Abstract
Climate change is driving a rise in the intensity and frequency of extreme weather events. Such events are characterised as thresholds beyond which cereal yields significantly change. We apply a threshold model to district-level data collected in India over 1966–2011 and objectively identify thresholds, measured by the Standardised Precipitation-Evapotranspiration Index, before estimating their yield effects, for rice, wheat, maize, millet, sorghum and barley. Heterogeneous, crop-specific thresholds are identified for all crops except wheat. Thresholds are identified at normal climatic conditions but have smaller negative marginal effects than those of thresholds identified at dry conditions. The extent to which agro-ecological conditions and irrigation influence the location of thresholds and the size of their marginal effects varies by crop. Thresholds identified at dry climatic conditions severely reduce yield yet are rarely crossed; those at normal conditions moderately affect yield but are frequently crossed. A threshold’s total impact on production is found to be inverse to the severity of its marginal effect. Severe-effect thresholds have been crossed with increasing frequency over time, contributing to growth in the size of total impacts. Our results have welfare implications and have the potential to inform predictions about the impacts of extreme weather events.
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Notes
According to Hansen (2000), there is no known distributional theory for models that use multiple threshold variables. To our knowledge, no threshold models for multiple threshold variables have been developed for a panel data setting.
We also test for stationarity of the dependent variable and apply several panel unit root tests (Table S.2). In all cases, the null of a unit root is rejected at the 1% level.
A 1% cut-off was selected because there is a trade-off in allowing the identification of thresholds as close as possible to the extremes (requiring a low trimming cut-off) and having a sufficient number of observations to allow for identification (requiring a higher cut-off). A similar cut-off was used by Hansen (1999) with a dataset of a similar size to our dataset.
In our case, we identify a maximum of two threshold values in all samples, with the model always rejecting the possibility of a third threshold.
In the case of yields, there is a good reason to believe that impacts of the SPEI may be asymmetric. Thus, a model that does not impose symmetry in the SPEI-yield relationship is desirable.
Note, however, that using coarser bins results in a loss of granularity which makes it more difficult to assess whether our linear relationship changes at the identified SPEI value.
For example, the difference in return periods between a SPEI value of −1 and − 2 is very large (in the range of 4–6 years and 50–60 years, respectively, in our samples). Therefore, knowing that the threshold lies somewhere between −1 and − 1.5 or between −1 and − 2 may not be very useful for policy-makers.
Here we note that it is possible to avoid this by changing the range of the SPEI used as the baseline category.
We choose 2005 because it is a recent year with relatively few drought-affected districts, so national prices were less likely to be affected by drought. A fixed year was chosen to ensure comparability of costs over space and time (see also SI – 4).
Threshold tests and the confidence intervals of thresholds are presented in Tables S.4 and S.5.
The estimated rainfall-yield relationship estimated in Tack et al. (2017) indicates that above 500-600 mm, additional rainfall may decrease crop yields.
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Acknowledgments
We thank Ben Groom, Daniel Osberghaus and three anonymous referees for useful comments. Fontes and Gorst also wish to thank the ESRC for their financial support during this paper as well as the Grantham Research Institute for access to research facilities.
Availability of data and material
All the agricultural data is sourced from the ICRISAT meso-level database, which can be accessed using the following URL (http://data.icrisat.org/dld/src/crops.html) and the SPEI data is also freely available from the following website (http://spei.csic.es/). Both datasets are publicly available.
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All the analysis was carried out using Stata 14.
Funding
Dr. Fontes and Dr. Gorst were recipients of ESRC doctoral grants during the period in which the research was conducted.
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FF designed the analysis, carried out most of the statistical analysis, interpreted the results and contributed to the writing of the paper. CP was mainly responsible for writing the paper and contributed to the research design and the interpretation of the results. AG was responsible for the data collection, treatment of spatial data, and contributing to some of the statistical analysis in the early versions of the paper.
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Fontes, F., Gorst, A. & Palmer, C. Threshold effects of extreme weather events on cereal yields in India. Climatic Change 165, 26 (2021). https://doi.org/10.1007/s10584-021-03051-x
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DOI: https://doi.org/10.1007/s10584-021-03051-x