Spatio-temporal economic planning weighted routing model for agricultural land-use management in peri-urban zones: a case study of the Kyiv agglomeration
DOI:
https://doi.org/10.31548/zemleustriy2026.02.05Keywords:
digital agriculture, weight-based routing, middleware, UAV–IoT–satellite integration, peri-urban Kyiv, logistics costs, land-use change, FAIR/OGC interoperability, decision support, sustainability metricsAbstract
Ukraine’s agricultural sector faces a compound set of economic and spatial pressures: peri-urban land-use transformation, disrupted logistics, volatile fuel costs, and restricted mobility in wartime conditions. These challenges are especially evident in the Kyiv agglomeration, where agricultural operations increasingly depend on rapid re-planning under changing constraints. This study presents and evaluates a weights-based real-time routing platform for digital agriculture in Ukraine, integrated into a FAIR/OGC-aligned middleware architecture that consolidates multi-source data streams (satellite EO, UAV imagery when permitted, IoT telemetry, road and congestion layers, and administrative–economic registers). The platform operationalizes routing as a continuous decision-support service by allowing users to tune explicit weights across four criteria: time, direct cost, CO₂e, and operational risk, while enforcing land-use and safety restrictions through hard spatial masks and soft penalty layers.
Empirical testing was conducted over four pilot weeks in the peri-urban belt of Kyiv using a controlled-scenario design with three weekly templates: Baseline (A), Stress (B: high fuel prices and higher transport risk), and Sustainability-adjusted (C). The middleware maintained continuous advisory generation under intermittent UAV availability and short-lived network outages by employing store-and-forward edge buffers, asynchronous refresh, and lineage capture; exported route advisories were reproducible as CSV/GeoJSON with full provenance. Scenario results show strong sensitivity of weekly logistics costs to fuel-driven cost bands: compared with Scenario A, Scenario B increased direct logistics costs by 12.44%. In comparison, Scenario C increased costs by 6.22%. Tactical re-weighting protected time performance (approximately −1.15% total travel time in B and C). In comparison, feasibility remained high (≥95% of jobs served within service windows), and restricted-edge violations remained zero due to enforced masks. CO₂e totals remained stable across scenarios under uniform emission factors, highlighting the need for differentiated low-carbon corridors or fleet classes in future pilots.
The results indicate that a standards-based middleware platform combined with weight-based routing can serve as a practical tool for land-use governance and agricultural economics by linking spatial constraints, cost dynamics, and auditable sustainability indicators within a single operational workflow.
Received: 27.03.2026;
Accepted: 07.04.2026;
References
1. Agrawal, J., & Arafat, M. Y. (2024). Transforming farming: A review of AI-powered UAV technologies in precision agriculture. Drones, 8(11), Article 664. https://doi.org/10.3390/drones8110664
2. Aich, S., Chakraborty, S., Lee, Y.-K., & Kim, H.-C. (2022). Digital twins in agriculture: A state-of-the-art review. ICT Express, 8(3), 300–312. https://doi.org/10.1016/j.icte.2021.11.006
3. 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. http://ceur-ws.org/Vol-3849/forum1.pdf
4. 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), Article 137. https://doi.org/10.3390/agriengineering7050137
5. Banerjee, S., Mukherjee, A., & Kamboj, S. (2025). Precision agriculture revolution: Integrating digital twins and advanced crop recommendation for optimal yield [Preprint]. arXiv. https://arxiv.org/abs/2502.04054
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), Article 440. https://doi.org/10.3390/info14080440
7. Guebsi, R., Mami, S., & Chokmani, K. (2024). Drones in precision agriculture: A comprehensive review of applications, technologies, and challenges. Drones, 8(11), Article 686. https://doi.org/10.3390/drones8110686
8. Lin, C., Choy, K. L., Ho, G. T. S., Chung, S. H., & Lam, H. Y. (2020). Green vehicle routing problem: A state-of-the-art review. International Journal of Production Economics, 228, Article 107749. https://doi.org/10.1016/j.ijpe.2020.107749
9. Liu, J., Zhang, D., Li, X., & Wang, L. (2024). Distribution path optimization of carbon emission-reducing agricultural cold chain logistics based on improved GA. Cleaner Logistics and Supply Chain, 12, Article 100164. https://doi.org/10.1016/j.clscn.2024.100164
10. 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), Article 3583. https://doi.org/10.3390/s25123583
11. 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. https://doi.org/10.31548/economics/2.2025.24
12. Nazarenko, V., & Ostroushko, B. (2024). Smart city management system utilizing micro-services and IoT-based systems. Energiya, 1(71), 29–38. https://doi.org/10.31548/energiya1(71).2024.029
13. Obayi, R., Choudhary, S., Nayak, R., & Ramanjaneyulu, G. V. (2025). Pragmatic interoperability for human–machine value creation in agri-food supply chains. Information Systems Frontiers. Advance online publication. https://doi.org/10.1007/s10796-024-10567-x
14. Qiao, Y., Ren, P., Tang, H., & Li, Z. (2023). An interoperable and service-oriented approach for real-time environmental modeling by coupling OGC WPS and SensorThings API. Environmental Modelling & Software, 162, Article 105651. https://doi.org/10.1016/j.envsoft.2023.105651
15. Roccatello, E., Pagano, A., Levorato, N., & Rumor, M. (2025). State of the art in Internet of Things standards and protocols for precision agriculture with an approach to semantic interoperability. Network, 5(2), Article 14. https://doi.org/10.3390/network5020014
16. 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, Article 109194. https://doi.org/10.1016/j.compag.2024.109194
17. 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, Article 1107026. https://doi.org/10.3389/fsufs.2023.1107026
18. 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, Article 105256. https://doi.org/10.1016/j.compag.2020.105256
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