I put this here as a warning. While growing degree-days (GDD) are well-known as an effective model to predict yields, they don’t perform so hot at the country-scale.
I used mean temperature GDDs, between 8 and 24 degrees C, estimated at many locations from station data, and then using the weighted average by production within each country. Here are the results:
| Barley | Maize | Millet | Rice | Sorghum | Wheat | |
|---|---|---|---|---|---|---|
| GDD / 1000 | -0.03 | 0.01 | -0.07** | 0.08 | 0.04* | -0.08*** |
| (0.01) | (0.01) | (0.03) | (0.06) | (0.02) | (0.02) | |
| Precip. (m) | 0.09 | 0.11*** | 0.12* | 0.02 | 0.14*** | -0.04 |
| (0.05) | (0.03) | (0.05) | (0.03) | (0.04) | (0.04) | |
| Country Cubic | Y | Y | Y | Y | Y | Y |
| R2 | 0.95 | 0.97 | 0.91 | 0.97 | 0.92 | 0.96 |
| Adj. R2 | 0.94 | 0.96 | 0.90 | 0.97 | 0.91 | 0.95 |
| Num. obs. | 1639 | 3595 | 1516 | 1721 | 2300 | 1791 |
| ***p < 0.001, **p < 0.01, *p < 0.05 | ||||||
As you can see, for most crops, these GDDs aren’t even significant, and as frequently negative as positive. This defies a century of agricultural research, but the same data at a fine spatial scale seems to work just fine.