Advanced study on agronomic strategies for crop cultivation

This article aims to provide a scientific basis for future crop planting planning in a rural area by first analyzing 2023 data on crop planting areas, rotation patterns, and legume cultivation to develop a model focused on maximizing planting profits. It incorporates expected sales changes, yield variations, and other market and environmental factors as constraints. Using advanced visualization tools and Deming regression models, the study examines the relationship between crop prices and expected sales, while addressing constraints like planting area, rotation, density, and sales volume through mixed integer and linear programming methods. To address future uncertainties in crop sales, yields, costs, and prices, the study employs robust optimization methods and Monte Carlo simulations to ensure stability and adaptability of optimal planting strategies. Furthermore, it explores crop substitutability and complementarity by analyzing relationships between sales, prices, and costs using K-Means clustering and Spearman correlation analysis, optimizing planting strategies to enhance economic benefits and reduce risks associated with market and climate changes.