Research on the Optimization Scheme of Rural Crops Planting in Mountainous Areas of North China

With the complications of modern agricultural production, how to formulate scientific and reasonable planting strategies in the limited land resources and highly uncertain market environment has become a key research topic in the agricultural field. The diversity of land types (such as terraced fields, sloping fields, and irrigated land) and the variety of crops in North China make the problem of planting optimization more complicated and practical. In this paper, a bi-level optimization model combining mixed integer programming (MIP) and stochastic programming (SP) is proposed to solve the problems of resource allocation and income maximization in crop planting. Under certain conditions, the planting scheme is constructed by a mixed integer programming model, and the effects of two over-processing strategies, namely “slow sales” and “lower price sales”, are analyzed. Under the condition of uncertainty, various random scenarios are generated by Monte Carlo simulation, and the influence of market demand and yield fluctuation per mu on planting strategy is explored by combining with a stochastic programming model.The experimental results show that the stochastic programming model is superior to the deterministic model in key indicators such as average income, land utilization rate, and income stability. Specifically, compared with the deterministic model, the land utilization rate of the stochastic programming model is increased by about 15% on average, the average income is increased by about 8%, and the fluctuation range of income is reduced by more than 50%. Sensitivity analysis further reveals the significant influence of market demand and the fluctuation range of yield per mu on income, and the stochastic programming model shows strong flexibility and robustness in adapting to uncertainty. At the same time, this study provides multi-dimensional decision support for agricultural operators by introducing multi-scenario weight optimization, which not only performs well in improving economic benefits but also provides an effective reference in resource allocation efficiency and risk management.This paper provides a new theoretical framework for agricultural planting optimization and has important practical value. The stochastic programming method can effectively deal with extreme market conditions and environmental uncertainties, and provide a decision-making basis for agricultural policy makers in subsidy distribution and market regulation. In addition, the research results provide technical support for the digital transformation and intelligent management of agricultural production and promote the development of precision agriculture. The optimization model proposed in this study also provides a reference for resource allocation and optimization in other fields.