Implications of neural network as a decision-making tool in managing Kazakhstan’s agricultural economy

• Autorzy: Kulisz Monika , Duisenbekova Aigerim , Kujawska Justyna , Kaldybayeva Danira , Issayeva Bibigul , Lichograj Piotr , Cel Wojciech

Identyfikatory

DOI

10.35784/acs-2023-39

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2023
• Tom: Vol. 19, no 4
• Strony: 121--135
• Opis fizyczny: Bibliogr. 35 poz., fig., tab.

Twórcy

autor

Kulisz Monika

• Lublin University of Technology, Faculty of Management, Department of Organization of Enterprise, Poland

• https://orcid.org/0000-0002-8111-2316

autor

Duisenbekova Aigerim

• L.N. Gumilyov Eurasian National University, Faculty of Economics, Department of Economics and Entrepreneurship, Kazakhstan, [e-mail], D.Serikbayev East Kazakhstan Technical University, School of Architecture, Civil Engineering and Energy, Kazakhstan

• https://orcid.org/0000-0001-9167-8076

autor

Kujawska Justyna

• Lublin University of Technology, Faculty of Environmental Engineering, Department of Biomass and Waste Conversion into Biofuels, Poland

• https://orcid.org/0000-0002-4809-2472

autor

Kaldybayeva Danira

• L.N. Gumilyov Eurasian National University, Faculty of Economics, Department of Economics and Entrepreneurship, Kazakhstan

• https://orcid.org/0000-0002-4370-4126

autor

Issayeva Bibigul

• L.N. Gumilyov Eurasian National University, Faculty of Economics, Department of Economics and Entrepreneurship, Kazakhstan

• https://orcid.org/0000-0002-8109-2896%5D

autor

Lichograj Piotr

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

• https://orcid.org/0000-0002-1351-4874

autor

Cel Wojciech

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

• https://orcid.org/0000-0002-8682-8428

Bibliografia

• [1] 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
• [2] 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
• [3] 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
• [4] 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
• [5] 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
• [6] 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/
• [7] 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
• [8] 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
• [9] 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
• [10] 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
• [11] 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
• [12] 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
• [13] 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
• [14] 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
• [15] 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
• [16] 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
• [17] 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
• [18] 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
• [19] 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
• [20] 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
• [21] 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
• [22] 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
• [23] 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
• [24] 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
• [25] 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
• [26] 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
• [27] 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
• [28] 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
• [29] 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.
• [30] 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.
• [31] 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
• [32] 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
• [33] 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
• [34] 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
• [35] 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