Integrated assessment and modelling of the impacts of cropping system diversification from field to landscape and agro-chain levels: the MAELIA multi-agent platform
Rui Catarino∗±1,2, Christian Bockstaller2, Frederique Angevin1 & Olivier Therond2
1 Eco-Innov, INRA, Avenue Lucien Brétignières, F – 78 850 Thiverval – Grignon
2 LAE, Université de Lorraine, INRA, 28 Rue de Herrlisheim, F-68000 Colmar
∗ Speaker
± Corresponding author: rui.catarino@inra.fr 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.
Fig.1 MAELIA implementation scheme : 1) integration of spatial data describing each farms present in each case study; (2) application, calibration and sensitivity analysis are performed before the simulation of different scenarios regarding cropping practices, climate and economic changes; (3) environmental and socio-economic indicators are retrieved, analysed and displayed.
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).
1. References
Canali, S., Bockstaller, C., Rui, C., Curran, M., Iocola, I., Schader, C., Stilmant, D., Therond, O., Vanhove, P., VanStappen, F., Angevin, F., 2019. Multi-criteria assessment tools to encompass the performance of diversified cropping systems at the rotation, farm, value chain, and landscape levels, in: European Conference on Crop Diversification, September 18th-21st 2019. Budapest.
Hamilton, S.H., ElSawah, S., Guillaume, J.H.A., Jakeman, A.J., Pierce, S.A., 2015. Integrated assessment and modelling: Overview and synthesis of salient dimensions. Environ. Model. Softw. 64, 215–229.
https://doi.org/https://doi.org/10.1016/j.envsoft.2014.12.005
Ponisio, L.C., Kremen, C., 2016. System-level approach needed to evaluate the transition to more sustainable agriculture. Proc. R. Soc. B 283, 20152913.
Ray, D.K., Ramankutty, N., Mueller, N.D., West, P.C., Foley, J. a, 2012. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293. https://doi.org/10.1038/ncomms2296
Rusch, A., Chaplin-Kramer, R., Gardiner, M.M., Hawro, V., Holland, J., Landis, D., Thies, C., Tscharntke, T., Weisser, W.W., Winqvist, C., Woltz, M., Bommarco, R., 2016. Agricultural landscape simplification reduces natural pest control: A quantitative synthesis. Agric. Ecosyst. Environ. 221, 198–204.
https://doi.org/https://doi.org/10.1016/j.agee.2016.01.039
Therond, O., Duru, M., Roger-Estrade, J., Richard, G., 2017. A new analytical framework of farming system and agriculture model diversities. A review. Agron. Sustain. Dev. 37, 21. https://doi.org/10.1007/s13593-017- 0429-7
Therond, O., Sibertin-Blanc, C., Lardy, R., Gaudou, B., Balestrat, M., Hong, Y., Louail, T., Panzoli, D., Sanchez-Perez, J.-M., Sauvage, S., 2014. Integrated modelling of social-ecological systems: The MAELIA high-resolution multi-agent platform to deal with water scarcity problems, in: 7th International Environmental Modelling and Software Society (IEMSs 2014). p. pp-1.
Vitousek, P.M., Naylor, R., Crews, T., David, M.B., Drinkwater, L.E., Holland, E., Johnes, P.J., Katzenberger, J., Martinelli, L.A., Matson, P.A., Nziguheba, G., Ojima, D., Palm, C.A., Robertson, G.P., Sanchez, P.A., Townsend, A.R., Zhang, F.S., 2009. Nutrient Imbalances in Agricultural Development. Science (80-. ).
324, 1519 LP-1520.
