Changes in climate that increase net farm income would be beneficial and would be harmful if they lead to decreases in net farm income.

# **5. Data requirements and sources**

The data for the analysis is based on a cross-sectional farm household survey conducted in a number of provinces and selected districts across the country. Figure 3 shows the provinces and the number of households sampled in each province. The survey covered most of the country’s provinces except two, which were omitted because of budgetary constraints. However, the sampled households give a fair representation of the farming systems in the country. The survey collected data for the 2002/03 and 2003/04 farming seasons for both crop and livestock production activities. It provided information about relevant socioeconomic variables such as farm size, household size, household assets (e.g. plows) and access to extension services, for use in the Ricardian analysis.

The surveyed districts were selected on the basis of agro-climatic and hydrological zones, provincial representation and latitude. The district sample of smallholder rainfed farmers was based on the proportion of district population to the total population of all selected districts. The target sample size was 1000 smallholder households, but because of budgetary constraints and the inaccessibility of some areas only 700 were finally surveyed. Only smallholder farmers were surveyed because former large scale and now resettled areas were not readily accessible. The fact that large scale farms were not included in the sample meant that the study also could not assess the effects of technology on net farm revenues.

The temperature and precipitation data came from Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric Association’s Climate Prediction Center, is based on ground station measurements of precipitation.

Soil data were made available by the Food and Agriculture Organization (FAO 2003). The FAO data provides information about the major and minor soils in each location as well as slope and texture. Data concerning the hydrology was obtained from the University of Colorado (Strzepek & McCluskey 2006). Using a hydrological model for Africa, the hydrology team calculated flow and runoff for each district.

# **_5.1 Defining variables_**

# _5.1.1 Dependent variable_

The absence of well-functioning land markets in Zimbabwe meant that there were no data to estimate land values that reflected the adjustments made by farmers to the normal climate conditions and could be used for estimating the climate response function. This study therefore used farm-level net revenue per hectare (Kumar & Parikh 2001) as a proxy for the land values. The net revenue per hectare was used as the dependent variable to estimate the climate response function for Zimbabwe.

For this study net revenue is defined as gross revenue less fertilizer and pesticide costs, cost of hired labor (valued at the median market wage rate), cost of transport, packaging and marketing costs, storage costs and post harvest losses. Valuation of both crops and inputs was based on median prices per district. Household labor was excluded from the definition of net farm revenue as it resulted in many households having negative net farm revenues. Bardhan 13