data come from the US Census of Population (2000 and 1990). Data concerning Brazil were collected by the Instituto Brasiliero de Geografia e Estatica (IBGE) from the Census of Agriculture (www.ibge.gov.br). The Census of Agriculture data from India were made available through Indiastat (www.indiastat.com).

The model is estimated using weighted OLS. We rely on the smallest administrative unit in each country that records agricultural activity: a county in the United States, a municipio in Brazil, and a district in India. Only rural observations are included to eliminate the unwanted influence of urban areas. Observations with population densities above 500 people/km<sup>2</sup> were dropped. The observations are weighted by the aggregate amount of cropland.

# **RESULTS**

This study explores the interaction between climate and agriculture in three countries: the Unite States, Brazil, and India. We measure two responses by agriculture: changes in net revenue and changes in the percent of land used for cropland. Both measures reflect net productivity and are sensitive to climate, soils, and economic conditions. We specifically test the relative importance of average climate (normals) versus climate variance (interannual variation).

Table 1 presents the net revenue results for the United States, Brazil, and India. The three regressions use net revenue as the dependent variable. The independent variables include climate normals, climate variance, soils, and economic variables. The coefficients in the three regressions are stable, indicating a robust relationship between these variables and net revenue. Only significant coefficients for climate variance, soils, and economic variables were kept unless omitting the variable strongly affected the model. The US and India regressions do a particularly good job of explaining the distribution of net revenues per hectare across the landscape, explaining over 80% of the variation. The results in Brazil are more mixed, explaining only 40% of the variation.

Both climate normal and climate variance terms are significant in every equation. Coefficients for every season are also significant implying that a seasonal description of climate is important. Quite often, quadratic terms are significant implying a nonlinear functional form for surface wetness and temperature.

Because many of the climate variables have both a linear and quadratic term and there are several seasonal variables, it is difficult to see exactly what each set of climate variables are doing. Table 2 takes the coefficients in Table 1 and calculates the marginal annual effect of temperature, soil moisture, temperature variance, and surface wetness variance. The central values presented in Table 2 show the effect of increasing each term by one standard deviation. For example, taking the standard deviation in temperature across the US, the value in Table 2 for temperature shows what a warming of this magnitude would do to land values. This is one way to understand the relative importance of temperature versus surface wetness and one way to compare climate normals against climate variance. The values in parentheses are the marginal effects of one more degree C or one more unit of surface wetness.

Table 2 reveals that climate normals are far more important than climate variance in the United States, almost an order of magnitude more important. In India, climate 64