Academic Journal of Computing & Information Science, 2025, 8(2); doi: 10.25236/AJCIS.2025.080210.
Renze Wei1, Boyue Zhang1, Zhengjie Huang2
1College of Information Engineering, Northwest A & F University, Yangling, 712100, China
2College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, 712100, China
The issue of global food security is becoming increasingly severe, and sustainable agricultural development has emerged as a focal point of global attention. This study takes rural areas in the mountainous regions of North China as the research object, investigating how to optimize planting schemes for terraces, sloped fields, irrigated lands, and greenhouses under cold climatic conditions and complex arable land environments through scientific crop rotation and intercropping strategies. The aim is to enhance farmland utilization efficiency, reduce environmental and market risks, and promote the sustainable development of rural economies. This research employs Monte Carlo simulations to estimate the expected sales volumes of crops. Based on two scenarios of handling surplus crop production beyond the expected sales volume—Scenario 1: disposal as waste; Scenario 2: surplus sold at a 50% discount—a linear programming model is constructed to optimize crop planting areas and types. The model is solved using a genetic algorithm. Comparative analysis of planting strategies under the two scenarios yields profitability forecasts for the period from 2024 to 2030. Under the surplus production scenario, the seven-year profit for Scenario 1 (disposal as waste) is 7,851,693 RMB, while for Scenario 2 (selling surplus at a 50% discount), the profit is 23,868,146 RMB.
Monte Carlo Simulation, Linear Programming, Genetic Algorithm, Crop Planting Scheme
Renze Wei, Boyue Zhang, Zhengjie Huang. Research on Crop Planting Strategies Based on Monte Carlo-Genetic Algorithm. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 2: 73-82. https://doi.org/10.25236/AJCIS.2025.080210.
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