The data for the analysis is based on cross sectional data from 400 randomly selected sample food crop producing farmers in kellem wollega zone found in the western part of oromia regional states of ethiopia. The data collection instrument focused on socioeconomic factors, climate change adaptation strategies used by the farmers, land, capital resource and agrochemicals used. Identification of climate information for the study area was made by taking the climatic data of the nearest meteorological station to the study site. Based on the number of meteorological stations in the area, sample farm households within a 50 km radius of the nearest meteorological station was selected. Climate data were collected from Meteorology Agency of Ethiopia which includes the monthly mean temperature and precipitation in the 2019/20. Average climatic data were calculated according to season based on classification: the winter (from December to February), summer (from June to August), spring (from March to May) and fall (from September to November).
The Ricardian approach is the common cross-sectional method that has been used to measure the impact of climate change on agriculture. Ricardian models completed in many countries and areas over the world; Europe (Passel et al., 2017),
united states (Mendelsohn et al., 1994,
1996); Italia (Bozzola et al., 2018),
in Africa (Seo et al., 2009);
in Ethiopia (Falco et al., 2012)
to examine the sensitivity of agriculture to changes in climate.
The Ricardian cross-sectional approach automatically incorporates farmer adaptation by including adaptations farmers would make to reduce the impact of a changing climate. Farmers adapt to climate change to maximize profit by changing the crop mix, planting and harvesting dates (Mendelsohn et al., 1994,
1996; Mendelsohn and Dinar, 1999)
. Available studies on effects of climate change on agriculture made use of this model, which capture climate variables like temperature and precipitation to look at the effect of climate on crop and livestock. In Ricardian model net revenue or land value were used as the dependent variable modelled against temperature and precipitation and other variables as the explanatory variables. This approach has been applied to examine the sensitivity of agriculture to changes in climate (Mendelsohn and Dinar, 2003)
and assess economic impacts of climate change on agriculture (Devkota and Phuyal, 2016
; Fonta et al., 2018; Huong et al., 2018; Mishra and Sahu, 2014)
As climate change has impact on crop production, this model makes it possible to account for the direct impact of climate on crop yields. It also indicates potential adaptation to a climate change by showing indirect as well as the indirect substitution among different inputs including the introduction of various activities. By regressing land values or net revenue on a set of environmental inputs, the Ricardian approach makes it possible to measure the marginal contribution of each input to farm income as capitalized in land value (Deressa and Hassan, 2009)
Following (Mendelsohn et al., 1994,
1996; Mendelsohn and Dinar, 2003)
the principle of Ricardian approach is captured by the following equations:
Pi = The market price of crop i.
Qi = The output of crop i.
X = A vector of purchased inputs (other than land).
C = A vector of climate variables.
Z = A vector of soil variables.
G = A vector of socio-economic characteristics.
Px = A vector of input prices.
The theoretical profit function states as in this study farm household always look to optimize their profits given available input change and they select crops, production type to maximize net income. Input demand of farm household relies on the market price of the input, where as the market price of the output expected under the impact of weather factors, climate and other factors. In Ricardo model it is hypothesized that output and input market prices are expected values in markets and each farmer will seek to maximize net farm revenues by choosing inputs (X) subject to climate, soils and socio-economic factors. The model relies on a quadratic formulation of climate. Consequently, the net value of the land can be expressed as follows (Mendelsohn and Dinar, 2003)
V= Land value.
C= A vector of climate variables.
Z= Set of soil variables.
G= Set of household’s socioeconomic variables (education level, farm size, household size and access to extension service and credit and irrigation) the β coefficient of the variables.
µ = An error term.
Equation (2) of net revenue climate response function is expressed by quadratic term to reflect the nonlinear shape which indicated how the marginal effect would change as one moves away from the mean (Mendelsohn et al., 1994).
The Ricardian approach developed to demonstrate the variant of the land value per hectare of cropland over climate (Mendelsohn et al., 1994;
Niggol Seo and Mendelsohn, 2008) and takes adaptation into account by measuring economic losses such as reduction in net income or the value of land due to environmental factors.
The marginal impact of a climate variable on net farm revenue evaluated at the mean is given by (Kurukulasuriya and Mendelsohn, 2008)
Changes in climate that increase net farm income would be beneficial and would be harmful if they lead to decreases in net farm income.