Speakers
Description
Introduction
To address agricultural challenges, engaging agroecological transition is crucial, necessitating a redesign strategy for productive and resilient biodiversity-based farming systems. However, implementing spatio-temporal design of diversified systems is complex due to the diverse factors that need to be considered, the large number of possible crops combinations in time and space, and the need to combine different forms of knowledge to take account of operational constraints, soil and climate conditions, and agroecological objectives. To support the design of agroecological cropping systems, we propose to combine together AI (constraint programming) which provides formalisms with a high level of expressivity (Challand et al., 2023) with co-design approaches that enable stakeholders with diverse skills and knowledge to collaborate, while putting the farmer at the centre of the design ecosystem. This approach has been applied to one of the most complex agrosystems, the mixed orchard market gardens, to explore crop allocation scenarios with the farmer that take tree growth into account.
Materials, methods
The model AGROECOPLAN, used in this study to generates a spatio-temporal crop allocation solution, has been described in Challand et al (2023). The model is composed of four sets of constraints to take into account pedoclimatic, operational and agroecological constraints: respect the return time of crops, forbid negative spatial interactions (spread of pests, incompatible cultivation operations, shade from neighbouring crops or trees…), forbid unfavourable precedents, forbid impossible locations. The model then optimizes two criteria to propose a cropping plan that maximizes the positive spatial and temporal interactions between crops.
The model was used in a case study one-hectare micro-farm in South of France. The objective was to assign the 60 crops from the cropping calendar to the 80 cropping beds, considering the crop assignments of the last 3 years and the farm's pattern. The co-design workshops were conducted in three steps : (i) identify and formalise the problem through a semi-directive interview with the farmers (ii) run the AGROECOPLAN model to propose a crop allocation scenario (iii) evaluate with the farmer the model's output and performance. If the solution is deemed unsatisfactory, the set of constraints is modified with the farmers to better specify the problem and the model is run again (repeating step 2) until a satisfactory solution is found.
Results
The co-design workshops led farmers to formulate three issues that guided the exploration of the scenarios. (i) How to add the maximum green manure beds to improve the agroecosystem performances? (ii) How will the growth of fruit trees change the layout of crops in the future? (iii) What crop area is needed to satisfy all the farmers' expectations? The model was able to find solutions that satisfied all the constraints for each of these three issues. This required several iterations each time to better specify and prioritize the constraints.
To answer the first question, two green manures were selected with the farmers: fodder beet and forage rye. By maximizing the number of green manures, 10 fodder beet beds and 24 forage rye beds were introduced, increasing the total number of positive interactions between crops by 16%. By taking into account the growth of the trees, we have been able to adapt the cultivation plan over long time, allowing crops that need or tolerate shade to benefit from it. Finally, exploration of the scenarios to answer the third question showed that 7 additional cropping beds were needed to find a cropping plan that met all the farmers' expectations. This corresponds to a 9% increase in cultivated area, which was feasible in this case study.
Discussion.
By integrating constraint programming into a co-design approach, we effectively managed the complex combinatorial nature of designing highly diversified farming systems and took account of farm-specific constraints and farmer expectations. This process introduced a disruptive solution for farmers, providing a basis for discussion on how to evolve their practices in order to strike a balance between integrating agroecological principles and maintaining acceptable operational management. This makes it possible to integrate many internal (soil-plant interactions) and external (management practices, climate) regulations that underpin the resilience of agro-ecosystems.
References.
Challand, M., Vismara, P., Justeau-Allaire, D., and de Tourdonnet, S. (2023). Supporting Sustainable Agroecological Initiatives for Small Farmers through Constraint Programming. In "Proceedings IJCAI-23, Macao, S.A.R.
| Keywords | constraint programming ; agroecological practices ; market gardening ; agroforestry |
|---|
