32
H. Ahumada and M. Cornejo
variables; the exogeneity of the variables used for modeling crop yields; the existence of nonlinearities; the presence of extreme events; disentangling short- and
long-run effects of climate change, and the problem of collinearities in a multivariate
framework.
Using data from 1973 to 2015, the results obtained from a multivariate system
estimation indicate that global temperature has a long-run negative effect of Argentine soybeans yields. Furthermore, we have also found negative short-run effects
associated with La Niña events and high temperatures during the growing season
of the plant. However, those global and local warming negative effects are partially
mitigated through the CO 2 fertilization effect.
The estimation of this model has shown that a multivariate framework, including
Niña events and the 30
◦ C threshold in temperature, and adopting a partial system
instead of a single-equation approach give different results with a clearer interpretation of the estimates. It is worth noting that, apart from the long-run linear effect
of global temperature anomalies, we found a short-run nonlinear effect derived from
the number of days in which the maximum temperature exceeded 30
◦ C during the
growing season of the plant.
So far, we have only focused on climate variables. However, when there are other
potential determinants as in the case of crop yields, the econometric model should
encompass other drivers like technology or economic factors. This appears as a future
route of research in particular when technology should be analyzed as an adaptation
response to climate change too.
Crop climate modeling may help identify which of the suggested adaptation strategies in the literature—the use of fertilizers, irrigation, change in planting date, no-till
practices, among others—has significant effect in a specific location. Adaptation to
climate variability and extreme events can help in reducing vulnerability to long-term
climate change.
Quantifying these effects will provide important insights into how much to spend
on mitigation and adaptation and thus, help policy-makers to develop those strategies.
References
Ahumada, H. and Cornejo, M. (2019), ‘Are soybean yields getting a free ride from climate change?
Evidence from Argentine time series’
Auffhammer M, Hsiang SM, Schlenker W, Sobel A (2013) Using weather data and climate model
output in economic analyses of climate change. Review of Environmental Economics and Policy
7(2):181–198
Auffhammer M, Ramanathan V, Vincent JR (2012) Climate change, the monsoon, and rice yield in
India. Climatic Change 111(2):411–424
Auffhammer M, Schlenker W (2014) Empirical studies on agricultural impacts and adaptation.
Energy Economics 46:555–561
Burke M, Emerick K (2016) Adaptation to climate change: Evidence from US agriculture. American
Economic Journal: Economic Policy 8(3):106–40
Chen, S., Chen, X. and Xu, J. (2013), ‘Impacts of climate change on corn and soybean yields in
China’, AAEA & CAES Joint Annual Meeting
H. Ahumada and M. Cornejo
variables; the exogeneity of the variables used for modeling crop yields; the existence of nonlinearities; the presence of extreme events; disentangling short- and
long-run effects of climate change, and the problem of collinearities in a multivariate
framework.
Using data from 1973 to 2015, the results obtained from a multivariate system
estimation indicate that global temperature has a long-run negative effect of Argentine soybeans yields. Furthermore, we have also found negative short-run effects
associated with La Niña events and high temperatures during the growing season
of the plant. However, those global and local warming negative effects are partially
mitigated through the CO 2 fertilization effect.
The estimation of this model has shown that a multivariate framework, including
Niña events and the 30
◦ C threshold in temperature, and adopting a partial system
instead of a single-equation approach give different results with a clearer interpretation of the estimates. It is worth noting that, apart from the long-run linear effect
of global temperature anomalies, we found a short-run nonlinear effect derived from
the number of days in which the maximum temperature exceeded 30
◦ C during the
growing season of the plant.
So far, we have only focused on climate variables. However, when there are other
potential determinants as in the case of crop yields, the econometric model should
encompass other drivers like technology or economic factors. This appears as a future
route of research in particular when technology should be analyzed as an adaptation
response to climate change too.
Crop climate modeling may help identify which of the suggested adaptation strategies in the literature—the use of fertilizers, irrigation, change in planting date, no-till
practices, among others—has significant effect in a specific location. Adaptation to
climate variability and extreme events can help in reducing vulnerability to long-term
climate change.
Quantifying these effects will provide important insights into how much to spend
on mitigation and adaptation and thus, help policy-makers to develop those strategies.
References
Ahumada, H. and Cornejo, M. (2019), ‘Are soybean yields getting a free ride from climate change?
Evidence from Argentine time series’
Auffhammer M, Hsiang SM, Schlenker W, Sobel A (2013) Using weather data and climate model
output in economic analyses of climate change. Review of Environmental Economics and Policy
7(2):181–198
Auffhammer M, Ramanathan V, Vincent JR (2012) Climate change, the monsoon, and rice yield in
India. Climatic Change 111(2):411–424
Auffhammer M, Schlenker W (2014) Empirical studies on agricultural impacts and adaptation.
Energy Economics 46:555–561
Burke M, Emerick K (2016) Adaptation to climate change: Evidence from US agriculture. American
Economic Journal: Economic Policy 8(3):106–40
Chen, S., Chen, X. and Xu, J. (2013), ‘Impacts of climate change on corn and soybean yields in
China’, AAEA & CAES Joint Annual Meeting