Hybrid Integer Programming Model for Optimizing Crop Planting Strategies Considering Dynamic Market and Seasonal Factors

This study explores the challenge of determining optimal crop allocation across various land types in the context of environmental degradation and resource scarcity. Focusing on the northern regions of China, this paper proposes a mixed-integer programming model designed to account for seasonal variations and market fluctuations, with the goal of maximizing cumulative planting income over multiple years. The model integrates a comprehensive analysis of both economic and environmental factors, including crop yield, costs, prices, and associated risks. In addition, the research employs entropy weighting and TOPSIS methods for effective risk assessment to address uncertainty. Validation of the model is conducted using datasets from several villages in China, highlighting its capability to predict income fluctuations while providing practical insights for agricultural planning. The findings indicate that this model not only stabilizes income but also improves resource efficiency and offers valuable references for effective agricultural market regulation. By addressing the complexities of crop allocation, this research contributes to sustainable agricultural practices and resource management strategies in the face of ongoing environmental challenges.