Yields That Forecast Themselves
a large row-crop agribusiness cooperative
yield improvement
less water and fertilizer used
mid-season forecast accuracy
Planting, irrigation, and input decisions rested on intuition and last season's outcomes, while yield forecasts swung wildly until harvest. Water and fertilizer went where they always had, not where the data said they should. Member farms varied enormously in soil, drainage, and microclimate, so blanket recommendations underperformed, and the cooperative needed field-level precision without asking farmers to become data scientists.
How we approached it
Fused satellite imagery, soil samples, weather history, and per-field harvest records into a unified field-level dataset across member farms.
Trained gradient-boosted yield models that forecast production per field and quantify how irrigation and input changes shift the outcome.
Built an agronomist copilot that translates model output into plain-language recommendations farmers can act on each week.
Piloted with a subset of member farms for one full season, comparing treated fields against controls before cooperative-wide rollout.
“Our members do not read model output, they read a weekly plan for their own fields. The yields speak for themselves, and we got there using less water, not more.”
The pilot season compared fields following model recommendations against control fields under conventional practice in the same regions, so weather affected both groups equally.
No. The models run on satellite imagery, weather data, soil samples, and harvest records the cooperative already collected, and recommendations arrive as a simple weekly plan.
The models are retrained after each harvest with the season's outcomes under a managed service arrangement, so accuracy improves year over year without an in-house data science team.
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