Year: 2024
Type: article
Abstract: This study focuses on the crop planting planning in a mountainous village in North China, aiming to optimize the planting strategies from 2024 to 2030. Utilizing the agricultural data of 2023, considering factors such as arable land resources, crop rotation requirements, sales situations, and various uncertainties, models are constructed through the Monte Carlo method, the greedy algorithm, and the genetic algorithm. In response to different sales situations (such as slow sales or sales at reduced prices) and changes in uncertain factors, the optimal planting schemes under various scenarios are solved to increase net profits and reduce risks. The results of the models show that reasonable planning can significantly improve yields, and the robustness of the models is verified through sensitivity analysis, providing strong support for rural planting decisions.
Authors: Xu Li, Jinlin Zhuo, Jiahao He, Zhenting Chen
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4409982480