Research on Crop Planting Strategies Based on Monte Carlo Simulation and Genetic Algorithm

Abstract

Sustainable agricultural development is challenged by global population growth and resource scarcity. Efficient land use and crop management are crucial for stable yields and maximising benefits from limited land. This paper aims to improve land profitability and marketability through scientific cropping plans that take into account soil nutrient cycling, crop rotation, seasonal adaptability and market dynamics. The study first developed a linear programming model to optimise crop planting in a mountainous area in northern China from 2024 to 2030. By constructing objective functions and constraints and solving them using a genetic algorithm, the maximum annual returns were 6.2 million yuan and 7.2 million yuan under two scenarios, with high model stability. Next, planting costs and selling prices were incorporated and the Monte Carlo algorithm was used to simulate changes in the indicators, further optimising the model to achieve a maximum annual return of 5.5 million yuan. Finally, taking into account crop substitution and complementarity, systematic clustering and multiple linear regression were used to derive the optimal planting configuration, increasing the maximum annual return to 5.6 million yuan. These results demonstrate the effectiveness of integrating advanced techniques to improve agricultural productivity and economic returns. Future research will focus on expanding the scope to more diverse regions and crops, integrating real-time data and advanced analytics to improve model adaptability and accuracy. In addition, incorporating external factors such as climate change and policy changes will improve the model's ability to deal with uncertainties in agricultural production. These efforts aim to support sustainable agriculture globally, ensuring higher productivity and resilience for food security and rural development. The ultimate goal is to provide policy makers and farmers with actionable insights to optimise agricultural practices in a rapidly changing world.