Predicting Winter Wheat Growth Stages with TAVAP
- Angeliki Milioti
- Sep 29
- 1 min read
Updated: 6 days ago
The Challenge
For farmers, timing is everything. In winter wheat production, operations such as fertilization, growth regulator application, and harvesting must align with specific crop growth stages. A delay of just a few days can reduce yields, waste inputs, and increase costs. Traditionally, farmers have relied on manual observation or calendar-based estimations, which are often imprecise and lead to inefficiencies.
The Solution
World from Space developed TAVAP, a digital service that integrates satellite data (Sentinel-1), weather records, soil data, and farm management information to predict key growth stages of winter wheat. Using advanced machine learning (XGBoost models), TAVAP provides accurate, parcel-level forecasts for stages like crop emergence, stem elongation, heading, maturity, and harvest. This allows farmers to schedule operations precisely, reduce unnecessary chemical use, and improve crop health.
The Results
The pilot, conducted in Germany with support from Deutsche Wetterdienst (DWD) phenology data, proved that satellite-driven growth stage prediction is feasible and effective:
Crop emergence was predicted with ~82% accuracy within 6 days of the actual event.
Heading (a critical stage for pesticide and fertilizer timing) reached ~67% accuracy within 6 days, helping optimize protective treatments.
Maturity and stem elongation, though harder to detect, achieved satisfactory results of ~55–60% accuracy, still valuable for operational planning.
Harvest detection worked well, identifying events within a 1-week window.
These results mean that, in practice, farmers can time interventions within a margin of ±1 week—a meaningful improvement over traditional methods.
The Impact
By improving timing accuracy, TAVAP helps farmers:
Reduce pesticide and fertilizer use by ensuring treatments are applied at the most effective stage.
Lower production costs while maintaining or increasing yields.
Strengthen sustainability by minimizing input waste and environmental impact.
Gain confidence in planning operations with science-based recommendations instead of guesswork.
Lessons Learned
Satellite data works best when combined with weather and soil information, highlighting the importance of integrated digital tools.
Some stages (like stem elongation) remain challenging, but continuous model refinement and improved training data will boost accuracy.
Farmers do not need exact day predictions—a 5–7 day accuracy window is practical and aligns with real-world field management.
Conclusion
The TAVAP pilot demonstrated how digital technologies can turn raw data into actionable intelligence for farmers. By predicting wheat growth stages with high reliability, TAVAP paves the way for smarter input use, reduced risks, and more sustainable farming practices.
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