Research On Optimization and Uncertainty Analysis of Crop Cultivation Based on The Monte Carlo Algorithm

Abstract

With population growth and climate change, agriculture faces challenges such as limited land, fluctuating market demand, and rising costs. This study focuses on optimizing crop yields and profits through scientific forecasting in uncertain environments. Using 2023 data on various crops and plot types, an integer programming model is developed to maximize profit, considering crop growth patterns, environmental constraints, and a requirement to plant legumes every three years to maintain soil fertility. The model incorporates non-negative and binary variables, along with quarterly constraints, to perform 0-1 programming. A Particle Swarm Optimization algorithm is used for iterative calculations, while the Monte Carlo method generates random inputs to account for uncertainty and reduce error. By analyzing different scenarios, the model identifies optimal planting strategies and predicts cropping plans for the next six years, providing a robust framework for decision-making in agricultural production.