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
Annamalai, N., & Johnson, A. (2023). Analysis and forecasting of area under cultivation of rice in India: Univariate time series approach. SN Computer Science, 4, 193. https://doi.org/10.1007/s42979-022-01604-0
DOI: https://doi.org/10.1007/s42979-022-01604-0
Google Scholar
Ansarifar, J., Wang, L., & Archontoulis, S. V. (2021). An interaction regression model for crop yield prediction. Scientific Reports, 11, 17754. https://doi.org/10.1038/s41598-021-97221-7
DOI: https://doi.org/10.1038/s41598-021-97221-7
Google Scholar
Yu Arkhipova, M., & Smirnov, A. I. (2020). Current trends in crop yield forecasting based on the use of econometric models. Voprosy Statistiki, 27(5), 65–75. https://doi.org/10.34023/2313-6383-2020-27-5-65-75
DOI: https://doi.org/10.34023/2313-6383-2020-27-5-65-75
Google Scholar
Beisekenov, N. A., Anuarbekov, T. B., Sadenova, M. A., Varbanov, P. S., Klemes. J. J., & Wang, J. (2021). Machine learning model identification for forecasting of soya crop yields in Kazakhstan. 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech) (pp. 1–6). IEEE. https://doi.org/10.23919/SpliTech52315.2021.9566376
DOI: https://doi.org/10.23919/SpliTech52315.2021.9566376
Google Scholar
Booranawong, T., & Booranawong, A. (2017). An exponentially weighted moving average method with designed input data assignments for forecasting lime prices in Thailand. Jurnal Teknologi, 79(6), 53-60. https://doi.org/10.11113/jt.v79.10096
DOI: https://doi.org/10.11113/jt.v79.10096
Google Scholar
Bureau of National Statistics of Kazakhstan. (2022). Statistics of agriculture. forestry. hunting and fisheries. https://stat.gov.kz/en/industries/business-statistics/stat-forrest-village-hunt-fish/
Google Scholar
Conradt, T. (2022). Choosing multiple linear regressions for weather-based crop yield prediction with ABSOLUT v1.2 applied to the districts of Germany. International Journal of Biometeorology, 66, 2287–2300. https://doi.org/10.1007/s00484-022-02356-5
DOI: https://doi.org/10.1007/s00484-022-02356-5
Google Scholar
Dahikar, S. S., & Rode, S. V. (2014). Agricultural crop yield prediction using artificial neural network approach miss. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 2(1), 683-686. https://api.semanticscholar.org/CorpusID:16167655
Google Scholar
Dharmaraja, S., Jain, V., Anjoy, P., & Chandra, H. (2020). Empirical analysis for crop yield forecasting in India. Agricultural Research, 9, 132–138. https://doi.org/10.1007/s40003-019-00413-x
DOI: https://doi.org/10.1007/s40003-019-00413-x
Google Scholar
Duisenbekova, A., & Daniłowska, A. (2021). Assessment of food security in the east Kazakhstan region. Zeszyty Naukowe SGGW w Warszawie, 21(3), 4–13. https://doi.org/10.22630/PRS.2021.21.3.9
DOI: https://doi.org/10.22630/PRS.2021.21.3.9
Google Scholar
Fan, C., Cao, P. G., Yang, T. J., & Fu, H. L. (2016). Research on the prediction model of grain yield based on the ARIMA method. 2015 4th International Conference on Sensors. Measurement and Intelligent Materials (ICSMIM 2015) (pp. 454–458). Atlantis Press. https://doi.org/10.2991/icsmim-15.2016.84
DOI: https://doi.org/10.2991/icsmim-15.2016.84
Google Scholar
Guo, W. W., & Xue, H. (2014). Crop yield forecasting using artificial neural networks: a comparison between spatial and temporal models. Mathematical Problems in Engineering, 2014, 857865. https://doi.org/10.1155/2014/857865
DOI: https://doi.org/10.1155/2014/857865
Google Scholar
Hemavathi, M., & Prabakaran, K. (2018). ARIMA model for forecasting of area. production and productivity of rice and its growth status in thanjavur district of Tamil Nadu, India. International Journal of Current Microbiology and Applied Sciences, 7(2), 149–156. https://doi.org/10.20546/ijcmas.2018.702.019
DOI: https://doi.org/10.20546/ijcmas.2018.702.019
Google Scholar
Islyami, A., Aldashev, A., Thomas, T. S., & Dunston, S. (2020). Impact of climate change on agriculture in Kazakhstan. Silk Road: A Journal of Eurasian Development, 2(1), 66–88. https://doi.org/10.16997/srjed.19
DOI: https://doi.org/10.16997/srjed.19
Google Scholar
Alani, L. A. F., & Alhiyali, A. D. K. (2021). Forecasting wheat productivity in Iraq for the period 2019-2025 using markov chains. Iraqi Journal of Agricultural Sciences, 52(2), 411–421. https://doi.org/10.36103/ijas.v52i2.1302
DOI: https://doi.org/10.36103/ijas.v52i2.1302
Google Scholar
Kim, T., Solanki, V. S., Baraiya, H. J., Mitra, A., Shah, H., & Roy, S. (2020). A smart. sensible agriculture system using the exponential moving average model. Symmetry, 12(3), 457. https://doi.org/10.3390/sym12030457
DOI: https://doi.org/10.3390/sym12030457
Google Scholar
Levin, E., Beisekenov, N., Wilson, M., Sadenova, M., Nabaweesi, R., & Nguyen, L. (2023). Empowering climate resilience: Leveraging cloud computing and big data for community Climate Change Impact Service (C3IS). Remote Sensing, 15(21), 5160. https://doi.org/10.3390/rs15215160
DOI: https://doi.org/10.3390/rs15215160
Google Scholar
Lwaho, J., & Ilembo, B. (2023). Unfolding the potential of the ARIMA model in forecasting maize production in Tanzania. Business Analyst Journal, 44(2), 128-139. https://doi.org/10.1108/BAJ-07-2023-0055
DOI: https://doi.org/10.1108/BAJ-07-2023-0055
Google Scholar
Murugan, R., Thomas, F. S., Geetha Shree, G., Glory, S., & Shilpa, A. (2020). Linear regression approach to predict crop yield. Asian Journal of Computer Science and Technology, 9(1), 40–44. https://doi.org/10.51983/ajcst-2020.9.1.2152
DOI: https://doi.org/10.51983/ajcst-2020.9.1.2152
Google Scholar
Nhu, A., Sahajpal, R., Justice, C., & Becker-Reshef, I. (2023). Improve state-level wheat yield forecasts in Kazakhstan on GEOGLAM’s EO data by leveraging a simple Spatial-Aware Technique. ArXiv, abs/2306.04646. https://doi.org/10.48550/arXiv.2306.04646
Google Scholar
Okorie, I. E., Afuecheta, E., & Nadarajah, S. (2023). Time series and power law analysis of crop yield in some east African countries. PLOS ONE, 18(6), e0287011. https://doi.org/10.1371/journal.pone.0287011
DOI: https://doi.org/10.1371/journal.pone.0287011
Google Scholar
Rai, S., Nandre, J., & Kanawade, B. R. (2022). A comparative analysis of crop yield prediction using regression. 2022 2nd International Conference on Intelligent Technologies (CONIT) (pp. 1–4). IEEE. https://doi.org/10.1109/CONIT55038.2022.9847783
DOI: https://doi.org/10.1109/CONIT55038.2022.9847783
Google Scholar
Rathod, S., Singh, K. N., Patil, S. G., Naik, R. H., Ray, M., & Meena, V. S. (2018). Modeling and forecasting of oilseed production of India through artificial intelligence techniques. The Indian Journal of Agricultural Sciences, 88(1), 22–27. https://doi.org/10.56093/ijas.v88i1.79546
DOI: https://doi.org/10.56093/ijas.v88i1.79546
Google Scholar
Rathod, S., Singh, K., Arya, P., Ray, M., Mukherjee, A., Sinha, K., Kumar, P., & Shekhawat, R. S. (2017). Forecasting maize yield using ARIMA-Genetic Algorithm approach. Outlook on Agriculture, 46(4), 265–271. https://doi.org/10.1177/0030727017744933
DOI: https://doi.org/10.1177/0030727017744933
Google Scholar
Romanovska, P., Schauberger, B., & Gornott, C. (2023). Wheat yields in Kazakhstan can successfully be forecasted using a statistical crop model. European Journal of Agronomy, 147, 126843. https://doi.org/10.1016/j.eja.2023.126843
DOI: https://doi.org/10.1016/j.eja.2023.126843
Google Scholar
Sadenova, M. A., Beisekenov, N. A., Rakhymberdina, M. Y., Varbanov, P. S., & Klemeš, J. J. (2021). Mathematical modelling in crop production to predict crop yields. Chemical Engineering Transactions, 88, 1225–1230. https://doi.org/10.3303/CET2188204
Google Scholar
Sadenova, M., Beisekenov, N., Varbanov, P. S., & Pan, T. (2023). Application of machine learning and neural networks to predict the yield of cereals, legumes, oilseeds and forage crops in Kazakhstan. Agriculture, 13(6), 1195. https://doi.org/10.3390/agriculture13061195
DOI: https://doi.org/10.3390/agriculture13061195
Google Scholar
Sellam, V., & Poovammal, E. (2016). Prediction of crop yield using regression analysis. Indian Journal of Science and Technology, 9(38), 1-5. https://doi.org/10.17485/ijst/2016/v9i38/91714
DOI: https://doi.org/10.17485/ijst/2016/v9i38/91714
Google Scholar
Senthamarai Kannan, K., & Karuppasamy, K. M. (2020). Forecasting for agricultural production using Arima Model. PalArch’s Journal of Archaeology of Egypt / Egyptology, 17(9), 5939–5949.
Google Scholar
Sharma, P. K., Dwivedi, S., Ali, L., & Arora, R. K. (2018). Forecasting maize production in India using ARIMA model, Agro Economist, 5(1), 1-6.
Google Scholar
Suieubayeva, S., Denissova, O., Kabdulsharipova, A., & Idikut Ozpenсe, A. (2022). The agricultural sector in the Republic of Kazakhstan: Analysis of the state, problems and ways of solution. Eurasian Journal of Economic and Business Studies, 66(4), 19–31. https://doi.org/10.47703/ejebs.v4i66.185
DOI: https://doi.org/10.47703/ejebs.v4i66.185
Google Scholar
Wing, I. S., De Cian, E., & Mistry, M. N. (2021). Global vulnerability of crop yields to climate change. Journal of Environmental Economics and Management, 109, 102462. https://doi.org/10.1016/j.jeem.2021.102462
DOI: https://doi.org/10.1016/j.jeem.2021.102462
Google Scholar
Yildirim, T., Moriasi, D. N., Starks, P. J., & Chakraborty, D. (2022). Using artificial neural network (ANN) for short-range prediction of cotton yield in Data-Scarce regions. Agronomy, 12(4), 828. https://doi.org/10.3390/agronomy12040828
DOI: https://doi.org/10.3390/agronomy12040828
Google Scholar
Yun, S. D., & Gramig, B. M. (2022). Spatial panel models of crop yield response to weather: Econometric specification strategies and prediction performance. Journal of Agricultural and Applied Economics, 54(1), 53–71. https://doi.org/10.1017/aae.2021.29
DOI: https://doi.org/10.1017/aae.2021.29
Google Scholar
Zhao, Y., Vergopolan, N., Baylis, K., Blekking, J., Caylor, K., Evans, T., Giroux, S., Sheffield, J., & Estes, L. (2018). Comparing empirical and survey-based yield forecasts in a dryland agro-ecosystem. Agricultural and Forest Meteorology, 262, 147–156. https://doi.org/10.1016/j.agrformet.2018.06.024
DOI: https://doi.org/10.1016/j.agrformet.2018.06.024
Google Scholar
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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