Digital transformation of land resource management and development of the national spatial data infrastructure using artificial intelligence

Received 12.04.2026
Revised 31.07.2026
Published 04.09.2026

Abstract

In the context of the digitalisation of the economy of the Kyrgyz Republic, the transformation of the land resource management system has become strategically important, as the efficient use of the national land fund is directly linked to ensuring the sustainable development of the agricultural sector, increasing the investment attractiveness of territories, and improving public regulatory mechanisms. The purpose of this study was to assess the role of artificial intelligence technologies and the national spatial data infrastructure in enhancing the efficiency of land resource management in the Kyrgyz Republic. The methodological framework of the study was based on system analysis, comparative analysis, economic and statistical methods, as well as geoinformation modelling and spatial data analysis. The research included a comprehensive assessment of the current state of digitalisation in the country’s land resource management system and identified the main institutional and technological constraints, including fragmented information resources, insufficient integration of digital platforms, limited access to spatial data, and a shortage of qualified specialists in geoinformation technologies. The findings demonstrated that the implementation of artificial intelligence tools improves the accuracy of land resource monitoring, automates spatial data processing, supports forecasting of land-use changes, and enhances the validity of managerial decision-making. The results also indicated that integrating geographic information systems with intelligent algorithms increases the transparency of land resource management, reduces the risks of inefficient land use, and optimises government control and regulatory processes. Priority directions for the development of the national spatial data infrastructure had been identified, including the standardisation of information flows, integration of digital services, expansion of interagency cooperation, and improvement of access to geospatial information resources. The practical significance of the study lay in the possibility of applying its findings in the development and implementation of national digital strategies for land resource management in the Kyrgyz Republic

Keywords

geographic information systems; digital cadastre; intelligent data analysis; land relations; territorial monitoring; management automation
Suggested citation
Belek uulu, E., Dyikanova, A., Zhumaliev, T., Begaliev, S., & Kerimov, T. (2026). Digital transformation of land resource management and development of the national spatial data infrastructure using artificial intelligence. Bulletin of the Kyrgyz National Agrarian University, 24(3), 20-34. https://doi.org/10.63621/bknau./3.2026.20

References

  1. Adewusi, A.O., Asuzu, O.F., Olorunsogo, T., Iwuanyanwu, C., Adaga, E., & Daraojimba, D.O. (2024). AI in precision agriculture: A review of technologies for sustainable farming practices. World Journal of Advanced Research and Reviews, 21(1), 2276-2285. doi: 10.30574/wjarr.2024.21.1.0314.
  2. Akhmatova, D.R. (2023). Economic potential of artificial intelligence: Global experience, Russian practice, and prospects of the EAEU countries. Economics and Innovation Management, 3, 15-24. doi: 10.26730/2587-5574-2023-3-15-24.
  3. Akintuyi, O.B. (2024). Adaptive AI in precision agriculture: A review: Investigating the use of self-learning algorithms in optimizing farm operations based on real-time data. Open Access Research Journal of Multidisciplinary Studies, 7(2), 16-30. doi: 10.53022/oarjms.2024.7.2.0023.
  4. Assis, K.C.D.C., Piantoni, J., & Azevedo, R.F. (2024). Technologies in smart agriculture: Efficiency and sustainability. Research Society and Development, 13(4), article number e7013445072. doi: 10.33448/rsd-v13i4.45072.
  5. Atapattu, A.J., Perera, L.K., Nuwarapaksha, T.D., Udumann, S.S., & Dissanayaka, N.S. (2024). Challenges in achieving artificial intelligence in agriculture. In S.S. Chouhan, A. Saxena, U.P. Singh & S. Jain (Eds.), Artificial intelligence techniques in smart agriculture (pp. 7-34). Singapore: Springer. doi: 10.1007/978-981-97-5878-4_2.
  6. Bachu, L., Kandibanda, A., Grandhi, N., Athina, D.P., & Ande, P.K. (2024). Machine learning for enhanced crop management and optimization of yield in precision agriculture. In 2024 8th international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC) (pp. 1289-1293). Kirtipur: IEEE. doi: 10.1109/i-smac61858.2024.10714733.
  7. Bhat, I.A., Ansarullah, S.I., Ahmad, F., Amir, S., Sidana, S., Sinha, A., Khalid, S., & Yazdani, G. (2025). Leveraging artificial intelligence in agribusiness: A structured review of strategic management practices and future prospects. Discover Sustainability, 6, article number 565. doi: 10.1007/s43621-025-01260-3.
  8. Chen, L., et al. (2023). Artificial intelligence-based solutions for climate change: A review. Environmental Chemistry Letters, 21, 2525-2557. doi: 10.1007/s10311-023-01617-y.
  9. Chowdhury, K., Rahman, M.A., Mallik, S.K., Chowdhury, L.N., & Nova, N.A. (2025). Bridging the global digital divide in agriculture: The role of AI in equitable technology access. International Journal of Sustainable Development and Planning, 20(6), 2469-2481. doi: 10.18280/ijsdp.200616.
  10. Çokkızgın, H., Çokkızgın, A., & Girgel, Ü. (2025). Reducing the carbon footprint of field crops: A comprehensive review of strategies and innovations. Applied Ecology and Environmental Research, 23(4), 7641-7661. doi: 10.15666/aeer/2304_76417661.
  11. Eze, V.H.U., Eze, E.C., Alaneme, G.U., Bubu, P.E., Nnadi, E.O.E., & Okon, M.B. (2025). Integrating IoT sensors and machine learning for sustainable precision agroecology: Enhancing crop resilience and resource efficiency through data-driven strategies, challenges, and future prospects. Discover Agriculture, 3, article number 83. doi: 10.1007/s44279-025-00247-y.
  12. Food and Agriculture Organization of the United Nations (FAO). (n.d.). Retrieved from https://www.fao.org.
  13. Ge, C., Zhang, G., Wang, Y., Shao, D., Song, X., & Wang, Z. (2025). Research status and development trends of artificial intelligence in smart agriculture. Agriculture, 15(21), article number 2247. doi: 10.3390/agriculture15212247.
  14. Gryshova, I., Balian, A., Antonik, I., Miniailo, V., Nehodenko, V., & Nyzhnychenko, Y. (2024). Artificial intelligence in climate-smart agriculture: Toward a sustainable farming future. Access to Science, Business, Innovation in Digital Economy, 5(1), 125-140. doi: 10.46656/access.2024.5.1(8).
  15. Gupta, G., & Pal, S.K. (2025). Applications of AI in precision agriculture. Discover Agriculture, 3, article number 61. doi: 10.1007/s44279-025-00220-9.
  16. Hamrani, A., Allouhi, A., Bouarab, F.Z., & Jayachandran, K. (2025). AI and robotics in agriculture: A systematic and quantitative review of research trends (2015-2025). Crops, 5(5), article number 75. doi: 10.3390/crops5050075.
  17. Law of Kyrgyz Republic No. 153 “On State Registration of Rights to Immovable Property and Transactions Therewith”. (1998, December). Retrieved from https://cbd.minjust.gov.kg/160/edition/1279572/ru.
  18. Lebid, O.V., Kiporenko, S.S., & Vovk, V.Yu. (2023). Use of artificial intelligence technologies in agriculture: European experience and application in Ukraine. Electronic Modeling, 45(3), 57-71. doi: 10.15407/emodel.45.03.057.
  19. Likhota, O.V. (2025). Artificial intelligence and quantum technologies and their role in the development of the U.S. economy. Problems of Economy, 1(63), 26-30. doi: 10.32983/2222-0712-2025-1-26-30.
  20. Mana, A.A., Allouhi, A., Hamrani, A., Rehman, S., el Jamaoui, I., & Jayachandran, K. (2024). Sustainable AI-based production agriculture: Exploring AI applications and implications in agricultural practices. Smart Agricultural Technology, 7, article number 100416. doi: 10.1016/j.atech.2024.100416.
  21. National Statistical Committee of the Kyrgyz Republic. (n.d.). Retrieved from http://www.stat.kg.
  22. Nehrey, M.V. (2023). Digital transformation of the agricultural sector: Prospects, challenges and solutions. Scientific Papers NaUKMA. Economics, 8(1), 94-100. doi: 10.18523/2519-4739.2023.8.1.94-100.
  23. Olawade, D.B., Wada, O.Z., David-Olawade, A.C., Fapohunda, O., Ige, A.O., & Ling, J. (2024). Artificial intelligence potential for net zero sustainability: Current evidence and prospects. Next Sustainability, 4, article number 100041. doi: 10.1016/j.nxsust.2024.100041.
  24. Open Data Portal of the Kyrgyz Republic. (n.d.). Retrieved from https://data.gov.kg/.
  25. Perekhodov, P. (2025). Agro-industrial complex development under sanctions pressure: The need for financial support. Vestnik Volgograd State University Economics, 26(4), 207-217. doi: 10.15688/ek.jvolsu.2024.4.16.
  26. Polwaththa, K.P.G.D.M., Amarasinghe, S.T.C., Amarasinghe, A.A.Y.D., & Amarasinghe, A.A.Y. (2024). Exploring artificial intelligence and machine learning in precision agriculture: A pathway to improved efficiency and economic outcomes in crop production. American Journal of Agricultural Science Engineering and Technology, 8(3), 50-59. doi: 10.54536/ajaset.v8i3.3843.
  27. Rudoy, D., Olshevskaya, A., Alentsov, E., Odabashyan, M., Prutskov, A., Onoiko, T., Vershinina, A., & Kutyga, M. (2023). Mathematical modeling in the agro-industrial complex: Basic problems and model construction. E3S Web of Conferences, 381, article number 01082. doi: 10.1051/e3sconf/202338101082.
  28. Ruksar, P., Kundathil, C., & Vardhan, M.S.C.N. (2025). Artificial intelligence in climate-smart agronomy. International Journal of Research in Agronomy, 8(7), 325-340. doi: 10.33545/2618060x.2025.v8.i7se.3424.
  29. Saha, S., Ghimire, A., Manik, M.M.T.G., Tiwari, A., & Imran, M.A.U. (2024). Exploring benefits, overcoming challenges, and shaping future trends of artificial intelligence application in agricultural industry. The American Journal of Agriculture and Biomedical Engineering, 6(7), 11-28. doi: 10.37547/tajabe/volume06issue07-03.
  30. Sakkaraeva, D., & Kumashev, M. (2024). Analysis of the agro-industrial sector of the Kyrgyz Republic. Ekonomika APK, 31(2), 41-50. doi: 10.32317/2221-1055.202402041.
  31. Satpati, S. (2026). AI-enabled precision agriculture for smallholder farmers. Research Square. doi: 10.21203/rs.3.rs-8808017/v1.
  32. Sherstiuk, O.V. (2025). Impact of digital technologies on the innovative development of agrarian enterprises: The technical and technological aspect. Business Inform, 6, 276-281. doi: 10.32983/2222-4459-2025-6-276-281.
  33. Shevchenko, A.A., Petrenko, O.P., & Kosyk, D.V. (2024). Artificial intelligence in crop production: Successful cases of agricultural enterprises. Modern Economics, 47, 130-137. doi: 10.31521/modecon.v47(2024)-19.
  34. Skvortsov, E.A. (2020). Prospects of applying artificial intelligence technologies in regional agriculture. Economy of Regions, 16(2), 563-576. doi: 10.17059/2020-2-17.
  35. Srikanthnaik, J. (2024). Artificial intelligence and machine learning for precision in agriculture: A comprehensive systematic review. International Journal of Research in Agronomy, 7(6), 762-767. doi: 10.33545/2618060x.2024.v7.i6j.2794.
  36. Srinivasu, P.N., Pavate, A., JayaLakshmi, G., Shafi, J., Choi, J., & Ijaz, M.F. (2026). Agentic AI for smart and sustainable precision agriculture. Frontiers in Plant Science, 16, article number 1706428. doi: 10.3389/fpls.2025.1706428.
  37. Truflyak, E.V., & Ragozin, L.V. (2025). Efficiency of variable rate application of nitrogen fertilizers using artificial intelligence model. Engineering Technologies and Systems, 35(3), 489-512. doi: 10.15507/2658-4123.035.202503.305-328.
  38. Ugwu, O.P.-C., et al. (2025). Implementing artificial intelligence and machine learning algorithms for optimized crop management: A systematic review on data-driven approach to enhancing resource use and agricultural sustainability. Cogent Food & Agriculture, 11(1), article number 2569982. doi: 10.1080/23311932.2025.2569982.
  39. Wei, H., Xu, W., Kang, B., Eisner, R., Muleke, A., Rodríguez, D., de Voil, P., Sadras, V., Monjardino, M., & Harrison, M.T. (2024). Irrigation with artificial intelligence: Problems, premises, promises. Human-Centric Intelligent Systems, 4, 187-205. doi: 10.1007/s44230-024-00072-4.
  40. Wu, K., Ji, Z., Wang, H., Shao, X., Li, H., Zhang, W., Kong, W., Xia, J., & Bao, X. (2025). A comprehensive review of AI methods in agri-food engineering: Applications, challenges, and future directions. Electronics, 14(20), article number 3994. doi: 10.3390/electronics14203994.
  41. Yang, Y., & Sun, Y. (2025). The values, challenges, and strategies of AI in empowering sustainable livelihoods for farmers. Frontiers in Sustainable Food Systems, 9 article number 1716572. doi: 10.3389/fsufs.2025.1716572.
  42. Ye, Z., Yin, S., Cao, Y., & Wang, Y. (2024). AI-driven optimization of agricultural water management for enhanced sustainability. Scientific Reports, 14, article number 25721. doi: 10.1038/s41598-024-76915-8.
  43. Yparraguirre, C.D.R., Rodríguez-Yparraguirre, A.J., Rodriguez, W.A.C., Saavedra-Vera, J.V., Lopez-Carranza, A.R., Olivares-Espino, I.M., Rojo, C.M., Epifania-Huerta, A.D., Guarniz-Vásquez, E., & Maco-Vásquez, W.A. (2026). Advances in emerging digital technologies for sustainable agriculture: Applications and future perspectives. Earth, 7(2), article number 63. doi: 10.20944/preprints202602.0211.v1.
  44. Zhang, Q., Wang, Q., Liang, Y., Zhang, K., & Hu, H. (2026). Artificial intelligence and the sustainable development of agricultural enterprises: A total factor productivity perspective. Frontiers in Sustainable Food Systems, 10, article number 1768115. doi: 10.3389/fsufs.2026.1768115.