Agriculture & food
Crop yield forecasting
Forecast yield and harvest timing by field, crop, and region as weather and crop conditions evolve.
Sound familiar?
- Harvest logistics booked for a yield the weather already changed
- Forward sales committed against production estimates that drift
- Problem fields spotted at harvest instead of mid-season
Harvest yield
projected t / ha · Live forecast
Driver
Weather
Driver
Soil moisture
Driver
Crop stage
Forecast horizon
2 weeks–1 season
Refresh cadence
Daily or weekly
Built for
Farm operations · Agronomy
What you can predict
One forecast can answer several operational questions.
Combine planting records, weather, soil, satellite indicators, irrigation, crop stage, and disease pressure to update production expectations.
Yield by field
Harvest volume
Harvest timing
Quality grade
Questions teams need answered
- Where is yield most at risk?
- How has the weather changed the outlook?
- When will each field be ready?
- What volume should logistics prepare for?
Data that can improve the forecast
Start with the history you already have. Add internal or external drivers only when backtesting shows that they improve the forecast on held-out periods.
What-if planning
Test a change before committing to it.
Compare a proposed change with the current baseline. See the expected direction, timing, range, and the assumptions behind the result.
Rainfall remains below normal
Compare yield and harvest timing
Increase irrigation
Estimate recoverable production
Harvest yield
projected t / ha · Scenario comparison
What if
Rainfall remains below normal?
Driver
Weather
Driver
Soil moisture
Driver
Crop stage
From forecast to action
Keep the people making the decision in the loop.
01 · MONITOR
Forecast continuously
Refresh harvest yield on a daily or weekly cadence as new data arrives.
02 · NOTIFY
Alert on meaningful changes
- Yield falls below plan
- Harvest timing shifts materially
03 · DECIDE
Put the result to work
From the handbook
How the forecast is actually built.
Guide
What is demand forecasting?
Demand forecasting estimates how much of a product or service people will want in a coming period, so you can buy, staff, and price before the rush — not after it has already walked in.
Guide
Time series forecasting, explained
Time series forecasting predicts the next values of something that was recorded in order — sales by day, load by hour, tickets by week — by learning from its own past, and from anything that regularly moves with it.
Guide
WAPE, explained
WAPE (weighted absolute percentage error) is total absolute error divided by total actuals. It behaves when some periods are zero, and it does not let tiny SKUs dominate the score the way MAPE does.
Build a crop yield forecast with your data.
Start with sample data, connect your own history, or talk with us about your target, horizon, and production requirements.