KG0 – FAO AGRIS data provider 2026
ISSN 1694-6286
e-ISSN 1694-9250
Submit Article
English
Bulletin of the Kyrgyz National Agrarian University

Search Article

The application of artificial intelligence in forecasting agricultural systems in Kyrgyzstan under climate change

A Ainura Dyikanova A Angysyn Seitmuratov A Abdikerim Kurbanaliev T Turgunbek Zhumaliev J Jamila Baialieva
Received 15.05.2025
Revised 16.08.2025
Published 10.09.2025

Abstract

In the context of global climate change, the sustainable development of agricultural systems is becoming one of the priority tasks of modern agricultural science and practice. Uneven precipitation, rising average annual temperatures and more frequent extreme weather events increase the risks of reduced yields and economic instability in agriculture. This paper examined the application of artificial intelligence methods for forecasting the sustainability of agricultural systems at the regional level. The aim of the study was to develop approaches to forecasting the productivity and adaptive potential of agricultural crops using neural networks and machine learning algorithms. The materials and methods of the study included the use of long-term statistical data on the yield of major crops, climatic indicators, and economic parameters of agriculture in the Kyrgyz Republic. Correlation-regression modelling, artificial neural networks, and clustering algorithms were used for the analysis. The results of the study showed that the use of intelligent algorithms can increase the accuracy of yield forecasts by 12-15% compared to traditional methods, as well as identify key climatic and economic factors that determine the sustainability of agricultural systems. The scientific novelty of the work lies in the integration of artificial intelligence methods with agroecological zoning to build adaptive models of sustainable development. The practical significance of the research lies in the possibility of applying the developed models in strategic planning, the formation of regional food security programmes, and risk management in the agricultural economy

Keywords

digital technologies in agriculture; adaptive farming; climate resilience; predictive models; big data; machine learning; agroecosystem management
Suggested citation
Dyikanova, A., Seitmuratov, A., Kurbanaliev, A., Zhumaliev, T., & Baialieva, J. (2025). The application of artificial intelligence in forecasting agricultural systems in Kyrgyzstan under climate change. Bulletin of the Kyrgyz National Agrarian University, 23(3), 74-84. https://doi.org/10.63621/bknau./3.2025.74

References

  1. Azarov, A., Kulikov, M., Sidle, R.C., & Zaginaev, V. (2025). Climate change and its impact on natural resources and rural livelihoods: Gendered perspectives from Naryn, Kyrgyzstan. Climate, 13(3), article number 57. doi: 10.3390/cli13030057.
  2. Baidybekova, S., Sauranbai, S., Yermekbayeva, D., & Bayetova, M. (2025). Current state and innovative development of the agrarian sector: Comparative analysis of trends in different regions. CABI Reviews, 20(1), article number 0048. doi: 10.1079/cabireviews.2025.0048.
  3. Bazarbaeva, R.S., Brovko, N.A., & Safronchuk, M.V. (2021). Global challenges of the beginning of the digital age and a green perspective for the development of the Kyrgyz Republic's economy. In E.B. Zavyalova & E.G. Popkova (Eds.), Industry 4.0: Exploring the consequences of climate change (pp. 137-150). Cham: Springer. doi: 10.1007/978-3-030-75405-1_12.
  4. Burkhanov, A.U., Azhibekova, A.T., Parakhina, V.N., & Meteleva, O.A. (2025). Climate-responsible innovations based on big data and AI for sustainable development of a smart vertical farm. In E.G. Popkova (Ed.), Technological horizons of decarbonization based on environmental innovations (pp. 157-162). Cham: Springer Nature. doi: 10.1007/978-3-031-82210-0_26.
  5. Burkhanov, A.U., Yadgarov, A.A., Saidova, M.E., & Tugizova, M.S. (2024). Agricultural insurance protection and food security in the face of global climate change. In M.P. Eshov, G.K. Abdurakhmanova, A.U. Burkhanov, N.B. Abdusalomova & S.T. Ergasheva (Eds.), Development of international entrepreneurship based on corporate accounting and reporting according to IFRS (Part B, pp. 75-79). Leeds: Emerald Publishing. doi: 10.1108/S1877-63612024000033B010.
  6. Chupin, A.L., Bespalko, V.G., Nemirova, G.I., Sergeev, I.V., & Yurchenko, O.A. (2025). Impact of artificial intelligence on the implementation of the UN SDG 9: Promoting sustainability in the EAEU economy. In E.G. Popkova (Ed.), Management of digital technologies in the innovative economy (pp. 239-243). Cham: Springer Nature. doi: 10.1007/978-3-031-83331-1_39.
  7. De Keyser, J., et al. (2023). Integrating open-source datasets to analyze the water-food-energy-climate nexus in Central Asia. Water, 15(19), article number 3482. doi: 10.3390/w15193482.
  8. European Centre for Medium-Range Weather Forecasts (ECMWF). (n.d.). Retrieved from https://www.ecmwf.int/.
  9. FAO. (2022). The state of food and agriculture 2022: Leveraging automation in agriculture for transforming agrifood systems. Rome: Food and Agriculture Organization of the United Nations. doi: 10.4060/cb9479en.
  10. Gerlitz, L., Vorogushyn, S., & Gafurov, A. (2020). Climate informed seasonal forecast of water availability in Central Asia: State-of-the-art and decision making context. Water Security, 10, article number 100061. doi: 10.1016/j.wasec.2020.100061.
  11. Getahun, S., Kefale, H., & Gelaye, Y. (2024). Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review. The Scientific World Journal, 2024, article number 2126734. doi: 10.1155/2024/2126734.
  12. Gryshova, I., Balian, A., Antonik, I., Miniailo, V., Nehodenko, V., & Nyzhnychenko, Ya. (2024). Artificial intelligence in climate-smart agriculture: Toward a sustainable farming future. Access Journal, 5(1), 125-140. doi: 10.46656/access.2024.5.1(8).
  13. Harikrishnan, S., Kaushik, D., Rasane, P., Kumar, A., Kaur, N., Reddy, C.K., Proestos, C., Oz, F., & Kumar, M. (2025). Artificial intelligence in sustainable food design: Technological and ethical considerations. Trends in Food Science & Technology, 163, article number 105152. doi: 10.1016/j.tifs.2025.105152.
  14. Imanbayeva, Z., Abuselidze, G., Bukharbayeva, A., Jrauova, K., Oralbayeva, A., & Kushenova, M. (2024). State regulation of the digital transformation of agribusiness in the context of the climate crisis intensification. Economies, 12(10), article number 270. doi: 10.3390/economies12100270.
  15. Kadyraliev, A., Oruntayeva, A., Kamchybekov, T., Abyshov, I., & Bigali, A. (2024). The impact of digital technologies on the effectiveness of management in the agricultural sector of the Kyrgyz Republic. Ekonomika APK, 31(5), 35-44. doi: 10.32317/ekon.apk/5.2024.35.
  16. Kamilaris, A., & Prenafeta-Boldú, F.X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90. doi: 10.1016/j.compag.2018.02.016.
  17. Liang, L., Zhang, F., & Qin, K. (2021). Assessing the vulnerability of agricultural systems to drought in Kyrgyzstan. Water, 13(21), article number 3117. doi: 10.3390/w13213117.
  18. Mai, J., & Liu, G. (2023). Modeling and predicting the effects of climate change on cotton-suitable habitats in the Central Asian arid zone. Industrial Crops and Products, 191(Part A), article number 115838. doi: 10.1016/j.indcrop.2022.115838.
  19. Meteoblue. (n.d.). Retrieved from https://www.meteoblue.com/.
  20. Mimenbayeva, A., Issakova, G., Tanykpayeva, B., Tursumbayeva, A., Suleimenova, R., & Tulkibaev, A. (2024). Applying machine learning for analysis and forecasting of agricultural crop yields. Scientific Journal of Astana IT University, 17, 28-42. doi: 10.37943/17LKYF9288.
  21. Ministry of Agriculture, Forestry and Water Resources of the Kyrgyz Republic. (n.d.). Retrieved from https://agroprod.kg/.
  22. Nakysbekova, K., Ismukhanova, L., Aldibayeva, L., Shimshikov, B., Torobekova, T., Osmonbekova, R., Asanova, A., & Kulnazarova, K. (2025). Role of information systems in ecology for achieving the Sustainable Development Goals (SDGs). Caspian Journal of Environmental Sciences, 23(3), 611-617.
  23. National Statistical Committee of the Kyrgyz Republic (NSCKR). (n.d.). Retrieved from https://stat.gov.kg/en/.
  24. Schmidhuber, J., & Tubiello, F.N. (2007). Global food security under climate change. Proceedings of the National Academy of Sciences, 104(50), 19703-19708. doi: 10.1073/pnas.0701976104.
  25. Wen, X., Zhao, G., Cheng, X., Chang, G., Dong, X., & Lin, X. (2022). Prediction of the potential distribution pattern of the great gerbil (Rhombomys opimus) under climate change based on ensemble modelling. Pest Management Science, 78(7), 3128-3134. doi: 10.1002/ps.6939.
  26. World Bank. (n.d.). Climate change knowledge portal. Retrieved from https://climateknowledgeportal.worldbank.org/.
  27. 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), article number 828. doi: 10.3390/agronomy12040828.