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

Monika KULISZ

m.kulisz@kop.pollub.pl
Lublin University of Technology, Faculty of Management, Department of Organization of Enterprise (Poland)
https://orcid.org/0000-0002-8111-2316

Aigerim DUISENBEKOVA


L.N. Gumilyov Eurasian National University, Faculty of Economics, Department of Economics and Entrepreneurship, D.Serikbayev East Kazakhstan Technical University, School of Architecture, Civil Engineering and Energy, (Kazakhstan)
https://orcid.org/0000-0001-9167-8076

Justyna KUJAWSKA


Lublin University of Technology, Faculty of Environmental Engineering, Department of Biomass and Waste Conversion into Biofuels (Poland)
https://orcid.org/0000-0002-4809-2472

Danira KALDYBAYEVA


L.N. Gumilyov Eurasian National University, Faculty of Economics, Department of Economics and Entrepreneurship (Kazakhstan)
https://orcid.org/0000-0002-4370-4126

Bibigul ISSAYEVA


L.N. Gumilyov Eurasian National University, Faculty of Economics, Department of Economics and Entrepreneurship (Kazakhstan)
https://orcid.org/0000-0002-8109-2896

Piotr LICHOGRAJ


John Paul II University of Applied Sciences in Biala Podlaska, Department of Technical Sciences, (Poland)

Wojciech CEL


Lublin University of Technology, Faculty of Environmental Engineering, Department of Renewable Energy Engineering (Poland)

Abstract

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 Kazakhstan's 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.


Keywords:

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

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Published
2024-01-05

Cited by

KULISZ, M., DUISENBEKOVA, A., KUJAWSKA, J., KALDYBAYEVA, D., ISSAYEVA, B., LICHOGRAJ, P., & CEL, W. (2024). IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY. Applied Computer Science, 19(4), 121–135. https://doi.org/10.35784/acs-2023-39

Authors

Monika KULISZ 
m.kulisz@kop.pollub.pl
Lublin University of Technology, Faculty of Management, Department of Organization of Enterprise Poland
https://orcid.org/0000-0002-8111-2316

Authors

Aigerim DUISENBEKOVA 

L.N. Gumilyov Eurasian National University, Faculty of Economics, Department of Economics and Entrepreneurship, D.Serikbayev East Kazakhstan Technical University, School of Architecture, Civil Engineering and Energy, Kazakhstan
https://orcid.org/0000-0001-9167-8076

Authors

Justyna KUJAWSKA 

Lublin University of Technology, Faculty of Environmental Engineering, Department of Biomass and Waste Conversion into Biofuels Poland
https://orcid.org/0000-0002-4809-2472

Authors

Danira KALDYBAYEVA 

L.N. Gumilyov Eurasian National University, Faculty of Economics, Department of Economics and Entrepreneurship Kazakhstan
https://orcid.org/0000-0002-4370-4126

Authors

Bibigul ISSAYEVA 

L.N. Gumilyov Eurasian National University, Faculty of Economics, Department of Economics and Entrepreneurship Kazakhstan
https://orcid.org/0000-0002-8109-2896

Authors

Piotr LICHOGRAJ 

John Paul II University of Applied Sciences in Biala Podlaska, Department of Technical Sciences, Poland

Authors

Wojciech CEL 

Lublin University of Technology, Faculty of Environmental Engineering, Department of Renewable Energy Engineering Poland

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