economic crop performance. A more detailed description of all the measurements and the interpolation technique is described in the next section.

The third section describes the data analyses. The climate measurements are regressed on net revenue per hectare, along with soils and economic control variables. Separate regressions are estimated for each country. By comparing the weather station and the satellite results, we test the predictive power of each variable in the climatic dataset and determine which variables are statistically significant. The paper concludes with some general observations and findings.

# **DATA**

We gathered climate normal data from available weather stations in Brazil, India, and the continental United States. There were 751 stations in Brazil, 378 stations in India, and 5,511 stations in the United States. We specifically collected January, April, July, and October monthly means for precipitation and average temperature for the period from 1961-90. Because the counties (districts or municipios) did not all have weather stations, we interpolated between available weather stations to the centroid of each county. We used a quadratic weighted regression for interpolation. The interpolation is based on weather stations in a 500-mile radius from each county. We then regressed the observed climate on latitude, longitude, altitude, and distance to sea weighting each observation by the square of the distance from the county. A separate regression was estimated for each county. This weighted regression obviously places much greater emphasis on weather stations that are close to the county. The predicted value at the centroid of each county was then used for each county. We present an example of this interpolation method in Table 1. In places that are relatively flat, the regressions explain a great deal of the variation. However, in mountainous terrain, the models cannot capture the complexity of the surface and the explanatory power falls.

We tested this interpolation method on the actual stations and found that temperature was very accurate but that there was some error associated with precipitation in all three countries. Table 2 presents the R-squared results from the regression of the predicted values on the actual values for all the weather stations in each country. Note that the temperature predictions are reasonably accurate. The surrounding weather stations do give a good prediction of what to expect at each station. The precipitation predictions are not at all accurate. The geographical complexity of the precipitation patterns is great and the simple model is not able to predict precipitation accurately at each location. Although weather stations are much better at measuring precipitation at the station than satellites, they do not lead to good predictions of what is happening between stations.

We rely on polar orbiting satellites from two programs: the Defense Meteorological Satellite Program (DMSP) and NOAA. The Defense satellite used channel measurements from the Special Sensor Microwave Imager (SSM/I) sensor on three separate DMSP polar orbiting satellites (F08, F11, and F13) from 1987 to 2002. These DMSP satellites provide sun synchronized overpasses at 6 A.M. and 6 P.M over the entire planet. These twice-daily satellite overpasses are processed into 1/3 x 1/3 degree "pixels" by NESDIS and archived at the National Climatic Data Center (NCDC). The SSM/I instrument measures the brightness temperature (reflections) at four 36