consistent with the available literature (Mendelsohn et al. 1994, 2003, Kurukulasuriya & Mendelsohn 2006). The Chow test results show that the overall models are significant at the 1% level of significance, but the R<sup>2</sup> shows that the models explain only between 3 and 13% of the total variation in net revenue.<sup>7</sup> Introducing flow and hydrological variables reduces the F statistic marginally from 3.73 to 3.27. However the R-squared increases by almost 100%. The results imply a hill-shaped relationship between mean flow and net revenue, and both coefficients are statistically different from zero at the 10% level. All soils except andosols turned out to be insignificant and reduce the significance of other variables considerably and we therefore dropped all other soils. The results indicate that andosols have a positive and significant impact on net crop revenue, which conforms to a priori expectations because andosols are quite fertile and thus suited for crop production.

Finally we tested the impact of some selected household level variables. Introduction of these variables raises the F statistic from 3.27 to 5.30, while the R squared doubles. Most of the household level variables have a significant impact on crop revenue. Livestock ownership dummy, farm size and wage rates are inversely correlated with crop revenue. Farm size exhibits a U-shaped relationship with crop revenue, implying that large farm size may be associated with higher productivity. Main and secondary occupation of household head, religion of household head and average number of years of education of the household members are positively correlated with net crop revenue. Household size, introduced as a proxy for household labor (or remotely population density) has a positive and significant impact on net crop revenue Livestock ownership dummy has a negative and significant impact on net revenue. This implies competition rather than complementarity between farming and livestock keeping. We did not discover any significant effect of education on crop productivity but the sign of the coefficient implies that education is associated with higher crop revenue. Irrigation has a large positive impact on crop revenue, implying the importance of adaptations to counter the impact of climate change.

# **_6.2 Marginal impacts and elasticities_**

In this subsection, we estimate the marginal impacts of climate on crop agriculture (Table 10). The results are based on the regression results in the second and fourth columns of Table 9. The marginal impacts for winter temperatures are positive, but summer temperatures have larger negative impacts on net crop revenue. Using the climate only model, crop revenue is inelastic (-0.55) with respect to changes in temperature. The seasonal marginal impacts with respect to summer temperature are statistically significant and thus different from zero, but the impacts for winter are insignificant. Using the model with all variables, the elasticity of

> 7 The results presented here omit households we suspected to be outliers, 92 in all. Most of the outlying households reported zero or very low revenues, or very high revenues or very high costs, making net revenues negative. We also excluded five households that reported very high crop land (group ranches). When these variables are included in the regression models, most variables are insignificant but they do not affect the signs of the coefficients. Their impact on the overall explanatory power of the model is also minimal. Median regressions which control for outliers are robust with Ordinary Least Squares (OLS) and so we present and discuss the latter.

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