OPTIMIZATION OF CROP PLANTING STRATEGIES BASED ON SPEARMAN-NORMAL STOCHASTIC LINEAR PROGRAMMING

This study considers a variety of planting conditions and uncertainties, and comprehensively analyzes and optimizes crop planting strategies to achieve profit maximization. This study is based on a detailed data set of crop planting information of a village in a mountainous area of North China in 2023, including expected sales volume, planting cost, per mu yield, and selling price. In the first phase of the study, assuming that the expected sales volume, planting cost, acre yield, and selling price of the crop remained stable as in the dataset, two scenarios were considered: excess sales were unsold and excess was sold at a 50% discount. The optimal planting scheme was solved by a simple linear programming model. In the second stage of the study, in order to solve the uncertainty and potential risk of expected sales volume, yield per mu, planting cost and selling price, a fluctuation factor is introduced, and a new objective function containing the fluctuation factor is constructed by using normal curve probability random fluctuation method. In the third stage of the study, taking into account the fungibility and complementarity among crops, as well as the correlation between expected sales volume, planting cost and selling price, Spearman correlation analysis was used to define and solve the substitution complementarity coefficient and correlation coefficient. These coefficients are introduced into the new constraints of the linear programming model to further optimize the model, and finally a more scientific and practical Spearman-Normal stochastic linear programming model is proposed to optimize crop planting strategies, which is conducive to facilitate field management, improve production efficiency, and reduce planting risks caused by various uncertainties.