Digital twins in the agricultural industry: concepts and practices

Purpose of the research. The methods and criteria for comprehensively assessing the effectiveness of a country's economic integration within the framework of the Eurasian Economic Union (EAEU) can be identified. Three main methods for evaluating the effectiveness of economic integration within the EAEU can be distinguished. The first method is the method of trade integration among EAEU member states, which assesses the dynamics, structure, and volumes of mutual trade between the countries of the union. The second method is the method of assessing the effectiveness of economic integration based on a set of strategic priorities for economic development. The third method is the method of national competitiveness, which evaluates how participation in the EAEU affects the overall competitiveness of the national economy. Despite the fact that certain aspects of evaluation, such as competitiveness analysis, are universal, these methods will require adaptation for countries outside the EAEU. This is due to differences in the specifics of regional cooperation (e.g., in the EU or ASEAN) and integration mechanisms (such as currency or customs unions with varying regulations). Thus, the proposed methods can be developed with consideration for the specifics of the EAEU, but with some adaptation, they could also be used to assess integration in other unions.
Methodology. A variety of methods were used in the course of the study: macroeconomic analysis, comparative analysis of national economic models, econometric modeling, and a comprehensive approach to assessing the achievement of economic benefits, stability, and competitiveness of the country within the framework of the EAEU. A literature review of academic works on the topic of the study was conducted, which allowed for an examination of Kazakhstan's role in the development of trade and economic relations between the member states of the EAEU.
Originality / value. The originality and value of the study lie in examining the impact of global and regional integration on Kazakhstan's economic stability and development, considering the changes in the country's economy since its accession to the Eurasian Economic Union (EAEU). This provides insights into the effectiveness of the integration and helps identify future development vectors for Kazakhstan, based on improving trade relations and strengthening economic ties among EAEU member states.
Findings. The study’s findings demonstrate that farm enterprises can be managed efficiently in real time by leveraging digital twins. Compared with traditional approaches, these models offer superior performance in process forecasting, optimal resource allocation, and adaptation to climatic or market fluctuations. Practical results from the IoF2020 project confirm the wide applicability of digital twins and their pivotal role in the digitalisation of the agri-food sector. All five IoF2020 pilot deployments quantitatively proved that digital twins significantly enhance both operational efficiency and sustainability. For example, in the “Potato Data Processing Exchange” pilot, potato growers used IoT devices to monitor the entire flow of produce from field to storage; as a result, average crop yield increased by 10 %, fuel consumption fell by 10 %, and overall margins rose by 5 %.

1. Verdouw, C., Tekinerdogan, B., Beulens, A. J. M. and Wolfert, S. (2021), "Digital twins in smart farming", Agricultural Systems, Vol. 189, p. 103046. DOI: 10.1016/j.agsy.2020.103046.

2. Grieves, M. W. (2005), "Product lifecycle management: The new paradigm for enterprises", International Journal of Product Development, Vol. 2 No. 1–2, pp. 71–84. DOI: 10.1504/IJPD.2005.006669.

3. Philpotts, M. (1996), "An introduction to the concepts, benefits and terminology of product data management", Industrial Management & Data Systems, Vol. 96 No. 4, pp. 11–17. DOI: 10.1108/02635579610117467.

4. Grieves, M. and Vickers, J. (2017), "Digital twin: Mitigating unpredictable, undesirable emergent behaviour in complex systems", in Kahlen, F.-J., Flumerfelt, S. and Alves, A. (Eds.), Transdisciplinary Perspectives on Complex Systems, Springer International Publishing, pp. 85–113. DOI: 10.1007/978-3-319-38756-7_4.

5. Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J. and Wang, L. (2010), Modeling, Simulation, Information Technology and Processing Roadmap: Technology Area 11, National Aeronautics and Space Administration (NASA). Retrieved November 26, 2023, from URL: https://www.researchgate.net/publication/280310295_Modeling_Simulation_Information_Technology_and_Processing_Roadmap.

6. Glaessgen, E. and Stargel, D. (2012), "The digital twin paradigm for future NASA and U.S. Air Force vehicles", in 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, American Institute of Aeronautics and Astronautics. DOI: 10.2514/6.2012-1818.

7. Fuller, A., Fan, Z., Day, C. and Barlow, C. (2020), "Digital twin: Enabling technologies, challenges and open research", IEEE Access, Vol. 8, pp. 108952–108971. DOI: 10.1109/ACCESS.2020.2998358.

8. Boschert, S. and Rosen, R. (2016), "Digital twin—The simulation aspect", in Hehenberger, P. and Bradley, D. (Eds.), Mechatronic futures: Challenges and solutions for mechatronic systems and their designers, Springer International Publishing, pp. 59–74. DOI: 10.1007/978-3-319-32156-1_5.

9. Verdouw, C. N., Beulens, A. J. M., Reijers, H. A. and van der Vorst, J. G. A. J. (2015), "A control model for object virtualization in supply chain management", Computers in Industry, Vol. 68, pp. 116–131. DOI: 10.1016/j.compind.2014.12.011.

10. Schleich, B., Anwer, N., Mathieu, L. and Wartzack, S. (2017), "Shaping the digital twin for design and production engineering", CIRP Annals, Vol. 66 No. 1, pp. 141–144. DOI: 10.1016/j.cirp.2017.04.040.

11. Verdouw, C. N., Wolfert, S., Beulens, A. J. M. and Rialland, A. (2016), "Virtualization of food supply chains with the Internet of Things", Journal of Food Engineering, Vol. 176, pp. 128–136. DOI: 10.1016/j.jfoodeng.2015.11.009.

12. Morchid, A., Et-taibi, B., Oughannou, Z. and El Alami, R. (2024), "IoT-enabled smart agriculture for improving water management: A smart irrigation control using embedded systems and server-sent events", Scientific African, Vol. 27, p. e02527. DOI: 10.1016/j.sciaf.2024.e02527.

13. Poornima, G. and Gowda, S. M. A. (2024), "Digital twin for smart farming", inData science foragricultural innovation and productivity, Bentham Science Publishers, pp. 1–16. DOI: 10.2174/9789815196177124010004.

14. Zhang, R., Zhu, H., Chang, Q. and Mao, Q. (2025), "A Comprehensive Review of Digital Twins Technology in Agriculture", Agriculture, Vol. 15 No. 9, p. 903. DOI: 10.3390/agriculture15090903.

15. Purcell, W. and Neubauer, T. (2023), "Digital twins in agriculture: A state-of-the-art review", Smart Agricultural Technology, Vol. 3, p. 100094. DOI: 10.1016/j.atech.2022.100094.

16. Schleich, B., Anwer, N., Mathieu, L. and Wartzack, S. (2017), "Shaping the digital twin for design and production engineering", CIRP Annals – Manufacturing Technology, Vol. 66 No. 1, pp. 141–144. DOI: 10.1016/j.cirp.2017.04.040.

17. Alam, K. M. and El Saddik, A. (2017), "C2PS: A digital twin architecture reference model for the cloudbased cyber-physical systems", IEEE Access, Vol. 5, pp. 2050–2062. DOI: 10.1109/ACCESS.2017.2657006.

18. Redelinghuys, A. J. H., Kruger, K. and Basson, A. H. (2020), "A six-layer architecture for digital twins with aggregation", in Borangiu, T., Trentesaux, D., Thomas, A. and Cavalieri, S. (Eds.), Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future, Springer, pp. 171–182. DOI: 10.1007/978-3-030-27477-1_13.

19. Wang, L. (2024), "Digital Twins in Agriculture: A Review of Recent Progress and Open Issues", Electronics, Vol. 13 No. 11, p. 2209. DOI: 10.3390/electronics13112209.

20. Awais, M., Wang, X., Hussain, S., Aziz, F. and Mahmood, M. Q. (2025), "Advancing Precision Agriculture Through Digital Twins and Smart Farming Technologies: A Review", AgriEngineering, Vol. 7 No. 5, p. 137. DOI: 10.3390/agriengineering7050137.