Optimized Design of Crop Planting Strategies Based on Nonlinear Programming

The advancement of agricultural technology has facilitated large-scale production. Efficient land use, optimal crop selection, and strategic planting decisions are crucial for improving productivity, minimizing cultivation risks, and promoting sustainable rural economic development. This paper focuses on optimizing crop planting strategies in a rural area of northern China.Data preprocessing included encoding and standardizing variable types. The actual crop yields in 2023 were used as the projected sales volumes for subsequent years. Based on problem analysis, the task is modeled as a nonlinear programming problem. A nonlinear model was constructed using crop planting area SSS as the decision variable, with constraints including land area, crop rotation, and legume planting frequency. An algorithm was designed to solve the model. Initially, the nonlinear solver from Python's SciPy library was applied, later optimized using a genetic simulated annealing algorithm. Sensitivity testing showed high responsiveness. Under Scenario 1, the total revenue reached 10.516 million RMB; under Scenario 2, the total revenue reached 16.26 million RMB.