Research on Optimal Crop Planting Strategy Based on Linear Programming with Sample Average Approximation

Abstract

Using linear programming, sample average approximation and other methods, this paper mainly focuses on the optimization of crop planting strategies in China, and conducts in-depth research to formulate the optimal planting scheme for the period from 2024 to 2030. Firstly, we preprocesses data on rural plot types and crop characteristics, and proposes the optimal crop planting scheme based on linear programming by considering crop sales, planting costs, mu yield, etc. Subsequently, this paper introduces uncertainties such as fluctuations in market demand and climate change, and constructs a dynamic planning model of planting scheme through the Sample Average Approximation (SAA) method. Finally, combined with the correlation analysis between crops, this paper proposes a comprehensive optimization model of planting scheme based on Spearman correlation, which effectively improves the overall return and ensures the robustness of the scheme.