Research on Crop Planting Strategy Based on Linear Programming Model

Abstract

This article addresses the long-term profit maximization needs of a rural village in the mountainous region of North China under limited arable land resources, focusing on the optimization of crop planting across multiple cycles. The study particularly tackles the challenge of coordinating yield overproduction risks with complex constraints. Existing research predominantly focuses on single-year or single-objective scenarios, lacking systematic integration of long-term dynamic planning and differentiated sales strategies. Our research constructs two profit functions based on a linear programming model, incorporating a seven-year cycle, land type restrictions, and crop rotation constraints to design a phased optimization strategy. By integrating land parcel information, crop attributes, and historical planting data, the impact of overproduction scenarios on profits is quantified. Under the assumption of a static market, the price reduction sales strategy outperforms the stockpiling strategy, increasing total profits by 12.5%-20%. Additionally, crop rotation constraints balance short-term gains with long-term soil health, and post-optimization, high-value crops such as golden oyster mushrooms dominate the planting area, validating the model's adaptability to complex constraints. This experiment proposes a planting optimization scheme that combines economic efficiency with sustainability by integrating multi-cycle dynamic planning and differentiated sales strategies, providing theoretical support for rural agricultural decision-making. Future work could enhance the model's dynamism by incorporating climate forecasting and blockchain technology.