauwenberg et al., 2007) where first, general principles are formulated as the overall goals to achieve sustainability (i.e.

preservation of abiotic and biotic resources). Second, from those principles criteria are drawn, which identify the impacts, the practice or the elements involved to accomplish the principles (i.e. water or air quality, soil fertility, climate change potential, biodiversity conservation). Third, indicators are used to measure the level of compliance with a criterion and assess the state of the system (i.e. nitrate leaching in watertable). Finally, reference value are defined to describe a quantitative threshold or target for the indicator (i.e. the maximum tolerate content of NO3 in watertable is 50 mg/l).

An indicator is "a variable which supplies information on other variables which are difficult to assess and which can be used as a benchmark to take a decision" [START_REF] Gras | Le fait technique en agronomie -Activité agricole, concepts et méthodes d'étude[END_REF]. The objective of indicators is to quantify phenomena and situation which are complex and cannot be directly measured. The output of an indicator is a value that describes the state of the environment or the pressure of a pollutant and that can be used to compare similar systems in which the same variables are involved. Indicators can be the simple measurement or estimation of a variable, or the composition of several variables aggregated together [START_REF] Bockstaller | How to validate environmental indicators[END_REF]. They could be classified into "means-based" (i.e. farming practices) or "effectbased" (i.e. effects of farming practices on the state of the system or on the environment): the latter usually require more data collection and are of not easy implementation, but their results describe an impact closer to reality [START_REF] Hansen | Is agriculture sustainability a useful concept[END_REF][START_REF] Van Der Werf | Evaluation of the environmental impact of agriculture at farm level: a comparison and analysis of 12 indicator-based methods[END_REF].

There is no minimum or limit number of indicators, nevertheless it should be as small as possible and as large as essential [START_REF] Bossel | Indicators for sustainable development: theory, method, application -A report to the Balaton Group[END_REF]. According to [START_REF] Gamborg | Sustainability in farm animal breeding : a review[END_REF], sustainability should be addressed by four to six principles (or concerns), each characterized by two to four criteria, which in turn could be assessed by two to four indicators. This means an overall sustainability evaluation could end up with 16-96 indicators.

Sustainability assessment methods

Sustainability assessment methods have been compared in many papers ( [START_REF] Van Der Werf | Evaluation of the environmental impact of agriculture at farm level: a comparison and analysis of 12 indicator-based methods[END_REF][START_REF] Peschard | Tools for evaluating the environmental impact of agricultural practices at farm level: analysis of 5 agri-environmental methods[END_REF]Halberg et al., 2005 (b); [START_REF] Thomassen | Evaluation of indicators to assess the environmental impact of dairy production systems[END_REF][START_REF] Galan | ISO 14 001 at the farm level: analysis of five methods for evaluating the environmental impact of agricultural practices[END_REF][START_REF] Ness | Categorizing tools for sustainability assessment[END_REF][START_REF] Van Cauwenbergh | SAFE -A hierarchical framework for assessing the sustainability of agricultural systems[END_REF] with different objectives, spacing from the proposition of evaluation guidelines, to the aid for choosing which indicators to implement, or the evaluation of results in terms of effectiveness of usefulness. Other authors have proposed indicators sets for the evaluation of one or more pillar of farm sustainability [START_REF] Bockstaller | Use of agro-ecological indicators for the evaluation of farming systems[END_REF][START_REF] Andreoli | Farm sustainability evaluation: methodology and practice[END_REF][START_REF] Castoldi | Integrated sustainability assessment of cropping systems with agro-ecological and economic indicators in northern Italy[END_REF] as well as tool for holistic sustainability assessment such as RISE [START_REF] Hӓni | RISE, a tool for holistic sustainability assessment at the farm level[END_REF] and MEACROS [START_REF] Mazzetto | MEACROS: a tool for multi-criteria evaluation of alternative cropping systems[END_REF]. Important criteria that should be considered when analyzing or comparing different methods are scientific soundness (or relevance), feasibility, utility, comprehensibility and cost, together with the comparison of output results and recommendations yielded from the different methods [START_REF] Bockstaller | Comparison of methods to assess the sustainability of agricultural systems. A review[END_REF]).

Here the objective is not to exhaustively compare different evaluation methods to establish which is the most suitable for our purposes, but to describe the methods that approached the sustainability assessment of dairy farming systems. Five kinds of approach have been found in the literature: input-output accounting of nutrients, ecological footprint, Life Cycle Assessment, Linear Programming model, Multi-criteria analysis.

Input-output accounting (IOA) is a set of indicators that can be applied either to quantify production fluxes for economic/budgetary survey, or to broader assess the environmental impact of a farm [START_REF] Goodlass | Input-output accounting systems in the European Community -an appraisal of their usefulness in rising awareness of environmental problems[END_REF]. The latter case is expressed as the surplus of nutrients, pesticides and energy originating from the flow of input entering the farm and of products leaving the farm (Halberg et al., 2005a). This surplus is considered to be lost to the environment (Goodlass et al., 2001). In Europe many IOA systems have been proposed to help farmers auditing the flow of potential pollutants in their farms and thus accomplish with EU environmental policies [START_REF] Goodlass | Input-output accounting systems in the European Community -an appraisal of their usefulness in rising awareness of environmental problems[END_REF]. IOAs have been developed especially in The Netherlands and Denmark, whose governments imposed environmental taxes based on the amount of nutrients surplus at farm level [START_REF] Ondersteijn | The Dutch Mineral Accounting System and the European N Directive: implications for N and P management and farm performance[END_REF], following the commitment to the Council Directive 91/676 EEC (Nitrate Directive) which expresses the hazards of groundwater pollution and eutrophication due to nitrogen and phosphorus leaching (EEC, 1991;[START_REF] Goodchild | EU Policies for the reduction of nitrogen in water: the example of the Nitrates Directive[END_REF]. An example of IOA for France is the set of Agro-ecological indicators proposed by [START_REF] Bockstaller | Use of agro-ecological indicators for the evaluation of farming systems[END_REF], which put the basis for the development of the tool

INDIGO 3 .
The concept of ecological footprint was introduced in the early 1990s by W.E. Rees and M.

Wackernagel [START_REF] Rees | Ecological Footprint and appropriated carrying capacity: what urban economics leave out[END_REF][START_REF] Rees | Ecological footprints and appropriated carrying capacity: Measuring the natural capital requirements of the human economy[END_REF], who later proposed its calculation [START_REF] Wackernagel | Our Ecological Footprint: Reducing Human Impact on the Earth[END_REF]. Ecological footprints is defined as the surface used directly or indirectly by an individual, a community or human population as a whole to produce its needed resources and to absorb its wastes [START_REF] Wackernagel | Tracking the ecological overshoot of the human economy[END_REF]. The surface "consumed" is also referred to as biosphere annual regenerating capacity, earth biological carrying capacity or biological productive area (BPA). This concept has been applied to estimate environmental impact of various economic activities, depletion of natural resource by countries towards its future generation, or the visioning of ecological future scenarios [START_REF] Van Vuuren | Exploring past and future changes in the ecological footprint for world regions[END_REF][START_REF] Venetoulis | Refining the ecological footprint[END_REF][START_REF] Ponthiere | The ecological footprint: an exhibit at an intergenerational trial? Environment[END_REF].

Life cycle assessment (LCA) is a product-related holistic method that evaluates the impact of a product on the environment through its complete life cycle. The impact is expressed in terms of resources consumption and pollutants emission, including those occurring during the production of the product's own inputs [START_REF] Guinée | Handbook on Life Cycle Assessment. An operational guide to the ISO Standards[END_REF]. The impact category is related to a functional unit which can express a global impact, for example kg of produced meat, milk or grain, or a local impact, i.e. ha of land [START_REF] Thomassen | Evaluation of indicators to assess the environmental impact of dairy production systems[END_REF][START_REF] Vries | Comparing environmental impacts for livestock production : a review if life cycle assessments[END_REF].

Linear programming is a model which allows to define ex ante the economic performances of an activity, e.g. a farm [START_REF] Kerselaers | Modelling farm-level economic potential for conversion to organic farming[END_REF]. It maximizes a selected aspect for a farm (f.i.

environmental impact) in order to see what changes/effects will occur to the other aspects, such as economic revenue. Thus it guides in defining the optimal technical solution for that farm. It is based on a matrix which crosses all farm activities with the constraints, internal and external, pressuring on the farm performances [START_REF] Berentsen | An environmental-economic model at farm level to analyse institutional and technical change in dairy farm[END_REF].

Multi-criteria analysis is an integrated assessment method used for systems in which competing evaluation criteria are present [START_REF] Ness | Categorizing tools for sustainability assessment[END_REF]. It permits to distinguish which factors favor or constrain sustainability, acting as decision aid tool which can be implemented either in an ex ante or ex post evaluation. It allows obtaining a trade-off between opposing or conflicting objectives which results in an overall sustainability value at the farm system level.

This single value can be break down into the lower-level criteria to observe the results yielded by the various objectives and thus their respective weight on the overall sustainability. Multi-criteria decision model such as MASC [START_REF] Sadok | MASC, a qualitative multi-attribute decision mode for ex ante assessment of the sustainability of cropping systems[END_REF], monitoring tool as RISE [START_REF] Hӓni | RISE, a tool for holistic sustainability assessment at the farm level[END_REF] and MOTIFS [START_REF] Meul | MOTIFS : a monitoring tool for integrated farm sustainability[END_REF], and farm evaluation method such as IDEA [START_REF] Vilain | La méthode IDEA: Guide d'utilisation. Troisième édition actualisée[END_REF] have been proposed to link sustainability evaluation theory to farming management practice.

Dairy farming systems evaluation

The literature review on dairy farming evaluation yielded two papers comparing or implementing IOA (Halberg et al., 2005a;[START_REF] Thomassen | Evaluation of indicators to assess the environmental impact of dairy production systems[END_REF], one for ecological footprint [START_REF] Thomassen | Evaluation of indicators to assess the environmental impact of dairy production systems[END_REF], five for LCA (Hass et al., 2001;[START_REF] Thomassen | Evaluation of indicators to assess the environmental impact of dairy production systems[END_REF]Thomassen et al., 2008a;[START_REF] Thomassen | Relating life cycle assessment indicators to gross value added for Dutch dairy farms[END_REF][START_REF] Van Der Werf | An operational method for the evaluation of resource use and environmental impacts of dairy farms by life cycle assessment[END_REF] and two main works on Linear programming model by [START_REF] Berentsen | An environmental-economic model at farm level to analyse institutional and technical change in dairy farm[END_REF] and by Van Calker et al. which produced several consequently related papers (2004; 2005; 2006; 2007; 2008). With regards to multi-criteria approaches, five methods have been retained: an integrated economic-environmental accounting framework as applied by Pacini et al. (2003) in an Italian context, IDEA method [START_REF] Zahm | Assessing farm sustainability with IDEA method -from the concept of agriculture sustainability to case studies on farms[END_REF], OTPA indicators guide (Guillaumin et al., 2007b) and the tools MOTIFS [START_REF] Meul | MOTIFS : a monitoring tool for integrated farm sustainability[END_REF] and RISE [START_REF] Hӓni | RISE, a tool for holistic sustainability assessment at the farm level[END_REF].

The methods introduced above present some differences and similarities with regards to parameters such as objective, sustainability aspects involved, spatial and temporal scale, type of indicators and their calculation, rating or scaling method, aggregation procedure. Additional differences are for example the time required to implement the method and the target end-users.

Input-output accounting system

Input-output accounting system was implemented by [START_REF] Thomassen | Evaluation of indicators to assess the environmental impact of dairy production systems[END_REF]

Ecological footprint

The ecological footprint assessment, due to its definition, yielded one single indicator: biological productive area (BPA) per kg of fat and protein corrected milk (FPCM). In the work of [START_REF] Thomassen | Evaluation of indicators to assess the environmental impact of dairy production systems[END_REF] ecological footprints was evaluated at farm scale. It does not consider a particular time scale because it can cover different time periods according to inputs and outputs production. Figure 2 present the parameters used by the authors to calculate the ecological footprint of an organic dairy farm.

Life cycle assessment

Life cycle assessment as proposed by [START_REF] Thomassen | Evaluation of indicators to assess the environmental impact of dairy production systems[END_REF], considered five environmental impact categories: land use, fossil energy use, global warming potential, eutrophication and acidification potential. In a study aimed to compare Dutch conventional and organic dairy production, Thomassen et al. (2008a) applied these same impact categories adding the distinction between on farm and off farm impact for each indicator. In a further study [START_REF] Thomassen | Relating life cycle assessment indicators to gross value added for Dutch dairy farms[END_REF] [START_REF] Haas | Comparing intensive, extensified and organic grassland farming in southern Germany by process life cycle assessment[END_REF] applied life cycle assessment to compare the environmental impact of intensive, extensified and organic grassland farming in Germany. The indicators set was richer than that of the Dutch studies presented above, for it included also impact of pesticides on animal and human toxicity, biodiversity preservation and animal welfare.

In general, LCA is based on the production period which could change from one farm to the other, as well as the farm's input production. Figure 3 present a possible graphic comparison of impact categories evaluated through LCA.

Linear Programming model

A LP model was set to determine the effects of possible changes at institutional, technical and market price level on dairy farm management, with regards to nutrient losses leading to environmental pollution and economic performances of the farm [START_REF] Berentsen | An environmental-economic model at farm level to analyse institutional and technical change in dairy farm[END_REF]. IDEA (Indicateurs de durabilité des exploitation agricoles, Farm sustainability indicators) is a method developed by [START_REF] Vilain | La méthode IDEA: Guide d'utilisation. Troisième édition actualisée[END_REF] for French Agriculture Ministry, that aims at being a self-assessment tool for farmers as well as a pedagogic tool for teacher and students [START_REF] Guillaumin | La contribution des exploitation d'élevage au développement durable : état des lieux des méthodes d'évaluation et rèsultats. 16 emes Rencontres autour des Recherches sur les Ruminants[END_REF]. The evaluation of farm performances is based on the three aspects of sustainability, which are referred to as agroecological, socio-territorial and economic, divided into ten criteria and measured by 41 indicators. A particular trait of IDEA is the visualization of the relationship between the indicators and the objectives of sustainability evaluation, realized with the help of a matrix. Indicators are generally composed of different qualitative or quantitative variables. They are calculated using the data available on the farm and the results is translated as score on a ascending scale, where higher scores mean higher sustainability for that criteria. The score of each indicator belonging to a criteria of sustainability is summed together, in order to obtain a single value for that specific criteria, from 0 (minimum score) to 30 (maximum score). These final values are used to build a spider graph on which the ten criteria of sustainability individuated by the method can be compared (Fig. 4). This evaluation allows the understanding of the strong aspects of farm management and the weak points towards sustainability.

Criteria scores can also be aggregated to obtain one score for the ecological, social and economic aspects. The overall sustainability score is expressed by the lowest of these three Economique) to be used for indicators calculation and to be recorded. The results are assembled in a table, which regroups indicators according to farming practices and that expresses their value related to a single campaign, a region or a farming system. To make the results easier to read and understand, an histogram graph for each farming practice is produced (Fig. 5). Each indicator is expressed as a horizontal bar whose value varies from 0 to 100. If the bar is oriented to the right (positive values), it will be assessed as sustainable, the opposite if it is oriented to the left (negative values). The longer the bar, the higher the sustainability or unsustainability of the related indicator. Since the indicators may be expressed by different unit of measurements, before the graph comparison they are translated into average values with respect to the sample of farms. However, at the moment of writing, the authors of the method have not defined an aggregation procedure to obtain a briefer overview of the results yet. Finally the method offers the possibility to pass from the farm to the territory scale, by expressing the results as rate of a territory reference surface (i.e. arable area, intercropped surface, irrigated surface etc.). IDEA and OTPA methods refer to the actual situation of the farm at the moment of the evaluation: time references are eventually specified for the required parameters of each indicator (f.i. annual application of organic matter for the indicator "organic matter management" or N and P fertilizer supply as average over two years for the indicator "fertilization" in IDEA).

MOTIFS (Monitoring Tool for Integrated Farm Sustainability) is based on a set of 47 indicators (not all defined and validated) evaluating the three dimensions of sustainability which are equally weighted [START_REF] Meul | MOTIFS : a monitoring tool for integrated farm sustainability[END_REF]. Indicators outputs are rescaled into scores between 0 and 100 in order to compare and aggregate the indicators by weighting. The tool output is visualized as a pie-radar graph (Fig. 6) of ten themes of sustainability, where the length of the segment represent the score obtained by the theme, the width defines the theme's weight, and the color corresponds to the belonging sustainability aspect.

Figure 6. Pie-radar graph used to visualize MOTIFS output and interpretation guidelines (source [START_REF] Meul | MOTIFS : a monitoring tool for integrated farm sustainability[END_REF].

RISE (Response-Inducing Sustainability Evaluation

) is a tool to assess sustainability and address practices optimization at farm level on yearly base [START_REF] Hӓni | RISE, a tool for holistic sustainability assessment at the farm level[END_REF]. Each of the twelve aspects is evaluated for its "State" (S) and its pressure on the system's sustainability ("Driving Force", D). State of the system and pressure are addressed by one or more factor, resulting in a score between 0 and 100, where 0 is the best score for pressure and 100 the best one for state. The overall "Degree of sustainability" (DS) is calculated as difference between S and D: it is considered as sustainable an aspect with a DS score higher than +10 or a farm with no aspect scoring lower than -10. The DS scores for each aspect are represented on a radar graph (Fig. 7). However, in the review paper it was not presented how to calculated most of the factors to obtain the D and S parameters, neither how to rescale them from 0 to 100. Furthermore a conceptual misleading is produced for the authors call "indicators" what indeed would be "aspects" or criteria of sustainability. Full Time Employees (FTE), while graph b present the results for a scenario in which cattle housing is optimised and renewable energy are used for machinery and heating (source [START_REF] Hӓni | RISE, a tool for holistic sustainability assessment at the farm level[END_REF]. It emerges here the conceptual difference between multi-criteria methods and indicators set based methods, which is the lack of criteria aggregation for the latter. Aggregation is a fundamental step of multi-criteria evaluation, which is meant to assess the holistic sustainability of a farm. Furthermore it permits to understand the role that each indicator plays on the related criterium. On the contrary, methods based on indicators sets cannot express sustainability as a whole, but only present separated quantitative criteria measurements. Multi-criteria analysis better fits sustainability evaluation than single indicators set, for sustainability is "more than the sum of the parts" (von Wirén-Lehr, 2001).

Another difference between multi-criteria and indicators set methods stands in the number of criteria considered in the evaluation. As one can see from table 1, multi-criteria methods use more criteria and indicators compared to all the other methods, also to those applying an economic and social evaluation such LP model.

Finally multi-criteria methods could be more easily used as decision aid tools because it allows to establish the relation between farming practices and sustainability criteria: on one side, the impacts of a single practice on different criteria could be identified, and on the other side the correlation and complementation of different practices on the same criterium could stand out. The first one is involved at the farm scale and works on the establishment of innovative production systems which integrate crops and animal breeding. The research is orientated towards sustainable management and evaluation of these particular farming systems. The second is involved at the territory scale in order to identify environmental risks linked to agriculture, especially for water quality, and how to limit the pollution or improve environmental condition through farming practices.

Context of the study

The study presented in this document was carried out in the context of the first theme.

The experimental farm of INRA-Mirecourt

The experimental farm of INRA-Mirecourt research unit is devoted to dairy farming research since the 1960s. In 2004 the conversion to organic farming started and was completed in 2006.

The objective of the research is to evaluate the technical and managerial performances of production systems on a long term scale (Coquil et al., 2009a). The aim of the experimental farm is to prototype a low input system, sustainable from environmental and agronomic point of view.

The economic viability is not considered as a prerequisite of the system because it would inevitably change the environmental and agronomic parameters set to run and study a sustainable farming management.

Two dairy systems are present on the experimental farm, one based on grassland (SH) and one based on mixed cereals and grassland (SPCE) (Fig. 8). The objectives of agro-environmental sustainability of the experimental farm accomplish three main criteria: abiotic resources preservation (water, air, fossil energy, soil fertility), biotic resources conservation (animal and vegetal biodiversity) and efficiency of farm production (crops and dairy production). The practices and agronomic choices are implemented in order to comply with those objectives and the organic farming rules: rotations and mechanical weeding play an important role since no pesticides are applied. The organization of soil labour, usually implemented before the seeding of spring cereals which were preceded by a winter cover or after the three years of temporary meadows/alfalfa, influences organic matter mineralization and fossil fuel consumption. The use of mixed cropping, cover crops, leguminous crops and meadows contribute to different aspects: soil fertility and nitrogen fixation, weed management, pest and disease control, but also to biodiversity in terms of crops and varieties number and favoring useful insects such as pollinators. With regards to production, the farm aims at being selfsufficient through the optimization of resource available on the farm surface and the off-setting of the two systems. The implementation of a low input system requires in fact as much autonomy from external input as possible and high complementarities at local territory scale This is obtained by regulating the feed and straw production on one side and organizing the milking period of the two herds over the all year as far as possible. Furthermore, the autonomy in straw utilization is very important in organic breeding in order to provide litter for the stable, on one hand to assure good housing for animals, as required by the European Regulation [START_REF] Leroux | Elevage bio : des cahiers des charges français à la réglementation européenne[END_REF], and on the other hand to obtain a stabilized manure. The crops-grazing system SPCE assures the availability of concentrate feed and litter straw for the two systems, while the grazing system SH complements the uptake from the first system with manure, closing the nutrient cycle [START_REF] Blouet | Polyculture-élevage: développer des complémentarités dans les exploitations et dans les territoires[END_REF].

The structure and organization of the two breeding systems of the experimental farm of Mirecourt depend on all the factors and interactions described above, to which other requirements for land use load and animal welfare has to be added. Although sold milk and wheat constitute a monetary revenue for the farm, economic viability is not a target of the experimentation. The objective is in fact to verify if such an autonomous system could be managed at the farm and local territory level: if it provides a benefit for the environment and resource preservation, then a potential economic support from the future policies could be justified.

Background for a multi-criteria evaluation

The setting-up and the management of a self-sufficient dairy farming system need to be verified in their effectiveness and impact on the territory. It is in this context that a multi-criteria analysis was developed to evaluate the two dairy systems implemented at INRA-Mirecourt. This evaluation aims at defining to which extent the set objectives are accomplished with regards to agro-environmental sustainability, in order to propose a valid model of sustainable dairy farming. In the past few years two studies have been conducted to propose a model for ex-ante evaluation of agro-environmental sustainability of the grazing system SH [START_REF] Gerber | Le modèle Dexi-SH* pour une évaluation multicritère de la durabilité agro-écologique des systèmes d'élevage bovins laitiers herbagers[END_REF] and of the cropping-breeding system SPCE [START_REF] Garnier | Rapport d'alternance: Élaboration d'une méthode d'évaluation multicritère et ex ante de la durabilité agro-environnementale des systèmes polyculture élevage bovin laitier: Le modèle DEXI SPCE[END_REF]. These two works used the tool Dexi, an integrated rule-based model that allows the sustainability assessment of an activity through the establishment of a tree where sustainability criteria are aggregated, weighted and attributed with qualitative classes [START_REF] Bohanec | Multi-attribute modelling of economic and ecological impacts of cropping systems[END_REF]. The Dexi-SH model aggregates sixteen indicators and seven expertise criteria, while Dexi-SPCE model aggregates about forty criteria, most of them yet to be completed, and twenty indicators derived from different available methods.

Objectives of agro-environmental sustainability

The present work aims at evaluating ex-post the agro-environmental sustainability of the two dairy systems of INRA-Mirecourt, using the criteria and indicators proposed in the Dexi-SPCE model. The evaluation tree proposed in this model is based on three main aspects, corresponding to the objectives of the experimental farm: abiotic resources preservation, biotic resources conservation and agro-ecosystem efficiency. The present work will develop the evaluation of the first aspect. The two latter aspects could not be investigated due to time restriction.

With regards to abiotic resources preservation, the Dexi-SPCE model proposed to evaluate the impact of farming practices on the water, the air, soil and fossil energy consumption (Fig. 9).

Each one of these main criteria is assessed by one or more level of sub-criteria: water quality is disaggregated into groundwater, which could be affected by pesticides residues and nitrate leaching, and surface water, affected by pesticides and allopathic treatments residues and eutrophication by phosphate. Air quality is affected by pesticides emissions, acidification potential due to ammoniac emissions from crops and animals, and global warming potential influenced by greenhouse gases emission and carbon storage in the soil. Soil quality is assessed as aggregation of physical quality, linked to compaction and erosion risk, and soil fertility, depending on organic matter content and phosphorus availability in the soil. Finally, the consumption of fossil energy is determined by the direct energy use and the indirect use due to mineral fertilizers.

The lowest level criteria in the tree could be evaluated through the use of indicators, which were proposed for most of criteria in the Dexi-SPCE model.

The investigation of those indicators is the starting point for this work, whose procedure to evaluate the impact of farming practices on abiotic resources is presented in the next chapter.

Materials and methods

Two low input organic dairy systems

The experimental farm is based on 240 ha of land and a herd of about 100 cows, breeds The choice of alfalfa or temporary meadows, as well as the presence of spring cereal in the rotation, is due to plots soil type. The five crop rotation systems of SPCE 6P, 6H, 8P, 8H and PP have 7, 11, 21, 4 and 24 plots respectively.

As one can observe from figure 10, cover crops are seeded before a spring cereal in order to maintain the soil with some vegetation during the winter. Soil tillage as well is usually implemented before spring cereals in order to destroy the vegetal cover and integrate the organic matter in the soil. Soil tillage could also be used after the three years of alfalfa or meadow.

Fertilization is usually applied at the end of the sixth year for the 8 years rotations, and at the end of the fourth year for the 6 years rotations (Fig. 10 red arrow) with about 25 t of manure per ha. Fertilization of grasslands for the two systems SH and SPCE depends on the number of cuts (about 15 t of liquid manure/ha per cut). Grasslands which are grazed do not receive further fertilization.

With regards to dairy production, in the campaign 2007/2008 the milk production per cow, for the two breeds Holstein and Montebéliarde respectively, was on average 5403 kg and 4887 kg for SH, and 6456 kg and 5411 kg for SPCE, with a total production of 206.642 kg for SH and 328.487 kg for SPCE, corresponding to 89% of the milk quota for the global farm (Coquil et al., 2009a).

More details on farming practices will be presented in the description of indicators calculation.

Evaluation of the impact on abiotic resources

For the evaluation of the impact on abiotic resources, the indicators implemented were chosen from four methods: INDIGO, gas emission from husbandry system proposed by [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF], PLANETE, OTPA.

Information about the four methods were gathered with different procedures. The Microsoft-Access based method INDIGO [START_REF] Bockstaller | Comparison of methods to assess the sustainability of agricultural systems. A review[END_REF] was demanded directly to the developers at INRA-Colmar and was obtained together with the indicators guidelines. Also PLANETE method was requested to its developers of SOLAGRO 13 : in exchange with the guidelines and spread sheet used to calculate the indicators, the results of the evaluation will be returned to the developers. The indicators proposed by Peigné were studied on a copy of her PhD

Indicators calculation

The calculation of indicators was established through three main steps: first, listing of data needed for indicators calculation, second, colle the form required by the methods. Finally the effective calculation was implemented through the filling in of the forms or spread sheets presented by the methods.

The results output for indicators of INDIGO method as well as those proposed by Peigné ( 2003) is classed on a scale from 0 to 10, where 7 is the reference value or the acceptability 14 Sinagri.com: http://rhone-alpes.synagri.com/synagri/synagri.nsf/

23

Thesis and calculated with the aid of the author herself. The guidelines of OTPA method and its indicators were downloaded from the agricultural chambers portal of Rhone 11 illustrates the tree of criteria and their related indicators used to evaluate the environmental sustainability with regards to the impact on abiotic resources. Physical quality of soil was not evaluated in this work because the two chosen indicators "t "erosion" from OTPA method needed to be better constructed, but this was not possible because Indicators tree implemented for the evaluation of the impact of farming practices on abiotic resources as a environmental sustainability.

Indicators calculation

The calculation of indicators was established through three main steps: first, listing of data needed for indicators calculation, second, collection of those data followed by the adaptatio the form required by the methods. Finally the effective calculation was implemented through the filling in of the forms or spread sheets presented by the methods.

The results output for indicators of INDIGO method as well as those proposed by Peigné ( 2003) is classed on a scale from 0 to 10, where 7 is the reference value or the acceptability alpes.synagri.com/synagri/synagri.nsf/ herself. The guidelines of OTPA method and its indicators were downloaded from the agricultural chambers portal of Rhone-Alps Region 14 .

11 illustrates the tree of criteria and their related indicators used to evaluate the environmental sustainability with regards to the impact on abiotic resources. Physical quality of wo chosen indicators "trampling risk" and better constructed, but this was not possible because Indicators tree implemented for the evaluation of the impact of farming practices on abiotic resources as a

The calculation of indicators was established through three main steps: first, listing of data followed by the adaptation to the form required by the methods. Finally the effective calculation was implemented through the

The results output for indicators of INDIGO method as well as those proposed by Peigné ( 2003) is classed on a scale from 0 to 10, where 7 is the reference value or the acceptability threshold below which the results are considered less and less sustainable. Since a similar tolerance value is not present by PLANETE method, it was proposed in this work for indicators CO 2 emission and energy consumption.

Finally, INDIGO indicators are first calculated at plot scale and after transformed into system scale scores through a weighted mean of plots surface.

Data listing

Through the studying of the different guidelines, a list of data necessary for the calculation of each indicator was completed. All the methods implemented explained clearly the data needed to be collected for the evaluation, either with a list for each indicator (J. Peigné, OTPA) or with a document for the gathering of information on the farm (INDIGO and PLANETE). However, in the second case the data requested in the document were not assigned to a specific indicator, because often they are used for the calculation of different indicators. Thus the listing of data for each indicator was more difficult and required more time.

Data collection

The primary source of data for indicators calculation was the research unit's data base called "ALAMB-ix". In this Microsoft-Access data base are linked the information from the crop database "ASTER-ix" [START_REF] Trommenschlager | Gérer et organiser les données agricoles et de recherche d'un site expérimental. Réalisation d'une base de données pour l'expérimentation-système en place sur l'installation expérimentale de l'unité ASTER Mirecourt[END_REF] and the animal database "ALADIN" [START_REF] Champciaux | ALADIN 97 : Application de la gestion des troupeaux Laitiers et Allaitants des Domaines INRA, Nouvelle version[END_REF]. Those two databases allow to insert new information in their tables and to extract the desired information with query or forms. During this work only extraction queries were implemented.

The database ASTERI-ix contains information about the cropping itineraries, the different working operations such as seeding, harvesting, fertilization and pesticides treatments, the fossil fuel consumption, the flux of organic matter between the two system SH and SPCE, the number of grazing days, the analysis on harvest, feed and fertilizers, data on pests and diseases occurred on the plots.

The different information listed above can be extracted according to the two systems SH and SPCE, to the different rotation systems, to each plot or to the fertility points present in the plots (54 for SPCE and 20 for SH). Fertility points correspond to the spots in the plots were samples for different analysis are collected. As requested by the different methods, information where gathered at the plots level for INDIGO and at systems level for the others.

The database ALADIN contains information of the two herds about the sex, parents, race, changes of age, location, nutrition, treatments, selling, death for each animal. It is possible to link each of these information thanks to the identification number assigned to each animal.

When some information were not available on the database, for example the surface of stables and manure stocking pools, they were requested to some operators of the research unit. If also this kind of information was not available, the necessary data were collected from the literature.

The data collected on the data base were often not in the good form and measurement unit required for indicators calculation. Thus a conversion and adaptation step was undertaken on spread sheets.

Indicators for abiotic resources

Water pollution risk

Four indicators were calculated to assess the risk of water contamination by nitrate leaching (INO 3 ), phosphate eutrophication (IPsol), pesticides in surface water (IPhyto Surface Water) and in groundwater (IPhyto Groundwater).

• INO 3

The indicator for nitrate leaching corresponds to the module INO 3 of the indicator "Azote" of INDIGO method. INO 3 is calculated as the sum of nitrates lost by leaching after fertilizer application (LP) [START_REF] Recous | Transformations et devenir de l'azote de l'engrais sous cultures annuelles et sous prairies[END_REF] and during the winter (LH) when the soil has little or no cover [START_REF] Machet | Maîtrise de l'azote dans les intercultures et les jachères[END_REF].

In order to calculate LP, the following formula ( 1) is applied in INDIGO method:

LP=∑L i = ∑(X i ' * l*(BH*t i ) *f) (1)
where L i is the nitrate leaching after an 'i' fertilization, X i is the quantity of N applied at each fertilization, l is the leaching coefficient [START_REF] Burns | Equations to predict the leaching of nitrate uniformly incorporated to a known depth or uniformly distributed throughout a soil profile[END_REF], BH is the winter drainage, t i is a coefficient expressing the temporal distance between the fertilization and the crop's highest N absorption capacity, and f is the frequency of rainfall after fertilization [START_REF] Bockstaller | Comparison of methods to assess the sustainability of agricultural systems. A review[END_REF].

LH is calculated as the balance of N after harvesting and of drained N (2):

LH = N post-harvesting Balance*l (2)
The N post-harvesting balance is calculated as the difference between N input and output (3): et al., 1996). Output factor Nabs supposes N absorbed by a cover crop as not subject to leaching anymore [START_REF] Carlotti | Recueil des bases de la préconisations de la fertilisation azotée des cultures[END_REF][START_REF] Chapot | Nitrogen uptake kinetics of six nonlegimunous cover crops after wheat to recover 100 kg/ha of residual nitrogen[END_REF]. The leaching concentration observed is compared to a maximum tolerance threshold of 50 mg NO 3 per liter of groundwater.

The data entered in INDIGO forms for each plot were soil type, texture, organic matter and P content, lime percentage and stone percentage, plot size (ha), crop type, expected and effective yield (q or t/ha), sowing and harvesting dates, presence of a cover crop, quantity of applied fertilizer (t/ha). For grassland also type of animal at grazing, animal load and number of grazing days were registered. Data such as average rainfall (mm), evapotranspiration (mm), temperature (°C), rainfall frequency and mineralization rate during winter were entered once for the overall farm.

• IPres

The indicator for phosphate loss and eutrophication risk was derived from the module IPres of the indicator "Phosphore" of INDIGO method. P leaching (kg P 2 O 5 /ha) is calculated as the difference between total P input (X) on the plot and the recommended P dose (X R ) (4). If this difference is negative, then the phosphate leaching is considered equal to zero, if not it is equivalent to the absolute value of this difference.

D = X -X R (4)

The total dose X is the sum of phosphorus obtained from fertilization [START_REF] Bodet | Fertiliser avec les engrais de ferme[END_REF], while the recommended dose is calculated by a form based on the program REGIFERT [START_REF] Pellerin | Evolution du raisonnement de la fertilisation phosphatée des grandes cultures. Etude par simulation des préconisations de fumure sur un échantillon test représentatif des sols et des successions de culture du Nord du Bassin Parisien[END_REF]INRA, 2003). To assess the final note of the indicator, the equation proposed by INDIGO for the indicator Phosphore was applied to the single module IPres (5,6,7): where C is T/3 and T is the tolerance threshold of 30 kg P 2 O 5 /ha [START_REF] Colomb | Le diagnostic de la fertilité phosphaté du sol et les règles de la fertilisation des grandes cultures, Références régionales et fertilisation raisonnée : le logiciel REGIFERT[END_REF].

If
Data necessary for the calculation were the same as INO 3 for soil type and occupation, fertilization, grazing, plus the superior and inferior threshold of phosphorus content in the soil, obtained from [START_REF] Pellerin | Evolution du raisonnement de la fertilisation phosphatée des grandes cultures. Etude par simulation des préconisations de fumure sur un échantillon test représentatif des sols et des successions de culture du Nord du Bassin Parisien[END_REF].

• IPhyto surface and groundwater

The impact of pesticides on water quality was evaluated with the indicator IPhy of INDIGO method, which present the results for three separated modules: pesticides in surface water (IPhy eaux surface), pesticides in groundwater (IPhy eaux profondes) and pesticides in the air (IPhy air). Since the experimental farm of Mirecourt is converted to organic farming, there is no use of chemical pesticides on crops. Thus in the form "Interventions" of INDIGO (Fig. 12) all the plots were selected for no treatment applications. It was not necessary to introduce other parameters.

• IPhyto Air

As explained above for IPhyto for surface and groundwater, it was not necessary to introduce data about pesticides in INDIGO form because no chemicals are used. A special form was used to select the plots with no pesticides application.

• INH 3

The indicator for ammonia emissions from volatilization was obtained from the indicator INH 3 proposed by [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF] and from INDIGO method module INH 3 of "Azote" indicator. As shown in figure 13, the total emission of NH 3 is calculated as the sum of three modules: emission originating from barn's litter, emission from manure stocking and emission from fertilization.

The first two modules separately consider the emissions due to slurry (or liquid system) and manure (solid system).

There are six barns at the experimental farm: one for SH cows with no litter, thus accounted as liquid system; two for SPCE cows and one for heifers of both systems that have a bedding area with litter and a stalling area without litter; one for calves and one for bulls of both systems with litter. Manure on the stalling area (i.e. scrape manure) was considered as liquid system because its consistency is similar to slurry since no straw is added and animals stamp on it. It is collected every day and stocked in three different piles: one for cows (only from SPCE), one for heifers (mixed) and one for calves (mixed).

was considered as solid system

Stocking sites for liquid system where taking into account only for SH, whose slurry from milking cows' barn is stocked into an open pit. (mixed) and one for calves (mixed). Manure collected from the bedding areas (straw manure) was considered as solid system: it is collected from the barn in autumn and composted on a plot.

Stocking sites for liquid system where taking into account only for SH, whose slurry from s' barn is stocked into an open pit.

indicator. Following the procedure of [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF], three modules are considered:

emissions from barns, stocking sites and manure spreading. The first two were calculated as proposed by the author, while the third module was calculated with INDIGO. Each module is composed by two sub parameters or formula are presented in the last box column.

Emissions coming from liquid systems are calculated with the equations 8 (barns) and 9 humidity [START_REF] Dewes | Effect of pH, temperature, amount of litter and storage density on ammonia emissions from stable manure[END_REF][START_REF] Kapuinen | Deep litter systems for beef cattle housed in uninsulated barns, Part 2: Temperatures and nutrients[END_REF]. The frequent addition of a stabilizing agent (like sphagnum moss or clay materials) to manure limits the emission of NH 3 from the barn floor [START_REF] Souloumiac | Maîtrise à la source des émissions gazeuses et de l'état final des effluents en élevage intensif de porcs sur litières[END_REF]McCroy and Hobbs, 2001), while too frequent revolting of composting piles or stocked manure increases NH 3 emissions [START_REF] Aubert | Le compostage des fumiers de volailles[END_REF]. These factors are summarized in figure 13. The output of fuzzy logic (FLb from barns and FLs from stocking) is a value from 0 to 10 which is converted into kg NH 3 /year with the logarithmic equations ( 10) and ( 11 where Mb and Ms are the quantities of respectively manure collected from the barns (bedding areas) and manure stocked (stalling areas + composted manure from bedding areas).

emission
Figure 14. Decision tree for the solid system of the module "NH 3 emissions from barns". F stands for 'favorable' and D stands for 'unfavorable' (source: [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF].

Figure 15. Decision tree for the solid system of the module "NH 3 emissions from manure stocking sites". F stands for 'favorable' and D stands for 'unfavorable' (source: [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF].

Finally, NH 3 emissions from fertilizers application was calculated with the module INH 3 of INDIGO "Azote" indicator. The amount of ammonia (kg NH 3 /ha/y) volatilizing from cropped surfaces and grasslands is obtained through equations ( 12) and ( 13) respectively:

NH 3 emissions = ∑(D i *v) (12) NH 3 emissions = ∑(D i *v + G*0,081) (13)
where D is the N content of a fertilizer input i (kg NH 3 /ha) and v is a volatilization coefficient characteristic of each type of fertilizer [START_REF] Sommer | Effects of dry mater content and temperature on ammonia loss from surface-applied cattle sludge[END_REF][START_REF] Menzi | Ammonia emissions following the application of solid manure to grassland[END_REF]Smith and Chambers (1997) in [START_REF] Freibauer | Emissions rates and emissions factors of greenhouse gas fluxes in arable and animal agriculture[END_REF]. The emissions from grazed surfaces depend also on the animal load and the number of grazing days to obtain the amount G of NH 3

excreted from manure and the potential volatilization of NH 3 .

The kg NH 3 /year emissions from barns and stocking piles are summed together and divided per the system's surface, in order to be added to emissions from manure spreading expressed as kg NH 3 /ha/year. The final amount of volatized NH 3 (Et) is transformed into a value from 0 to 10 with equation ( 14) proposed by [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF]:

INH3=10*℮ ((-0,0192)*Et) (14)
To built this formula a tolerance threshold of 20 kg NH 3 per ha was validated by [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF]. where Qa is the amount of manure produced per animal in one day (kg or m3/an/d), according [START_REF] Ziegler | Engrais de ferme. Valeur fertilisante, gestion, environnement[END_REF]; Nex is the g N/kg of excreted manure according to Tillie and [START_REF] Capdeville | Etude sur les déjections bovines[END_REF]; An is the number of animal belonging to the same age category and farming system; Db is the number of days spent in the barn by the animals and Ds the number of manure stocking days; N2O-N emitted is a coefficient estimating g N2O emitted/kg N in man according to [START_REF] Freibauer | Regionalised inventory of biogenic greenhouse gas emissions from European agriculture[END_REF]; and S is the surface (ha) of the system. The different coefficients used in these two modules calc 31 O indicator. Following the procedure of [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF], three modules are emissions from barns, stocking sites and manure spreading. The first two were calculated as proposed by the author, while the third module was calculated with INDIGO. Each module is composed by two sub la are presented in the last box column.

Emissions from barns were calculated as presented by equations ( 15) and ( 16) where Qa is the amount of manure produced per animal in one day (kg or m3/an/d), according Nex is the g N/kg of excreted manure according to Tillie and [START_REF] Capdeville | Etude sur les déjections bovines[END_REF]; An is the number of animal belonging to the same age category and farming system; Db is the number of days spent in the barn by the animals and Ds the number of manure N emitted is a coefficient estimating g N2O emitted/kg N in man ); and S is the surface (ha) of the system. The different coefficients used in these two modules calculations are reported in table 2.

O
O indicator. Following the procedure of [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF], three modules are considered: emissions from barns, stocking sites and manure spreading. The first two were calculated as proposed by the author, while the third module was calculated with INDIGO. Each module is composed by two sub-modules whose required and (16):

emitted)/S (15) N emitted)/S (16)

where Qa is the amount of manure produced per animal in one day (kg or m3/an/d), according to Nex is the g N/kg of excreted manure according to Tillie and [START_REF] Capdeville | Etude sur les déjections bovines[END_REF]; An is the number of animal belonging to the same age category and farming system; Db is the number of days spent in the barn by the animals and Ds the number of manure N emitted is a coefficient estimating g N2O emitted/kg N in manure ); and S is the surface (ha) of the system. The different coefficients [START_REF] Capdeville | Etude sur les déjections bovines[END_REF][START_REF] Freibauer | Regionalised inventory of biogenic greenhouse gas emissions from European agriculture[END_REF] The third module considers the amount of N 2 O emitted from plots surface due to fertilizers application, soil tillage, irrigation and soil type (Fig. 16). This amount, expressed as kg N 2 O/ha/y, is calculated as presented in equations ( 17) and ( 18 [START_REF] Bouwman | Direct emission of nitrous oxide from agricultural soils[END_REF][START_REF] Freibauer | Emissions rates and emissions factors of greenhouse gas fluxes in arable and animal agriculture[END_REF], Ctillage is a coefficient for soil tillage [START_REF] Mummey | Assessment of alternative soil management practices on N2O emissions from US Agriculture[END_REF], Cfert (is a coefficient due to fertilizer application method [START_REF] Dosch | Reducing N losses (NH3, N2O, N2) and immobilization from slurry through optimized application techniques[END_REF][START_REF] Chadwick | Nitrous oxide and ammonia emissions from grassland following applications of slurry: potential abatement practices[END_REF][START_REF] Clemens | Ammonia and nitrous oxide emissions after landspreading of slurry as influenced by application technique and dry matterreduction. II. Short term nitrous oxide emissions[END_REF] and Cirr a coefficient due to irrigation effect [START_REF] Qian | Soil denitrification and nitrous oxide losses under corn irrigated with high-nitrate groundwater[END_REF][START_REF] Xu | Simulating the impact of management practices on nitrous oxide emissions[END_REF][START_REF] Simojoki | Effect of nitrogen fertilization, cropping and irrigation on soil air composition and nitrous oxide emission in a loamy clay[END_REF]. In the case of grasslands a factor of 0,7 is used if hay is cut more than three times (Ccut) and a factor of 0,02 for animal grazing (Cgraze) [START_REF] Freibauer | Emissions rates and emissions factors of greenhouse gas fluxes in arable and animal agriculture[END_REF].

The three modules are then summed together and total amount Et of kg N 2 O emitted/ha/y is transformed into indicator value varying from 0 to 10 with the formula (19) proposed by [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF]:

IN 2 O=10*℮ ((-0,3072)*Et) (19)
The tolerance threshold applied by this formula is 1 kg N 2 O emissions per ha as proposed by [START_REF] Bouwman | Scaling of Nitrogen Gas fluxes from Grasslands[END_REF].

Data collected for the calculation of IN 2 O where the same as for INH 3 (see paragraph above).

• ICH 4

The indicator used for methane emissions evaluation was implemented from the one proposed by [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF], where two modules are summed together, one considering the emissions due to enteric fermentation and the other considering emissions from barns and stocking piles. According to [START_REF] Crutzen | Methane production by domestic animals, wild ruminants, other herbivorous fauna, and humans[END_REF] the production of methane from each animal (first module) is calculated as shown in equation ( 20 Where EGb is the ingested gross energy [START_REF] Vermorel | Prédictions gazeuses et thermiques résultant des fermentations digestives[END_REF] and EB is the ingested digestible energy [START_REF] Jarrige | Alimentation des bovins, ovins et caprins[END_REF]. EGb and EB are two coefficients depending on the type of feed and animal age, which were drawn from the literature review proposed by [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF]. The kg of CH 4 /day/animal obtained in this way are then multiplied for the number of animals (NA) of the same age category (atelier i ) and the number of days (D) they received that specific feed. This calculation is repeated for each type of feed (hay, concentrates etc.) and summed together (21).

The emission of CH 4 (kg/year) for the systems SH and SPCE is obtained by adding three ateliers (milking cows, heifers and calves) as shown in equation ( 22). The second module sums together the emission produced from barns and stocking piles.

These two sources are again calculated as solid and liquid systems. For sub-module barns and stocking, the formula in equation ( 23 where VS is the volatile solids produced per day per system (kg/d), B 0 is the CH 4 production potential (m 3 CH 4 /kg VS) [START_REF] Consulting | Emission inventory improvement program. Volume 8 Chapter 7: Method for estimating greenhouse gas emissions from manure management[END_REF], MCF (%) is a methane conversion factor expressing the percentage of methane emitted from stocking piles [START_REF] Zeeman | Methane production/emission in storages for animal manure[END_REF]IPCC, 1996) and NA is the animal number for an age category. The number obtained is multiplied for 0,67 kg/m 3 to convert CH 4 emission unit from kg to m 3 .

The total emission from barns and stocking piles (kg CH 4 /ha/y) are multiplied per 365 days because the indicator is calculated for a system over one year period, and divided by the system surface to obtain the CH 4 emission per ha.

To calculate VS the equation ( 25) was applied:

VS (kg/day) = Qa*%MS*%VS (25

)
where Qa is daily individual manure production, %MS is manure percentage of dry matter [START_REF] Ziegler | Engrais de ferme. Valeur fertilisante, gestion, environnement[END_REF] and %VS is the quantity of volatile solids expressed as percentage of dry matter [START_REF] Barth | Agricultural Waste Characteristics[END_REF].

The total emission Et (kg CH 4 /ha/y) were transformed into indicator's value from 0 to 10 with the equation ( 26) proposed by [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF], where the tolerance threshold for CH 4 emission is 30 kg/ha.

ICH 4 = 10*℮ ((-0,0116)*Et) (26)

Data were collected at system level for number of animals and age, type of feed and quantity, number of days for the same feed. All the other factors were derived from tables in [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF].

• CO 2 emissions

To evaluate carbon dioxide emissions from the two systems the indicator CO 2 of PLANETE method was applied [START_REF] Bochu | Consommation d'énergie et émissions de GES des exploitations en agricolture biologique : synthèse des résultats PLANETE 2006[END_REF]. This indicator is calculated on a spread sheet where different parameters of energy consumption are transformed into CO 2 equivalents by the use of literature coefficients. The accounted energy comes from direct consumption such as fuel, electricity and irrigation, and indirect consumption from production of mineral fertilizers, organic amendments, pesticides, seeds, animal feed, construction of farm buildings and farm machines and equipment. The output of the spread sheet calculation is the amount of equivalents t CO 2 /year and equivalents t CO 2 /ha.

CO 2 emissions from dairy farming have been estimated to vary between 0,8 and 1,4 kg CO 2 eq/kg milk on global average [START_REF] Sevenster | A sustainable dairy sector: Global, regional and life cycle facts and figures on greenhouse gas emissions[END_REF][START_REF] Vries | Comparing environmental impacts for livestock production : a review if life cycle assessments[END_REF].

According to [START_REF] Weiske | Mitigation of greenhouse gas emissions in European conventional and organic dairy farming[END_REF], in the continental areas of Europe (to which north-east regions of France belong to) the yearly CO 2 emissions is 0,133 kg CO 2 /kg milk for a conventional dairy model farm and 0,072 kg CO 2 /kg milk for an organic model farm [START_REF] Weiske | Mitigation of greenhouse gas emissions in European conventional and organic dairy farming[END_REF]. The lowest emissions were found in an organic model farm (0,058 kg CO 2 /kg milk) while the highest in a conventional model farm (0,186 kg CO 2 /kg milk), both from Atlantic areas. To built the indicator's curve, the emission amount of 0,058 kg CO 2 /kg milk was used as reference threshold 7. The optimum value 10 was assigned for emissions equal to zero.

According to a LCA study for IMPRO (Environmental Improvement Potentials of Products, EC project) [START_REF] Weidema | Environmental Improvement Potentials of Meat and Dairy Products[END_REF] the total emission of a dairy product (from field to consumer) is of 2,4 kg CO 2 eq/kg milk, where the off-farm emission due to milk processing and transport contribute to 10-20%. The on-farm CO 2 emission is estimated to be 17% of total GHG emission for conventional farming and 13% for organic farming, while CO 2 is the main responsible for off-farm GHG emission (De Boer, 2003;[START_REF] Sevenster | A sustainable dairy sector: Global, regional and life cycle facts and figures on greenhouse gas emissions[END_REF]Thomassen, 2008b). Thus the amount of 0,806 kg CO 2 /kg milk value of 0, corresponding to a farm scenario with a CO Figure 17. Procedure to calculate the CO line on-farm CO 2 emissions are calculated as multiplication of total GHG emission of a dairy product (2,4 kg CO eq/kg milk) per the on-farm share of these emission (80%) per the share of CO N 2 O and CH 4 (17%). In the second line calculates off and for GHG N 2 O and CH 4 is 100%. On the top, references for those data are reported.

Since the evaluation carried out in this work assesses the performa a whole, a functional unit expressed as t CO CO 2 eq/kg milk. According to an European Commission study (EC, 2000), the average farm for continental France has a surface of 70 ha, a herd of 28 milk/cow/year. Thus the amount milk for values 7 and 0 respectevely) t CO2 eq/ha=(kg CO 2 eq/kg milk 35 /kg milk was calculated (Fig. 17) and used to assess value of 0, corresponding to a farm scenario with a CO 2 emission excess.

Figure 17. Procedure to calculate the CO 2 emission amount corresponding to a value 0 for CO calculated as multiplication of total GHG emission of a dairy product (2,4 kg CO farm share of these emission (80%) per the share of CO 2 emission among the other GH gas . In the second line calculates off-farm emissions, whose share for product emissions is 20% is 100%. On the top, references for those data are reported.

Since the evaluation carried out in this work assesses the performance of the dairy system as a whole, a functional unit expressed as t CO 2 eq/ha for CO 2 indicator output is preferable than kg eq/kg milk. According to an European Commission study (EC, 2000), the average farm for continental France has a surface of 70 ha, a herd of 28 milking cows and a production of 4551 kg milk/cow/year. Thus the amounts used to built the indicator's curve (0,058 and 0,806 kg CO milk for values 7 and 0 respectevely) were converted into t CO 2 eq/ha as follow ( 27 used to assess the indicator's emission amount corresponding to a value 0 for CO 2 indicator. In the first calculated as multiplication of total GHG emission of a dairy product (2,4 kg CO 2 emission among the other GH gas farm emissions, whose share for product emissions is 20% nce of the dairy system as indicator output is preferable than kg eq/kg milk. According to an European Commission study (EC, 2000), the average farm for and a production of 4551 kg 058 and 0,806 kg CO 2 /kg as follow ( 27):

)*(4551 kg milk/cow/y)*(28 cows)*(1/1000 t/kg CO 2 eq)/70

The curve and its function are represented in figure 18. The function was used to transform PLANETE output (t CO 2 eq/ha) into indicator's value from 0 to 10.

Figure 18. Curve and function of the indicator CO 2 , where the reference value 7 corresponds to 0,24 t CO 2 eq/ha for an average farm of 70 ha, 28 milking cows and a production of 4551 kg milk/cow/year.

Data collected, at system level, for the implementation of this indicator were: number of animals for each age category, sold animals, solid and liquid manure produced, total milk production, type of crops for soil occupation, surface (ha) for each crop, yield, self-consumed produce percentage, total fertilized surface (ha), fossil fuel and electricity consumed for the system, quantity of bought seeds, type, number, age and yearly usage (hours) of tractors, and finally type, number, age and yearly usage (ha) for the other farm machinery (machines for seeding, fertilizing, harvesting, hay conditioning etc.).

Soil quality

Soil fertility was assessed with two indicators, one for organic matter content (IMO) and one for phosphorus availability (IPsol).

• IMO

To evaluate the content of organic matter influencing soil fertility, the indicator IMO of INDIGO was applied. The indicator value is obtain by multiplying the reference value 7 for the ratio between the humus Ax incremented into the soil and the recommended quantity of humus A R that should be incorporated into the soil (28). Ax is obtained by summing the different humus inputs on the plots due to crop residues and manure in the last four years. Those input are calculated as k1*m where k1 is the isohumic coefficient and m is the organic matter content of crop residues and manure [START_REF] Boiffin | Systèmes de culture et statute organique des sols dans le Noyonnais: application du modèle de Hénin-Dupuis[END_REF]. A R values are already registered in the form "Soil-fertilisations/Apport d'humus recommandé)" of INDIGO, and are based on values of optimal soil organic matter content by [START_REF] Rémy | L'entretien organique des terres[END_REF].

IMO=7*(A

Data collected for each plot were soil type, soil clay and organic matter content, soil tillage and labour depth, crop residues management, type and quantity of manure applied, type and yield of crops for the last four years.

• IPsol

To evaluate the content of phosphorous in the soil available for plants, the module IPsol of the indicator "Phosphore" of INDIGO method was implemented. IPsol is calculated as the difference between the P dose the crop can absorb (X A ) and the recommended P dose (X R ) as shown in equation ( 29). If this difference is positive, then the lack of phosphorus in the soil is considered equal to zero, if not it is equivalent to the absolute value of this difference.

D = X A -X R (29)

As for IPres, the total dose X A is the sum of phosphorus obtained from mineral and/or organic fertilization [START_REF] Bodet | Fertiliser avec les engrais de ferme[END_REF], while the recommended dose is calculated by a form based on the program REGIFERT [START_REF] Pellerin | Evolution du raisonnement de la fertilisation phosphatée des grandes cultures. Etude par simulation des préconisations de fumure sur un échantillon test représentatif des sols et des successions de culture du Nord du Bassin Parisien[END_REF]INRA, 2003) where C is T/3 and T is the tolerance threshold for phosphorus deficit of 30 kg P 2 O 5 /ha [START_REF] Colomb | Le diagnostic de la fertilité phosphaté du sol et les règles de la fertilisation des grandes cultures, Références régionales et fertilisation raisonnée : le logiciel REGIFERT[END_REF].

Data collected were the same as for module IPres (see above).

Energy consumption • • • • Energy

The indicator "Analyse energetique" of the method PLANETE was implemented to calculate the energy consumed at the system level. It is calculated as the sum of the direct use of energy (MJ/year) from fuel, electricity, irrigation and plastic material, and indirect use of energy for feed, fertilizers, pesticides, seeds, animal bought, stables and buildings and machinery (Ferrier et al., 1997).

The output of PLANETE energy indicator is expressed either as liter fuel equivalent per ha (or per kg of milk or per LSU/ha) or as MJ/ha. For its indicator of energy dependence, IDEA method [START_REF] Vilain | La méthode IDEA: Guide d'utilisation. Troisième édition actualisée[END_REF] assesses points from -1 to 8 according to the quantity of fuel eq/ha consumed, to which 1 or 2 points can be added if some renewable energies. To transform this amount into an indicators rank between 0 and 10, the formula from INDIGO indicator for energy consumption [START_REF] Pervanchon | Assessment of energy use in arable farming systems by means of an agro-ecological indicator: the energy indicator[END_REF] was applied (33):

IEn = 8,75544 10-9 (Et)² -6,5492 10-4 (Et) + 12,184 (33

)
where Et is the amount of consumed energy in MJ/ha. The tolerance value 7 is equivalent to a consumption of 7000 MJ/ha, corresponding to an integrated farming system [START_REF] Viaux | Une 3ème voie en grande culture[END_REF].

When this consumption is lower than 3500 MJ/ha a value of 10 is given, when it is above 34900 MJ/ha a value of 0 is assessed to the indicator.

Since it is calculated with the same spread sheet as CO 2 emissions indicator the same data were required and registered (see above paragraph CO 2 ).

Data management for INDIGO

The program INDIGO is made of different forms: some are used to enter the required data, others contain different parameters and coefficients about soil, crops, fertilizers, etc. Since not all the parameters already present in those form corresponded to the actual situation on the experimental farm, some adjustments had to be made to use the collected data.

Furthermore, some data as soil type, soil analysis, plot size, crop type, yield, sowing and harvesting dates, fertilizer input, grazing animals and days do not need to be registered for each indicator because they are common to different calculation and indicators.

Soil type

The experimental farm of INRA-Mirecourt is characterized by an heterogeneous soil type, varying from clay to lime-clay and lime-sand especially for the plots bordering with two streams.

Each plot may be characterized from many soil types different for clay content, depth, parent material (dolomite or shale or river deposit). Soil analysis, carried out in 2006, are available for fertility points: thus only plots containing a fertility point, and only the soil type characterizing the fertility point, could be linked to a specific soil type.

To calculate the indicators of INDIGO method it is necessary to register for each plot a soil type and its analysis (class, clay content, soil depth, phosphorus, organic matter and limestone content). Assigning to each soil type of each plot the status of plot, would have meant the introduction of almost 400 plots for the overall farm. Thus it was decided to assign only one soil type for each of the 67 plots of SPCE and for the 45 plots of SH. First the analysis from fertility points corresponding to the same soil type were compared to see if the results were not too

For each new crop new coefficients needed to be assigned:

-humus input (Ax) for IMO;

-crop P exigency and P assimilation (kg P2O5/ha)for the calculation of the recommended P dose for IPsol and IPres;

-N need (kg N/q or t of yield) for the calculation of the recommended N dose for INO3;

-parameter expressing the number of days the crop need to absorb 50% of N received by fertilization for LP calculation in INO3;

-residues mineralization coefficient (Nres) for INO3;

-N not absorbed by the crop (Ic) for INO3;

-N absorbed by the crop before winter as kg/ha (Nabs);

-water consumption as mm/ha (Conseau);

To obtained those coefficient, a weighted mean of the same coefficients for the crops alone was implemented. The weight was assigned as the proportion of the crop seeded in the mix.

With regards to the animals, the age categories and the corresponding livestock unit (LSU)

were detailed as in table 4.

Aggregation

The aggregation of the indicators was carried out with the aid of the tool DEXi [START_REF] Bohanec | Multi-attribute modelling of economic and ecological impacts of cropping systems[END_REF], which allows to create a hierarchical structure where input attributes (lower level of the structure) are aggregated into one or more intermediate levels represented by aggregated attributes. The latter are aggregated into a single root attribute, which assess the overall system evaluation. Aggregation is obtained through an utility function F. Input and aggregated attributes correspond respectively to the indicators and criteria used to evaluate that system.

DEXi allows to weight and define a qualitative ranking scale for each attribute.

In the present work, the hierarchical structure (here after called 'indicators tree') aggregating the different attributes levels was inspired by MASC (Multi-Attribute Assessment of Sustainability of Cropping systems), as well as weights and qualitative scales. MASC is a model for the multi-criteria evaluation of cropping system [START_REF] Sadok | Ex ante assessment of the sustainability of alternative cropping systems: implications for using multi-criteria decision-aid methods. A review[END_REF][START_REF] Sadok | MASC, a qualitative multi-attribute decision mode for ex ante assessment of the sustainability of cropping systems[END_REF] which has been applied to evaluate systems differing for crop type, cropping condition, farming management, geographical location [START_REF] Bohanec | SMAC Advisor: A decision-support tool on coexistence of genetically-modified and conventional maize[END_REF][START_REF] Bohanec | A qualitative multi-attribute model for economic and ecological assessment of genetically modified crops[END_REF]INRA, 2010). In the model MASC environmental sustainability is assessed through three main aggregated attributes: impact on environmental quality, pressure on resources and biodiversity conservation.

In this work, the environmental assessment was limited to the first attribute, called "Impact on abiotic resources" , whose structure was adapted to the indicators tree elaborated for the dairy farming systems of INRA-Mirecourt (presented in figure 11 In order to assign a qualitative value for each input attribute of the tree, the respective indicators' scores ranked from 0 to 10 were converted into a qualitative scale (Table 6).

Afterwards, a qualitative ranking scale was assigned to each aggregated and root attributes (table 7). The chosen scales allows to directly read the effect of the evaluated attribute: for instance level of pesticides in surface water could be "very low" (très faible), water pollution risk could be "from low to medium", impact on abiotic resources could be "from medium to high", and agro-environmental sustainability could be "very high". For any attribute it is possible to assign type of scales differing for level number, level name, increasing or decreasing order. In this work, following the suggestions of [START_REF] Angevin | MASC 1.0 : Une méthode d'évaluation multicritère de la durabilité des systèmes de culture. Présentation générale et principes d'utilisation[END_REF] proposed in MASC indicators guidelines, the choice was to assign four levels scale to all attributes, excepted those for soil quality and energy consumption. With regards to soil chemical quality, the score of indicators IMO and IPsol is considered "acceptable" when it is higher than the tolerance threshold 7, meaning that a favorable soil quality is obtained when soil fertility is conserved, through a positive humus balance and a regular P availability. IMO and IPsol results are unfavorable (défaborable) when they are between 4 and 7, meaning a lack of organic matter and phosphorus in the soil, reducing soil fertility. A very unfavorable soil quality (très défavorable) is the result of IMO and IPsol score lower than 4.

For energy consumption as well, three scale were adopted. A score of non renewable energy indicator higher than 7 correspond to a low (faible) energy use, a score between 4 and 7 means medium (moyenne) energy use, while a score below 4 correspond to a high energy use.

For GHG emission and NH 3 volatilization indicators, four scales were assigned: low (faible) for scores higher than 9, medium (moyenne) for scores between 7 and 9, high (élevée) for scores between 4 and 7, and very high (très élevée) for scores below 4. This choice might be penalizing especially for tolerated or good levels, but it was linked to the fact that GHG and NH3 emissions are a weak point of all livestock systems and dairy systems in particular (FAO, 2006).

Finally, all other attributes were ranked with four scales, where two extreme (very high and good to medium (from low to medium, "faible à moyenne") from a (from medium to high, "moyenne à élevée Afterwards all attributes were weighted level they belonged to (Fig. 19 except for GHG emission which is not proposed by the authors. The weights of the three GHG N 2 O-CH 4 -CO 2 were inspired by the repartition of these gases on the emission of a conventional dairy farm, expressed as CO respectively (Sevenster and de Jong "Impact on abiotic resources" had to be modified from Sadock the tree developed in this work included also weights were supposed to be almost equal for the four attributes assessing water, air, soil quality and energy consumption. (from low to medium, "faible à moyenne") from a medium to bad from medium to high, "moyenne à élevée").

all attributes were weighted according to their influence on the upper evaluation level they belonged to (Fig. 19). Attributes weights were drawn from Sadock except for GHG emission which is not proposed by the authors. The weights of the three GHG were inspired by the repartition of these gases on the emission of a conventional dairy farm, expressed as CO 2 equivalents, which are estimated to be 32%, 51% and 17%

de [START_REF] Sevenster | A sustainable dairy sector: Global, regional and life cycle facts and figures on greenhouse gas emissions[END_REF]. Also the weights of the final aggregation level "Impact on abiotic resources" had to be modified from Sadock et al. (2009) proposition, because the tree developed in this work included also the aggregated attribute "energy consumption". The weights were supposed to be almost equal for the four attributes assessing water, air, soil quality environmental sustainability tree and attributes weight. The number in the square boxes correspond to the weigh (%) assigned to each attribute. medium to bad situation to their influence on the upper evaluation ). Attributes weights were drawn from Sadock et al. (2009), except for GHG emission which is not proposed by the authors. The weights of the three GHG were inspired by the repartition of these gases on the emission of a conventional to be 32%, 51% and 17% , 2008). Also the weights of the final aggregation level (2009) proposition, because the aggregated attribute "energy consumption". The weights were supposed to be almost equal for the four attributes assessing water, air, soil quality

The number in the square boxes correspond Results of aggregated levels are obtained through a utility functions F (Fig. 20) that combines the qualitative values of corresponding input or aggregates attributes through a set of decision rules. Decision rules can be established by hand, assigning to each attribute combination its final qualitative value, or can be automatically assigned by DEXi. In the second case, it is possible to base decision rules on attribute weights and/or on attribute scale values. In this work the latter option was applied, maintaining attributes weights for decision rules construction. The list of decision rules is presented in the annex. Options a n corresponds to the indicators scores (from 0 to 10). Input attributes (X 1 -X 3 ) are aggregated into aggregate attribute X 6 through a utility function#1 F(X 1 ,X 2 , X3). Aggregate attribute X 6 and input attributes X 4 -X 5 are aggregates together into the root attribute Y throgh utility function#2 whose decision rules are shown in the top left box. For example if X 4 obtained a qualitative value "medium", X 5 obtained "very low" and X 6 obtained "low", then the overall result for Y will be "very low". The same functioning is applied to the lower level utility function#1. Utility functions can be defined manually for each combination or automatically by DEXi. In the latter case weights need to be assigned to the attributes participating in the function, and the first and last combinations have to be entered (source Sadock et al., 2009). [START_REF] Payne | A general algorithm for analysis of variance[END_REF], as it happens to be for rotation systems and crop types evaluated in this work.

Statistical analysis

For indicator INO 3 a further analysis was carried out to verify the crossed influence of two factors at a time. Three combinations were analyzed: rotation system-crop type, soil typerotation system and soil type-crop type. The first combination aims to deepler understand the relationships between the two factors previously analyzed through unbalanced ANOVA. The latter two combinations were chosen because soil characteristics such as N mineralization rate and water holding capacity have a role on INO 3 calculation. These characteristics vary according to the soil type, thus this factor could have an influence on indicator's result. Five soil types were determined according to soil texture (see paragraph 4.3.4.1.): "Clay" (A), "Clay-Loamy" (AL), "Loamy" (L), "Loamy-Clay" (LA) and Loam-Sandy"(LS).

The combined factors analysis for INO 3 was carried out with a REML (Residual Maximum Likelyhood), a linear mixed model that allows to analyze mixed groups of fixed and random factor with many missing terms [START_REF] Gilmour | AIREML, an efficient algorithm for variance parameter estimation in linear mixed models[END_REF]. The statistical significance was tested by a Wald statistic.

Results

Indicators results

The indicators' results are ranked on a scale from 0 to 10, where 7 is the tolerance value or the acceptability threshold below which the related criteria are considered less and less sustainable. Evaluation results are displayed on the radar graph of figure 22 unbalanced ANOVA to test the differences between the 6 rotation systems (6H, 6P, 8H, 8P, PP belonging to SPCE and PP-SH) and between the crop types. Indicator INO 3 was also analized with a REML linear mixed model in order to test the effects of combined crop type-rotation system, soil-type-rotation system and soil type-crop type on indicator's result.

Indicator I-Phyto Surface water, I-Phyto Groundwater and I-Phyto Air were not analyzed because all systems and crops obtained a value of 10. Indicators ICH 4 , CO 2 and non-renewable energy use were not analyzed because they were calculated at farm system level, so only one global value was available for SPCE and one for SH. Plot Saumnaumont 12 was integrated to SPCE permanent meadows system but in the evaluated campaign it was still treated as a temporary meadow. Globally speaking, the two dairy systems SPCE and SH obtained a value of 6,9 and 8,4 respectively. Looking at the resu distinguished: one with a low value around 5,5 (i.e. 6P), one with an acceptable average to tolerance value 7 (i.e. 8P) and one with a good value over 8 represented by the permanent meadows of SH. Ob (value lower than 7) depend eith a nitrate absorbing function between surplus between nitrogen input and output For SPCE 17 plots out of 67 (25%) where concerned by a value between 3 and 7 caused by the absence of cover cropping. Those plots were all occupied by winter cereals which in the rotations plan are not preceded by a cover crop. Eleven plots (16%) were char

(Nitrate leaching)

Globally speaking, the two dairy systems SPCE and SH obtained a value of 6,9 and 8,4 respectively. Looking at the results of the crop rotations (Fig. 24a), three different groups can be distinguished: one with a low value around 5,5 (i.e. 6P), one with an acceptable nce value 7 (i.e. 8P) and one with a good value over 8 represented by the of SH. Observing the results displayed in INDIGO either on an over-fertilization or on the absence of a cover crop with between two consecutive cereals. The latter is displayed as balance input and output. plots, where two temporary meadows, were concerned by the phenomenon described above. The four outsider values of SH (Fig. 24b) correspond to the four plots which obtained a low value for INO 3 (minimum 5,4). The over-fertilization of those permanent meadows could be due to the input of manure from animal grazing and from after cuts-spreading which overpass the N needs of the pasture. The result is a surplus of N that enhance nitrate leaching risk.

From the analysis of crop type influence on INO 3 results (Fig. 24c), we can notice that not only winter cereals but also some spring cereals (TpPf and TriP) obtained a low value for INO 3 .

In these cases, since a cover crop was implemented and no fertilization or a N dose under the recommended one was applied, we cannot use the same explanation as for winter cereals. It might be due to the soil type, which would require a further analysis.

Crossing INO 3 results for rotation systems and crop types with REML linear mixed model, we obtained that there is an influence of the two factors but no interaction between this them. In order to verify the influence of soil type on indicators results, first an unbalanced ANOVA was carried out using INO 3 values as variate and soil texture as fixed factor. Secondly, a REML linear mixed model analysis was applied to verify the crossed influence on INO 3 results of soil type and rotation systems, and of soil type and crop types.

Unbalanced ANOVA analysis, summarized in table 11, assessed that there is a significant difference between the soil texture "Loam-Sandy"(LS) and the other four soil textures "Clay" (A), "Clay-Loamy" (AL), "Loamy" (L) and "Loamy-Clay" (LA). However, REML analysis did not yield any interaction for INO 3 between the combinations "soil type and rotation system" and the combinations "soil type and crop type". The results for the first analysis "soil type and rotation system" are presented in table 12 and visualized in figure 26. One can notice that not all soil types are represented in the crop rotations. Only the soil type "Loam-Clay" (LA) is present in all rotation systems with a minimum of one plot (8H) and a maximum of 13 plots (8P). On the other side, PP-SH (which corresponds to the whole dairy system SH) is the only rotation system where all soil types are represented. Also for the REML analysis of crossed soil type and crop type no interaction was found. In this case the number of observed combination is even lower than the one presented in the previous analysis. Table 13 shows for each crop type (lines) the number of observation, mean INO 3 value and standard error related to soil type (columns). The mean values are represented on figure 27. One can observe that no soil type is correlated to all crop types and vice versa.

The only crop present in all soil type is permanent meadows of SH because it is the only soil occupation for this system. Apparently nitrate leaching is not influenced by soil type in this system, since there is no significant difference between the scores obtained by INO 3 on the different soil types. The results obtained by SPCE and SH where respectively 8,3 and 10.

With regard to crop rotation system the same group which is significatively differen meadows (PP and PP-SH) of both systems Among the 4 crop-based rotation systems, only 8H is below 7 (6,1). Since the level of 7 is assigned to those plots where the o can say that all rotation system different quantity resulting in a more positive balance for the grassland. From the box plot (Fig. 28b) we can also notice that while permanent meadows result (10), the other crop rotation systems have a big variat causes of this variation we could look

56

(Organic matter content)

The results obtained by SPCE and SH where respectively 8,3 and 10. crop rotation systems (Fig. 28a), it emerges that 6H, 6P, 8H and 8P the same group which is significatively different from the group composed of permanent SH) of both systems.

. IMO results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop rotation systems, only 8H is below 7 (6,1). Since the level of 7 is those plots where the organic matter input is higher than the recommended input, we can say that all rotation systems, 8H excluded, bring enough organic matter in the soil, bu different quantity resulting in a more positive balance for the grassland. From the box plot we can also notice that while permanent meadows plots obtained all the same indicator crop rotation systems have a big variation within the plots. To explain the ation we could look to the histogram of figure 28c where the different crops are analyzed. There are four different groups of crops: group a includes those with a value lower than 7 which are winter oat + faba bean (Avhfev), winter barley (Oh), spring barley (Op) and rye (Se). These crops are present on 2 plots out of 11 in 6H (18%), on 0 plot in 6P, on 2 plots out of 4 in 8H (50%) and on 4 plots out of 21 (19%). Group b includes wheat (Bh), spring triticale with (TpPf) and without fodder pea (TriP) with a mean value around 7. Group c includes alfalfa+dactyls (LuDac), temporary meadows and triticale with a mean value around 9. Group d includes spelt (Ep), permanent meadows but also alfalfa+dactyls and triticale (Tri), for a group mean between 9 and 10. Results variation among crop types could be positively related to the number of plots occupied by that crop type (Fig. 28d, see also table 10 for crop distribution).

Thus we cannot suppose that the difference is due to a particular system management but the management of the single plots.

Since the indicator value is based on the quantity of organic matter accumulated in the soil calculated as the balance between the input of organic matter (through fertilization and crop residues) and the recommended input, those crops which received no fertilization or whose residues are not incorporated into the soil are penalized by the indicator. Plots with cereal crops such as wheat and winter triticale that were fertilized because they were in the fertilization year of the rotation obtained a good value (f.i. Hauts de Vigne 31 with Tri obtained 9, Ravenel 5 with Bh obtained 10). Other winter cereals with no fertilization and exportation of residues yielded a note lower than 7. Spring cereals (2 plots TriP and 1 plot TpPf) obtained respectively a note of 9.7, 5.5 and 7.2. The first one was fertilized with 28 t/ha and preceded by a cover crop, the second and the third ones were only preceded by a cover crop. The difference between the note of the latter two could be explicated by the higher organic matter input coefficient assigned to the crop mix TpPf. Alfalfa plots and grasslands received notes higher than 7 because they are both fertilized and soil is covered all year long. Temporary meadows that were not fertilized (plots Domvallier 2, Haut du Bois 1 and 2) or that received slurry poor in organic matter (plot Justice 9) received a note lower than 7.

IPsol (Phosphorus availability)

The indicator IPsol yielded a value of 7,3 for SPCE and 8,9 for SH.

From the histogram of figure 29 by only one rotation 8H with a mean mean value of 6,8, 7,2 and 7,5 meadows systems PP and PP-SH with variation between the plots, as it can be noticed from the box present the largest variance (7,157). The reason for this influenced by the type of crops present in the rotati cereals and meadows. The indicator IPsol yielded a value of 7,3 for SPCE and 8,9 for SH. of figure 29a three rotation's groups can be noticed: group a composed n 8H with a mean value of 4,6, group b composed by 6H, 6P and 8P with value of 6,8, 7,2 and 7,5 respectively, and a third group c composed by the two permanent SH with a mean value of 8,8. All the rotation variation between the plots, as it can be noticed from the box plot in figure 29 present the largest variance (7,157). The reason for this difference between groups might by the type of crops present in the rotation, that could be differentiated between To see the influence of crop types, the respective analysis is presented in figure 29c and29d.

The high variances of Se (12) and TriP (13,96) can be explained as the number of plots occupied by these crops is very limited (3 and 2) and the value obtained by the crop is very different (10 and 4 for Se and 9,3 and 4 for TriP). The crop Bh is present on 9 plots that have a results variance of 6,295. It presents two outsider data corresponding to two plots of 8P (Ravenel 5 and Saumnomomnt 11) that obtain a value of 9,9 and 10, which are very different from the values of the other 7 plots (3,9).

When the indicator IPsol has a value below 7 it means that there is a lack of phosphorus in the soil, or in other words that the phosphorus input by fertilizers was not sufficient to compensate the absorption from the plants. This is mirrored by the results, where plots that received no fertilization input or spreading of slurry poor in phosphorus content obtained a value under the reference threshold. The shape of the histogram for IPsol (Fig. 29c) is very similar to the histogram of IMO (Fig. 28c), demonstrating that for these two indicators fertility of soil is strictly linked to nutrients input.

Observing the histogram of figure 29c it could be established the flow of phosphorus during the rotation: during the years of temporary or permanent meadows or alfalfa the availability of P is high thanks to the frequent manure input from fertilization and animal grazing. The winter wheat following those crops usually leaves a low P content in the soil because it is not fertilized.

The following year a secondary winter cereal is sown without fertilization (8H, 8P), with fertilization (6H) or a spring cereal preceded by cover crop (8P) or cover crop and fertilization (6P). For the first and third case the content of P will be low, while for the other two it will be acceptable. This difference could explain the variation among secondary winter cereals such as Avhfev, Oh, Op (first case) and Ep, Tri (second case) and among spring cereals such as TpPf (third case) and TriP (fourth case). Rye (Se), although belonging to the first case group, presents an irregularity for one of its plots (Petite Fin 22) which obtained a value of 10. This might be due to the limited surface of the plot (0,42 ha) and the low yield (3,9 q/ha) compared to the expected yield (31 q/ha).

IPres (Phosphorus losses)

The two systems SPCE and SH obtained a value of 9 and 9,3 respectively for the indicator IPres.

As shown in figure 30a, there is no significant difference between the rotation systems with regards to phosphorus lost into the water that could cause eutrophication. By the way plots present outsider results IPsol, because IPsol and IPres are two complementary modules of the indicator IP of INDIGO method: if on one side a plot have a good level of P availability in the soil due to its fertilization, on the other side one part of this P could be lost into t soil and plants absorption capacity.

With regards to crop types (Fig. 30 and the others, all crops obtained a value higher than the reference value can be found also for crop types ( The two systems SPCE and SH obtained a value of 9 and 9,3 respectively for the indicator there is no significant difference between the rotation systems with regards to phosphorus lost into the water that could cause eutrophication. By the way (Fig. 30b). Those are the crops which received a good value IPsol, because IPsol and IPres are two complementary modules of the indicator IP of INDIGO method: if on one side a plot have a good level of P availability in the soil due to its fertilization, on the other side one part of this P could be lost into the environment if the input was superior to soil and plants absorption capacity. types (Fig. 30c), even if there is a difference in the results between Ep , all crops obtained a value higher than the reference 7. The presence of outsider value can be found also for crop types (Fig 30d) The two systems SPCE and SH obtained a value of 9 and 9,3 respectively for the indicator there is no significant difference between the rotation systems with regards to phosphorus lost into the water that could cause eutrophication. By the way a lot of ). Those are the crops which received a good value for IPsol, because IPsol and IPres are two complementary modules of the indicator IP of INDIGO method: if on one side a plot have a good level of P availability in the soil due to its fertilization, he environment if the input was superior to ), even if there is a difference in the results between Ep

The modules of IN 2 O emission

proposed by [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF], assigned to all systems a value of 9,5. Thus the differences between the two groups are due to some plots characteristics linked to manure spreading. Rotation 6P is similar to both groups maybe because half of its plots are value similar to permanents meadows

From figure 31c we can see that meadow O emissions from barns and stocking sites, calculated with the procedure proposed by [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF], assigned to all systems a value of 9,5. Thus the differences between the two groups are due to some plots characteristics linked to manure spreading. Rotation 6P is e because half of its plots are temporary meadows that obtained a value similar to permanents meadows' mean value (between 2,2 and 4,8 [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF], assigned to all systems a value of 9,5. Thus the differences between the two groups are due to some plots characteristics linked to manure spreading. Rotation 6P is temporary meadows that obtained a s (temporary or permanent) do not have a different but are not different between each other, O emitted, this means that those crops emit a similar quantity of N and their continuous input of nitrogen through manure. The outsider either to plots with very high value (10) due to no grazing and fertilization at all (3 plots) or to plots with very low values (<2) probably because of over could not be explained by fertilization because there was no manure input. It could probably be linked to the soil type and mineralization rate

INH 3 (Ammonia volatilization)

The results of INH 3 were 6,9 and 7,1 for SPCE and SH respectively.

Total emissions were 19,7 and 17,5 kg NH presented in figure 32a show that the permanent meadows permanent meadows' system of SH and that both are different from the crop rotation 8P. The other crop rotations are in a way set between PP and PP and 8P (8H). This indicator, as IN 2 O, was estimated as the sum of the emissions from barns, manure stocking sites and fertilization. The results for the module barn and manure stocking were the same for the two system (8) with 11,7 kg NH3/ha for SPCE and 9,5 kg NH3/ha for SH. Thus the difference lies once again in the emission coming from fertilization, obtained through INDIGO.

The lower value of PP compared to PP-SH might be due to a higher manure input linked to animal load. The best value obtained by 8P might be explained by the fact that in this crop rotation system there were no temporary meadows but alfalfa + dactyle which is not fertilized.

But from the histogram in figure 32c we can see that there is no difference between these two crops. Another reason could be found in the number of plots belonging to each system, whose good or bad value could gain a different weight on a global note (21, 11, 7 and 4 plots for 8P, 6H, 6P and 8H respectively). The outsider data of 8P correspond to a wheat (Bh) plot, a triticale + fodder pea (TpPf) plot and alfalfa+dactyle (LuDac). Since none of these crops was fertilized, the reason for higher NH 3 volatilization could be due to the soil type and nitrogen mineralization rate. From the box plot in figure 32d we can see that cereal crops performed less NH 3 volatilization compared to meadows, excepted mixed cereals and proteaginous (Avhfev and TpPf.

Aggregation results

According to the qualitative scales assigned to each input attribute level in DEXi program (see paragraph 4.4), the 12 evaluated indicators were ranked as shown in table 14.

65

. Indicators aggregation results for SPCE. The colours indicate the result of the related attribute using an green is the best result, yellow is a good to medium result, orange is Indicators aggregation results for SH. The colours indicate the result of the related attribute using an green is the best result, yellow is a good to medium result, orange is . Indicators aggregation results for SPCE. The colours indicate the result of the related attribute using an orange is a medium to bad result . The colours indicate the result of the related attribute using an orange is a medium to bad result

Discussion

During the development of this work many questions arose about how to best carry out the agro-environemental sustainability evaluation, always respecting the fixed objectives. Since at each step of this multi-criteria evaluation different decisions had to be taken, at least the same number of alternative possibilities were left aside.

On one side, this work's final result is strongly influenced by the object of the evaluation itself, meaning the structure and organization of INRA-Mirecourt experimental farm, which played an important role on indicators calculation, consequently their results and the analysis of the latter.

On the other side, the final result mirrows the choices undertaken at the moment of indicators selection and aggregation.

These decision related questions will be discussed in the following paragraphs, together with the issue of indicators validation that could not be developped in this work.

Evaluation results

The multi-criteria evaluation applied in this work evaluated agro-environmental sustainability as the impact of farming practices on abiotic resources. Thus the impacts of the two organic dairy systems SPCE and SH of INRA-Mirecourt on water quality, air quality, soil quality and energy consumption were evaluated through twelve indicators.

As shown by the aggregation results, SH perfomed a better impact on agro-environmental sustainability compared to SPCE. This is due to the fact that the risk of water pollution for SH is lower than that of SPCE. In the latter system in fact, leaching of nitrate is more important because the rotation systems includes cereals that leave for a part of the year a bare or almost bare soil, thus more exposed to nitrate leaching. Intensively-managed permanent meadows have been found worse than arable crops for N losses in groundwater [START_REF] Hack-Ten Broeke | Effects on nitrate leaching and yield potential of implementing newly developed sustainable land use systems for dairy farming on sandy soils in The Netherlands[END_REF] However in this evaluation permanent meadows appeared to be better performing as nitrates traps, maybe because of a less intensive management that determines a lower input of manure through animal grazing and after-cut fertilizations, and a soil cover all year long.

Risk of surface and ground water pollution was "very low" and equal for both systems SPCE and SH, because no pesticides are used since the two systems are managed under organic farming regulation.

Also phosphorus losses was qualified "very low" for both systems, meaning that eutrophication risk is feeble. However, this result depends on the choice for P losses indicator, which will be discussed in the following paragraph.

unconsistency could be related to the construction of the two indicators CO 2 and non-renewable energy use proposed by PLANETE method, which have not been scientifically validated.

Finding the appropriate indicators

As mentioned above, the choice of an indicator instead of another to evaluate a criteria might influence the evaluation results. In fact indicators proposed by different methods, as presented in paragraph 2.2, could be based on different parameters and calculation procedures. farming system and distinguish their impacts (i.e. nitrate leaching, acidification potential and global warming potential). This distinction allows to compare the results with other methods as for example LCA or input-output accounting systems [START_REF] Thomassen | Evaluation of indicators to assess the environmental impact of dairy production systems[END_REF].

However, especially for nitrate leaching, balance based indicators cannot well estimate nitrate leaching on annual basis [START_REF] Laurent | Effets de la conduite de la prairie pâture sur la lixiviation du nitrate. Proposition pour une maitrise du risque à la parcelle[END_REF].

N emission calculated with INO 3 , INH 3 and IN 2 O were integrated with the indicators proposed by [START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF] in order to account also for the emission originated by animals.

The indicator for soil organic matter content IMO, was considered an interesting indicator because it evaluates the organic matter brought into the soil over a rotation scale. However, the rotation length considered is of 4 years, while the rotations at the experimental farm are of 6 and 8 years. If the evaluation is observed at systems level (SPCE and SH), probably the result would not be much altered because the rotation is represented by the sum of plots. This could be less true if the evaluation is observed at rotation system level (6H, 6P, 8H, 8P) because the cropping sequence might be incomplete (i.e. some crops of the rotation are missing for the evaluated campaign and the previous 3 years).

With regards to phosphorus related indicators, it was mentioned in paragraph 5.4.1 that they are complementary modules of IP indicator of INDIGO method. The objective of IP is to evaluate the impact of phosphorus fertilization on soil chemical quality and on the use of nonrenewable energies. Module IPsol evaluates if a too low P input could decrease soil fertility, while IPres evaluates if P input could be a hazard for natural resources depletion. In this work the role of the module IPsol has been maintained, while the role of IPres has been transformed from account of mineral P extracted from non-renewable sources and consumed [START_REF] Mengel | Agronomic measures for better utilization of soil and fertilizer phosphates[END_REF], to amount of P potentially lost from soil into surface water. This amount is calculated as balance between the P input and the quantity already present in the soil: it is considered that all the surplus could be leached to surface and groundwater [START_REF] Oenema | Effects of lowering nitrogen and phosphorus surpluses in agriculture on the quality of groundwater and surface water in The Netherlands[END_REF]. This choice is questionable, as this balance considers the potential and not the effective P loss. Other factors that influence P runoff thus eutrophication, such as soil type, rainfall and tillage [START_REF] Gilley | Nutrient concentrations of runoff during the year following manure application[END_REF][START_REF] Kleinman | Effect of mineral and manure phosphorus sources on runoff phosphorus[END_REF] need to be considered, as well as the fact that manure application can induce higher P runoff than mineral fertilization [START_REF] Eghball | Phosphorus and nitrogen in runoff following beef cattle manure or compost application[END_REF][START_REF] Bertol | Phosphorus loss by surface runoff in notill system under mineral and organic fertilization[END_REF], saturating the soil P capacity in the long term [START_REF] Brock | Phosphorus leaching through intact soil cores as influenced by type and duration of manure application[END_REF]. A new tool which considers many of these factors has been proposed and validated by a USDA study [START_REF] Vadas | Estimating phosphorus loss in runoff from manure and fertilizer for a phosphorus loss quantification tool[END_REF]: it might be interesting to implement it in a further step of the evaluation.

Finally, the two PLANETE indicators for CO 2 emission and energy consumption are based on a LCA logic (accounting of all production step) and on an input-output calculation of the final emission/energy consumed. They have been chosen for their user friendliness, since they have been constructed in order to be used by extentionist and farmers [START_REF] Bochu | PLANETE : méthode pour l'analyse énergétique de l'exploitation agricole et l'évaluation des émission de gaz à effet de serre[END_REF]. However the data available of PLANETE farms evaluation in France is not readable as a statistical reference [START_REF] Bochu | Consommation d'énergie et émissions de GES des exploitations en agricolture biologique : synthèse des résultats PLANETE 2006[END_REF]. Also INDIGO method proposes an indicator for energy consumption at farm level, but it is built for cropping systems, thus energy consumed from animal production is not considered. For this reason PLANETE indicator was used.

How to manage the aggregation of multiple criteria

Aggregation of criteria and indicators allows to easily visualize the evaluation results and assess farm's performance level. However, this single yielded value could be ambiguous [START_REF] Van Der Werf | Evaluation of the environmental impact of agriculture at farm level: a comparison and analysis of 12 indicator-based methods[END_REF]. In fact the complexity and interaction of factors behind the final aggregation value is not discernible anymore.

Furthermore, aggregation could be conducted with a compensatory or non-compensatory approach [START_REF] Van Calker | Maximising sustainability of Dutch dairy farming systems for different stakeholders: a modelling approach[END_REF]: the first implies a low indicator value (or a not good criteria value) is traded off by a high value of another indicator (or criteria). In the second case, each indicator is independent on the final result. This work aimed at using a non-compensatory approach, through the assignment of weights and outranked scales to indicators and criteria. The type of multi-attribute utility function proposed by DEXi allows to build a set of all the possible combination of indicators and criteria, where the aggregated value tends at maximizing negative values (Sadock et al., 2009). This type of aggregation is more realistic than simple weighting of indicators results (Benoit and Rousseaux, 2003).

The choice of ranking scales could be questioned for the fact that neither the same type of scale nor the same number of scale levels was applied to all indicators. The decision, which followed the expert suggestions [START_REF] Angevin | MASC 1.0 : Une méthode d'évaluation multicritère de la durabilité des systèmes de culture. Présentation générale et principes d'utilisation[END_REF], was led by the will to attribute more precise (if possible) judgement to indicators that have a more precise calculation procedure (f.i.

INDIGO versus PLANETE

). Furthermore, it would have been possible to detect more differences for those criteria that have a higher influence on dairy farming sustainability, such as GHG emission or nitrate leaching.

Finally, a numeric ranking scale could be use for sustainability evaluation, as applied by [START_REF] Van Calker | Maximising sustainability of Dutch dairy farming systems for different stakeholders: a modelling approach[END_REF], where qualitative values are replaced by numeric levels.

Validation questions

Any sustainability assessment should accomplish a set of principles in order to conduct a coherent and valid evaluation of a development activity. According to the "Bellagio Principles", a set of guidelines to help in the choice and design of indicators for sustainability assessments (Hardi and Zdan, 1997).

Furthermore, a sustainability assessment and its indicators should be validated. The validation of an indicator aims at verifying if it's a priori set criteria are met in a scientifically acceptable way [START_REF] Meul | Validating sustainability indicators: focus on ecological aspects of Flemish dairy farms[END_REF]. According to [START_REF] Addiscott | Critical evaluation of models and their parameters[END_REF] in [START_REF] Bockstaller | How to validate environmental indicators[END_REF] the validation of an indicator or a model expresses the probability of those tools to be disproved: if this probability is high, then the indicator is rejected.

Although the validation is considered as an important step in the construction of sustainability assessment tools and indicators (Van der [START_REF] Van Der Werf | Evaluation of the environmental impact of agriculture at farm level: a comparison and analysis of 12 indicator-based methods[END_REF], few authors developed a well defined procedure, except for simulation models [START_REF] Bockstaller | How to validate environmental indicators[END_REF][START_REF] Meul | Validating sustainability indicators: focus on ecological aspects of Flemish dairy farms[END_REF].

In

Structural limits

The increasing heterogeneity of the INRA-Mirecourt experimental farm from the farm to the plot level, brought a certain level of difficulties for indicators calculations, especially those evaluated at the plot level (INDIGO).

A number of factors could vary from a rotation system to the other, from a plot to the other belonging to the same rotation: soil type, crop type, fertilization, manure type, soil labour, yield etc. If on one side this diversity allows to test in practice many different cropping situations, on the other side it complicates the evaluation, in terms of data collection and registration and in terms of incertainty of evaluation results.

An example of this complexity is given by soil data management. ). This led to the attribution of different soil types at farming system level and to different soil analysis for all plots. If this on one side was meant to be representative of the farm soil diversity, on the other side it is translated into a variability difficult to manage at statistical level.

The unbalanced ANOVA on soil type carried out for INO 3 determined that there is a difference between soil types, at least between those with a clay-loamy texture and those with a loamy-sandy texture. By the way, when soil types were combined with rotation systems and with crop types, no significant difference was determined between the combinations because of too many missing data.

To verify the influence of soil type on indicators results, a different approach could have also been applied assigning to all the plots of the two systems SPCE and SH the same soil type.

Repeating the evaluation for each soil type would have permitted to define the influence of the different soil types on each indicators. If on one side this procedure would have flatten the diversity inherent to the farm, on the other side it could have established a sort of sensibility test of indicators with regards to soil type.

Conclusions

This work aimed at evaluating the agro-environmental sustainabiliy of the two low input organic dairy systems of INRA-Mirecourt experimental farm.

A multi-criteria analysis was applied aggregating twelve environmental indicators to define the impact on abiotic resources of the two farming systems.

This approach permitted to assign a global evaluation of the two dairy systems, but also to distinguish the strong and weak aspects that could effect their agro-environmental sustainability.

If on one side this kind of approach allows an holistic view of a complex system, on the other side its effective implementation is a true analytical process: in order to calculate environmental indicators, the farming practices and their guiding rules have to be desaggregated and analyzed into details.

This process allows the user to get to know all the different components of the system and the interaction between those components. Furthermore a comparison with other management systems and other evaluation methods could enrich the system's re-conception process. Infact, if one of the aim of multi-criteria evaluation is to define the performance of the studied system, it should not be left aside the important role on system's improvement through the feedbacks that can be drawn from the evaluation results.

However this work is not definitive: the validation of applied indicators by comparison with indicators results from other methods and by the comparison with measured data is a challenging as well as interesting following step that should be undertaken.

ANNEX

The following tables show the decision rules built with the tool DEXi [START_REF] Bohanec | Multi-attribute modelling of economic and ecological impacts of cropping systems[END_REF]:

-Page 1: decision rules for "Global sustainability" and "Agro-environmental sustainability";

-Page 2: decision rules for "Impact on abiotic resources", aggregating the attributes "Water pollution risk" (25%), "Air pollution risk" (25%), "Soil quality" (25%) and "Energy consumption" (25%);

-Page 4: decision rules for "Water pollution risk", aggregating the attributes "Risk due to pesticides in the water" (33%), "NO3 leaching" (34%) and "P losses" (33%);

-Page 4: decision rules for "Risk due to pesticides in the water", aggregating the attributes "Pesticides in surface water " (50%) and "Pesticides in groundwater" (50%);

-Page 5: decision rules for "Air pollution risk", aggregating the attributes "Pesticides loss in the air" (33%), "GHG emission" (34%), "NH3 volatilization" (33%);

-Page 6: decision rules for "GHG emission", aggregating the attributes "N2O emission" (32%), "CH4 emission" (51%) and "CO2 emission" (17%)

-Page 6: decision rule for "Chemical quality of soil", aggregating the attributes "Organic matter content" (50%) and "Phosphorus availability" (50%).

-Page 6: decision rules for "Energy consumption", only one attribute "Non renewable energy use".

Starting from the left page side, the tables present on the first line the name of the lower attribute levels. At the right side is the upper level attribute which aggregates the previous ones.

The second line of the tables shows the weights (%) for each the attributes aggregated together.

The following lines present all the possible decision rules to obtain a qualitative value for the aggregated attribute. The number of decision rules depends on the number of attributes aggregated together and on the number of qualitative scales they have been assigned. For example, to assess "air pollution risk" three attributes are aggregated, each having four possible qualitative value, which makes a minimum of 4 3 =64 decision rules.

When there is a star * instead of a qualitative value, it means that any values can be put in that place to obtained the final aggregated value. <=faible a moyenne tres defavorable acceptable moyen a eleve moyen a eleve:faible a moyen tres elevee <=defavorable acceptable moyen a eleve moyen a eleve:faible a moyen <=moyenne a elevee tres defavorable acceptable moyen a eleve >=moyen a eleve tres elevee tres defavorable acceptable moyen a eleve moyen a eleve tres elevee >=defavorable >=defavorable moyen a eleve moyen a eleve <=moyenne a elevee defavorable >=defavorable moyen a eleve moyen a eleve <=moyenne a elevee >=defavorable defavorable moyen a eleve moyen a eleve <=faible a moyenne defavorable defavorable moyen a eleve moyen a eleve:faible a moyen tres elevee defavorable >=defavorable moyen a eleve moyen a eleve:faible a moyen tres elevee >=defavorable defavorable moyen a eleve moyen a eleve:faible a moyen <=moyenne a elevee defavorable defavorable moyen a eleve >=moyen a eleve tres elevee defavorable defavorable moyen a eleve moyen a eleve tres elevee favorable * moyen a eleve moyen a eleve <=moyenne a elevee favorable <=defavorable moyen a eleve moyen a eleve <=faible a moyenne favorable tres defavorable moyen a eleve moyen a eleve:faible a moyen tres elevee favorable <=defavorable moyen a eleve moyen a eleve:faible a moyen <=moyenne a elevee favorable tres defavorable moyen a eleve >=moyen a eleve tres elevee favorable tres defavorable moyen a eleve moyen a eleve moyenne a elevee <=defavorable >=defavorable moyen a eleve moyen a eleve moyenne a elevee * defavorable moyen a eleve moyen a eleve moyenne a elevee:faible a moyenne tres defavorable >=defavorable moyen a eleve moyen a eleve moyenne a elevee:faible a moyenne <=defavorable defavorable moyen a eleve moyen a eleve >=moyenne a elevee tres defavorable defavorable moyen a eleve moyen a eleve:faible a moyen moyenne a elevee tres defavorable >=defavorable moyen a eleve moyen a eleve:faible a moyen moyenne a elevee <=defavorable defavorable moyen a eleve moyen a eleve:faible a moyen moyenne a elevee:faible a moyenne tres defavorable defavorable moyen a eleve >=moyen a eleve moyenne a elevee tres defavorable defavorable moyen a eleve moyen a eleve moyenne a elevee defavorable * moyen a eleve moyen a eleve moyenne a elevee >=defavorable <=defavorable moyen a eleve moyen a eleve moyenne a elevee:faible a moyenne defavorable <=defavorable moyen a eleve moyen a eleve moyenne a elevee:faible a moyenne >=defavorable tres defavorable moyen a eleve moyen a eleve >=moyenne a elevee defavorable tres defavorable moyen a eleve moyen a eleve:faible a moyen moyenne a elevee defavorable <=defavorable moyen a eleve moyen a eleve:faible a moyen moyenne a elevee >=defavorable tres defavorable moyen a eleve moyen a eleve:faible a moyen moyenne a elevee: <=moyen a eleve <=moyen a eleve tres eleve tres eleve * tres eleve tres eleve <=moyen a eleve tres eleve <=moyen a eleve tres eleve <=moyen a eleve <=moyen a eleve tres eleve tres eleve * tres eleve tres eleve tres eleve tres eleve >=moyen a eleve faible a moyen moyen a eleve <=moyen a eleve moyen a eleve >=faible a moyen moyen a eleve <=moyen a eleve moyen a eleve:faible a moyen faible a moyen moyen a eleve <=faible a moyen moyen a eleve faible a moyen moyen a eleve tres eleve >=faible a moyen moyen a eleve:faible a moyen moyen a eleve <=moyen a eleve faible a moyen moyen a eleve:faible a moyen moyen a eleve <=moyen a eleve >=faible a moyen moyen a eleve moyen a eleve <=faible a moyen faible a moyen moyen a eleve moyen a eleve moyen a eleve <=moyen a eleve >=faible a moyen moyen a eleve moyen a eleve <=faible a moyen faible a moyen moyen a eleve moyen a eleve:faible a moyen tres eleve >=faible a moyen moyen a eleve moyen a eleve:faible a moyen <=moyen a eleve faible a moyen moyen a eleve >=moyen a eleve tres eleve faible a moyen moyen a eleve moyen a eleve moyen a eleve >=moyen a eleve moyen a eleve moyen a eleve moyen a eleve:faible a moyen moyen a eleve:faible a moyen moyen a eleve moyen a eleve >=moyen a eleve moyen a eleve moyen a eleve moyen a eleve:faible a moyen moyen a eleve moyen a eleve:faible a moyen moyen a eleve moyen a eleve:faible a moyen moyen a eleve:faible a moyen moyen a eleve moyen a eleve >=moyen a eleve moyen a eleve moyen a eleve moyen a eleve moyen a eleve faible a moyen <=faible a moyen moyen a eleve moyen a eleve >=faible a moyen <=moyen a eleve moyen a eleve moyen a eleve:faible a moyen faible a moyen <=moyen a eleve moyen a eleve moyen a eleve:faible a moyen >=faible a moyen tres eleve moyen a eleve faible a moyen tres eleve >=moyen a eleve moyen a eleve faible a moyen <=moyen a eleve moyen a eleve:faible a moyen moyen a eleve faible a moyen <=faible a moyen moyen a eleve moyen a eleve >=faible a moyen tres eleve moyen a eleve:faible a moyen moyen a eleve >=faible a moyen <=moyen a eleve moyen a eleve moyen a eleve faible a moyen moyen a eleve <=faible a moyen moyen a eleve faible a moyen moyen a eleve:faible a moyen <=moyen a eleve moyen a eleve faible a moyen >=moyen a eleve tres eleve moyen a eleve >=faible a moyen moyen a eleve <=moyen a eleve moyen a eleve <=moyen a eleve

Figure 1 .Figure 2 .Figure 3 .Figure 4 .Figure 7 .Figure 8 .Figure 14 .Figure 18 .Figure 20 .FigureFigure 33 .

 12347814182033 Figure 1. Farm gate P-surplus (kg P/ha) by farm type and amount of P supplied with manure.....5 Figure 2. Elements and computation methods for the calculation of the ecological footprint of an organic dairy farm ………………………………………………………………………..6 Figure 3. Comparison of LCA results applied to conventional and organic dairy farms in five different countries (Germany, Sweden, The Netherlands, New Zealand and France)…..7 Figure 4. Graphic representation of results obtained with IDEA method .………………………9 Figure 5. OTPA horizontal bar graph used to represent farming practices evaluation results .....10 Figure 6. Pie-radar graph used to visualize MOTIFS output and interpretation guidelines …….11 Figure 7. Radar graphs visualizing the sustainability degree of a mixed livestock and crop farm evaluated by the tool RISE ...……………………………………………………………12 Figure 8. Functioning of the two bovine milk systems conducted at the experimental farm of INRA-Mirecourt ………………………………………………………………………...18 Figure 9. Dexi-SPCE tree for the evaluation of the impact of the farming systems on the agroenvironmental sustainability aspect "abiotic resources preservation" ………………….20 Figure 10. The four rotation systems of the cropping-breeding system SPCE …………………21 Figure 11. Indicators tree implemented for the evaluation of the impact of farming practices on abiotic resources as a component of agro-environmental sustainability ………………..23 Figure 12. INDIGO form "Interventions" from which it is possible to enter data about pesticides treatments, fertilizers input, irrigation and machine operations ...………………………27 Figure 13. Structure of INH3 indicator …………………………………………………………28 Figure 14. Decision tree for the solid system of the module "NH 3 emissions from barns" …….29 Figure 15. Decision tree for the solid system of the module "NH 3 emissions from manure stocking sites" ...…………………………………………………………………………29 Figure 16. Structure of IN2O indicator …………………………………………………………31 Figure 17. Procedure to calculate the CO 2 emission amount corresponding to a value 0 for CO 2 indicator …………………………………………………………………………………35 Figure 18. Curve and function of the indicator CO 2 …………………………………………….36 Figure 19. Agro-environmental sustainability tree and attributes weight ……………………....45 Figure 20. General structure and functioning of a decision rule-based, qualitative hierarchical multi-attribute model ……………………………………………………………………46 Figure 22. Radar graph summarizing the results obtained for each indicator used in the evaluation of the two dairy systems SPCE (blue line) and SH (red line) ……………….48 Figure 23. Radar graph with the results of crop rotation systems evaluation ...…………………50 Figure 24. INO3 results …………………………………………………………………………51 Figure 25. Distribution of crop type among the six rotation systems …………………………...52 Figure 26. Distribution of soil type among the six rotation systems ……………………………54 Figure 27. Distribution of soil type among the crop types ……………………………………...55

  in a survey to compared different environmental evaluation methods on Dutch dairy farms. Halberg et al. (2005b) compared ten different voluntary applications of IOA systems on north European farms and the indicators used to evaluate the environmental impact of nutrients, pesticides and energies at farm level. In both cases applied indicators are quantitative simple indicators applied on a yearly base. Figure 1 present a graph in which different farms are compared for their phosphorus surplus by input-output accounting.

Figure 1 .

 1 Figure 1. Farm gate P-surplus (kg P/ha) by farm type and amount of P supplied with manure, source Halberg et al. (2005b).

Figure 2 .

 2 Figure 2. Elements and computation methods for the calculation of the ecological footprint of an organic dairy farm (source Thomassen and De Boer, 2005).

  those indicators have been related to gross value added for Dutch dairy farms. Two more indicators were thus included in the LCA: gross value added per kg FPCM and labour productivity (GVA/labour unit). Van der Werf et al. (2009) implemented the environmental impact of 47 Brittany dairy farms using the valuation tool EDEN (Evaluation de la Durabilité des ExploitatioNs), which is based on LCA integrated with economic and social categories. Compared to the previous studies on LCA, the authors considered also terrestrial toxicity as impact category of farm practices.

Figure 3 .

 3 Figure 3. Comparison of LCA results applied to conventional and organic dairy farms in five different countries (Germany, Sweden, The Netherlands, New Zealand and France). Bars represent the impact of the farms on the environment, expressed as kg of PO 4 equivalents per 1000 kg of milk for eutrophication potential, kg of SO 2 equivalents per 1000 kg of milk for acidification potential, kg of CO 2 equivalents per 1000 kg of milk for climate change (or global warming) potential, GJ per 1000 kg of milk for energy use and m 2 per year per 1000 kg of milk for land use (source Van der Werf et al., 2009).

  Some environmental indicators were used to evaluate nutrient balance at farm, herd and soil level, NH 3 emission, nitrate leaching and phosphate surplus. The proposed economic indicators were milk production, manure spreading costs, costs per animal (health care + breeding + milking energy + interest + animal replacement), housing capacity, feed production, purchase of feed and concentrates, labour costs and fixed costs for machinery.Van Calker et al. (2004) adapted this existing dairy farm LP model on an experimental Dutch farm to study the effects of environmental policy and management changes on the economic and environmental sustainability of the farm. One economic indicator and seven ecological indicators were applied in the model. In a successive study (Van Calker et al., 2007) the model was completed with social indicators that the authors divided into one internal social or physical health indicator and four external social or societal indicators.2.2.5. Multicriteria analysisPacini et al. (2003) applied an integrated economic-environmental accounting framework to three Central Italy mixed farms, in order to evaluate their financial and environmental sustainability. The first was assessed with a farm gross margin indicator. The second was assessed through an environmental accounting information system. The evaluation is proposed at field, site and farm levels and uses data from a reference period of three years(1998)(1999)(2000) to compare annual average results of indicators over time.

  scores. The validation of indicators has been carried out for few of them in the form of comparison with other indicators output or expert supervision.

Figure 4 .

 4 Figure 4. Graphic representation of results obtained with IDEA method. The radars group the ten evaluation criteria and allow to visualize the performances of the farm with regards to each criteria, expressed by the point on the axis, where 30 is the best performance and 0 the worst. The farm's results (continuous line) are compared with the average results of the group of farms it belongs to (source Zahm et al., 2008).

Figure 5 .

 5 Figure 5. OTPA horizontal bar graph used to represent farming practices evaluation results. In this case fertilization in husbandry system. Bars represent the indicators involved in the evaluation (for example N balance, mineral nutrients balance, percentage of bare soil, cover crops, etc.). The length of the bars expresses the placement of the farm with respect of the farm group average (source Guillomin et al., 2007a).

Figure 7 .

 7 Figure 7. Radar graphs visualizing the sustainability degree of a mixed livestock and crop farm evaluated by the tool RISE. Graph a) present the evaluation results of the actual farm management (19 ha; 1,5 Large animal units/ha; 2,5

Figure 8 .

 8 Figure 8. Functioning of the two bovine milk systems conducted at the experimental farm of INRA-Mirecourt: a grazing system (SH) and a cropping-breeding system (SPCE) complementary at the local territory scale (source: Coquil et al., 2009b).

Figure 9 .

 9 Figure 9. Dexi-SPCE tree for the evaluation of the impact of the farming systems on the agro-environmental sustainability aspect "abiotic resources preservation" (source: Garnier, 2009).

  Holstein and Montbéliarde in equal proportion. Two dairy systems have been conducted since the conversion: one based on grazing (système herbager, SH) constituted of 80 ha of grassland for about 40 cows, and one based on mixed cereals and grazing (système polyculture-élevage, SPCE). The latter is composed of 50 ha of grassland and 110 ha of crops, divided into four rotation systems, two organized in a six-years rotation and the other two in a eight-years rotation, for a herd of about 60 cows. Among the crops cultivated in the SPCE there are wheat, sold to a local mill to produce bread organic flour, and secondary cereals as barley, oat, triticale and spelt, often in mixed cropping with proteaginous crops as pea and faba bean, used to produce concentrate fodder for the cattle. The six-years rotation is based on three years of temporary meadows and three years of cereals (Fig.10 a) and b)), while the eight-years rotation is based on three years of alfalfa meadow and five years of cereals (Fig.10 c) and d)).

Figure 10 .

 10 Figure10. The four rotation systems of the cropping-breeding system SPCE. The number 6 or 8 in the rotation name stands for the rotation's number of years, while the letter P and H stands for the presence of spring (Printemps) cereals or only winter (Hiver) cereals in the rotation. PT: temporary meadow, Luz/Dac: alfalfa and grass, BH: winter wheat, CSH: secondary winter cereals, CSP: secondary spring cereals, +prot: mixed with proteaginous crops, nu: bare soil. The pie charts show how cover crop (InterCrop) is seeded before a spring cereal, in order to keep a vegetal cover on the soil during the winter. The red arrows indicate manure application for cereals.

  Figure11illustrates the tree of criteria and their related indicators used to evaluate the environmental sustainability with regards to the impact on abiotic resources. Physical quality of soil was not evaluated in this work because the t "erosion" from OTPA method needed to of a lack of time.

Figure 11 .

 11 Figure 11. Indicators tree implemented for the evaluation of the impact of farming practices on abiotic resources as a component of agro-environmental sustainability.

N

  post-harvesting Balance = Nrr + Nre + Nmr + Nmh + NXm + NXo-Nabs (3) Input factors consider N incompressible surplus (Nrr), N from over-fertilization (Nre), from mineralization of crop residues (Nmr) and soil humus (Nmh), from fertilizers applied in autumn (N Xm ) and N captured by a cover crop if any is present (N Xo ) (Taureau

Figure 12 .

 12 Figure 12. INDIGO form "Interventions" from which it is possible to enter data about pesticides treatments, fertilizers input, irrigation and machine operations. The red font button permits the selection of plots that received no pesticides treatments: to those plots a value of 10 is assessed for IPhy.

Figure 13 .

 13 Figure 13. Structure of INH 3 indicator. Following the procedure of Peigné (2003), three modules are considered:

  from barn (kg/y): Fb*Sb*D*RSD*RL (8) emission from stocking sites (kg/y): Fp*Sp*D*RCF*RB (9) Fb and Fp are respectively the quantity of slurry in the barn and stocked (kg/d/m2), Sb and Sp are respectively the barn surface and the pit surface (m2), D is the number of days spent in the barn (8) or of slurry stocking (9), RSD and RL are coefficients related to barn floor type and floor cleaning modality respectively, RCF and RB are reduction coefficients for the presence of a pit cover and pit aeration respectively. These parameters of equations 8 and 9 are also described in the right boxes of figure 13. anure collected from the bedding areas (straw manure) : it is collected from the barn in autumn and composted on a plot. Stocking sites for liquid system where taking into account only for SH, whose slurry from indicator. Following the procedure of Peigné (2003), three modules are considered: emissions from barns, stocking sites and manure spreading. The first two were calculated as proposed by the author, module is composed by two sub-modules whose required Emissions coming from liquid systems are calculated with the equations 8 (barns) and 9 emission from barn (kg/y): Fb*Sb*D*RSD*RL (8) emission from stocking sites (kg/y): Fp*Sp*D*RCF*RB (9) Fb and Fp are respectively the quantity of slurry in the barn and stocked (kg/d/m2), Sb and Sp are respectively the barn surface and the pit surface (m2), D is the number of days spent related to barn floor type and floor cleaning modality respectively, RCF and RB are reduction coefficients for the presence These parameters of equations 8 and 9 are also Emissions from solid systems are calculated with fuzzy logic (Fig.14 and 15) for each animal category (milking cows, heifers and calves). Factors influencing NH 3 emissions are the C availability for ammonifying bacteria expressed by litter C/N (Jeppsson, 1999) and litter

  ): NH 3 Emission from barns (kg/y) = (Ln10)/0,6556 -1/0,6556*Ln(FLb))*Mb (10) NH 3 Emission from stocking site (kg/y) = (Ln10)/0,2336 -1/0,2366*Ln(FLs))*Ms (11)

  Also INDIGO considered the same reference threshold. This choice is in accordance with the proposition ofBobbink et al. (1998) and[START_REF] Kuylenstierna | Critical loads for nitrogen deposition and their exceedance at European scale[END_REF].The data necessary for the calculation of INH 3 modules barns and stocking were: surface of barns divided into stalling and bedding surface, surface of manure pits, number of animals in the barn for each age category, number of days spent in the barn, type of manure stored (liquid or solid), type of barn soil. Those data were collected at systems level (SH and SPCE). Instead for the calculation of INH 3 module, fertilizer input were collected for each plot as required by INDIGO. Together with the soil, crop and fertilization data mentioned above and common for the other INDIGO based indicators, age of grazing animals, grazing load and number of grazing days were required.•IN 2 OThe indicator for nitrous oxide volatilization was obtained by summing the emissions produced from manure in the barns and stocking piles, calculated per animal system the indicator IN 2 O proposed by[START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF], and the emissions from the fields, calculated per plot with INDIGO method module IN 2 O of "Azote" indicator (Fig.16). The types of manure for liquid and solid systems were the same as INH3.

Figure 16 .

 16 Figure 16. Structure of IN 2 O indicator. Following the procedure of[START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF], three modules are emissions from barns, stocking sites and manure spreading. The first two were calculated as proposed by the author, while the third module was calculated with INDIGO. Each module is composed by two sub parameters or formula are presented in the last box column.

  emission from barn (kg/y/ha): (Qa*Nex*An*Db*N2O-N emitted)/S (15 n from stocking sites (kg/y/ha): (Qa*Nex*An*Ds*N2O-N

  is the total N dose without NH 3 lost by volatilization (i.e. Di of INH3), Nre is N from over-fertilization (see INO3), Csoil is a coefficients for soil type

  ) is applied. The total CH 4 emission for the systems SH and SPCE is obtained by summing barns and stocking piles emissions (24): CH 4 emitted (barns or stocking) (kg/year) = ∑ atelier (VS *Bo *MCF*0,67 kg/m 3 *NA) (23) Et=CH 4 emission (kg/ha/year)=(CH 4 barns emission+CH 4 stocking emission)*365d/S (24)

  . To assess the final note of the indicator, the equation proposed by INDIGO for the indicator Phosphore was applied to the single module IPsol (30, 31, 32):

Figure 19 .

 19 Figure19. Agro-environmental sustainability tree and attributes weight. to the weigh (%) assigned to each attribute.

Figure 20 .

 20 Figure 20. General structure and functioning of a decision rule-based, qualitative hierarchical multi-attribute model.Options a n corresponds to the indicators scores (from 0 to 10). Input attributes (X 1 -X 3 ) are aggregated into aggregate attribute X 6 through a utility function#1 F(X 1 ,X 2 , X3). Aggregate attribute X 6 and input attributes X 4 -X 5 are aggregates together into the root attribute Y throgh utility function#2 whose decision rules are shown in the top left box. For example if X 4 obtained a qualitative value "medium", X 5 obtained "very low" and X 6 obtained "low", then the overall result for Y will be "very low". The same functioning is applied to the lower level utility function#1. Utility functions can be defined manually for each combination or automatically by DEXi. In the latter case weights need to be assigned to the attributes participating in the function, and the first and last combinations have to be entered(source Sadock et al., 2009).

  indicators (INO 3 ,IMO, IPsol, IPres, INH 3 , IN 2 O) were analyzed with an

Figure 24 .

 24 Figure 24. INO3 results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop type; d) box plot for crop type.

.

  INO3 results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for cropFor SPCE 17 plots out of 67 (25%) where concerned by a value between 3 and 7 caused by the absence of cover cropping. Those plots were all occupied by winter cereals which in the rotations plan are not preceded by a cover crop. Eleven plots (16%) were char the two dairy systems SPCE and SH obtained a value of 6,9 and 8,4), three different groups can be distinguished: one with a low value around 5,5 (i.e. 6P), one with an acceptable value close on nce value 7 (i.e. 8P) and one with a good value over 8 represented by the form, nitrate leaching the absence of a cover crop with two consecutive cereals. The latter is displayed as balance . INO3 results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for cropFor SPCE 17 plots out of 67 (25%) where concerned by a value between 3 and 7 caused by the absence of cover cropping. Those plots were all occupied by winter cereals which in the rotations plan are not preceded by a cover crop. Eleven plots (16%) were characterized by over the same time high nitrogen balance: those plots were temporary (2) or permanent (9) meadows, for which a low INO 3 value meant an unbalance between N input and output due to over-fertilization. Rotation 6P had a low INO 3 value probably because 5 out of 7

Figure 25

 25 Figure 25 allows to visualize the repartition of crops within the 6 rotation systems with the average value obtained for INO 3 . Seven out of fourteen crops (the permanent meadows of SPCE and SH excluded) are present in only one rotation system. Wheat is the only crop present in all four crop-based rotation systems, with 9 occupied plots (1 in 6H and 8H, 2 in 6P and 5 in 8P), a mean value of 4,3 but a high variance between plots results as shown in the box plot of figure 24d.

Figure 25 .

 25 Figure 25. Distribution of crop type among the six rotation systems. The cones height represents the mean value for INO 3 obtained by crossing crop types and rotation systems with REML linear mixed model.

Figure 28 .

 28 Figure 28. IMO results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop type; d) box plot for crop type.

  it emerges that 6H, 6P, 8H and 8P belong to t from the group composed of permanent . IMO results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop rotation systems, only 8H is below 7 (6,1). Since the level of 7 is input is higher than the recommended input, we bring enough organic matter in the soil, but in different quantity resulting in a more positive balance for the grassland. From the box plot plots obtained all the same indicator ion within the plots. To explain the where the different crops Boxplot for IMO-Organic matter

Figure 29 .

 29 Figure 29. IPsol results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop type; d) box plot for crop type.

.

  IPsol results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop s groups can be noticed: group a composed 6, group b composed by 6H, 6P and 8P with a c composed by the two permanent 8. All the rotation systems present a big in figure 29b. Rotation 8P difference between groups might be on, that could be differentiated between . IPsol results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop

Figure 30 .

 30 Figure 30. IPres results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop type; d) box plot for crop type.

  .

  results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop d)

  7. The presence of outsider results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop The results of IN 2 O for the two systems SPCE and SH were r In figure 31a two different groups can be distinguished between the rotation systems: one composed by the permanent meadows crops in rotation with temporary meadows (6H, 6P, 8H and 8P).

Figure 31 .

 31 Figure 31. IN 2 O results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop type; d) box plot for crop type.

  result. All other crop types are different from meadows excepted TpPf. Since the value i two systems SPCE and SH were respectively 6,8 and 5,8. two different groups can be distinguished between the rotation systems: one permanent meadows of both systems (PP and PP-SH), and one composed by crops in rotation with temporary meadows (6H, 6P, 8H and 8P).

O

  results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop

  can be distinguished between the rotation systems: one SH), and one composed by O results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop from barns and stocking sites, calculated with the procedure proposed by

Figure 32 .

 32 Figure 32. INH 3 results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop type; d) box plot for crop type.

  similar quantity of N 2 O. The high emission from meadows is and their continuous input of nitrogen through manure. The outsider data with very high value (10) due to no grazing and fertilization at all (3 plots) or to 2) probably because of over-animal load. The emission from TpPf could not be explained by fertilization because there was no manure input. It could probably be linked to the soil type and mineralization rate. volatilization) were 6,9 and 7,1 for SPCE and SH respectively. Total emissions were 19,7 and 17,5 kg NH 3 /ha for the two systems. The crop rota show that the permanent meadows' system of SPCE ' system of SH and that both are different from the crop rotation 8P. The are in a way set between PP and PP-SH (6H and 6P) and between PP results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop value (10) due to no grazing and fertilization at all (3 plots) or to load. The emission from TpPf could not be explained by fertilization because there was no manure input. It could probably be /ha for the two systems. The crop rotations results ' system of SPCE is different from the ' system of SH and that both are different from the crop rotation 8P. The SH (6H and 6P) and between PP-SH results: a) histogram of crop rotation systems; b) box plot for crop rotations; c) histogram for crop

  SPCE obtained the best rank (green font) for 7 indicators (I-Phyto Ground Water -Surface Water -Air, IPres, IMO, IPsol and Energy consumption), and the bad to medium rank (black bold font) for the other five indicators (INO 3 , INH 3 , IN 2 O, ICH 4 , CO 2 ). SH obtained the best rank for 7 indicators like SPCE (I-Phyto Ground Water -Surface Water -Air, IPres, IMO, IPsol and Energy consumption), the good to medium rank for three indicators (black classic font) (INO 3 , INH 3 ,CO 2 ), the medium to bad rank for one indicator (IN 2 O) and the worst rank (red font) for indicator ICH 4 .

  INDIGO indicators have been developed by an INRA research team on a French based context. They appeared to be feasible for the evaluation of the dairy experimental farm of INRA-Mirecourt. However, they are based on cropping systems and later adapted to include meadow grazing as well. A new indicator of N lossess from grassland has been proposed by Pervanchon et al. (2005), which could be somehow integrated to the already existing one. The indicators INO 3 , INH 3 and IN 2 O were drawn from the modules of INDIGO indicator for N losses in the environment, in order to separate the different sources of N emitted from a

  this work, validation of indicators could not be carried out because of time lack. A part for the indicator INH3 proposed and validated by Peigné (2003) all the other indicators are not validated. This step of the evaluation should be carried out in particular for CO2 emission indicator to verify the validity of the equation built in this work, and for non-renewable energy use indicator to verify its possible redundancy with CO2 emission and the validity of the applied function derived from INDIGO energy indicator.

Table 1 .

 1 Comparison of the methods with regards to the applied indicators ……………………13

Table 2 .

 2 Coefficients used for the calculation of N2O emitted from barns and stocking sites …32

Table 3 .

 3 Soil type registered in INDIGO form "Classe sol" ……………………………………39

Table 4 .

 4 Animal groups and corresponding livestock unit (LSU) ………………………………40

Table 5 .

 5 Organic fertilizers and analysis as they have been registered in INDIGO "Fertilisants" form ……………………………………………………………………………………..41

Table 6 .

 6 Attribution of ranges and corresponding qualitative scales to the indicators used in the evaluation ……………………………………………………………………………….43

Table 7 .

 7 Attribution of ranges and corresponding qualitative scales to the criteria (=aggregated attributes and root attributes) used in the evaluation ……………………………………44

Table 8 .

 8 Results of the indicators evaluated on the two systems SPCE and SH ………………..48

Table 9 .

 9 Number of plots occupied by the same crop for each rotation systems ……………….49

Table 10 .

 10 Results of crop rotations evaluation ………………………………………………….50 Table 11. INO 3 average values related to soil type ……………………………………………..53

Table 12 .

 12 Number of observation, INO 3 mean value and standard error in the six rotation systems (6H, 6P, 8H, 8P, PP, PP-SH) for the different soil types ……………………………….53

Table 13 .

 13 Number of observation, INO 3 mean value and standard error in the 13 crop types for the different soil types …………………………………………………………………..55

Table 14 .

 14 Indicators results and their respective qualitative value as ranked with the tool DEXi……………………………………………………………………………………..64

Table 15 .

 15 Evaluation of SPCE with DEXi tool …………………………………………………66

Table 16 .

 16 Evaluation of SH with DEXi tool ….…………………………………………………66

Table 1

 1 summarizes the methods comparison presented above. Columns represent the five methods and in particular each reviewed paper for that method. For LCA only the latest work of

	Thomassen et al. (2009) is shown, for it completes the indicators proposed in the previous
	studies. Lines represent the indicators found in the papers. Sustainability criteria are listed in the
	first column, in order to group together indicators belonging to the same criteria. In the cells of
	each methods is reported the specific used indicator and, where available, the unit of
	measurement. This summary does not visualize the actual list of indicators or the indicators
	"tree" for multi-criteria methods as founded in the peer-review papers: it is an artificial
	clustering with the objective of allowing a comparison between methods applying similar
	indicators. For example, land use is implemented as agro-environmental indicator by ecological
	footprint and two LCA studies, but for IDEA it is considered into the criteria "ethics and human
	development" of the social pillar.

PILLAR CRITERIA METHODS Input-Output Accounting Ecological Footprint LCA (Life cycle assessment) LP (Linear Programming) model Multi-criteria analysis INDICATORS Thomassen and De Boer, 2005 Halberg et al., 2005 Thomasse n and De Boer, 2005 Thomasse n et al., 2009 Haas et al., 2001 Van der Werf et al., 2009 Berentsen and Giesen, 1995 van Calker et al. (2004;2007) IDEA (Vilain, 2003) Pacini et al., 2003 OTPA MOTIFS (Meul et al., 2008) RISE (Hӓni et al., 2003) AGRO-ENVIRONMENTAL Nitrate leaching potential (ground water quality)

		NO3 emission	N surplus (kg N/ha farm)	N surplus (kg/ha) or N use efficiency (%)		N farm gate balance; N leaching potential		N potential losses -N denitrificat ion	NO3 concentration in groundwater (NO3-mg/l)	N losses (GLEAMS) 4	N surplus (N input-N output)	N surplus; efficiency N use	N balance
		NO3 et NOx			kg NO3	N and P
	Eutrophication potential (surface water quality)	emission NH3 emission PO4	P surplus (kg P/ha farm)	P surplus (kg P/ha farm)	eq/ha farm 5 or kg NO3 eq/kg FPCM 6	surplus as kg PO4 eq/ha (P balance; % drained surface)	kg PO4 eq/ha farm or kg PO4 eq/1000 kg FPCM	P2O5 surplus	kg NO3 eq/ha	P losses (GLEAMS)	P surplus (P input-P output)	P surplus; P use efficiency	P balance
	Land use	Land use		m2 BPA/kg of FPCM	ha/kg FPCM		m2/year/ 1000 kg FPCM		world population feeding contribution
				energy use
	Resources consumption	direct and indirect fossil energy use		(MJ/ha) or energy efficiency (MJ/kg	MJ/ha farm or MJ/kg FPCM	primary energy use; P and K fertilizers	GJ/ha farm or GJ/kg FPCM		energy dependence missing) (description		energy use (l fuel eq/year)		Energy use/ha
				product)
	Global warming potential	CO2 emission N2O emission CH4 emission		CO2 emission (kg CO2/kg product)	kg CO2 eq/kg FPCM or kg CO2 eq/ha farm	t CO2 eq/ha	kg CO2 eq/ha farm or kg CO2 eq/1000 kg FPCM		kg CO2 eq/t FPCM		t CO2 eq/ha
								NH3 from
	Acidification potential	NH3 emission NOx emission SO2 emission	NH3 ha farm/year	NH3 ha farm/year	kg SO2 eq/ha farm or kg SO2 eq/kg FPCM	kg SO2 eq/ha	kg SO2 eq/ha farm or kg SO2 eq/1000 kg FPCM	barn and manure spreading	kg SO2 eq/ha

Table 1 .

 1 Comparison of the methods with regards to the applied indicators.

PILLAR CRITERIA METHODS Input-Output Accounting Ecological Footprint LCA (Life cycle assessment) LP (Linear Programming) model Multi-criteria analysis INDICATORS Thomassen and De Boer, 2005 Halberg et al., 2005 Thomasse n and De Boer, 2005 Thomasse n et al., 2009 Haas et al., 2001 Van der Werf et al., 2009 Berents en and Giesen, 1995 van Calker et al. (2004;2007) IDEA (Vilain, 2003) Pacini et al., 2003 OTPA MOTIFS (Meul et al., 2008) RISE (Hӓni et al., 2003) ECONOMIC Farm income

			Thomassen and De Boer, 2005	Halberg et al., 2005	Thomasse n and De Boer,2005	Thomasse n et al., 2009	Haas et al., 2001	Van der Werf et al., 2009	Berentsen Giesen,19 95	van Calker et al. (2004;2007)	IDEA	Pacini et al., 2003	OTPA	MOTIFS	RISE
			Wild diversity									HPBI 7 ; APBI	% Natura 2000; % E.E.	D. of wild species	ZEC; plot size
		Biodiversity	Grassland/hedges biodiversity Crop diversity Labour profitability Return of the farm Labour productivity	species nb/density-diversity-care		GVA/kg FPCM GVA/labo ur unit	grassland, hedges, field margins		Variable and fixed costs	Net farm income	annual and perennial crops Economic feasibility	HBI 3 CDI 3 Farm gross margin	% meadows hedges length; Income/ labour Habitat diversity unit Farm profit/ x capital unit nb crops; % main crop ha Crops used x VA/labour unit	Farm income Return on assets
		Economic productivity and efficiency	Land productivity Animal diversity Capital productivity								livestock diversity; genetic conservation Economic specialization	Insect biodiversity (no calculation) 8	nb of species VA/land use unit Animals and races used VA/farm capital
		Farming practices Farm independence Farm continuity Working conditions (internal social)	Crop rotation Fertilization Efficiency Pesticides use Financial self-sufficiency Subsides support Allopathic treatments Economic transmissibility Soil erosion Soil quality Work intensity Professional pride Life quality	comparison of PEC 9 with public standards			pesticide application antibiotics application			Physical load index (PLI)	x x x treated surface/total farm surface x x Treatments x per animal/ total herd soil conservation x x	no calculation 4 no calculation 4 Soil salinity; S.O.M. content (no calculation) 4	x x treatments nb Total/optimal production Risk of pesticides x x use x soil erosion minimum tillage; compaction content O.M; P; K x Farmer emancipation Disposable x income	Risk; a.i. quantity ha paction content ;com Nutrients FTE 10 income/MRI
	SOCIAL	Agroecosystem efficiency	Energy efficiency								Energy, water, minerals, biological resources water			Kg product/ energy unit Kg	Consumption /workforce
			water use				m3/ha			m3/ha	resources		m3/ha	product/l	m3/ha
											manag.			water
		Others		Heavy metals accumulation				Terrestrial toxicity (kg 1,4-DCB eq/ ha or 1000 kg FPCM)		Aquatic and terrestrial ecotoxicity		Dangerous waste load; livestock load;		Waste water contaminat ion risk	Waste production disposal
		10 FTE=full time equivalent; MRI= minimum regional income
								15

Social quality (external social)

	All the methods presented above are goal-oriented, meaning they aim at defining the
	sustainability of a farming system with regards to its site specific conditions, as opposed to
	means-oriented framework whose objective is to define which practices are a priori sustainable
	(Von Wirén-Lehr, 2001). However, two groups can be distinguished: multi-criteria methods and
	the single indicators sets (i.e. LCA, IOAs, Ecological footprint and Linear programming). The
	second group's methods use "raw" quantitative data as indicators' output (f.i. kg of CO 2 emitted
	per ha) to compare different farming situations or systems. Thus the most (or least) sustainable
	practice is assessed as the least (or most) emitting. In a multicriteria analysis those quantitative
	data are transformed into scores on a scale (from 1 to 10 for example) in order to aggregate raw
	data output which differ for measurement unit and calculation.
		Hygiene and security		Chain food safety index	x	x
		Animal welfare	Housing system condition	Tier GerechtheitsInde x-35L	x	Parameters of living conditions
		Animal health		Animal health index		Lesions and diseases rate
				Agricultural	Landscape and	Nature
		Landscape value		nature norm	patrimony	x	conservation,
				analysis (ANNA)	valorization	elements
		Social involvement			x	x	Green care
		Local market valorization			x	x	Entrepreneurship Local sales %
	Services/others	Multi-functionality			x	x
		Active				Local
		population			x	x	workforce
		occupation				level

Table 2 .

 2 Coefficients used for the calculation of N 2 O emitted from barns and stocking sites among those proposed in the literature selected by[START_REF] Peigné | Méthode d'évaluation des pratiques agri-biologiques sur la qualité de l'air à l'aide d'indicateurs agri-environnementaux[END_REF]. Among the values of N 2 O-N emitted proposed by[START_REF] Freibauer | Regionalised inventory of biogenic greenhouse gas emissions from European agriculture[END_REF] (minimum-average-maximum) the average value 2 g N 2 O/kg N was retained.

	Animal category	System type	Qa (kg ou m 3 /an/d)	Nex (g N/kg manure)	Db	Ds	N 2 O-N emitted (g N 2 O/kg N)
	Calves	Solid (100% straw)	8,5	5,8	120 (SH), 180 (SPCE)	120	2
	Heifers	Solid (50% straw)	14	5,8	120 (SH), 180 (SPCE)	270	2
		Liquid (50% straw)	9	3,5	120 (SH), 180 (SPCE)	270	2
	Milking cows	Solid (50% straw)	25	5,1	120 (SH), 180 (SPCE)	210	2
		Liquid (50% straw)	14	3,5	120 (SH), 180 (SPCE)	180	2
		Liquid (100% straw)	45	3,5	120 (SH), 180 (SPCE)	317	2
	References		Heduit, 1991 Ziegler and	Tillie and

Table 4 .

 4 Animal groups and corresponding livestock unit (LSU). The last column contains the acronym of animal groups as they are registered in the database of the unit.

	Group	LSU	Animal group
	Calves	0,30	VeFem -VeMal
	Bovines 1 year	0,40	GS1an -MaS1a
	Bovines 1-2 years 0,60	G12aP -G12aV -Ma12a
	Bovines 2 years	0,70	Boeuf -G2VVP -G2VVV
	Dry cows	0,80	TxSNa -VaBPI -VaBVi -VaTPI -VaTVi
	Milking cows	1,00	VLMCP -VLMCV -VLMPP -VLMPV -VLPCP -VLPCV -VLPPP -VLPPV
	Mixed <2 years	0,50
	Mixed	0,50
	4.3.4.3.Fertilizers
	Fertilizers produced at the experimental farm are solid manure for SPCE and solid and liquid
	manure for SH. Some slurries are also obtained from the stocking sites where manure produced
	in different stables from different manure could be stocked. manure types were distinguished
	according to their nature (composted manure, stocked manure, manure for stable scraping,
	slurry), producing system, producing animals and year of production. Manure analysis were
	available for the manure spread in the period 18/12/2006-13/11/2009: for the manure spread in
	the three years before the evaluated campaign (i.e. 2005-2007), needed for IMO indicator, an
	average of values from the available analysis was calculated. Table 5 summarizes all the manure

types and their analysis entered into INDIGO form "Fertilisants". In the same form, the coefficient of mineral fertilizers equivalence and the coefficient of NH 3 volatilization were completed for the new registered fertilizers.

Table 5 .

 5 Organic fertilizers and analysis as they have been registered in INDIGO "Fertilisants" form.

	YEAR	NAME	ANIMAL PRODUCER	System	Quantity (t)	C/N	Ntot kg/t %Nmin/Ntot P2O5 kg/t K2O kg/t MO kg/t	k1	humus kg/t
	2007	Eaux Vertes et Blanches	VL + Génisses	SH-SPCE	552,5	3,7	0,36	61,23	0,10	1,24	2,34		0
	2007	Fumier Dépôt	VL	SPCE	74,0	14,4	5,20	19,83	1,49	5,29	132,12	0,30 2
	2007	Fumier Dépôt	Veaux	SPCE	11,8	12,4	5,58	15,56	2,78	9,59	127,44	0,30
	2007	Fumier Dépôt	VL + Veaux	SPCE	94,2	18,5	3,84	5,41	3,61	8,08	128,13	0,30
	2007	Fumier Raclage	Génisses	SPCE	27,7	13,6	4,87	12,90	3,44	9,37	121,97	0,40 3
	2007	Fumier Raclage	VL	SPCE	103,9	15,7	4,14	26,51	2,15	8,26	117,33	0,40
	2007	Purin	VL + Veaux + Génisses	SH-SPCE	141,3	5,0	0,61	59,54	0,10	4,18	4,89		0
	2008	Eaux Vertes et Blanches	VL + Génisses	SH-SPCE	596,0	5,6	0,14	84,06	0,08	0,68	1,05		0
	2008	Fumier Composte	Génisses	SH	34,4	17,7	4,58	7,12	5,19	4,95	144,76	0,40
	2008	Fumier Composte	Génisses	SPCE	55,5	19,1	5,62	7,23	3,53	15,26	195,71	0,40
	2008	Fumier Composte	VL + Veaux + Génisses	SPCE	85,5	12,6	5,06	2,77	3,77	11,57	124,98	0,40
	2008	Fumier Dépôt	VL	SPCE	210,0	15,0	5,36	17,90	2,49	7,75	144,54	0,30
	2008	Fumier Dépôt	Veaux	SPCE	28,6	18,4	4,58	21,57	4,03	11,94	157,30	0,30
	2008	Fumier Dépôt	VL + Veaux	SPCE	63,6	12,8	5,67	4,97	3,06	10,88	128,66	0,30
	2008	Fumier Pailleux	Génisses	SPCE	64,9	11,9	6,04	13,85	3,54	16,47	130,59	0,25
	2008	Fumier Raclage	VL	SPCE	468,5	17,7	4,13	25,65	2,47	7,38	131,16	0,40
	2008	Purin	VL + Veaux	SH-SPCE	101,2	6,2	0,22	67,20	0,05	1,91	2,00		0
	2008	Purin	VL + Veaux	SPCE	133,0	6,3	0,13	68,82	0,04	1,23	1,27		0
	2008	Purin	VL + Veaux + Génisses	SH-SPCE	76,0	7,0	0,27	58,61	0,10	1,48	2,71		0
	SH	Eaux Vertes et Blanches	VL	SH-SPCE	453,0	7,6	0,20	60,72	0,13	0,69	2,50		0,00
	SH	Eaux Vertes et Blanches	VL + Génisses	SH-SPCE	354,0	4,4	0,21	68,90	0,09	0,88	1,60		0,00
	SH	Fumier Composte	VL + Veaux + Génisses	SH	85,9	15,7	4,33	7,19	4,82	7,27	136,62	0,40	54,65
	SH	Fumier Dépôt	VL + Génisses	SH	48,4	13,4	6,23	5,62	3,41	13,51	157,96	0,30	47,39
	SH	Lisier	VL	SH	1302,4	14,7	1,56	29,15	0,94	3,49	40,81		22,00
	SH	Purin	VL + Veaux + Génisses	SH	150,5	14,7	0,04	101,27	0,06	0,64	1,02		0,00
	SH	Purin	VL + Veaux + Génisses	SH-SPCE	433,3	4,7	0,33	60,04	0,08	1,81	2,39		0,00
	05-07	Eaux Vertes et Blanches	VL + Génisses	SH-SPCE	220,0	4,6	0,25	72,64	0,09	0,96	1,69		0
	05-07	Fumier Composte	VL		87,5	16,5	5,08	5,70	4,17	10,59	155,15	0,40
	05-07	Fumier Dépôt	VL	SPCE	25,0	14,7	5,28	18,86	1,99	6,52	138,33	0,30
	05-07	Fumier Dépôt	VL + Génisses	SPCE	41,3	15,6	4,75	5,19	3,34	9,48	128,39	0,30
	05-07	Fumier Raclage	Génisses	SPCE	38,7	13,6	4,87	12,90	3,44	9,37	121,97	0,40
	05-07	Fumier Raclage	VL	SPCE	122,6	16,7	4,14	26,08	2,31	7,82	124,25	0,40
	05-07	Fumier Raclage	VL + Veaux (+ Génisses)		61,3	15,6	4,38	21,69	2,69	8,34	123,49	0,40
	05-07	Purin	VL + Veaux + Génisses	SH-SPCE	72,0	6,0	0,44	59,08	0,10	2,83	3,80		0

1

For the cells in light blue color the humus input is from INDIGO program form "fertilisants" 2 For the cells in yellow color k1 is from

Boiffin et al.1986

3 For the cells in pink color k1 is from

[START_REF] Rémy | L'entretien organique des terres[END_REF]

Table 6 .

 6 Attribution of ranges and corresponding qualitative scales to the indicators used in the evaluation. Green font indicates a positive result, while red font indicates a negative result. Black font indicates an intermediate result.When there are two black font ranks, the classic font stands for a "good to medium" result, while the bolt font stands for "medium to bad" result. Très faible=very low, faible à moyenne=low to medium, moyenne à élevée=medium to high, très élevée=very high, acceptable=acceptable, stable=stable, défavorable=unfavorable.

	2 nd level aggregated attributes	3 rd level aggregated attributes	Input attribute (indicator)	INDICATOR RANGE	QUALITATIVE SCALE
		Risk due to pesticides	Pesticides in surface water (I-Phyto Surface water)	>9 7≤I-Phyto SW<9 4≤ I-Phyto SW<7 <4	Très faible Faible à moyenne Moyenne à élevée Très élevée
	Water pollution risk	in the water	Pesticides in groundwater (I-Phyto Groundwater)	>9 7≤I-Phyto GW<9 4≤ I-Phyto GW<7 <4 >9	Très faible Faible à moyenne Moyenne à élevée Très élevée Très faible
			Nitrate leaching	7≤INO3<9	Faible à moyenne
			(INO3)	4≤ INO3<7	Moyenne à élevée
				<4	Très élevée
				>9	Très faible
			Phosphorus losses	7≤IPres<9	Faible à moyenne
			(IPres)	4≤ IPres<7	Moyenne à élevée
				<4	Très élevée
			Pesticides loss in the air (I-Phyto Air)	>9 7≤I-Phyto Air<9 4≤ I-Phyto Air<7 <4	Très faible Faible à moyenne Moyenne à élevée Très élevée
				>9	Faible
			N 2 O emission	7≤IN2O<9	Moyenne
			(IN2O)	4≤ IN2O<7	Elevée
				<4	Très élevée
				>9	Faible
	Air pollution risk	GHG emission	CH 4 emission (ICH4)	7≤ICH4<9 4≤ ICH4<7	Moyenne Elevée
				<4	Très élevée
				>9	Faible
			CO 2 emission	7≤CO2<9	Moyenne
			(CO2)	4≤ CO2<7	Elevée
				<4	Très élevée
				>9	Faible
			NH 3 volatilization	7≤NH3<9	Moyenne
			(INH3)	4≤ NH3<7	Elevée
				<4	Très élevée
	Soil quality	Chemical quality	Organic matter content (IMO) P availability (IPsol)	≥7 4≤ IMO<7 <4 ≥7 4≤ IPsol<7 <4	Acceptable Défavorable Très défavorable Acceptable Défavorable Très défavorable
	Energy consumption		Non-renewable Energy use	≥7 4≤ IPsol<7 <4	Faible Moyenne Elevée

Table 7 .

 7 Attribution of ranges and corresponding qualitative scales to the criteria (=aggregated attributes and root attributes) used in the evaluation. Green font indicates a positive result, while red font indicates a negative result. Black font indicates an intermediate result. When there are two black font ranks, the classic font stands for a "good to medium" result, while the bolt font stands for "medium to bad" result. Très faible=very low, faible à moyenne=low to medium, moyenne à élevée=medium to high, très élevée=very high, acceptable=acceptable, stable=stable, défavorable=unfavorable

  Statistical analysis were applied with GenStat® software (VSN International Ltd, Hempstead, UK) to analyse some factors that could influence indicators results.

	An unbalanced ANOVA was carried out to compare rotation systems (SPCE:6P, 6H, 8P, 8H,
	PP; SH: PP-SH) and crop types (Winter oat + faba bean=Avhfev; Winter wheat=Bh; Spelt=Ep;
	Alfalfa+dactyls=LuDac; Winter barley=Oh; Spring barley=Op; Permanent meadows-SPCE=Prp;
	Permanent meadows -SH=Prp-SH; Temporary meadows=Prt; Rye=Se; Spring triticale+Fodder
	pea=TpPf; Winter triticale=Tri; Spring triticale=TriP). This kind of analysis was chosen because
	it allows to analyze not homogeneous groups

Table 8 .

 8 Results of the indicators evaluated on the two systems SPCE and SH.

		Phyto Ground Water	Phyto Surface Water	INO 3	IPres	Phyto Air	INH 3	IN 2 O	ICH 4	CO 2	IMO	IPsol Energy
	SPCE	10,0	10,0	6,9	9,0	10,0	6,9	6,8	4,2	6,4	8,3	7,3	8,9
	SH	10,0	10,0	8,4	9,3	10,0	7,1	5,8	2,9	7,5	10,0	8,9	8,7
	Tolerance	7,0	7,0	7,0	7,0	7,0	7,0	7,0	7,0	7,0	7,0	7,0	7,0
	Max	10,0	10,0	10,0	10,0	10,0	10,0	10,0	10,0	10,0	10,0	10,0	10,0

Figure 22. Radar graph summarizing the results obtained for each indicator used in the evaluation of the two dairy systems SPCE (blue line) and SH (red line).

  The different crop types and the number of plots they occupied in the campaign 2007/2008 for the different rotation systems are shown in table 9. Evaluation results for crop rotations are summarized in table 10 and displayed on the radar graph of figure 23.

Table 9 .

 9 Number of plots occupied by the same crop for each rotation systems. Grey cells stands for crops not included in the rotation system. The first column shows the extended name of the crops while the second one the acronyms (from French). The last column shows the surface percentage occupied by each crop for the overall systems SPCE and SH in the campaign 2007/2008.

			6H	6P	8H	8P	PP	PP-SH	SPCE	Surface%
	Plots number		11	7	4	21	24	45	67
	Winter oat + faba bean	Avhfev			1				1	2,2%
	Spring oat + faba bean	Avpfev							0,0%
	Winter wheat	Bh	2	1	1	5			9	18,1%
	Spelt	Ep				3			3	1,6%
	Alfalfa+dactyls	LuDac			1	8			9	14,7%
	Winter barley	Oh	2		1				3	3,8%
	Spring barley	Op				1			1	1,0%
	Spring barley +Lupin	OpLup							0,0%
	Permanent meadow	Prp					23		23	32,6%
	Permanent meadow -SH	Prp-SH						45	100,0%
	Temporary meadow	Prt	6	4			1 1		11	17,2%
	Rye	Se				3			3	2,4%
	Winter triticale +Fodder pea	ThPf							0,0%
	Spring triticale + Fodder pea	TpPf				1			1	1,3%
	Winter triticale	Tri	1						1	2,1%
	Spring triticale	TriP		2					2	3,0%
	1

Table 10 .

 10 Results of crop rotations evaluation. Indicators values were obtained as weighted mean of plots output referred to plots surface. Figure 23. Radar graph with the results of crop rotation systems evaluation.

		SDC	Phyto Ground water	Phyto Surface water	INO3	IPres	Phyto Air	INH3	IN2O	ICH4	CO2	IMO	IPsol	Energy
	SPCE	6H	10,0	10,0	6,6	10,0	10,0	6,8	7,0	4,2	6,4	7,8	6,8	8,9
		6P	10,0	10,0	5,6	10,0	10,0	6,8	6,5	4,2	6,4	7,4	7,0	8,9
		8H	10,0	10,0	6,0	10,0	10,0	7,2	9,0	4,2	6,4	6,1	4,4	8,9
		8P	10,0	10,0	7,2	9,5	10,0	7,6	8,5	4,2	6,4	7,5	7,3	8,9
		PP	10,0	10,0	7,3	9,2	10,0	6,4	4,7	4,2	6,4	10,0	9,1	8,9
	SH	PP-SH	10,0	10,0	8,4	9,3	10,0	7,1	5,8	2,9	7,5	10,0	8,9	8,7
		Tolerance	7,0	7,0	7,0	7,0	7,0	7,0	7,0	7,0	7,0	7,0	7,0	7,0

Table 11 .

 11 INO 3 average values related to soil type. The unbalanced ANOVA yielded a significant difference between soil type "Loam-Sandy" (b) and the other four types.

Table 12 .

 12 Number of observation, INO 3 mean value and standard error in the six rotation systems (6H, 6P, 8H, 8P, PP, PP-SH) for the different soil types: Clay (A), Clay-Loamy (AL), Loamy (L), Loamy-Clay (LA) and Loam-Sandy (LS).Figure 26. Distribution of soil type among the six rotation systems. The cones height represents the mean value for INO 3 obtained by crossing soil types and rotation systems with REML linear mixed model.

	Soil class	INO 3 mean value	Standard error	Number of values
	Clay (A)	7.812 b	0.284	41
	Clay-Loamy (AL)	7.144 b	0.371	24
	Loamy (L)	7.704 b	0.504	13
	Loamy-Clay (LA)	7.599 b	0.343	28
	Loam-Sandy (LS)	5.370 a	0.741	6

Table 13 .

 13 Number of observation, INO 3 mean value and standard error in the 13 crop types for the different soil types: Clay (A), Clay-Loamy (AL), Loamy (L), Loamy-Clay (LA) and Loam-Sandy (LS).Figure 27. Distribution of soil type among the crop types. The cones height represents the mean value for INO3 obtained by crossing soil types and crop types with REML linear mixed model.

	Soil type		A			AL			L			LA			LS
		N	Mean	Se	N	Mean	Se	N	Mean	Se	N	Mean	Se	N	Mean	Se
	Avhfev	0	*	*	0	*	*	0	*	*	1	4,960 1,309		*	*
	Bh	2	4,475	0,925	1	3,360 1,309	1	5,560 1,309	3	4,947 0,755		3,170 0,925
	Ep	1	5,840	1,309	0	*	*	2	7,135 0,925	0	*	*		*	*
	LuDac	1	8,960	1,309	1	8,950 1,309	1	8,930 1,309	6	8,850 0,534		*	*
	Oh	0	*	*	2	7,110 0,925	0	*	*	0	*	*		9,120 1,309
	Op	0	*	*	0	*	*	0	*	*	1	9,120 1,309		*	*
	Prp	11	7,349	0,395	7	6,207 0,495	1	5,950 1,309	4	7,652 0,654		*	*
	Prp-SH	20	8,556	0,293 10 8,119 0,414	8	8,180 0,463	6	7,898 0,534		8,950 1,309
	Prt	4	8,803	0,654	2	8,320 0,925	0	*	*	3	8,227 0,755		3,905 0,925
	Se	0	*	*	0	*	*	0	*	*	3	7,910 0,755		*	*
	TpPf	0	*	*	1	3,640 1,309	0	*	*	0	*	*		*	*
	Tri	1	4,590	1,309	0	*	*	0	*	*	0	*	*		*	*
	TriP	1	4,780	1,309	0	*	*	0	*	*	1	4,340 1,309		*	*

Table 14 .

 14 Indicators results and their respective qualitative value as ranked with the tool DEXi. Green font indicates a positive result, while red font indicates a negative result. Black font indicates an intermediate result. When there are two black font ranks, the classic font stands for a "good to medium" result, while the bolt font stands for "medium to bad" result. Très faible=very low, faible à moyenne=low to medium, moyenne à élevée=medium to high, très élevée=very high, acceptable=acceptable, stable=stable, défavorable=unfavorable.Figure33. Indicators aggregation results for SPCE. The colours indicate the result of the related attribute using an increasing scale were green is the best result, yellow is and red is the worst result.

	The aggregation of indicators into aggregated attributes (criteria) yielded the results shown in
	figures 33 and 34 for SPCE and SH respectively. In those figures an increasing colored scale is
	used to help visualizing the results, where green correspond to the best result, yellow to a better
	intermediate result (from good to medium), orange to a worse intermediate result (from medium
	to bad) and red correspond to the worst result obtainable.

  To evaluate plot scale indicators (i.e. INO 3 , IPres, IMO, IPsol, I-Phyto GW, I-Phyto SW, I-Phyto Air) INDIGO program required soil type and soil analysis data for each plot. The choice in this work was to assign to each plot a soil type that could be the closest to the actual situation (see paragraph 4.3.4.1.

Pesticides in surface water Pesticides in groundwater Risk due to pesticides in the water

	DEXi					Dur_environ_03_07 -english.dxi 08/07/2010	Page 6
	Pesticides loss in the air N2O emission CH4 emission CO2 emission GHG emission GHG emission NH3 volatilization Air pollution risk
	33% 33%	51%		16%	33%		33%
	tres elevee 1 tres elevee	tres eleve	*	tres elevee tres elevee <=elevee	tres elevee
	tres elevee 2 tres elevee	<=eleve	<=elevee tres eleve tres elevee tres elevee	tres elevee
	<=moyenne a elevee 3 <=elevee tres eleve	<=eleve	tres elevee tres elevee tres elevee	tres elevee
	tres elevee 4 tres elevee	eleve:moyen	<=elevee eleve:moyen elevee	>=moyenne	moyenne a elevee
	tres elevee 5 <=elevee	eleve		>=eleve	<=moyenne elevee	moyenne	moyenne a elevee
	<=moyenne a elevee 6 <=moyenne eleve		eleve	tres elevee elevee	>=moyenne	moyenne a elevee
	<=moyenne a elevee 7 tres elevee moyen		<=moyen	<=elevee elevee	moyenne	moyenne a elevee
	<=faible a moyenne 8 <=elevee moyen		tres elevee tres eleve elevee	moyenne	moyenne a elevee
	tres elevee 9 elevee	<=eleve	>=moyen	elevee	elevee	>=elevee	moyenne a elevee
	tres elevee elevee:moyenne tres eleve	>=moyen	elevee:moyenne elevee:moyenne elevee	moyenne a elevee
	tres elevee >=elevee	tres eleve	moyen	>=elevee elevee	elevee	moyenne a elevee
	<=moyenne a elevee elevee eleve		*	elevee	elevee	elevee:moyenne	moyenne a elevee
	<=moyenne a elevee elevee eleve:moyen	elevee:moyenne elevee tres eleve elevee	moyenne a elevee
	<=faible a moyenne elevee:moyenne eleve		<=eleve	elevee	elevee	elevee	moyenne a elevee
	tres elevee moyenne	tres eleve	*	moyenne elevee	<=moyenne	moyenne a elevee
	tres elevee moyenne	<=eleve	<=eleve	>=moyenne elevee	<=elevee	moyenne a elevee
	<=moyenne a elevee >=moyenne tres eleve	<=moyen	moyenne elevee	<=elevee	moyenne a elevee
	<=moyenne a elevee <=elevee >=moyen	faible	>=moyenne moyenne	tres elevee	moyenne a elevee
	*	<=faible a moyenne moyen		faible	moyenne moyenne	tres elevee	moyenne a elevee
	moyenne a elevee <=elevee faible		*	tres elevee moyenne	>=elevee	moyenne a elevee
	moyenne a elevee <=moyenne faible		<=moyen	<=elevee moyenne	elevee:moyenne	moyenne a elevee
	*	moyenne a elevee faible		<=moyenne tres eleve moyenne	elevee	moyenne a elevee
	moyenne a elevee:faible a moyenne tres elevee elevee >=moyen >=eleve moyenne	elevee:moyenne	moyenne a elevee
	moyenne a elevee:faible a moyenne <=elevee elevee:moyenne >=moyen eleve:moyen moyenne	elevee	moyenne a elevee
	>=moyenne a elevee >=elevee moyen		>=eleve	tres elevee moyenne	elevee	moyenne a elevee
	moyenne a elevee moyenne >=eleve	moyen	elevee	moyenne	<=moyenne	moyenne a elevee
	moyenne a elevee >=moyenne eleve:moyen	>=moyen	elevee:moyenne <=elevee moyenne	moyenne a elevee
	moyenne a elevee moyenne >=moyen	<=moyen	>=elevee moyenne	tres elevee	moyenne a elevee
	moyenne a elevee:faible a moyenne elevee >=moyenne moyen *	moyenne	<=elevee	moyenne a elevee
	moyenne a elevee:faible a moyenne elevee:moyenne tres elevee >=moyenne >=moyen tres eleve moyenne	moyenne a elevee
	>=moyenne a elevee faible <=moyen	faible	elevee	moyenne	tres elevee	moyenne a elevee
	faible a moyenne faible eleve:moyen	*	tres elevee moyenne	<=moyenne	moyenne a elevee
	faible a moyenne faible >=eleve	<=elevee tres eleve moyenne	<=elevee	moyenne a elevee
	faible a moyenne >=moyenne faible		faible	<=moyenne faible	tres elevee	moyenne a elevee
	>=faible a moyenne faible faible		>=eleve	tres elevee faible	<=elevee	moyenne a elevee
		>=faible a moyenne			<=elevee	tres elevee	moyenne a elevee
	<=moyenne a elevee <=faible a moyenne Chemical quality Soil quality <=moyenne a elevee 100% <=faible a moyenne tres defavorable tres defavorable moyenne a elevee defavorable defavorable moyenne a elevee:faible a moyenne elevee:moyenne faible >=moyenne faible moyenne faible faible >=moyenne faible moyenne >=elevee faible favorable favorable	faible a moyenne faible a moyenne faible a moyenne faible a moyenne faible a moyenne faible a moyenne
		>=moyenne a elevee			elevee		faible	faible a moyenne
	moyenne a elevee Organic matter content Phosphorus availability Chemical quality >=moyenne >=moyenne	faible a moyenne
	50%	moyenne a elevee:faible a moyenne moyenne 50%	>=moyenne	faible a moyenne
	moyenne a elevee:faible a moyenne >=moyenne tres defavorable *	moyenne tres defavorable	faible a moyenne
	>=moyenne a elevee <=defavorable	<=defavorable	moyenne	moyenne tres defavorable	faible a moyenne
	*	moyenne a elevee	faible tres defavorable		>=elevee tres defavorable	faible a moyenne
	moyenne a elevee:faible a moyenne faible defavorable acceptable		elevee:moyenne defavorable	faible a moyenne
	>=moyenne a elevee acceptable	defavorable	faible		elevee defavorable	faible a moyenne
	faible a moyenne acceptable	acceptable	<=moyenne	faible favorable	faible a moyenne
		>=faible a moyenne			<=elevee	faible	faible a moyenne
	faible a moyenne faible a moyenne Non renewable energy use Energy consumption elevee:moyenne >=moyenne >=elevee moyenne	faible a moyenne faible a moyenne
	>=faible a moyenne 100%			elevee		>=moyenne	faible a moyenne
	>=faible a moyenne <=moyen	defavorable	elevee:moyenne moyenne	faible a moyenne
	faible a moyenne faible	acceptable	moyenne	>=elevee	faible a moyenne
		faible a moyenne			>=moyenne	elevee:moyenne	faible a moyenne
		>=faible a moyenne			moyenne	elevee:moyenne	faible a moyenne
		>=faible a moyenne			>=moyenne	elevee	faible a moyenne
		faible a moyenne			faible		<=moyenne	faible a moyenne
		>=faible a moyenne			faible		<=elevee	faible a moyenne
		tres faible				<=elevee	>=moyenne	faible a moyenne
		tres faible				<=moyenne	moyenne	faible a moyenne
		tres faible				elevee		>=elevee	faible a moyenne
		tres faible				elevee:moyenne elevee:moyenne	faible a moyenne
		tres faible				>=elevee	elevee	faible a moyenne
		tres faible				moyenne	<=moyenne	faible a moyenne
		tres faible				>=moyenne	<=elevee	faible a moyenne
		>=faible a moyenne			faible		faible	tres faible
		tres faible				>=moyenne	faible	tres faible
		tres faible				faible		>=moyenne	tres faible
		39%			61%
		tres elevee			tres elevee		tres eleve
		tres elevee			moyenne a elevee:faible a moyenne moyen a eleve
		<=moyenne a elevee		moyenne a elevee	moyen a eleve
		moyenne a elevee		<=moyenne a elevee	moyen a eleve
		>=moyenne a elevee		tres elevee		moyen a eleve
		<=moyenne a elevee		tres faible		faible a moyen
		moyenne a elevee		>=faible a moyenne	faible a moyen
		>=moyenne a elevee		faible a moyenne	faible a moyen
		>=faible a moyenne		moyenne a elevee:faible a moyenne faible a moyen
		>=faible a moyenne		tres faible		tres faible

Rodale Institute: http://www.rodaleinstitute.org/new_farm

FIBL-Suisse: http://www.fibl.org/fr/suisse/site-ch.html

INDIGO: http://www.inra.fr/indigo/

HPBI: herbaceous plant biodiversity index; APBI: Arboreous plant biodiversity index; HBI: Hedge biodiversity index; CDI: crop biodiversity index

Calculations not presented in the paper

PEC: predicted environmental concentrations

INRA-SAD (2010): http://www.inra.fr/sciences_action_developpement/

INRA-Mirecourt (2009): http://www.nancy.inra.fr/l_inra_en_lorraine/implantations/mirecourt/aster

Acknowledgements I would like to thank you the director of INRA-Mirecourt research unit Catherine Mignolet and my supervisor Michel Marie for the opportunity I've been given to carry out my MSc internship and thesis at the experimental site of Mirecourt. I would also like to thank my supervisors from ISARA-Lyon Alexander Wezel and from Turin University Carlo Grignani for supporting my Master thesis work. I am very grateful to all the personnel working at INRA-Mirecourt for the help and time I've been given during my work.

It was with pleasure and joy that I spent these last six months in Mirecourt, and this is

List of abbreviations

different in order to obtain an average value for each soil type present on the farm. Then the soil analysis of each plot were assigned as weighted average of the values of the different soil present of that plot. The soil type name assigned corresponded to the one occupying the biggest surface on the plot. Final soil types are listed in table 3. 16. Evaluation of SH with DEXi tool. Green font indicates a positive result, while red font indicates a negative result. Black font indicates an intermediate result. When there are two black font ranks, the classic font stands for a "good to medium" result, while the bolt font stands for "medium to bad" result. Très faible=very low, faible à moyenne=low to medium, moyenne à élevée=medium to high, très élevée=very high, acceptable=acceptable, stable=stable, défavorable=unfavorable.

Indicators

Input attributes First level aggregated attributes obtained the same results for the two systems: a "very low" risk of water pollution, a "high" GHG emission and a "favorable" soil chemical quality. Second level aggregated attributes obtained the same results for Air pollution risk ("low to medium"), Soil quality ( "favorable", thus with a tendency to the best situation) and Energy consumption (best situation or "acceptable"). Water pollution risk obtained a "low to medium" rank for SPCE and the best rank ("very low") for SH. This is due to the better rank obtained by the input level "nitrate leaching" (INO 3 ) for SH compared to SPCE.

Water pollution risk

Très faible

Impact on abiotic resources

Très faible

Agroenvironmental sustainability

The impact on abiotic resources of SPCE is "low to medium" while the impact of SH is "very low". Finally, the agro-environmental sustainability of the SPCE is "medium to high" while for SH it is "very high".

Criteria for soil quality and energy consumption, and their related indicators, obtained the best performance for both systems. With regard to soil quality, we can say that average organic matter content in the soil for the two systems SPCE and SH is good, with respectively 1207 and 1692 kg humus/ha brought into the soil, where the recommended amount calculated by INDIGO was 682 and 282 kg humus/ha respectively. These results mean that the rotation, as it is managed at system level, can accumulate organic matter in the soil. Looking at the single plot management level, practices as organic fertilization, cover cropping and mixed cropping favor accumulation of organic matter in the soil. On the contrary, the exportation of crop residues has a negative impact on soil fertility. Nevertheless this practice is unavoidable in the management of the experimental farm in order to assure straw for the barns. It could be suggested to implement mixed cropping for all cereals, but the only data we have on winter cereal mixed with proteaginous crop (Avhfev) did not received a good score (4). This might be due to the fact that the plot was not fertilized in the previous four years and that the percentage of faba bean assigned to the mix was 10% (obtained from seeding proportion).

Phosphorus availability, the other indicator for soil chemical quality, is evaluated as acceptable for both systems SPCE and SH. Phosphorus deficit in the soil in fact is about 25 kg P 2 O 5 /ha for SPCE and 9 kg P 2 O 5 /ha for SH, with regard to a threshold limit of 30 kg P 2 O 5 /ha. The worst performing criteria was air pollution risk. In particular GHG emission was attributed a "high" impact qualitative rank, to both SPCE and SH. This is not a surprising result, since dairy systems are responsible of 3% of global GHG emission of which 1,2% is produced on-farm [START_REF] Sevenster | A sustainable dairy sector: Global, regional and life cycle facts and figures on greenhouse gas emissions[END_REF]. At global level dairy farming is responsible for 36% of methane emission (18% through enteric fermentation and 18% through manure handling), 11% of nitrous oxide emission and a smaller share of carbon dioxide (FAO, 2006). SPCE obtained "high" emission for all three GH gases, CH 4 , N 2 O and CO 2 , while SH obtained "very high" emission of CH 4 and "medium" emission amount of N 2 O and CO 2 .

Methane emission are mainly due to enteric fermentation which is linked to animal feeding [START_REF] Crutzen | Methane production by domestic animals, wild ruminants, other herbivorous fauna, and humans[END_REF]. Feed rich in easy digestible cellulose, such as hay, inhance microbial fermention thus methane emission, while concentrate feed with a lower digestibility inhibit this fermentation [START_REF] Vermorel | Prédictions gazeuses et thermiques résultant des fermentations digestives[END_REF]. Furthermore methane emission is higher for liquid substrates than for solid ones [START_REF] Martinez | Emissions de méthane au cours du stockage des déjections animales[END_REF]. SH cows which are fed only with hay and whose excretions are stocked in the form of slurry, emit a higher amount of CH 4 (105,7 kg/ha) compared to SPCE cow (74,1 kg/ha), fed partly with concentrates (about 25% of the feed) and whose manure is stock in solid form. However if the emission are related to number of milking cows, SPCE have a higher emission (228 kg/milking cow) than SH (208 kg/milking cow).

Nitrous oxide emission is mainly due to manure handling, in particular on-field spreading.

The production of N 2 O from organic matter is linked to many interacting factors linked to substrate quality (temperature, humidity, carbon availability, NH 4 + and NO 3 -concentration) [START_REF] Granli | Nitrous oxide from agriculture[END_REF][START_REF] Beauchamp | Nitrous oxide emission from agricultural soils[END_REF] and to soil conditions [START_REF] Germon | Mécanismes de production, régulation et possibilités de limitation des émissions de N2O à l'échelle agronomique[END_REF] Finally carbon dioxide emission were lower for SH (0,2 t CO 2 /ha) than for SPCE (0,3 t CO 2 /ha). CO 2 total emission is due to fuel consumption for field operations, irrigation and electricity, and CO2 emission during fertilizers and chemical pesticides production, concentrate feed production, and machinery and buidings construction. Since no chemical fertilizers nor pesticides are used in the two systems, concetrate feeds are produced on the farm, no irrigation is used, and the machinery and buildings used are the same for SPCE and SH, it could be concluded that the differences of CO 2 emission lies in the amount of fuel consumed for field works, higher for cereals (SPCE) than for meadows (SH), and of electricity use (higher for SPCE because of the bigger size of the herd).

Nevertheless, factors for direct energy use in field operations, irrigation and electricity, and indirect energy use for fertilizers, chemicals and concentrated feed production are also accounted in non-renewable energy use indicator. In the latter the amount of direct energy use was almost the same for both systems (94 fuel eq/ha or 3477 MJ/ha for SH and 93 fuel eq/ha or 3333 MJ/ha for SPCE), while the indirect energy use was slightly higher for SH than for SPCE (68 fuel eq/ha or 2314 MJ/ha for SH and fuel 59 eq/ha or 2104 MJ/ha for SPCE). This higher consumption of indirect energy by SH could be explained by the fact that a little amount of concetrate feeds produced on SPCE are used for SH calves.

From these results it appears that direct fuel consumption for field works and electricity is the same for the two systems, denying the conclusions drawn for CO 2 indicator. However, this PLANETE) were calculated in order to evaluate the impact on abiotic resources: nitrate leaching, phosphorus losses, pesticides loss in surface and groundwater, pesticides loss in the air, ammonia volatilization, nitrous oxide, methane and carbon dioxide emission, soil organic matter content, phosphorus availability and non-renewable energy use. Indicator results were ranked on a scale from 0 to 10, where 7 is the tolerance value. Indicators were aggregated into four main criteria (water and air pollution risk, soil quality and energy consumption), weighted and attributes a qualitative ranking scale with the tool DEXi. Data were collected at plot and farm scale for the period 2007-2008. The first results of this evaluation showed that SH has a "very high" and SPCE a "medium to high" agro-environmental sustainability. The impact on abiotic resources is "very low" for SH and "medium to low" for SPCE. However both systems had a medium to low impact on water pollution, soil quality and energy consumption. Air pollution obtained the worst result ("medium to bad" impact), due to high GHG emission for both systems. In fact the indicators of nitrous oxide, methane and carbon dioxide emission obtained respectively a score of 5,8, 2,9 and 7,5 for SH, and 6,8, 4,2 and 6,4 for SPCE. These results correspond to an emission amount of 2 kg N 2 O/ha, 105,7 kg CH 4 /ha and 0,2 t CO 2 /ha for SH, and 1,6 kg N 2 O/ha, 74,1 kg CH 4 /ha and 0,3 t CO 2 /ha for SPCE. These results underline the role of dairy farming on global GHG emission, especially of methane emission from enteric fermentation. All other indicators yielded a score above the tolerance value, excepted nitrate leaching (6,9) and ammonia volatilization (6,9) for SPCE. Further work should be carried out in order to validate the proposed indicators and aggregation.

Total number of volumes: 1 Number of pages of the main document: 87