Supporting Sustainable Agroecological Initiatives for Small Farmers through Constraint Programming

Supporting Sustainable Agroecological Initiatives for Small Farmers through Constraint Programming

Margot Challand, Philippe Vismara, Dimitri Justeau-Allaire, St├ęphane de Tourdonnet

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
AI for Good. Pages 5924-5931. https://doi.org/10.24963/ijcai.2023/657

Meeting the UN's objective of developing sustainable agriculture requires, in particular, accompanying small farms in their agroecological transition. This transition often requires making the agrosystem more complex and increasing the number of crops to increase biodiversity and ecosystem services. This paper introduces a flexible model based on Constraint Programming (CP) to address the crop allocation problem. This problem takes a cropping calendar as input and aims at allocating crops to respect several constraints. We have shown that it is possible to model both agroecological and operational constraints at the level of a small farm. Experiments on an organic micro-farm have shown that it is possible to combine these constraints to design very different cropping scenarios and that our approach can apply to real situations. Our promising results in this case study also demonstrate the potential of AI-based tools to address small farmers' challenges in the context of the sustainable agriculture transition.
Keywords:
AI for Good: Constraint Satisfaction and Optimization
AI for Good: Multidisciplinary Topics and Applications
AI for Good: Planning and Scheduling