FORECASTING AGRICULTURAL SECTOR INDICATORS USING MACHINE LEARNING METHODS: AN ECONOMIC ASPECT

Esenbek Belek uulu, Turgunbek Zhumaliev, Samyibek Begaliev, Ainura Dyikanova, Samara Zhusupbekova, Tologon Kerimov
Received: 10.12.2025
Revised: 05.06.2026
Accepted: 01.07.2026
Pages: 154 –168
Retrieved from: Volume 4, No. 2, 2026
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Abstract

With global population growth, which could increase by 2 billion people by 2050, food demand will increase by 60%. This poses a challenge for the agricultural economy, requiring a transition from traditional planning methods to highly accurate digital models. Machine learning is becoming an "agricultural accelerator," enabling the optimization of production cycles and the minimization of economic risks. The aim of this study is to investigate the effectiveness of machine learning algorithms for forecasting key economic indicators in the agricultural sector. The study utilized data mining methods, including random forest algorithms, support vector machines, and gradient boosting. Model accuracy was assessed using the mean absolute percentage error (MAPE). It was found that the use of ensemble methods ensures accurate yield and market price forecasts with an error of no more than 10%. Data analysis shows that the implementation of artificial intelligence technologies contributes to the growth of total factor productivity in agricultural enterprises, reducing logistics and resource costs (fertilizers, water). External factors, such as weather conditions, were found to have a more significant impact on forecast accuracy than soil genotype. The resulting models can be used by farmers and government agencies to make informed management decisions, ensure food security, and increase the competitiveness of the agricultural sector in the global market.

Keywords

artificial intelligence, digital transformation of the agro-industrial complex, neural networks, regression analysis, agro-industrial complex, food security, mathematical modeling

Suggested citation

Belek uulu, E., Zhumaliev, T., Begaliev, S., Dyikanova, А., Zhusupbekova, S., & Kerimov, Т. (2026). FORECASTING AGRICULTURAL SECTOR INDICATORS USING MACHINE LEARNING METHODS: AN ECONOMIC ASPECT. Global Scholars’ Space, 4(2), 154-168.

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