Exploration of Sustainable Crop Cultivation Strategies Under Resource Constraints Based on Integration of Simulation and Algorithms

With the increasing concern for sustainable agriculture and efficient resource utilization, the study of crop planting strategy optimization under resource constraints has become an important topic. In this paper, an integrated framework combining simulation and optimization algorithms is proposed for developing sustainable crop cultivation strategies. A 0–1 linear programming model is constructed to maximize economic returns under resource constraints, taking into account constraints such as land area, crop rotation, and resource allocation. A simulated annealing algorithm was introduced to optimize the minimum planting area allocation of a single plot to ensure the rationality of spatial and temporal distribution. Based on the 2023 data of a rural area in the mountainous region of North China, the optimal planting strategies from 2024 to 2030 were predicted. The results show that the proposed method significantly improves resource utilization efficiency, ensures the stability of economic returns, and flexibly adapts to different resource constraint scenarios. This study provides theoretical support for sustainable agricultural development and useful reference for land use optimization.