Table I. Different models used to... | Emerald Publishing

Table I.

Different models used to measure impact of climate change on agriculture

Method	Description	Strengths	Weaknesses
Crop simulation models	Crops are grown under controlled experiments and predictions are made about climate effects (Hebbar, 2008)	These models can predict and forecast impact of climate on crop production under different scenarios (Geethalakshmi et al., 2011)	These models are considered only agriculture oriented as they focus on plant physiology and compare productivity levels under different climate scenarios (Eitzinger et al., 2003)
Production function approach	A mathematical function that links agriculture inputs to output	Explicitly measures macroeconomic effects of weather variability on agriculture (Adams, 1989)	Unable to capture adaptation behaviour of farmers popularly known as “dumb farmer” phenomenon
Ricardian approach	It is a cross-sectional, across climate method to measure how climate affects land values and net revenues (Mendelsohn and Dinar, 2003; Kavikumar, 2009)	Accounts for direct effect of climate on yield of different crops and indirect substitution of different inputs (Mendelsohn et al., 1994)	It does not include transition costs and does not capture the impact of space invariant variable (Sohngen et al., 2002). It also assumes prices as constant (Cline, 1996)
GEMs	These models link agriculture to climate change considering its link with other sectors of the economy (Calzadilla et al., 2010a)	Asses complex system of relationship simultaneously (Calzadilla et al., 2013)	Supress the special characteristics of variables (Mendelsohn and Dinar, 2009)
IAMs	Combine agriculture data and economic models (Mikiko et al., 2003)	Incorporate information from other disciplines. Describe cause and effect of climate change (Mikiko et al., 2003)	Complex in nature and take climate as exogenous variable (Dinar and Mendelsohn, 2011)
Panel data models	Used to see the impact of environment on agriculture yield (McCarl et al., 2008)	Capture time and space specific characteristics of the variables (Saravanakumar, 2015)	Use deviation from country specific means that leads to large measurement errors (Schlenker and Lobell, 2010)

Method	Description	Strengths	Weaknesses
Crop simulation models	Crops are grown under controlled experiments and predictions are made about climate effects (Hebbar, 2008)	These models can predict and forecast impact of climate on crop production under different scenarios (Geethalakshmi et al., 2011)	These models are considered only agriculture oriented as they focus on plant physiology and compare productivity levels under different climate scenarios (Eitzinger et al., 2003)
Production function approach	A mathematical function that links agriculture inputs to output	Explicitly measures macroeconomic effects of weather variability on agriculture (Adams, 1989)	Unable to capture adaptation behaviour of farmers popularly known as “dumb farmer” phenomenon
Ricardian approach	It is a cross-sectional, across climate method to measure how climate affects land values and net revenues (Mendelsohn and Dinar, 2003; Kavikumar, 2009)	Accounts for direct effect of climate on yield of different crops and indirect substitution of different inputs (Mendelsohn et al., 1994)	It does not include transition costs and does not capture the impact of space invariant variable (Sohngen et al., 2002). It also assumes prices as constant (Cline, 1996)
GEMs	These models link agriculture to climate change considering its link with other sectors of the economy (Calzadilla et al., 2010a)	Asses complex system of relationship simultaneously (Calzadilla et al., 2013)	Supress the special characteristics of variables (Mendelsohn and Dinar, 2009)
IAMs	Combine agriculture data and economic models (Mikiko et al., 2003)	Incorporate information from other disciplines. Describe cause and effect of climate change (Mikiko et al., 2003)	Complex in nature and take climate as exogenous variable (Dinar and Mendelsohn, 2011)
Panel data models	Used to see the impact of environment on agriculture yield (McCarl et al., 2008)	Capture time and space specific characteristics of the variables (Saravanakumar, 2015)	Use deviation from country specific means that leads to large measurement errors (Schlenker and Lobell, 2010)