Combining constraint programming and a participatory approach to design agroecological cropping systems

Context

Agroecology implementation around the world have shown that increasing the complexity of the agroecosystem leads to increased resilience, lower dependence on synthetic inputs, the provision of ecosystem services and improved performance. However, designing diversified agroecosystems is particularly complex because of the diverse factors to take into account for each specific local context and the range of possible spatiotemporal crop combinations.

Objective

Here we propose an iterative agroecological design approach combining artificial intelligence with constraint programming and co-design workshops with farmers to explore and optimize spatiotemporal cropping arrangements in diversified cropping systems.

Methods

Our iterative approach comprises a three-step loop for designing new cropping systems: 1) identifying problem data and spatiotemporal constraints; 2) applying a flexible constraint programming model, and refining/removing constraints iteratively with farmers' input until a solution is found; and 3) evaluating solutions through model assessment and workshops with farmers, leading to the design of a new scenario if necessary (repeating step 2). We applied our approach to a case study involving diversified mixed fruit tree–vegetable cropping systems in southern France, whereby farmers were involved in co-design workshops with an agronomist.

Results and conclusions

The constraint programming model simulated most important farmers' constraints while adapting to the input of new information during the design process. The workshops facilitated knowledge elicitation, with progressive questioning of farming practices, while fostering a learning process through farmer-agronomist discussions. Meanwhile, the scope of the problem was iteratively outlined during the process, driven by the need to seek trade-offs between all of the constraints, and informed by model feedback. This approach allowed farmers to explore and assess disruptive scenarios, in turn facilitating informed decisions that jointly addressed agroecological and operational objectives on their farms.

Significance

The framework presented and illustrated in this study provides a basis for exploring and optimizing spatiotemporal cropping arrangements in diversified cropping systems.