Optimizing agricultural planting decisions requires addressing the multi-scale impacts of market and climatic uncertainties on production efficiency. This study proposes a decision-making framework integrating probabilistic modeling and stochastic programming. Based on farmland data from a mountainous village in North China, we construct a stochastic programming model using Monte Carlo simulation to maximize profits by quantifying uncertainties. First, a deterministic constraint model is established, incorporating constraints such as plot area limits, crop rotation rules, seasonal planting restrictions, and field management requirements. Next, using normal distributions probabilistic modeling methods are introduced to quantify yield fluctuations (±10%) and price fluctuations (±5%). Finally, a solver is employed for Monte Carlo simulation to generate numerous stochastic scenarios. The top ten optimal strategies yield annual profits ranging from 50.43 million to 52.90 million CNY, validating the model’s effectiveness. This framework provides dynamic decision support for agricultural management, optimizes resource allocation, reduces uncertainty risks, and enhances economic benefits and sustainable development capabilities in agrarian production.

Yunbao Xu, Xiaodan Yang, Jie Zhang. Optimization of Crop Planting Strategy Based on Monte Carlo Simulation. Academic Journal of Mathematical Sciences (2025), Vol.6, Issue 2: 41-47. https://doi.org/10.25236/AJMS.2025.060206.

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