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2021 | OriginalPaper | Buchkapitel

How Econometrics Can Help Us Understand the Effects of Climate Change on Crop Yields: The Case of Soybeans

verfasst von : Hildegart Ahumada, Magdalena Cornejo

Erschienen in: The Economics of Climate Change in Argentina

Verlag: Springer International Publishing

Abstract

Climate econometrics is a new field, which is proving to be a fruitful approach to give a rigorous treatment to many hypotheses related to climate change. This chapter illustrates how econometrics can help understand the effects of climate change on the time behavior of crop yields at a country-level scale. We use a multivariate framework to discuss issues related to non-stationarity of climate variables, the existence of nonlinearities, and collinearities. We also examine the exogeneity of the explanatory variables in crop yields models, the presence of extreme events, and the short- and long-run effects of climate change. Global climate change may affect the incorporation of new lands to production and the rise of crop yields to meet the increasing demand for food and energy. However, adaptation, trade, the declining share over time of agriculture in the economy, and carbon fertilization have reduced the harmful impacts of global warming. In particular, carbon fertilization refers to the positive effects that increasing carbon dioxide concentrations have on some plants that use CO\(_2\) during photosynthesis. We focus on soybeans in Brazil, USA, and particularly in Argentina, as interesting examples of mitigation and adaptation to global and regional climate fluctuations.

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Fußnoten
1
ISAAA. 2016. Global Status of Commercialized Biotech/GM Crops: 2016. ISAAA Brief No. 52. ISAAA: Ithaca, New York.
 
2
It should be noted that Granger causality is a different statistical concept which is not a necessary condition for weak exogeneity, but for strong exogeneity (Engle et al. 1983).
 
3
The carbon dioxide data on Mauna Loa constitute the longest record of direct measurements of CO\(_2\) in the atmosphere.
 
4
A VAR model is a multivariate stochastic process model that can be used to capture the linear interdependences among the variables analyzed in the system. It generalizes the time-series AR model.
 
5
These variables, that we previously selected according to their statistical significance, were included unrestrictedly in the system, that is, outside the co-integration vector.
 
6
The VAR model passes all diagnostic tests at traditional levels and included a linear trend in the long run since the variables can co-integrate but may have different deterministic trends. These results are not reported but can be obtained from the authors upon request.
 
7
Given the goodness of fit that has been obtained.
 
8
Those variables include the variance coefficient, the rainfall gini index, the precipitation concentration index, and the cumulative precipitation during the growing phase of the plant.
 
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Metadaten
Titel
How Econometrics Can Help Us Understand the Effects of Climate Change on Crop Yields: The Case of Soybeans
verfasst von
Hildegart Ahumada
Magdalena Cornejo
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-62252-7_2