Yield estimation
Yield outlooks combining vegetation condition, weather context, and historical performance — production intelligence before harvest, at regional scale.
- Pre-harvest outlooks
- Weather-adjusted models
- District-level granularity
Benefits
What yield estimation unlocks
Short-term optimization
- Plan storage, logistics, and procurement earlier
- Flag underperforming regions within the season
- Support credit decisions with objective signals
Long-term impact
- Yield history baselines per district and crop
- Improved forecast accuracy as seasons accumulate
- Stress-tested supply-chain planning
Approach & methodology
How the analytics work
We are transparent about data sources, models, and limits — so you can trust what you act on.
- Data sources
- Vegetation indices, satellite-derived weather variables, and historical yield statistics.
- Approach
- Statistical and ML models relating in-season condition curves to end-of-season yield.
- Update cadence
- Monthly outlooks from mid-season; final estimate near harvest.
- Limitations
- Model skill varies by crop and region; first seasons run with wider confidence bands.
Deliverables
Expected outputs
- GIS-ready data layers (GeoJSON / KML / SHP / GeoTIFF)
- Decision-ready PDF report with interpretation
- Interactive smart map with time-series view
Process
Project stages
4–6 weeks for first regional outlook
Define area of interest and indicators with your team
Acquire and process multi-date satellite imagery
Run analytics models and validate outputs
Deliver layers, maps, and report; set up recurring monitoring