2020
DOI: 10.1038/s41467-020-17707-2
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Using insurance data to quantify the multidimensional impacts of warming temperatures on yield risk

Abstract: Previous research predicts significant negative yield impacts from warming temperatures, but estimating the effects on yield risk and disentangling the relative causes of these losses remains challenging. Here we present new evidence on these issues by leveraging a unique publicly available dataset consisting of roughly 30,000 county-by-year observations on insurance-based measures of yield risk from 1989–2014 for U.S. corn and soybeans. Our results suggest that yield risk will increase in response to warmer t… Show more

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Cited by 43 publications
(38 citation statements)
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References 55 publications
(31 reference statements)
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“…Temperature variables that were included in the model were measured by the time exposure to certain temperature ranges: C increment is estimated from a sinusoidal function of daily maximum and minimum temperature [24,30]. From the multiple temperature range sets, we choose the cutoff temperatures (26 • C and 36 • C in AS; 26 • C and 30 • C in AD) that best fit the data [4]. Because our purpose is to see the detrimental effect of temperature on aflatoxin that has positively associated with high temperature, we used time exposure to temperature ranges rather than degree days, a common temperature index.…”
Section: Historical Datamentioning
confidence: 99%
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“…Temperature variables that were included in the model were measured by the time exposure to certain temperature ranges: C increment is estimated from a sinusoidal function of daily maximum and minimum temperature [24,30]. From the multiple temperature range sets, we choose the cutoff temperatures (26 • C and 36 • C in AS; 26 • C and 30 • C in AD) that best fit the data [4]. Because our purpose is to see the detrimental effect of temperature on aflatoxin that has positively associated with high temperature, we used time exposure to temperature ranges rather than degree days, a common temperature index.…”
Section: Historical Datamentioning
confidence: 99%
“…However, indemnity varies due to corn price, insurance contract type, and coverage level. To minimize bias from aggregation, we normalized indemnity amount by using indemnity dollar paid per premium dollar paid [4]. To examine the impact of temperature and drought in the corn growing stage, and not storage-related events, we collected insurance claims data for only the months of June through September, when aflatoxin events are most likely to occur in the field.…”
Section: Historical Datamentioning
confidence: 99%
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“…The accurate representation of precipitation response of maize yield in crop models is essential to better predict crop yield losses caused by extreme climate. It also assists design novel insurance policies to compensate farmers' losses at finer scales (Perry andYu 2020, Diffenbaugh et al 2021). For example, the emerging weather index insurance established by linking weather conditions (mainly precipitation-related index) and yield losses has been demonstrated to be a cost-efficient way to improve drought risk management (Dalhaus et al 2018, Bucheli et al 2021, Vroege et al 2021.…”
Section: Implications For Climate Change Adaptationmentioning
confidence: 99%
“…The COL database contains the insurance payment information by specific causes such as cold wet weather, drought, freeze, frost, heat, and excess moisture 3 . The recently, researchers have been utilizing the COL data to gain more insights on the nature of crop losses (Lobell et al, 2011; Perry et al, 2020). Since our focus is to understand the role of temperature on crop losses, we limit our attention to temperature‐related losses.…”
Section: Data and Variablesmentioning
confidence: 99%