SUSTAINABLE CROP PLANNING IN A MOUNTAINOUS VILLAGE USING LINEAR PROGRAMMING

This study focuses on optimizing crop planning in a mountainous village in North China, where the cold climate permits only one growing season per year. The village’s arable land is distributed across 34 plots of various types, including flat drylands, terraces, slopes, and irrigated fields. A linear programming model is developed to maximize planting revenue, considering crop rotation requirements, land suitability, planting costs, yields, and market prices. To simulate future conditions, crop prices in 2030 are modeled using normally distributed random numbers. The model sets two sales scenarios: (1) surplus production beyond expected sales cannot be sold, resulting in waste; and (2) surplus is sold at 50% of the 2023 market price. To address uncertainties in yield, cost, price, and demand, as well as potential planting risks, a particle swarm optimization algorithm is employed with random factors to simulate changing conditions. Results show that the village can achieve annual revenues between 8 to 9 million RMB under both deterministic and stochastic scenarios. The optimized strategies satisfy agronomic and environmental constraints and offer scientific support for sustainable agricultural development in resource-limited mountainous regions.