Table 6 presents satellite temperatures and moisture indices. The long-term mean annual and seasonal satellite temperatures for Kenya are estimated at 19 _°_ C, with Coast Province reporting the highest mean of 25 _°_ C. In contrast to the ARTES temperatures where Central Province had the lowest mean values, here Nyanza and Western Provinces report the lowest mean temperatures at about 17 _°_ C. The moisture index is unit-less but low values indicate dry conditions and higher values moist ones. An index of less than 2 indicates very dry conditions, 2–4 vegetated areas such as forests and woodlands, and 5–8 croplands. Values greater than 8 suggest conditions ranging from abundant water to permanently inundated lands. The estimates for moisture indices are quite modest. The country’s annual and seasonal averages are about 3 implying that, on average, Kenya does not have enough moisture for crop farming. Most provinces report a mean of about 2. The lowest are observed for Eastern and Central Provinces. It is surprising that Coast Province seems to have more moisture than these two provinces, given that it has the highest temperatures. Western Province has a mean of about 4, while Nyanza has the highest at about 6. The reason for the very low estimates for moisture is not clear, given that temperature estimates are within reasonable range. According to the classification in Table 6, only Nyanza would seem suitable for crop farming, yet the actual situation is that this province has more wetlands than any other province.

In the empirical analysis, we tested the impact of climate using the ARTES precipitation and satellite temperature data. The latter is preferred to the ARTES temperatures because the ARTES dataset is based on province level estimates, while the satellite data is based on district level ones and is therefore more reliable as it allows for more variability. However, the satellite moisture index has some temperature component embedded in it. It is therefore not very reliable for predicting the impact of precipitation on agriculture. We therefore rely on the ARTES precipitation estimates.

# _Hydrological data_

The other datasets used in this study were obtained from the IWMI (International Water Management Institute) and the University of Colorado and include estimates of runoff and flow data based on monthly values from 1961–1990 time series. The final values were estimated using hydrological models for Africa (STRZEPEK & MCCLUSKEY 2006). The original data was made available to us in mean monthly values and long-term variance for each of the sampled districts. The mean runoff for the country is estimated at about 39mm, with a high standard deviation of 25mm, implying that there is high variability in the mean runoff across the six provinces. The highest estimate is for Nyanza Province at 73mm, followed by Western Province at 63mm; the lowest estimates are for Coast and Eastern Provinces, at only 12mm. Provinces with very high runoff estimates also have the highest estimates for surface flow. For instance, Nyanza Province has an estimated mean flow of about 452,000m<sup>3</sup> , compared to a national estimate of 301,000m<sup>3</sup> . Central and Western Provinces follow with 397,000 m<sup>3</sup> and 373,000 m<sup>3</sup> respectively. Rift Valley Province has the lowest at 186,000 m<sup>3</sup> .

# _Soil data_

Soil data were obtained from the Food and Agricultural Organization (FAO 2003). Kenya has at least 28 different types of soil but we focused only on the key types in the six sampled 11