Optimization of crop cultivation based on Monte Carlo simulation and linear programming

Yishuai Liu; Shuo Liu; Shuo Liu

doi:10.54097/r6jyze18

Authors

Yishuai Liu
Shuo Liu
Shuo Liu

DOI:

https://doi.org/10.54097/r6jyze18

Keywords:

Monte Carlo simulation, linear programming model, crop cultivation optimization.

Abstract

With the advancement of agricultural modernization, crop planting optimization faces challenges of price fluctuation risk and diversified constraints, necessitating the combination of Monte Carlo simulation with linear planning for scientific decision-making. This study quantifies crop planting rules based on 2023 data including expected sales volume, planting cost, yield, and sales price, establishing a comprehensive planning model. The sales price of each crop is obtained through Monte Carlo simulation method. By ensuring full land utilization and minimizing crop waste to maximize benefits, the model yields a maximum gain of 52.672 billion yuan. When adjusting the objective function to calculate excess production at 50% of the sales price, the maximum gain becomes 23.671 billion yuan. This research provides scientific decision support for agricultural production, optimizes land resource allocation, enhances risk resistance capacity, and promotes agricultural efficiency. By incorporating agronomic requirements such as legume crop planting, the model supports sustainable agricultural development and digital transformation, significantly contributing to agricultural modernization and rural economic development.

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References

[1] NI Xuezhi,YU Xiaoyuan. Cropland rotation, agricultural cropping structure and lasting food security in China[J]. Exploration of Economic Issues,2018,(07):78-88.

[2] Tian Guoping. Research on the impact of temperature change on China's agricultural economy[D]. Southwest University of Finance and Economics,2021.

[3] Mao Yu, Mechanism of price influence on feed grain variety substitution in the context of food consumption structure evolution(D). Southwest University of Finance and Economics,2023.

[4] Su Xiaofeng. Research on crop planting decision-making model based on big data analysis technology[J]. Heilongjiang Grain,2023,(10): 88-90.

[5] WU Menghan, WANG Yi. Optimal adjustment of multi-objective planting of crops in Shache Irrigation District of Xinjiang[J]. People's Yellow River,2024,46(01):120-125+131.

[6] Li Yaqi. Agribusiness value assessment based on Monte Carlo simulation[D]. Central University of Finance and Economics,2022.

[7] WANG Jing, FANG Feng, WANG Suping, LI Zhenqi. Risk assessment of crop production in China based on probabilistic statistical methods[J]. Meteorological and Environmental Science,2023,46(02):9-18.

[8] LI Guizhou, ZHOU Xinguang. Bayesian autoregressive prediction analysis of concrete carbonation depth[J]. Journal of Yantai University (Natural Science and Engineering Edition),2013,26(04):282-286+291.

[9] YU Guo,LI Haitao,FANG Yizhu. Construction of target uncertainty prediction model for conventional gas planning production in Sichuan Basin[J] Petrochemical Applications,2024.43(07):30-35+51.

[10] Li Junxian. Effects of legume crop rotation on ammonia balance and productivity of farming systems in semi-arid areas (D). Gansu Agricultural University,2019.