many factors besides the effects of climate change. Efforts in the Ricardian studies to control for this problem through including other variables, such as soil quality, market access and solar radiation, are hampered by the difficulty of obtaining perfect measures of these variables (Mendelsohn 2000). The result is that many of these factors may not be taken into account when assessing the impacts on farm revenues. According to Mendelsohn (2000), this is a common problem in developing countries where data is often incomplete. Household labor and animal power are two important variables in many developing country farms that are difficult to control for. The agronomic approach, on the other hand, does not face this problem of extraneous variables as it relies on carefully controlled experiments.

Assuming that prices will remain constant is another limitation of the Ricardian approach (Cline 1996). Mendelsohn et al. (1994), as cited in Kurukulasuriya and Rosenthal (2003), agree that including price effects is problematic and that the Ricardian approach is weaker in that respect. Existing cross-sectional studies rely on a cross section within a country where there is little price variation across farms, with the result that the studies have not been able to estimate the effects of prices. The assumption in the Ricardian studies that prices are constant leads to bias in the welfare calculations (Cline 1996). The cross-sectional approach only measures the loss as producer surplus from the climate change and ignores the price change that would occur if supply changed, and as a result omits consumer surplus from the analysis. The result is that damages are underestimated (omit lost consumer surplus) and benefits are overestimated (overstate value of increased supply) (Mendelsohn 2000). The argument however, is that this also applies to all agro-economic models that are confronted with the same difficulty of predicting domestic price changes when changes in agricultural prices due to climate change are determined at the global level (Kurukulasuriya & Rosenthal 2003). Despite the failure to address this problem, Mendelsohn et al. (1994) contend that the bias is less than 7% (Kurukulasuriya & Rosenthal 2003).

Another limitation of the Ricardian approach is that measuring impact in a static spatial model would only be valid if technology, policy or any other temporally varying factor that would affect land use and farmers’ production management decisions does not change, or if the value of alternative uses of the land does not change (Antle 1995). For instance, technological changes would alter the relationship between environmental characteristics and land values and thus the approach would give inaccurate effects of climate change on land values (Antle 1995).

Failure to take account of water supply is another important criticism that has been raised concerning existing cross-sectional models (Darwin 1999; Kurukulasuriya & Mendelsohn 2006). In examining the effect of country climate on country production the existing models do not take into account water that might come from distant countries through rivers and other water supplies. According to Mendelsohn (2000), there has not been data available predicting the magnitude of these water supplies and how they in turn would be affected by climate change. In addition, the cross-sectional models have not considered the effects from flooding. Integrating water systems into agricultural analysis will be important to all approaches and Mendelsohn and Dinar, (2003) have made a significant contribution by testing the sensitivity of net farm revenues to other sources of water.

Research has shown that agronomic-economic and agro-ecological zone models produce reliable results relating agricultural yield and climate variables. However, these methodologies are complex and have high requirements and they do not incorporate farmers’ adaptation strategies to changing climate in their analysis. For the purpose of this study, the modified Ricardian cross-sectional approach, which is simpler and takes into account farmer 10