Mendelsohn et al. (2003) use the same approach to analyze the relationship between climate and rural income based on country data for two US states and municipos from Brazil. The results suggest that favorable climate increases agriculture net revenues and thus per capita incomes. They conclude that climate is an important determinant of household welfare and therefore that providing new technology and capital may be an ineffective strategy for increasing rural incomes in hostile climate regions.

Mendelsohn et al. (2000) explore climate change impacts on African agriculture using the IPCC (Intergovernmental Panel on Climate Change) forecast of future CO2 levels in the atmosphere by 2100. Because of the lack of African studies that calibrate climate sensitivity, the authors rely on studies of climate sensitivity for the US. Their results show that the most pessimistic forecast implies that African countries may lose 47% of their agricultural revenue because of global warming, while a cross-sectional forecast suggests losses of only 6% of agricultural GDP. With the expected fall in the contribution of agriculture to GDP over time, the authors conclude that the damage from climate change to African agriculture may be expected to range from 0.13% to 2% of GDP by 2100. They further argue that every region in Africa will experience some negative climate change impacts. They caution that their findings may be quite optimistic given that they are based on US climate response functions, and they call for African countries to estimate the climate effects and to understand adaptation options for Africa.

Seo et al. (2005) employ the Ricardian approach to measure the impact of climate change on Sri Lankan agriculture, focusing on four major crops. The authors find that global warming is expected to be harmful to Sri Lanka but increases in rainfall will be beneficial. They also find that with warming the already dry regions are expected to lose large proportions of their current agriculture, but the cooler regions are predicted to remain the same or increase their output. They conclude that climate change damages could be extensive in tropical developing countries but will depend on actual climate scenarios.

Tol (2002) assesses the impacts of climate change on agriculture, forestry and other aspects of human welfare using GCM (Global Circulation Model) based scenarios of climate change. This study is based on a number of countries. The results show that a 1 _°_ C increase in the global mean surface air temperature would have a positive impact on the OECD, China and Middle East countries but a negative effect on others. The author further argues that the distributional aspects of climate change and the uncertainty about the impacts can be extremely large.

Kumar et al. (1998) use farm level data to examine and the agricultural impacts and adaptation options of climate change in India. They find that adverse climate change would lead to huge loses in agricultural revenues, even if farmers were to adapt their farming practices to climate change. Molua (2002), in an analysis of the impact of climate on agriculture in Cameroon, finds that increased precipitation is beneficial for crop production and that farm level adaptations are associated with increased farm returns. Etsia et al. (2002) find that a combination of increasing CO2, temperature and rainfall is likely to have adverse effects on agricultural production in Tunisia. These results support findings by Rosenzweig and Parry (1994) who find that increased CO2 and temperatures reduce rice production in India.

Deressa et al. (2005), use the Ricardian model to analyze the impact of climate on South African sugarcane production, using time series data for both irrigated and dry land farming. The authors show that climate change has significant non-linear impacts on net revenue with 14