countries. Which EO products produce the largest or smallest coefficients varies from country to country suggesting the how well an EO product captures the truth about weather depends on the geography and climate of where it is looking.

Moving to mean daily temperature (see Figure 13), the figures are presented with the same column and row structure, but instead of having six ranking places, there are only three. This somewhat simplifies the view. Beginning with Ethiopia, in the weather only specification, ERA5 produces the largest coefficients while CPC and MERRA-2 which places for the smallest. When we include FEs, CPC and MERRA continue to switch places based on the dependent variable but ERA5 now always produces the smallest coefficients. Next, in Malawi, we see a clear and consistent ordering of outcomes: MERRA-2 always produces the largest coefficients while CPC and ERA5 switch regarding which one produces the smallest based on the dependant variable. Like with Nigeria and rainfall, one get similar results in Malawi regardless of what EO product was selected. Turning to Niger, MERRA-2 produces largest coefficients in the weather only specifications while ERA5 produces the smallest. This order changes once FEs are added to the specification. We next consider Nigeria, which does not present a consistent pattern in the weather only specification but produces a consistent ordering of EO products once FEs are added. For Tanzania, a similar but less consistent pattern exists. There is very little stability in ordering of coefficient size in the weather only specification but a fair amount of consistency in the specifications that include FEs, with MERRA-2 always producing the smallest coefficients. Finally, in Uganda, there exists a pattern very similar to Malawi. CPC always produces the largest coefficients while MERRA-2 and ERA5 swap places for the smallest coefficient. Overall, while there is must fewer changes in ordinality for EO temperature data compared to rainfall data within a country, there again is little consistency across country. In Ethiopia and Uganda, CPC tends to produce the largest coefficients while in Malawi and Nigeria it is MERRA-2 and in Niger and Tanzania it is ERA5. But ERA5 produces the smallest coefficients in Ethiopia, Malawi, and Uganda, while MERRA-2 produces the smallest coefficients Niger and Tanzania. The EO product that produces the largest or smallest coefficients varies from country-to-country and again suggests that the accuracy of these products is a function of geography and climate. Not all products perform equally well across space. This means what results a researcher gets will depend on their choice of EO product as well as their country of study.

Taken together, the preponderance of evidence from our regressions leads us to conclude that our results are not robust to the choice of remote sensing EO weather product. The correlation between rainfall or temperature and agricultural productivity varies not only across countries but within a country based on the econometric specification or how productivity is quantified. Our findings show that different measurement methods from different EO sources result in different ordering of coefficients and not just a change in the relative size of the coefficients.

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