Research On Agricultural Planting Strategy Based on Nonlinear Programming Model

This study developed a nonlinear programming model for optimizing crop cultivation strategies in resource-constrained mountainous regions of northern China that integrates agronomic, economic, and ecological aspects. By synthesizing yield, cost, and price data for six different cropland types (e.g., irrigated fields, rain-fed plots, and greenhouses) and 41 crops, the model dynamically evaluated the trade-offs between residual wastage (Scenario 1) and discounted redistribution at a 50% market price (Scenario 2), and addressed post-harvest supply chain uncertainty. The constraint system rigorously incorporates agroecological principles, including a ban on crop rotation to mitigate soil degradation, a mandatory legume planting cycle to fix nitrogen, and land-type-specific seasonal rules (e.g., a fungus-exclusive second season in greenhouses). The computational solution using Python showed a 16.4% increase in profit for Scenario 2 ($9,790,138.2) compared to Scenario 1 ($8,409,800), highlighting the economic value of adaptive surplus management. Sensitivity analyses show that strategic spatial allocation of high-value cash crops combined with prioritization of food security for staple foods can significantly improve profitability under land fragmentation constraints. The model's embedded ecological safeguards, such as minimum acreage thresholds (30% per plot) and biodiversity conservation rotations, align economic incentives with long-term soil health. These findings provide a scalable framework for sustainable agricultural planning in marginal environments, emphasizing the synergies between precision agriculture, market adaptability, and ecological resilience. Future research could incorporate stochastic climate models to improve the robustness of decision-making in the face of increasing environmental volatility.