EarthDaily has secured a €480,000 grant from the French government to support agricultural intelligence work for Ukraine, in a programme that will use satellite data, analytics, and artificial intelligence to track crop conditions across one of the world’s most closely watched grain-producing regions.
The award has been made under France’s FASEP programme and routed through GEOSYS, the French business EarthDaily acquired in 2021. The project will provide in-season crop area estimates and yield trends at oblast level, covering key crops including winter wheat, winter barley, winter rapeseed, sunflower, and corn. The aim is to give the Ukrainian government a more consistent view of regional production patterns during a period when farming conditions remain highly unstable.
EarthDaily said the work will draw on its existing agricultural analytics models as well as data from the EarthDaily Constellation, which the company says is due to become operational this summer. That gives the project a dual role. At one level it is a practical monitoring tool for Ukrainian ministries trying to assess harvest risk, production volumes, and regional variation during the growing season. At another, it is an early live demonstration of how calibrated Earth observation systems are being positioned as operational infrastructure for food security, rather than simply a source of periodic imagery.
Cécile Tartarin, general manager for Europe at EarthDaily, said “reliable agricultural intelligence is critical for food security, economic stability, and global commodity markets”. In Ukraine’s case, that intelligence is not just about headline yields. It also shapes decisions around procurement, logistics, export expectations, and the broader confidence that buyers and policymakers place in official crop estimates.
The French government’s involvement also gives the project an industrial angle beyond the immediate Ukraine brief. FASEP is designed to back the international deployment of French technology, and this scheme puts agricultural analytics, AI models, and Earth observation capability into that export frame. If the programme delivers credible in-season forecasting under difficult operating conditions, it will strengthen the case for wider adoption of this kind of monitoring in national agriculture systems elsewhere.
