Research on Optimization Model of Crop Planting Strategy Based on Linear Programming

Zhidan Cai; Wenjie Chen; Xinyi Chen

doi:10.62051/mm41rk73

Authors

Zhidan Cai
Wenjie Chen
Xinyi Chen

DOI:

https://doi.org/10.62051/mm41rk73

Keywords:

Linear Programming, Robust Optimization, Penalty Factor, Monte Carlo.

Abstract

With the advancement of China's rural revitalization strategy, exploring scientific planting strategies is becoming increasingly important for achieving sustainable development of rural economy. This article takes a village in Huabei Mountain District as the research object and establishes a linear programming model to optimize the planting strategy of crops, to maximize the economic benefits of the village from 2024 to 2030. Firstly, based on the crop yield per mu, cost, and sales price data in 2023, a linear programming model was established with the goal of maximizing total revenue. Then, by analyzing the constraints of crop selection, continuous planting, legume crop rotation, and area allocation in the plot, the constraints of the objective function were defined. For the overproduction part, this article calculates the objective function under the conditions of unsold waste and 50% price reduction for sale. The results show that under the condition of unsold waste, the maximum total revenue of the village from 2024 to 2030 is approximately 2.38 million; under a 50% price reduction, the maximum total revenue for the village is approximately 2.59 million, and the provided planting plan meets all constraints.

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References

[1] Liu Xinrui. Research on the Modernization of Rural Governance System in the New Era [D]. Jilin University, 2023.

[2] Zhang Yutong. Research on Water Footprint and Water-Saving Cultivation of Main Grain Crops in North China [M]. China Institute of Water Resources and Hydropower Research, 2019.

[3] Huang Feng, Yang Xiaolin, Fang Yu, Wang Sufen, Kang Shaozhong. Study on Agricultural Planting Layout in North China Adapted to Water and Soil Resources [J]. Engineering Sciences, 2022(05): 89-96.

[4] Liu Huaixun, Zhang Chaoxiang, Wu Aili. Factors Affecting Crop Yields in Hebei Province and Improvement Strategies [J]. Rural Science and Technology, 2021(35): 40-42.

[5] Qiu Lei, Zhong Yuchun, Cui Yunwen. Optimization of Agricultural Planting Structure in Major Grain-Producing Areas Based on the Water-Energy-Grain Nexus: A Case Study of Jilin Province [J]. Chinese Journal of Agricultural Resources and Regional Planning, 2022(10): 51-62.

[6] Su Yuandong. Analysis and Prediction of the Factors Affecting the Volatility of China's Agricultural Product Futures Prices [D]. Southwest Jiaotong University, 2023.

[7] Wu Junjie. Optimization of Agricultural Planting Structure Based on a Linear Programming Model [J]. New Farmers, 2024(03): 28-30.

[8] Wu Qiang. Monitoring of Wheat Growth and Yield Prediction in Hetao Irrigation District Based on UAV Remote Sensing and Machine Learning [D]. Inner Mongolia Agricultural University, 2023.

[9] Chen Jian, Wu Liuge, Zhang Xin, Deng Aixing, Song Zhenwei, Zhang Weijian, Zheng Chengyan. Characteristics and Differences of Crop Yields and Greenhouse Gas Emissions under Different Planting Systems in the Huang-Huai-Hai Region [J]. Chinese Journal of Eco-Agriculture, 2024: 1-12.

[10] Chen Jianghua, Yang Hui, Li Qiusheng. Research on the Impact of Agricultural Socialization Services on Farmers' Willingness to Grow Rice: A Study Based on Double-Cropping Rice [J]. Research on Agricultural Modernization, 2024: 1-12.

[11] Gu Jinhang. Research on the Application of Smart Agriculture in Greenhouse Cultivation [J]. Smart Agriculture Journal, 2024(18): 5-8+12.

[12] Xu Changqing. Research on the Design of Precision Smart Agriculture Greenhouse System Based on the Internet of Things [J]. World Tropical Agriculture Information, 2024(07): 91-92.

[13] Sun Liquan. Application of Dynamic Programming Model in Determining Optimal Crop Planting Scheme [J]. Jilin Agriculture, 2015(08): 84.

[14] Yu Hongyang. Research on the Optimization of Agricultural Planting Structure in Jilin Province Based on a Linear Programming Model [J]. China Agricultural Information, 2013(09): 290-291.