Optimization study of crop planting strategies based on dynamic linear and correlation

The purpose of this paper is to study the crop planting strategy of a rural village in North China from 2024 to 2030 to cope with market fluctuations and uncertainty of planting risks. Firstly, a dynamic linear programming model is constructed and combined with Gaussian white noise to simulate random fluctuations, and Lingo software is used to solve the optimization model of crop revenue under the consideration of uncertainty. Then, on the one hand, we analyze the substitutability and complementarity between crops by introducing Spearman's correlation coefficient; on the other hand, we conduct regression analysis on the expected sales volume and sales price, planting cost, introduce the correlation coefficient to correct the existing model, and finally, we use Monte Carlo simulation to generate virtual data to solve the corrected model. The results of the study show that under the dynamic model that takes uncertainty into account, the total planting return in the village is 3,268,000 yuan, the total planting return is adjusted to 3,185,000 yuan after introducing the correlation constraint, and the total return of the grain category is increased by substituting sweet potatoes with pumpkins. This study provides a decision-making framework for agricultural planting strategies that consider economy and risk control, which is of realization significance for rural revitalization and agricultural modernization.