study may be able to control for non-climatic factors more carefully than the studies that depend on aggregate data. On the other hand, detailed farm-level data may pose new challenges as the analysis will have to explain variations between farmers within a specific climate zone in addition to variations among farmers between climate zones. As a result, the choice of the type of dataset crucially depends on the nature of the question being posed. Whether it is advantageous to move to individual data is therefore an empirical question that needs to be resolved.

The remainder of this paper is organized as follows. The next section outlines the empirical strategy relied on in this study. The third section outlines the data used in the analysis along with some background about the agriculture sector in Sri Lanka. The fourth section presents results of applying the economic approach using household-level data from Sri Lanka. The fifth section then presents our predictions for climate change on the smallholder agriculture sector in Sri Lanka for three climate models. Finally, the sixth section concludes.

# **METHODOLOGY**

Much of the existing quantitative evidence of the magnitude of climate impacts in developing countries is based on agronomic (also known as the production function) studies. Carefully controlled crop simulation experiments that measure damages on crop growth due to variation in inputs such as temperature, precipitation and C02 are used for this purpose (see for example, <u>Rosenzweig and Parry 1994).</u> However, economists assert that agronomic studies tend to overestimate negative climate impacts and underestimate positive impacts because they fail to account for adaptations that farmers continuously undertake in order to cope with climate pressures (Adams et al 1990; <u>1999; Mendelsohn</u> _<u>et al.</u>_ <u>1994; 1999). In other words, the agronomic</u> studies ignore that sufficient reductions in yields will lead farmers to switch to a different crop that will better suit the new climatic conditions. Similarly, any positive impacts of climate change are likely to be underestimated in an agronomic model because it does not account for the behavioral response of farmers not producing the optimal crop who will switch into cultivating the optimal crop.

In response, Mendelsohn _<u>et al.</u>_ <u>(1994)</u> propose an alternative economic approach, which makes use of cross-sectional data to capture the influence of climatic as well as economic and other factors on land values (or farm income). The Ricardian technique captures the flexibility of farmers better than the agronomic method. The method examines how land values (or rents) shift with climate and other control variables. Because farmer adaptations are reflected in land values, the approach accounts for the costs and benefits of adaptation. The Ricardian model has been applied in a variety of countries, including the United States (Mendelsohn et al <u>1994; Mendelsohn and Dinar 2003), England and Wales (Maddison 2000), India (Dinar et al 1998; Mendelsohn et al 2001), and Cameroon (Molua 2002). Notably, all of these studies are based on aggregate</u> district level data except for <u>Maddison (2000)</u> and <u>Molua (2002). Madison (2000)</u> measures the productivity of farmland characteristics in England and Wales using individual farmland values. Molua (2002) examines revenues of farmers in a single agroecological zone of Cameroon.

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