higher R squares than the weather station results. This is an interesting result given the abundance of weather station data in the US.

Another interesting result in these final regressions is to compare the marginal impact of higher annual temperatures evaluated at the mean temperature of agricultural production in the US. Summing across the seasons, the marginal impact of higher temperatures would reduce land values by 13.1% according to the weather station data but only 7.3% according to the satellite data. The weather station data implies that the negative slope of the land value temperature relationship is steeper. It is also interesting to note that although the marginal impact of surface wetness is slightly negative (-2.0%) that increased precipitation is positive (13.4%). This, of course, may have less to do with the measuring device and more to do with the difference between surface wetness and precipitation that was discussed earlier. Interannual variance has mixed effects depending upon the season, the climate variable, and the measuring device.

In Table 4, we conduct a similar analysis for Brazil. In columns 1 and 4, we regress only temperature and temperature squared for the months of January, April, July, and October onto average net revenue per hectare. Since the vast majority of Brazil’s agricultural land is in the Southern Hemisphere, January represents the middle of the growing season. The weather station temperature coefficients are generally not significantly different from zero. Approximately half of the satellite temperature coefficients are significant. The overall results, however, indicate that the satellite data can explain only slightly more of the cross sectional variation in average net revenue than the weather station data.

Columns 2 and 5 of Table 4 contain the remaining climate variables of precipitation, surface wetness, and interannual variance. The precipitation variables are highly significant whereas the surface wetness variables are generally insignificant. In addition, more of the interannual variance measures taken by the weather stations are significant, as compared with the satellite variance measures. The overall result is that the weather station data explains more of the variance in the second analysis than the satellite data.

In the third analysis (columns 3 and 6 in Table 4), we include soils and economic variables as further controls. This weakens the significance of some of the climate coefficients indicating that they are correlated with soil and economic parameters. Overall, the power of the regression improves, but it remains that the weather station data is slightly more effective in explaining net revenues than the satellite data. At least in Brazil, it appears that having precipitation data rather than surface wetness data is important, giving the weather station data an advantage. This may reflect the numerous low-lying wet areas scattered across Brazil, which affect the surface wetness measure at a 30 km resolution.

We also compare the marginal impact of temperature in Brazil. According to the weather station data, a one degree increase in annual temperature would reduce net revenue by -4.5%. The negative marginal effect of temperature according to the satellite data is -14.2%. The marginal effect of increased precipitation is virtually zero on annual net revenues in Brazil. Increasing surface wetness increases net revenues by 7.6%. According to the satellite data, interannual variance is harmful in the few seasons that it is 39