IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY

Kulisz M. Duisenbekova A. Kujawska J. Kaldybayeva D. Issayeva B. Lichograj P. Cel W.
2023 Polish Association for Knowledge Promotion

Applied Computer Science
2023 #19 Issue 4 121 - 135 pp.

This study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstans economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems.

agriculture , artificial neural network , decision-making , economy , management

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Lublin University of Technology, Faculty of Management, Department of Organization of Enterprise, Poland
L.N. Gumilyov Eurasian National University, Faculty of Economics, Department of Economics and Entrepreneurship, Kazakhstan
D.Serikbayev East Kazakhstan Technical University, School of Architecture, Civil Engineering and Energy, Kazakhstan
Lublin University of Technology, Faculty of Environmental Engineering, Department of Biomass and Waste Conversion into Biofuels, Poland
John Paul II University of Applied Sciences in Biala Podlaska, Department of Technical Sciences, Poland
Lublin University of Technology, Faculty of Environmental Engineering, Department of Renewable Energy Engineering, Poland

Lublin University of Technology
L.N. Gumilyov Eurasian National University
D.Serikbayev East Kazakhstan Technical University
Lublin University of Technology
John Paul II University of Applied Sciences in Biala Podlaska
Lublin University of Technology

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