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We evaluate candidate signals against real Agreste yields. Higher |r| means a stronger relationship; sign matters for interpretation.
Peak EVI is the wrong signal — negative correlation in Coop 63 (r=−0.30) means high peak EVI can coincide with low seed maize yield (non-seed maize types may be overrepresented in RPG random sampling). EVI Integral (area under the seasonal curve, r=+0.62 within Coop 63) and Half-Max DOY (r=−0.56, earlier = better) are much stronger candidates, consistent with Konstantin's insight on the call.
Growing-season aggregates (Apr-Sep) from ERA5-Land via the ARCO point-query endpoint, at the centroid of Puy-de-Dôme. Climate is now the single strongest signal we have — heat-day count captures drought years (2019, 2022) without depending on satellite cloud cover.
2022 dropped — no vegetation-class Sentinel-2 observations (Massif Central cloud cover).
Only SCL=4 (vegetation-classified) pixels shown; sparse points in some years reflect persistent cloud cover over the Massif Central.
Each point is one département-year (2017–2024). Colors are by dept; the red cluster highlights Coop 63 (dept 63).
Using total-maize yield anomaly as target. R² negative in most years reflects the fundamental challenge: EVI features alone cannot predict yield anomaly at dept level without irrigation and climate covariates. This motivates adding ERA5 climate features (growing-degree-days, drought index) and parcel-level targeting via Coop 63 contract IDs.
s3://digifarm-limagrain-yield/ ├── agreste/maize_yield_dept.csv Yield data (91 depts × 15 years) ├── rpg/2023/maize_parcels.parquet 707K maize parcels + IDs ├── samples/centroids.parquet 240K sampled field centroids ├── evi/evi_timeseries.parquet 199K Sentinel-2 observations ├── outputs/ │ ├── features.parquet Aggregated dept × year × signal │ ├── model/yield_model.pkl Trained model │ └── reports/coop63_calibration.md This report └── client_docs/ Vivadour contracts + 2025 polygons
github.com/digifarmio/limagrain-yield (private)