crop revenue with respect to changes in temperature falls drastically in absolute terms (from 0.55 to 0.071). These results show that high temperatures are harmful for productivity (elasticity is negative), confirming that global warming is likely to have devastating effects on agriculture unless farmers take adaptation measures to counter the impact of climate change (Kurukulasuriya & Mendelsohn 2006).

The marginal impacts of precipitation are more modest than for temperatures, but the elasticities are higher. The last row of Table 10 shows that crop revenue is highly elastic with respect to changes in precipitation, and that increased precipitation increases productivity. The elasticity of revenue with respect to precipitation in the all variable model is larger (3.25) than in the first model. A 1% increase in rainfall would lead to a 3.25% increase in net crop revenue, though a similar change in temperature would lead to only a 0.07% fall in revenue.

# **_6.3 Predicting impact of global warming on Kenyan agriculture_**

Results from the Ricardian analysis show that climate has important effects on agriculture in Kenya. In this subsection, we use the regression results for Model 3 to project the impact of global warming on Kenyan agriculture. To simulate the impact of different climate scenarios, two General Circulation Models (GCMs) were used, namely the Canadian Climate Model (CCC) and the Geophysical Fluid Dynamics Laboratory model (GFDL). These models have been found to give reasonable climate forecasts for Kenya. The CCC and GFDL models predict an average increase in temperature of 3.5 _°_ C and 4 _°_ C respectively with the doubling of CO2 by the year 2030. For rainfall, evidence from Kenya shows that there have been very large geographical disparities in the trend patterns. Estimates show that there has been a tendency for annual rainfall to decrease in the arid and semi-arid areas and increase over Lake Victoria and the coastal and neighboring regions. This implies that some regions may gain from global warming while others may be adversely affected. Both models predict, however, that on average, Kenya will experience a 20% decrease in rainfall by the year 2030.

Using the regression results in the fourth column of Table 9, we simulated the expected impact of climate change on net crop revenue, using the CCC and GFDL models. We added the predicted change in temperature to the benchmark values, and then evaluated the impact on the baseline net crop revenue. We also adjusted benchmark precipitation by the predicted percentage to get the new precipitation levels. For the CCC model, we simulated the impact of an increase in temperature of 3.5 _°_ C combined with a 20% decrease in rainfall and took a similar scenario for the GFDL model but with a 4 _°_ C change in temperature. We applied the scenarios separately for medium and low potential zones on the one hand and high potential zones on the other, and then for the country as a whole.<sup>8</sup> This was because it is expected that the effects of climate change on agriculture will not be uniform across continents or even within a country. Some regions may gain while others may experience losses (Gbetibouo & Hassan 2005; Deressa et al. 2005; Seo et al. 2005).

The results in Table 11 show that with precipitation remaining the same, changes in temperature predicted by the CCC model would result in a 1% (US$3.54 per hectare) gain in high potential zones but a 21.5% (US$54 per hectare) loss in medium and low potential

> 8 The definition of zones is based on Table 3. We define high potential zones as including Central Western and Nyanza Provinces (agro-ecological zones I, II and III) and all other provinces in the sample as medium and low potential zones (Zones IV–VII). We combined the latter zones because of the difficulties of accurately separating them into two categories.

20