Mathematical Model Of Optimal Allocation Of Resources In Agriculture

Authors

  • Shixiyev Raxim Muxammetovich Karakalpak State University

DOI:

https://doi.org/10.31150/ajebm.v8i8.3925

Keywords:

resource potential, optimization, agriculture, information system, mathematical modeling, efficiency

Abstract

Sustainable development of agriculture lies in the effective use of agronomic inputs in the era when the world is faced with increased food demand that is simultaneously accompanied by declining natural resources. In this regard, mathematical modelling and optimisation systems are becoming more used in the assignment of land, water and labor in agricultural enterprises. However, current models hardly reflect the internal uncertainty and the temporality of agribusiness systems, thus limiting their adaptability to real applications. The current paper suggests a model of similarities which operates simultaneously using linear and nonlinear programming, stochastic modelling and dynamic optimisation methods with the objective of maximising resources in operation within an agribusiness given uncertainties in the process. The effectiveness of the model is ascertained given that empirical simulations reveal that the overall efficiencies are improved by 23 % and profits increased by 15 %, respectively. The inclusion of dynamic and stochastic modeling along with multi-criteria decision making approach forms a holistic approach to agricultural resources management that artifacts the aspects of uncertainty and time simultaneously. This study, therefore, provides a theoretical framework through which agricultural decision-makers can invest in highly powerful optimisation models, optimize available resources and embrace the idea of sustainable agricultural activities against the backdrop of compounding global pressures.

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Published

2025-08-15

How to Cite

Muxammetovich, S. R. (2025). Mathematical Model Of Optimal Allocation Of Resources In Agriculture. American Journal of Economics and Business Management, 8(8), 4035–4040. https://doi.org/10.31150/ajebm.v8i8.3925

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