A robust multi-objective optimization model for grid-scale design of sustainable cropping patterns: A case study

In the face of growing population, water scarcity, and increasing food demand, there is a pressing need to shift towards optimized, resource-efficient, climate-resilient, and sustainable agricultural practices. In light of that, designing a sustainable cropping pattern that considers the procedure of resource allocation based on land capabilities and crop features is vital for ensuring long-term food production and safeguarding the delicate balance of ecosystems. Motivated by this imperative, this study proposes a comprehensive framework that integrates Geographic Information System (GIS), System Dynamics (SD), and optimization to address the sustainable design of cropping patterns. The framework assesses grid-scale land suitability, models dynamic water resource interactions, and optimizes resource allocation based on crop calendar considerations. For the first time, a dynamic crop inventory is integrated into the cropping pattern optimization process, addressing food security concerns in a comprehensive manner. In order to evaluate the effect of uncertainties on the designed system, a robust optimization model is developed based on convex sets. The results demonstrate the advantages of the robust model in situations with uncertainty. Despite a 5% reduction in profit compared to the deterministic solution, the robust design achieves a 25% decrease in irrigation, highlighting the cost of ensuring sustainability. The deterministic approach prioritizes crops based on their economic value, whereas the robust solution considers the volume of irrigation required for a sustainable design. The managerial implications emphasize the importance of prioritizing water-efficient and climate-resilient agricultural practices to guarantee long-term food security.