Optimization of Crop Planting Strategy Based on Mixed-Integer Programming under Multiple Scenarios

Abstract

Agriculture serves as the bedrock of the rural economy, directly determining household incomes and food security while providing essential support for extending industrial chains and fostering related industries. Selecting suitable crops requires 2 with regional climate and soil conditions, while rational planting strategies must balance profitability with ecological considerations—both being crucial for achieving sustainable rural development. This study examines a village with diverse farmland types—flat dryland, terraced fields, slopes, irrigated land, and both standard and smart greenhouses. By optimizing annual crop selections across these terrains and applying mixed-integer programming (MIP) to planting strategies, it tracks yield fluctuations under varying approaches. This methodology proves effective in minimizing resource wastage, boosting profitability, and mitigating market volatility. The results indicate that by fully considering the specific demand trends for different crop categories over the years, the real-time fluctuation patterns of market vegetable prices during different seasons and holidays, as well as the substitutability in planting space and growth cycles and the ecological complementarity among various crops, the resulting planting strategy can effectively reduce waste of resources such as land, water, fertilizers, and labor. It significantly increases farmers' planting income, providing a solid and practical scientific basis for steadily increasing crop yields, sustained growth in agricultural economic benefits, and the sustainable development goal of balancing rural ecological conservation with economic growth.