Research on Optimal Agricultural Planning for Risk-Adjusted Crop Yields Based on Monte Carlo Simulation and Bi-Objective Optimization Model

This paper proposes a bi-objective optimization framework to address the challenges of agricultural planning under uncertainty. The framework integrates Monte Carlo simulation to model uncertainties in crop yields, prices, and costs, and a simulated annealing algorithm to optimize planting strategies. The model aims to maximize total profitability while minimizing income variability, ensuring a balance between economic returns and risk. Experimental results demonstrate the effectiveness of the proposed approach, achieving a total profit of 47,776,161 yuan over a seven-year planning horizon while adhering to constraints such as crop rotation and land use limitations. The optimized strategies allocate high-yield crops to favorable parcels and distribute stable crops to mitigate risk, promoting sustainability and resilience. This framework provides a practical decision-support tool for policymakers and farmers in resource-constrained agricultural regions.