Rural Crop Planting Strategy Based on the Simulated Annealing Algorithm

Abstract

Rural agricultural economies need tailored strategies and intensive cultivation for sustainable development. In China, with limited arable land and a push for agricultural modernization, choosing the right crop planting strategies is essential to meet people's needs and boost agricultural production and the economy.For Problem 1, we created an integer programming model aiming for stable annual economic returns, with average annual planting revenue as the objective function. Two scenarios were considered: excess crops wasted or sold at half price. Using the simulated annealing algorithm, we found that the average annual revenue is 7,882,002.50 yuan in the first scenario and 8,659,569.25 yuan in the second.For Problem 2, we built a robust optimization model to account for the dynamic nature of the agricultural market, including potential risks from rising costs, falling prices, and declining demand. The model focuses on worst-case scenarios to develop a resilient planting strategy, reducing risks from market volatility. It uses minimum annual planting revenue as the objective function and parameter uncertainty sets to create a plan less sensitive to disturbances and effective under most conditions.For Problem 3, we constructed evaluation indicators to explore the substitutability and complementarity between crops, as well as their sales, planting costs, and prices. A grey relational analysis model was used to assess crop similarity. Prioritizing crops with higher returns and stable prices, we selected 29 crops, including legumes, for a new planting strategy. Compared to the strategy from Problem 2, the new plan has fewer crop rotations, more stable economic returns, and easier field management.This paper summarizes and analyzes the established models, providing a comprehensive evaluation of their advantages and limitations.