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Data foundation and modeling framework analysis for optimizing farmland resource allocation and crop planting structure in mountainous rural areas of north China
Abstract
This study develops crop-planting plans for fragmented farmland in mountainous rural areas of North China. The model covers grain crops, vegetables, legumes, and edible fungi, and accounts for differences in land conditions. It specifies planting area, expected and surplus yield, crop-plot suitability, planting decisions, and market fluctuation parameters for subsequent linear and intelligent optimization. The dataset covers 1,201 mu of open-field farmland and 20 mu of greenhouse land. Plots include flat dryland, slopes, terraces, conventional greenhouses, and smart greenhouses. Price ranges were represented by their mean values, and baseline sales volumes were reconstructed from the historical census year. Flat dryland and terraces carried most field-crop production, while greenhouses supported vegetables and edible fungi. Under stable conditions, the optimized profit was about 3.0 million yuan when surplus was discarded and about 15.0 million yuan when it was sold at 50% of the original price. Under market uncertainty, particle swarm optimization reached about 18.0 million yuan in the discounted-surplus case. The regression model produced an R² of 0.835 and an adjusted R² of 0.816. The resulting scenario-based planting plans account for land use, rotation, legume requirements, profit, and market uncertainty
