A Comparison of Optimization Techniques for Large-scale Allocation of Soybean Crops

The optimal allocation of crops to different parcels of land is a problem of paramount practical importance, not only to improve food and feed production, but also to address the challenges posed by climate change. However, this optimization problem is inherently complex due to the large number of agricultural parcels available which generates a vast search space that renders traditional optimization techniques impractical. Moreover, as maximizing average production may generate solutions characterized by high year-by-year instability and lead to large and unrealistic cultivated areas, it is necessary to optimize crop allocation considering several objectives at the same time. In order to tackle this complex optimization problem, we propose a multi-objective approach, simultaneously maximizing the average production, minimizing the year-on-year production variance, and minimizing the total cultivated surface. The approach exploits an established multiobjective evolutionary algorithm, and employs a machine learning model able to predict crop production from weather and soil conditions, trained on historical data, making it possible to tackle allocation problems of large size. As a reference, we also present a comparison with a quadratic programming algorithm specifically tailored to the target problem. A case study focusing on the allocation of soybean crops in the European continent for the years 2000-2023 shows that the proposed methodology is able to identify informative trade-offs between the conflicting objectives, and identify realistic and meaningful crop allocations for supporting stakeholders’ decisions.