IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY
Monika KULISZ
m.kulisz@kop.pollub.plLublin 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, agricultureReferences
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Authors
Monika KULISZm.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 DUISENBEKOVAL.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 KUJAWSKALublin 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 KALDYBAYEVAL.N. Gumilyov Eurasian National University, Faculty of Economics, Department of Economics and Entrepreneurship Kazakhstan
https://orcid.org/0000-0002-4370-4126
Authors
Bibigul ISSAYEVAL.N. Gumilyov Eurasian National University, Faculty of Economics, Department of Economics and Entrepreneurship Kazakhstan
https://orcid.org/0000-0002-8109-2896
Authors
Piotr LICHOGRAJJohn Paul II University of Applied Sciences in Biala Podlaska, Department of Technical Sciences, Poland
Authors
Wojciech CELLublin University of Technology, Faculty of Environmental Engineering, Department of Renewable Energy Engineering Poland
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