Digital agronomy: smart decision-support workflow for climate‑resilient farming in the Kyiv agglomeration
DOI:
https://doi.org/10.31548/zemleustriy2025.03.0%25pKeywords:
precision agriculture, digital agronomy, peri-urban farming, land use change, logistics costs, policy integration, Kyiv agglomerationAbstract
Rapid metropolitan growth is reshaping agrarian viability in peri-urban regions. Using the Kyiv agglomeration as a data-rich testbed, this study couples a modular UAV–AI decision-support workflow with empirical constraints emerging from land use, labour markets, logistics, and land rents. Regional evidence shows agricultural land has been squeezed to 0.21% of the area (≈0.18 k ha), against >54% green zones; wage differentials (16,500 UAH food processing vs 14,000 UAH agriculture) and transport costs of 20–250 UAH/km undermine farm margins and labour retention, while prime‑zone rents up to 25 million UAH/ha/year intensify conversion pressure (Kyiv & oblast baseline tables and figures). These structural frictions motivate digital agronomy that is explicitly policy‑ and cost-aware. We therefore prototype a decision-support workflow that fuses UAV/satellite imagery, in-field IoT, and historical climate/crop data with administrative‑economic layers (rents, wage gradients, haulage costs). The system translates multisource inputs into actionable stress detection, irrigation timing, and input allocation recommendations. At the same time, a logistics module evaluates route/vehicle choices under peri-urban cost profiles—a stakeholder‑co-design process (farmers, processors, and planners) anchors usability and transferability. We report (1) the peri-urban baseline for Kyiv (land, wages, logistics, enterprise distribution), (2) the architecture of the UAV–AI workflow and integration points with farm CRMs and public agri‑data, and (3) an evaluation framework linking agronomic KPIs to spatial‑economic constraints for resilient adoption. The approach is designed for cross-border replication (Ukraine - Germany) and to inform respectable policy outputs on digital land management and peri-urban agrifood resilience.
Keywords: precision agriculture, digital agronomy, UAV, AI decision support, peri-urban farming, land use change, logistics costs, Kyiv agglomeration, resilience, policy integration.
References
1. Agrawal, J., & Arafat, M. Y. (2024). Transforming farming: A review of AI-powered UAV technologies in precision agriculture. Drones, 8(11), 664. DOI: https://doi.org/10.3390/drones8110664.
2. Guebsi, R., Mami, S., & Chokmani, K. (2024). Drones in precision agriculture: A comprehensive review of applications, technologies, and challenges. Drones, 8(11), 686. DOI: https://doi.org/10.3390/drones8110686.
3. Miller, T., Mikiciuk, G., Durlik, I., Mikiciuk, M., Łobodzińska, A., & Śnieg, M. (2025). The IoT and AI in agriculture: The time is now—A systematic review of smart sensing technologies. Sensors, 25(12), 3583. DOI: https://doi.org/10.3390/s25123583.
4. Urdu, D., Berre, A. J., Sundmaeker, H., Rilling, S., Roussaki, I., Marguglio, A., … Wolfert, S. (2024). Aligning interoperability architectures for digital agri-food platforms. Computers and Electronics in Agriculture, 224, 109194. DOI: https://doi.org/10.1016/j.compag.2024.109194.
5. Obayi, R., Choudhary, S., Nayak, R., & Ramanjaneyulu, G. V. (2024). Pragmatic interoperability for human–machine value creation in agri-food supply chains. Information Systems Frontiers. DOI: https://doi.org/10.1007/s10796-024-10567-x.
6. Falcão, L., Matar, S., Rauch, E., Elberzhager, F., & Koch, K. (2023). Next-generation interoperability: A framework for data spaces and OPC UA-based automations. Information, 14(8), 440. DOI: https://doi.org/10.3390/info14080440.
7. Open Geospatial Consortium (OGC). (2022). OGC SensorThings API Part 1: Sensing (v1.1). Available at: https://docs.ogc.org/is/18-088/18-088.html. (Standards reference for middleware/FAIR alignment.)
8. Arz von Straussenburg, S., Aldenhoff, T., & Riehle, D. (2024). Improving OGC SensorThings API for industrial IoT use cases: Lessons from real-world scenarios. In Proceedings of the 27th AGILE Conference on Geographic Information Science. CEUR-WS.
9. Awais, M., Wang, X., Hussain, S., Aziz, F., & Mahmood, M. Q. (2025). Advancing precision agriculture through digital twins and smart farming technologies: A review. AgriEngineering, 7(5), 137. DOI: https://doi.org/10.3390/agriengineering7050137.
10. Bahmutsky, S., Grassauer, F., Arulnathan, V., & Pelletier, N. (2024). A review of life cycle impacts and costs of precision agriculture for cultivation of field crops. Sustainable Production and Consumption. DOI: https://doi.org/10.1016/j.spc.2024.11.010.
11. Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256. DOI: https://doi.org/10.1016/j.compag.2020.105256.
12. Yousaf, H., Kayvanfar, V., Mazzoni, S., & Elomri, A. (2023). From decision support to Agriculture 4.0: A systematic review of data-driven systems and adoption challenges. Frontiers in Sustainable Food Systems, 7, 1107026. DOI: https://doi.org/10.3389/fsufs.2023.1107026.
13. Nazarenko, V., & Martyn, A. (2025). Urban growth and agrarian dynamics: Evaluating the Kyiv agglomeration’s economic landscape. Economics and Business Management, 2(16), 24-41.
14. Nazarenko, V. (2023). Urban futures reimagined: Advanced ecological-economic frameworks for sustainable growth [Monograph]. National University of Life and Environmental Sciences of Ukraine. Available at: https://dglib.nubip.edu.ua/handle/123456789/10692.
15. Nazarenko, V., Ostroushko, B., (2024). Smart city management system utilizaing micro-services and iot-based systems. Energiya, (1), 29-38. DOI: https://doi.org/10.31548/energiya1(71).2024.029.
16. United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development. United Nations. Available at: https://sdgs.un.org/2030agenda.
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