Digital agronomy: smart decision-support workflow for climate‑resilient farming in the Kyiv agglomeration

<ns0:p>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.</ns0:p>