significant. The interannual results are more mixed with the weather station data with both positive and negative effects.

In Table 5, we examine the results for India. In the first case, climate measures were regressed on net revenues in a single year, 1997 (columns 1 and 4). The results for the temperature only case reveal that the satellite data slightly outperformed the weather station data. The overall explanatory power of both regressions is weak. Using only a single year of net revenue data is problematic because it reflects the weather of that year not just the long-term climate. Adding the other climate variables (columns 2 and 5) improves the performance of the regression notably. In India, precipitation and surface wetness may be more important than temperature in explaining cross-sectional variation. With the surface wetness variables and interannual effects, the satellite data clearly outperforms the weather station data, since it generates a much higher R squared value.

The third analysis (columns 3 and 6 in Table 5) includes locational and socioeconomic data. This considerably improves both models even further, but especially the weather station model. Nonetheless, even with all the control data in the equation, the satellite data still clearly outperforms the weather station data in this last set of regressions.

With all the controls, April and October linear and squared temperature coefficients are significant. The same two seasons are significant with precipitation as well. Examining marginal impacts reveals that the weather station data predicts that higher temperature has a large positive effect on net revenue (26%). With the satellite data, higher temperatures reduce net revenues by 17.5%. Given the already high temperatures in India, it was expected that higher temperatures would be harmful. The satellite results reflect what was expected. Higher precipitation is beneficial, increasing net revenues by 3.5%. Higher surface wetness is also very beneficial, increasing net revenues by 61.3%. The weather station data suggests that April interannual temperature variation is beneficial and that April and July precipitation variance is also mildly beneficial. The satellite data suggests that temperature variance is insignificant in India and that precipitation variance is mixed with positive effects in January and negative effects in April. The satellite results conform more closely to expectations. Off-season weather variation (January) is easier for farmers to adapt to since crops have not yet been planted. However, in-season weather variation (April) is more harmful because often the growing season has already begun and little adaptation is possible.

In addition to the measures of climate discussed above, the satellites provided additional measurements that have never been tested in a Ricardian study before: maximum temperature, Normalized Difference Vegetation Index (NDVI), and polarization. In all three cases, these variables have been added to the final regressions (column 6) in Tables 3, 4, or 5. Table 6 presents the results for maximum temperature for Brazil and India. Maximum temperatures could be important to crops because they may reflect heat stress in warm seasons and reduced frost in cold seasons. Only the coefficients of the additional variables are reported in Table 6. All four seasonal coefficients for maximum temperature in India are significant. The coefficients have an interesting pattern with a positive value in January and October and a negative value in April and July. Given that April and July are the hottest months, one could interpret the negative results as reflecting heat stress. In the cooler months of January and October, 40