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International audience ; Plusieurs formes d'agriculture ont récemment émergé pour faire face aux externalités négatives de l'agriculture. Certaines visent à réduire les impacts. D'autres comme l'agroécologie propose de fonder l'agriculture sur les services écosystémiques. De ce fait, il coexiste actuellement deux grilles d'évaluation mal articulées caractérisant, l'une les impacts sur les ressources naturelles et l'environnement, l'autre les services fournis par la biodiversité à l'agriculture et à la société (séquestration du carbone.). Nous présentons un cadre d'analyse basé sur les services écosystémiques et l'économie circulaire, articulant ces deux démarches, pour évaluer les systèmes agricoles. Puis, à partir des données de la littérature, nous comparons trois exemples : agriculture raisonnée (AR), biologique (AB) et de conservation des sols (ACS). Nous montrons que l'AB a des impacts plus importants que l'AC et l'ACS lorsque l'on considère les indicateurs les plus couramment considérés (émissions de gaz à effet de serre, besoin en terre). Par contre, les résultats sont revisités pour les services à la société : plus faibles pour l'AR, et plus élevés pour l'AB et l'ACS. Une évaluation similaire est faite pour les systèmes d'élevage. Nous discutons comment ces évaluations pourraient être mobilisées par les politiques publiques pour relever les grands défis à l'agenda de l'agriculture.

International audience ; Plusieurs formes d'agriculture ont récemment émergé pour faire face aux externalités négatives de l'agriculture. Certaines visent à réduire les impacts. D'autres comme l'agroécologie propose de fonder l'agriculture sur les services écosystémiques. De ce fait, il coexiste actuellement deux grilles d'évaluation mal articulées caractérisant, l'une les impacts sur les ressources naturelles et l'environnement, l'autre les services fournis par la biodiversité à l'agriculture et à la société (séquestration du carbone.). Nous présentons un cadre d'analyse basé sur les services écosystémiques et l'économie circulaire, articulant ces deux démarches, pour évaluer les systèmes agricoles. Puis, à partir des données de la littérature, nous comparons trois exemples : agriculture raisonnée (AR), biologique (AB) et de conservation des sols (ACS). Nous montrons que l'AB a des impacts plus importants que l'AC et l'ACS lorsque l'on considère les indicateurs les plus couramment considérés (émissions de gaz à effet de serre, besoin en terre). Par contre, les résultats sont revisités pour les services à la société : plus faibles pour l'AR, et plus élevés pour l'AB et l'ACS. Une évaluation similaire est faite pour les systèmes d'élevage. Nous discutons comment ces évaluations pourraient être mobilisées par les politiques publiques pour relever les grands défis à l'agenda de l'agriculture.

International audience ; Introduction During the last decades food production has increased thanks to intensive use of non-renewable and agrochemical inputs and simplification of landscapes (Therond et al., 2017). Yet, the present inefficient use of pesticides and fertilizers and the increasing prevalence of intensive and simplified cropping systems are leading to negative impacts on biodiversity and its associated ecosystems services (Rusch et al., 2016). Actually, these are the same systems that may push agriculture over a sustainability tipping point (Ray et al., 2012). Thus, it is a priority to develop innovative forms of agriculture that stabilizes both yields and farmers' profitability while developing ecosystem services (Therond et al., 2017). Cropping diversification is regarded as a key path for a strong sustainable development of multifunctional agroecosystems (Ponisio and Kremen, 2016). However, little is still known on the potential synergic and trade-off effects brought by the implementation of different crop diversification strategies at different spatial and temporal scales. Integrated Assessment and Modelling (IAM), a multicriteria and multilevel assessment based on modelling platforms and involvement of stakeholders may help to deal with this challenge. As part of the EU H2020 DiverIMPACTS project, this work uses MAELIA, an IAM platform of agricultural landscape (Therond et al., 2014), to perform on three contrasted European case studies (CSs), an assessment of the benefits and drawbacks brought by diversification from field and farm to landscape and agro-chain levels. Materials and Methods MAELIA is a high-resolution multi-agent platform, that allows to simulate at fine spatial resolution the daily dynamic and interactions between human activities (e.g. farming practices), ecological processes (e.g. crop growth), and governance systems (e.g. agricultural regulations). It is applied in three very different CSs used as pilot test for the implementation of different diversified agricultural strategies. A first CS is located in Germany, is composed by 25 farmers with a common goal of improving water quality in catchment basin trough new valuable and sustainable farming strategies based on diversified rotations, as well as to increase the cooperation and trade between local farmers. A second CS in Romany is composed by four very large farms (800 to 14000 ha each) in which the objective is to assess in an ex-ante way the effects of including in the rotation legumes and other winter crops on soil quality, yield stability and economic returns. The third CS, in France, corresponds to ten farmers where the challenge is to develop exchanges between cereal and livestock farmers, in order to offer diversification opportunities (e.g. forage production), while dealing with water quality and availability. The IAM process was structured in three main participatory steps. Firstly, data and knowledge were collected and integrated to develop in MAELIA a concrete model of the current functioning of each CS. Second, a participatory design process led by stakeholders was conducted to specify changes in the cropping systems. Thirdly, the integrated assessment of these scenarios was carried out with MAELIA, and results were analysed and discussed with stakeholders. Results A large set of pre-defined indicators (8 for the economic pillar, 19 for environmental sustainability, 2 for social dimension; see Canali et al. 2019) and stakeholder-oriented indicators (e.g. biological regulations issues) will be calculated to assess the direct and indirect effects of diversification over time (intra and inter year variabilities, rotation and long term) and space (field, cropping system, farm and territory). Based on outputs of scenarios simulations, this conference will provide opportunity to present and discuss the first key results of the IAM procedure. Discussion and Conclusions To support policy makers, it is necessary to define policies and strategies at the relevant levels at which impacts (e.g. biodiversity and economic return) are managed, such as farm or landscape. These impacts are also intrinsically linked with local biophysical processes, defined at very fine scales, such as soil-plant level. Here, we propose an IAM approach that allows simulating and assessing the main aspects of cropping systems, including social, environmental and economical, steering a transition to more sustainable food. Namely, the discussion is focused on the effects of cropping system diversification on a large range of generic and stakeholders oriented indicators. We will focus above all on the viability of agricultural holdings and the robustness/resilience to technical, climate and socioeconomic changes; stability of production and economic returns; soil (e.g. quality, erosion); GHG balance and non-renewable resources; water quality and availability; agricultural environmental impacts (e.g. nitrogen leaching); and, farmers' quality of life (e.g. workload).

The rapid evolution of economie and environmental constraints on farming systems require cropping systems design or assessment to be conducted in the context of market instability as well as economie (c.g. CAP in EU) and environmental policy changes. The Nitrate Directive (91/676/EC) is one of the oIdest EU environmental policy, designed to reduce water pollution by nitrate from agricultural sources, through a set of measures, defined at regional level, and mandatory for farmers of vulnerable zones

The reconciliation of economy and environment is a key factor in achieving sustainability.The European Union wishes to achieve the sustainability of its agriculture in order to produce high quality food materials and to manage energy crisis and the risks related to climate and market fluctuations. These risks can be mitigated by reducing negative impacts of agricultural activities on the environment. Therefore, this study was designed to derive and promote the potential tools to increase the land area under grain legumes in Midi-Pyrenees region (France) where it currently stands at only 1 to 3%. For this purpose modeling chain APES-FSSIM Indicator was used to assess different alternative scenarios of proposition of new grain legumes based cereals rotations, provision of higher premium on grain legumes, increase in sale price and yield of grain legumes, reduction in price and yield variability of grain legumes and combination of all these scenarios. Results showed that alternative scenario of provision of more premiums on grain legumes was more efficient in increasing the grain legume area than other alternative scenarios, but this would require a level of subsidies much higher than the currentcrop-specific subsidies in EU. However, in case of combination of all these scenarios, the increase in grain legumes area was maximum for all three selected farms from the study area. In addition farm income was increased by 11 to 26% and energy consumption was decreased by 4 to 9% for the selected farms. It is concluded that grain legumes area in Midi-Pyrenees farming systems can be increased by following the above mentioned alternative strategies.

International audience ; Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfill these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20 and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green LAI, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.

International audience ; Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfill these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20 and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green LAI, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.

International audience ; Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfill these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20 and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green LAI, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.

International audience Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfill these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20 and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green LAI, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.

This paper presents the experience gained related to the development of an integrated simulation model of water policy. Within this context, we analyze particular difficulties raised by the inclusion of multi-level governance that assigns the responsibility of individual or collective decision-making to a variety of actors, regarding measures of which the implementation has significant effects toward the sustainability of socio-hydrosystems. Multi-level governance procedures are compared with the potential of model-based impact assessment. Our discussion is illustrated on the basis of the exploitation of the multi-agent platform {MAELIA} dedicated to the simulation of social, economic and environmental impacts of low-water management in a context of climate and regulatory changes. We focus on three major decision-making processes occurring in the Adour-Garonne basin, France: (i) the participatory development of the Master Scheme for Water Planning and Management (SDAGE) under the auspices of the Water Agency; (ii) the publication of water use restrictions in situations of water scarcity; and (iii) the determination of the abstraction volumes for irrigation and their allocation. The {MAELIA} platform explicitly takes into account the mode of decision-making when it is framed by a procedure set beforehand, focusing on the actors' participation and on the nature and parameters of the measures to be implemented. It is observed that in some water organizations decision-making follows patterns that can be represented as rule-based actions triggered by thresholds of resource states. When decisions are resulting from individual choice, endowing virtual agents with bounded rationality allows us to reproduce (in silico) their behavior and decisions in a reliable way. However, the negotiation processes taking place during the period of time simulated by the models in arenas of collective choices are not all reproducible. Outcomes of some collective decisions are very little or not at all predictable. The development and simulation of a priori policy scenarios capturing the most plausible or interesting outcomes of such collective decisions on measures for low-water management allows these difficulties to be overcome. The building of these kind of scenarios requires close collaboration between researchers and stakeholders involved in arenas of collective choice, and implies the integration of the production of model and the analysis of scenarios as one component of the polycentric political process of water management.