# **INTRODUCTION**

As the threat of climate changes becomes more evident, there is increased interest in understanding the role of climate on agriculture and other facets of our lives and economy (Houghton et al., 2001; McCarthy et al 2001). In order to use cross-sectional data to quantify the effects of climate on farm productivity and various other activities, analysts need climate measures across space. This paper examines how best to measure climate carefully over the landscape. There are two available tools at the moment: ground measurements from weather stations or satellite observations. The weather station data are very accurate at recording what is happening at ground level but only in the immediate vicinity of the station. However, weather stations are expensive and require constant recording of measurements and periodic maintenance. Weather stations are consequently sparsely distributed, especially in developing countries. Subject areas are rarely closely covered and require interpolation between stations. In contrast, satellites see entire landscapes and so they are able to provide precise measurements at every location. However, satellites cannot make ground measurements as accurately as weather stations. Whether the accuracy of the individual measurements or the completeness of the spatial coverage provides superior observations is an important empirical question.

This paper compares climate measurements by satellites and interpolated weather station data in an analysis of agricultural net revenues in order to determine which measurement tool is more effective. The study examines broad cross sections in the United States, Brazil, and India. The Ricardian model is used to test how well climate explains agricultural performance across the landscape in each country (Mendelsohn, Nordhaus, and Shaw 1994). The Ricardian approach regresses climate, soils, and other control variables on farm value or net revenues per hectare. Past Ricardian studies of the United States, Brazil, and India relied strictly on ground weather station observations alone (Mendelsohn et al 1994; Mendelsohn et al 2001; Mendelsohn and Dinar 2003). This analysis compares the ground measurement results directly with satellite results in order to make a full assessment of the effectiveness of satellite data in each country.

We obtain long-term temperature and precipitation data from weather stations in all three countries. We interpolate between weather stations to provide climate estimates for each agricultural area (county, district, or municipio, in the United States, India, and Brazil, respectively) in the study. We also collect corresponding climate measurements from polar orbiting meteorological satellites from the Department of Defense. We also collect the maximum temperature from National Oceanic and Atmospheric Administration (NOAA) satellites. Using this satellite data, we calculate the temperature, maximum temperature, and surface wetness for the centroid (center) of each studied agricultural district.

In addition to these climate measures, we also examine two other satellite measures that directly examine crops. From the NOAA satellites, we calculate the Net Difference Vegetation Index (NDVI). From the Defense satellites, we calculate polarization. These two measures are direct measures of plants on the surface, not climate. However, it was interesting to test how well the satellite observations explained 35