Among significant coefficients, most remote sensing sources produce positive coefficients. In Ethiopia, coefficient signs are divided evenly for CPC but for the other five products a majority of coefficients are positive. For Malawi, all remote sensing products report only positive coefficients. In Niger, MERRA-2 reports negative signs on six of seven significant coefficients, while the other five products report a majority of significant coefficients with positive signs. In Nigeria and Uganda, the vast majority of significant coefficients are positive regardless of remote sensing product. Though unlike Malawi, there are a couple negative and significant coefficients. Finally, in Tanzania, CHIRPS, CPC, and MERRA-2 produce results in which the number of days with rain is positively correlated with output, while ARC2, ERA5, and TAMSAT have the number of days with rain negatively correlated with output.

Synthesizing the evidence from the number and share of rainy days, most remote sensing sources are in agreement. The one exception is MERRA-2, which tends to produce fewer significant coefficients. MERRA-2 is also at odds with the other products in Niger, where the more days with rain reduces crop output while more days with rain, as measured by the other five products, increases crop output. Based on this evidence, MERRA-2 appears to suffer from some measurement error in counting the number of days in which there was precipitation. This result is unsurprising, given that descriptive statistics reveal that MERRA-2 tends to under report precipitation. What is surprising, and difficult to explain, is the sharp division in how the number of days with rain affects output in Tanzania. Data from all six products results in a majority of regression coefficients being significant. But among those significant coefficients, CHIRPS, CPC, and MERRA-2 report mostly positive coefficients, with only 11 _−_ 14% of coefficients being negative. By contrast, ARC2, ERA5, and TAMSAT all report mostly negative coefficients, with 60 _−_ 71% of coefficients being negative. While it is unclear what is driving these results, what is clear is that counts or shares of days in which it rained is a poor metric for use in Tanzania.

The results regarding remote sensing temperature products are more ambiguous than those for rainfall. Already we have observed that while mean and median daily temperature produce the most significant coefficients, the signs on these coefficients are nearly evenly split, which violates our assumption about the consistency of sign. As a consequence of this, we also include variance of daily rainfall, which while producing fewer significant coefficients, those coefficients are consistently negative. Figure 36 presents results for mean daily temperature and Figure 37 presents results for median daily temperature. Results for both metrics are similar, which is expected as the correlation between these two metrics is 0 _._ 99. Results are also similar across country. In general, about half of coefficients are significant. Among significant coefficients, about 60% of coefficients using MERRA2 data are negative, while between 45 to 55% of coefficients from ERA5 and CPC are negative. The one exception to this is Niger, where coefficients tend to be either negative or not significant, with very few positive coefficients.

Examining the specification charts reveals one reason for this near equal division between pos40