Big data analytics in the agro-Industrial complex of Kazakhstan: effective technologies

The relevance of the topic is driven by the importance of digital transformation in agriculture. The application of big data technologies in the country’s agro-industrial complex contributes to income growth, cost reduction, and increased efficiency of production processes.

The goal is to identify the challenges of analyzing large volumes of information in the functioning of the agrarian sector in the context of global trends and challenges. The country’s agro-industrial production faces a shortage of qualified personnel, high costs of digital transformation, and outdated solutions. A new challenge has emerged – the training of specialists in working with large data sets is complicated by the lack of sufficient practical experience among producers and integrators of innovative models.

Methods – analytical, comparative analysis, and graphical methods were used to visualize materials and substantiate conclusions. The study includes analysis of case studies from Russia, the USA, Ukraine, Israel, and Kazakhstan, demonstrating practical adaptation of scientific approaches.

Results – limitations restricting the interpretation of digital document flow in the agro-industrial complex were identified, and barriers hindering its widespread adoption in the sector were highlighted. An expert assessment was given on the current state and prospects for the implementation of analytical tools based on a wide range of factual data in the agriculture of several foreign countries, with an emphasis on the potential for transferring successful practices to the agro-industrial complex of the Republic of Kazakhstan. The use of satellite monitoring and unmanned aerial vehicles (drones) was demonstrated, providing high-precision and real-time data on the condition of agricultural land.

Conclusions – priority directions for the implementation of these nanotechnologies in the short term (5 years) were determined, and a conditional structuring of stages for the medium and long term (10 years) was proposed in the form of three sequential phases.

1. Baseca, C.C., Sendra, S., Lloret, J., & Tomas, J. (2019). A Smart Decision System for Digital Farming. Agronomy, 9 (5), 216.doi:10.3390/agronomy9050216

2. Tibbetts, John H. (2018) The Frontiers of Artificial Intelligence. Bioscience, 68(1), 5 –10.doi:10.1093/biosci/bix136

3. Bjerke, L. Innovation in agriculture: An analysis of Swedish agricultural and non-agricultural firms// L.Bjerke, S. Johansson // Food Pol icy.-2022.-Vol 109. - P. 1–14.

4. Moorthy, J., Lahiri, R., Biswas,N., Sanyal,D., Ranjan,J., Nanath,K., & Ghosh,P. (2015). BigData: Prospects and Challenges.Vikalpa, 40(1), 74–96. doi:10.1177/0256090915575450

5. Zayats O. A., Nazarova Yu. N., Strizhakova E. A. [i dr.] Tekhnologii Big Data v sel’skom hozyajstve // Fundamental’nye issledovaniya. 2022. № 7. S. 35–40. doi: 10.17513/fr.43280.

6. Vedomstvennyj proekt «Cifrovoe sel’skoe hozyajstvo»: ofi cial’noe izdanie. M.: FGBNU «Rosinformagrotekh», 2019. 48 s. URL: https://mcx.gov.ru/upload/iblock/900/ (data obrashcheniya: 19.12.2024).

7. Technologic, kotorye prokormyat chelovechestvo. URL: https://trends.rbc.ru/trends/industry/6221f6aa9a794 7184f151d22 (data obrashcheniya: 19.12.2024).

8. «Rostselmash» i Cognitive Technologies vpervye v Rossii proveli polevye ispytaniya bespilotnogo kombajna. URL: https://agbz.ru/news/rostselmash--i-CognitiveTechnologies-vpervyie-v-rossii-proveli-polevyie-ispyitaniyabespilotnogo-kombayna/ (data obrashcheniya: 19.12.2024).

9. Abdelkader A., Hafi da A. Predictive Analysis for Big Data: Extension of Classification and Regression Trees Algorithm // International Journal of Computer and Systems Engineering. 2019. Vol. 13, № 8. P. 450–454.

10. Guild, M. & Danaher, T. (2014). Big Data Comes to the Farm. Financial Sense. Retrieved from http: //www. financialsense.com/contributors/guild/big-data-farm.

11. Faulkner, A., & Cebul, K. (2014). Agriculture Gets Smart: The Rise of Data and Robotics, Clean tech Agriculture Report. Cleantech Group.

12. Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F.X. (2017). Are view on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture,143 (December), 23–37.doi:10.1016/j.compag.2017.09.037

13. Sonka, S. (2015). Big Data: From hype to agricultural tool. Farm Policy Journal,12, 1–9.

14. Oussous, A., Benjelloun, F.Z., Lahcen, A.A., & Belfkih, S. (2018). Big Data technologies: Asurvey. Journal of King Saud University-Computer and Information Sciences, 30(4), 431–448.

15. Zilberman, D., Lipper, L., McCarthy, N., & Gordon, B. (2018). Innovation in Response to Climate Change. In Climate Smart Agriculture (pp.49–70).Springer.doi:10.1007/978-3-319-61194-5_4

16. Capalbo, S.M., Seavert,C., Antle, J.M., Way,J., & Houston, L. (2017). Understanding tradeoffs in the context off arm-scale impacts an application of decision support tools for assessing climate smart agriculture. In L.Lipper, N. McCarthy, D.Zilberman, G.Branca, & B.Blankespoor (Eds.), Climate Smart Agriculture: Building Resilience to Climate Change. Rome: FAO.

17. Jones,J.W., Antle,J., Basso,B., Boote,K., Conant,R., Foster,I., & Wheeler,T.et al. (2017).Towards a new generation of agricultural system data, models and knowledge products: State of agricultural systems science. Agricultural Systems,155,269–288.doi:10.1016/j.agsy.2016.09.021PMID:28701818

18. Lokers,R., Knapen,R., Janssen,S., vanRanden,Y., & Jansen,J. (2016). Analysis of Big Data technologies For use in agro-environmental science. Environmental Modelling & Software,84,494–504.doi:10.1016/j.envsoft.2016.07.017

19. Baranyi, J., Jóźwiak, A., Varga, L., Mézes, M., Beczner,J., & Farkas, J. (2013). Application Potentials of Network Science, Bio informatics and Systems Biology to Food Science. Hungarian Science, 174(9),1094-1102.

20. Bennett, J.M. (2015). Agricultural Big Data: Utilisation to discover the unknown and instigate practice change. Farm Policy Journal,12, 43–50.

21. Castillo, M.J., Boucher,S., &Carter,M. (2016). Index insurance: Using public data to benefit small-scale agriculture. The International Food and Agribusiness Management Review,19, 93–114.

22. Sawant, M., Urkude,R., & Jawale,S. (2016). Organized Dataand Information for Efficacious Agriculture Using PRIDE™Model.The International Food and Agribusiness Management Review, 19, 115–130.

23. Ob innovatsionnoy deyatel'nosti pred priyatii v Respublike Kazakhstan: statisticheskiy byulleten' [On the innovation activity of enter prises in the Republic of Kazakhstan: statistical bulletin] (2013-2023). Available at: https://www. stat.gov.kz/ru/industries/social-statistics/stat-edu science-inno/spreadsheets/ (date of access: 25.01.2025) [in Russian].