Why national averages mislead
Brazil's average rainfall is dominated by the Amazon, where almost no soybeans grow. A dry week in Mato Grosso can be invisible in a national number and decisive for the crop. Weighting by crop area fixes that, and also lets the same country have a different weather signal for each crop.
How it is built
Start with gridded weather: a reanalysis such as ERA5 from the Copernicus Climate Change Service for history, and a forecast model such as ECMWF's IFS for recent days and the coming two weeks. Overlay a map of harvested area for each crop, weight each grid cell by its area, and aggregate to regions and countries.
Comparing with the same calendar days over the last 5 or 10 years turns raw values into anomalies, which is what moves markets. Expressing anomalies in standard deviations makes regions with very different climates comparable.
Reading it well
A few habits help:
- Look at accumulated rain over 10 to 30 days, not single days
- Focus on the crop's sensitive stage: pollination, pod filling, flowering
- Check the forecast against observed data as it rolls in
- Filter out regions where the crop is marginal so they do not dilute the signal
Frequently asked questions
- What is ERA5?
- ERA5 is the fifth-generation global climate reanalysis from ECMWF, produced by the Copernicus Climate Change Service. It provides hourly weather from 1940 onwards on a consistent grid.
- How far ahead are weather forecasts useful for crops?
- Deterministic and ensemble forecasts are most reliable for about 10 to 15 days; beyond that, seasonal outlooks give probabilities rather than specific values.