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A vulnerability index for priority targeting of agricultural crops under a changing climate

Climatic Change volume 166, Article number: 34 (2021) Cite this article

Abstract

In this paper, we evaluate a single-index polymorphic production function that relates agricultural output to temperature and precipitation. The advantage of this new approach to measuring agricultural vulnerability under climatic change is that a single-index measure of vulnerability can capture a range of climate responses including plateau effects. The approach identifies plateau effects in the crop yield-weather relationship and provides overall fits consistent with higher-order polynomial fitting. We apply the technique to corn, soybeans, wheat, and cotton at the USA county level. We illustrate its computation and use as a critical policy variable.

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Availability of data and material

Publicly available.

Code availability

Code and data necessary to fully reproduce the results of this study are housed at a permanent repository at the Cornell Institute for Social and Economic Research: [DOI Link TBD].

Notes

  1. Because of logarithmic scaling, however, values close to 1 or −1 tend to be highly unstable so we limited the range to fall between −0.99 and 0.99.

  2. Holling (1973) was referring to bifurcations in natural ecosystems leading to new stable equilibria after a period of adaptation. The agricultural response to climate change discussed in this paper leads to new equilibria but by man’s design. Gunderson (1999) was considering issues in resource management in which the human desire is to catch a potentially irreversible phase transition and reverse engineer the process to its initial state or equilibrium.

  3. The term “resilience capacity” has broad usage across disciplines (e.g., Smith and Frankenberger 2018; Kontokosta and Malik 2018; d’Errico et al. 2018; Carlson et al. 2012).

  4. This includes empirical measurement of sustainability, vulnerability, and resilience. Lestari and Pigawati’s (2018) approach to climate vulnerability uses a traditional mathematical formulation based on the IPCC framework. Vulnerability is the sum score of exposure (probability), sensitivity, and adaptive capacity measured at the village level. Reilly et al. (2003). They approach the climate change effect on three major crops (Maize, Wheat, and Potato) in the USA by analyzing the trends in yield variability. Jayathilaka et al. (2012) approach crop/climate sensitivity mapping by spatial assessment. They connect the dynamics of the climate indicator and major crop yield in a geo-referred map by using the levels of crop vulnerability classes including precipitation, relative humidity, temperature, evapotranspiration, and yield class for each crop. Nonlinear models proposed by Schlenker and Roberts (2009) include the step function, eighth-order polynomial function, and piecewise linear specification. However, they analyze and evaluate the nonlinear temperature and yield relationship on a large (national) scale which makes it difficult from a policy perspective to identify regional “hot spots” of vulnerability. Nonetheless, as we have previously cited, their crop response functions, and in particular their cotton response function, suggest significant plateaus that are very consistent with what our vulnerability index aims to capture. Ortiz-Bobea et al. (2019) estimate the climate change effect on the different periods of the growing season for different types of crops based on county level data and also the contribution of different climate drivers on the crop yield. Simelton et al. (2009) propose the use of the socioeconomic model to solve crop vs. climate change vulnerability index when facing the contradictions between significant yield losses with minor drought and minor yield losses This tranche of research is in line with the broader paradigm on sustainability offered in Turner et al. (2003) who focus attention on the coupled human-environment systems of which sustainability and vulnerability are predicated on the synergies between human and biophysical subsystems.

  5. Our selection of 0.5 is somewhat arbitrary but worked quite well against alternative measures.

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Authors and Affiliations

  1. Dyson School of Applied Economics and Management, S.C. Johnson Graduate School of Management, Cornell University, 450A Warren Hall, Ithaca, NY, 14853, USA

    Calum G. Turvey, Jiajun Du, Yurou He & Ariel Ortiz-Bobea

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  1. Calum G. Turvey
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  2. Jiajun Du
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  3. Yurou He
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  4. Ariel Ortiz-Bobea
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Contributions

Calum G. Turvey developed the central ideas and conceptual/analytical framework to this paper and prepared the final manuscript. Jiajun Du contributed the analytical framework including all initial statistics, coding, mapping, and figures as part of his MS thesis at Cornell University. Ariel Ortiz-Bobea contributed materially to the conceptual and analytical framework including development of R code, mapping, and design of direct and robustness tests. Yurou He ensured the reproducibility of the project and polished the code and figures in the paper. All authors contributed to the drafting of this manuscript.

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Correspondence to Calum G. Turvey.

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Turvey, C.G., Du, J., He, Y. et al. A vulnerability index for priority targeting of agricultural crops under a changing climate. Climatic Change 166, 34 (2021). https://doi.org/10.1007/s10584-021-03135-8

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Keywords

  • Adaptive capacity
  • Agriculture
  • Climatic change
  • Holling resilience
  • Single-index measure of vulnerability
  • Vulnerability index