Author(s)

ZhuoFan Yang^1*, JinTao Hu², XiaoHan Yang¹, XinYu Wang³

Affiliation(s)

¹Computer College, Guangzhou Maritime University, Guangzhou 510725, Guangdong, China.

²Electronic and Electrical Engineering College, Zhaoqing University, Zhaoqing 526061, Guangdong, China.

³Navigation College, Guangzhou Maritime University, Guangzhou 510725, Guangdong, China.

Corresponding Author

ZhuoFan Yang

ABSTRACT

This study considers a variety of planting conditions and uncertainties, and comprehensively analyzes and optimizes crop planting strategies to achieve profit maximization. This study is based on a detailed data set of crop planting information of a village in a mountainous area of North China in 2023, including expected sales volume, planting cost, per mu yield, and selling price. In the first phase of the study, assuming that the expected sales volume, planting cost, acre yield, and selling price of the crop remained stable as in the dataset, two scenarios were considered: excess sales were unsold and excess was sold at a 50% discount. The optimal planting scheme was solved by a simple linear programming model. In the second stage of the study, in order to solve the uncertainty and potential risk of expected sales volume, yield per mu, planting cost and selling price, a fluctuation factor is introduced, and a new objective function containing the fluctuation factor is constructed by using normal curve probability random fluctuation method. In the third stage of the study, taking into account the fungibility and complementarity among crops, as well as the correlation between expected sales volume, planting cost and selling price, Spearman correlation analysis was used to define and solve the substitution complementarity coefficient and correlation coefficient. These coefficients are introduced into the new constraints of the linear programming model to further optimize the model, and finally a more scientific and practical Spearman-Normal stochastic linear programming model is proposed to optimize crop planting strategies, which is conducive to facilitate field management, improve production efficiency, and reduce planting risks caused by various uncertainties.

KEYWORDS

Linear programming model; Normal curve probability random fluctuation method; Spearman correlation analysis; Spearman-Normal stochastic linear programming; Optimize crop planting strategies

CITE THIS PAPER

ZhuoFan Yang, JinTao Hu, XiaoHan Yang, XinYu Wang. Optimization of crop planting strategies based on spearman-Normal stochastic linear programming. Eurasia Journal of Science and Technology. 2025, 7(1): 44-54. DOI: https://doi.org/10.61784/ejst3066.

REFERENCES

[1] Lee C C, Zeng M, Luo K. How does climate change affect food security? Evidence from China. Environmental Impact Assessment Review, 2024, 104: 107324.

[2] Guo X X, Li K L, Liu Y Z, et al. Toward the economic-environmental sustainability of smallholder farming systems through judicious management strategies and optimized planting structures. Renewable and Sustainable Energy Reviews, 2022, 165: 112619.

[3] Yang L, Wang L, Chu J, et al. Improving soil quality and wheat yield through diversified crop rotations in the North China Plain. Soil and Tillage Research, 2024, 244: 106231.

[4] Filippi C, Mansini R, Stevanato E. Mixed integer linear programming models for optimal crop selection. Computers & Operations Research, 2017, 81: 26-39.

[5] Maa H. Optimization of Crop Planting Strategies Based on Linear Programming and Monte Carlo Simulation. 2024.

[6] Annetts J E, Audsley E. Multiple objective linear programming for environmental farm planning. Journal of the operational research society, 2002, 53(9): 933-943.

[7] Alotaibi A, Nadeem F. A review of applications of linear programming to optimize agricultural solutions. International Journal of Information Engineering and Electronic Business, 2021, 15(2): 11.

[8] Liang K, Lei J, Wu F, et al. Analysis of optimal planting scheme based on the North China region//Journal of Physics: Conference Series. IOP Publishing, 2024, 2898(1): 012004.

[9] Soleimani-Damaneh M. Modified big-M method to recognize the infeasibility of linear programming models. Knowledge-Based Systems, 2008, 21(5): 377-382.

[10] Fradi A, Feunteun Y, Samir C, et al. Bayesian regression and classification using Gaussian process priors indexed by probability density functions. Information Sciences, 2021, 548: 56-68.

[11] Ali Abd Al-Hameed K. Spearman's correlation coefficient in statistical analysis. International Journal of Nonlinear Analysis and Applications, 2022, 13(1): 3249-3255.