Those studies employing farm value data typically rely on geographically aggregated sale price data. The use of such data, however, implies a loss of control since one is aggregating over potentially diverse farming enterprises. Indeed, there is an important difference between farm values which might reflect structural attributes such as farm buildings, and bare land values. Furthermore, even when individual farm sale price data is available one needs to account for the fact that in any given year only a very small fraction of farmland is likely to be put on the market. Whatever sales take place might be of farms which are unrepresentative, thereby giving rise to sample selection problems (Koundouri & Pashardes 2003).

In this paper we use farm level data on farmer-perceived rather than actual sale value. Any remaining sample selection issues are explicitly addressed. The relative advantages and disadvantages of this choice are discussed below. We also pool data from different countries but include country dummies in an attempt to control for market segmentation.

# **3. Data**

This paper uses a large dataset containing detailed information on farming activities in 11 African countries.<sup>11</sup> Almost 10,000 farms were surveyed during 2003 and survey districts were deliberately chosen to ensure that the sample was representative of farms in different agro-climatic areas across Africa. The questionnaire included questions on household characteristics, employment of household members and the quantity of labor used in agricultural production. Detailed information was sought with respect to the type of crops grown, the amount of land used and other crop farming related costs such as seeds, fertilizer and pesticides, light, heavy and animal machinery, and farming related buildings. The survey also requested information on the type of livestock, poultry and other animals farmed. Each farm surveyed was assigned a unique identifying code enabling it to be matched with spatially referenced data on climate, runoff and land quality.

Two main sources of climate data were used. Satellite data was taken from the US Department of Defense (Basist et al. 2001). Satellites are equipped with sensors that can detect both surface temperature and wetness. The African Rainfall Temperature Evaluation System (ARTES) data was created by the National Oceanic and Atmospheric Association’s Climate Prediction Centre (World Bank 2003). The ARTES data is based on ground station measurements of precipitation and minimum and maximum temperature.

The rationale for combining these two different sources of data is as follows. First, the ARTES dataset is at the provincial level as opposed to the satellite data that is available at the district level. Second, in the case of satellite-measured wetness, this measure is actually an index which partly depends on temperature. We therefore use satellite measurements for temperature and ground station measurements for precipitation.

The climate data are in the form of three-month averages, where December, March, June and September are the middle months. A particular issue arises when one is analyzing climate data drawn from countries some of which are in the northern hemisphere, some in the

> 11 The data are obtained from an ongoing project entitled _Climate, water and agriculture: Impacts on and adaptations of agro-ecological systems in Africa_ and funded by the Global Environmental Facility and the World Bank.

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