Negative result: country-wide growing degree-days

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:

Statistical models
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.

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