A Study of Crop Planting Schemes Based on Linear Programming and Monte Carlo Simulation
DOI:
https://doi.org/10.54097/ykd45j38Keywords:
Linear Programming, Monte Carlo simulation, Dynamic programming, Second-order Clustering, Relevance.Abstract
In this paper, based on linear programming and Monte Carlo simulation, the optimal cropping scheme for crops from 2024 to 2030 in a rural village is studied and modeled. First, considering the characteristics of different cultivated land types and the growth habit of different crops, all cultivated land is divided into six categories, and a single-objective linear programming model is established to maximize the net return. Then, considering the fluctuations brought by market, climate and other factors on the expected sales volume, mu yield, planting cost and sales price, a dynamic planning model based on Monte Carlo simulation was established, while dynamic constraints were added to analyze the optimal planting scheme of crops in this countryside. Finally, the relationship between crops is introduced, and the substitutability and complementarity between crops, the correlation between the expected sales volume and sales price, and the planting cost need to be considered, and a dynamic planning model based on Monte Carlo simulation and multi-factor variables is established to optimize the allocation of limited arable land resources in the countryside and rationally plan the planting of crops.
Downloads
References
[1] Wang, S., Z.. Research on ecological optimization of crop cultivation [J]. Agricultural Science, 2019(5): 45-50.
[2] Wang Z. A review of intelligent optimization methods in agricultural production [J]. Modern Agricultural Science, 2020(8): 78-85.
[3] Fan Menghan. Agricultural planting planning based on reinforcement learning and price dynamics [D]. University of Chinese Academy of Sciences, 2022.
[4] Li H. Research on agricultural optimization model based on dynamic planning [D]. Peking University, 2021.
[5] XU Wei-Chao. A review of correlation coefficient studies [J]. Journal of Guangdong University of Technology,2012,(3): 12-17.
[6] Li Lingjing, Wang Cunyou, Yu Jiayuan. Proof of the rule for automatic determination of the number of clusters in two-order clustering [J]. Statistics and Decision Making, 2010,(20): 28-29.
[7] Zhu Yingchun. Second-order clustering model and its application [J]. Market Research, 2005,(1): 40-42.
[8] Wang Xiuhong. Agricultural Material Inputs and the Potential Risk Assessment for Vegetable Production in China[J]. Journal of Resources and Ecology, 2016, 7(4): 269-274. DOI:10.5814/j.issn.1674-764x.2016.04.005.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
