# **SUMMARY**

This paper measures the economic impact of climate on crops in Kenya. We use crosssectional data on climate, hydrological, soil and household level data for a sample of 816 households. To collect the requisite household data, we adopted the common questionnaire used by all countries in the regional GEF/World Bank project. The countries involved in the project adopted the same survey design in terms of sampling and used the same questionnaire designed jointly by the School of Forestry and Environmental Studies of Yale University and the Centre for Environmental Economics and Policy in Africa (CEEPA), University of Pretoria. Climate satellite data were provided by the US Department of Defense and we used data from the Africa Rainfall and Temperature Evaluation System (ARTES). The monthly means were estimated from approximately 14 years of data (1988–2003) to reflect long-term climate change normals (Basist et al. 1998, 2001). The hydrological data were obtained from the International Water Management Institute and the University of Colorado. The runoff and flow data estimates were based on monthly values from 1961–1990 time series data. The final values were estimated using hydrological models for Africa (Strzepek & McCluskey 2006). Soil data were obtained from the Food and Agricultural Organization (FAO 2003).

Since we did not discover any important impact of dry and wet condition climate variables on revenue, we settled for a seasonal Ricardian model. Our results show that climate affects agricultural productivity. Increased winter temperatures are associated with higher crop revenue, but increased summer temperatures have a negative impact. Increased precipitation is positively correlated with net crop yield. The results further show that there is a non-linear relationship between temperature and revenue on the one hand and between precipitation and revenue on the other. Further, our results suggest a hill-shaped relationship between mean flow and net crop revenue. Andosols, irrigation and household size are positively correlated with revenue, but livestock ownership, farm size and wage rates are inversely correlated with crop revenue.

Estimated marginal impacts of climate variables suggest that global warming is harmful for agricultural productivity and that changes in temperature are much more important than changes in precipitation. This result is confirmed by the predicted impact of various climate change scenarios on agriculture. For prediction purposes, we use two Global Circulation Models: the Canadian Climate Model (CCC) and the Geophysical Fluid Dynamics Laboratory (GFDL) model, which predict 3.5 _°_ C and 4 _°_ C changes in temperature by the year 2030 respectively. The models both predict a 20% change in precipitation over the same period. The prediction results confirm that global warming will have a substantial impact on net crop revenue, and that the impact will be more pronounced in medium and low potential zones than in high potential zones. Based on the CCC model, we predict 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 zones. The GFDL model predicts a loss of US$32 per hectare in high potential zones compared to losses of US$178 in medium and low potential zones by the year 2030. The results further confirm that the temperature component of global warming is much more important than precipitation.

We analyze farmers’ perceptions of climate variations and their adaptation to these, and also constraints on adaptation mechanisms. The results suggest that farmers in Kenya are aware of short-term climate change, that most of them have noticed an increase in temperatures, and that some have taken adaptive measures. The analysis also shows differences in perceptions and adaptations between farmers in medium/low potential zones and those in high potential zones. Diversification (changes in crop mix) is the most common adaptation measure, 3