Dataset Information

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 temperatures, with a 1 °C increase associated with yield risk increases of approximately 32% and 11% for corn and soybeans, respectively. Using cause of loss information, we also find that additional losses under warming temperatures primarily result from additional reported occurrences of drought, with reported losses due to heat stress playing a smaller role. An implication of our findings is that the cost of purchasing crop insurance will increase for producers as a result of warming temperatures.

SUBMITTER: Perry ED

PROVIDER: S-EPMC7486405 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Using insurance data to quantify the multidimensional impacts of warming temperatures on yield risk.

Perry Edward D ED Yu Jisang J Tack Jesse J

Nature communications 20200911 1

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 respo ...[more]

PMID: 32917888

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Project description:BackgroundHuman mobility is fundamental to understanding global issues in the health and social sciences such as disease spread and displacements from disasters and conflicts. Detailed mobility data across spatial and temporal scales are difficult to collect, however, with movements varying from short, repeated movements to work or school, to rare migratory movements across national borders. While typical sources of mobility data such as travel history surveys and GPS tracker data can inform different typologies of movement, almost no source of readily obtainable data can address all types of movement at once.MethodsHere, we collect Google Location History (GLH) data and examine it as a novel source of information that could link fine scale mobility with rare, long distance and international trips, as it uniquely spans large temporal scales with high spatial granularity. These data are passively collected by Android smartphones, which reach increasingly broad audiences, becoming the most common operating system for accessing the Internet worldwide in 2017. We validate GLH data against GPS tracker data collected from Android users in the United Kingdom to assess the feasibility of using GLH data to inform human movement.ResultsWe find that GLH data span very long temporal periods (over a year on average in our sample), are spatially equivalent to GPS tracker data within 100 m, and capture more international movement than survey data. We also find GLH data avoid compliance concerns seen with GPS trackers and bias in self-reported travel, as GLH is passively collected. We discuss some settings where GLH data could provide novel insights, including infrastructure planning, infectious disease control, and response to catastrophic events, and discuss advantages and disadvantages of using GLH data to inform human mobility patterns.ConclusionsGLH data are a greatly underutilized and novel dataset for understanding human movement. While biases exist in populations with GLH data, Android phones are becoming the first and only device purchased to access the Internet and various web services in many middle and lower income settings, making these data increasingly appropriate for a wide range of scientific questions.

Dataset Information

Using insurance data to quantify the multidimensional impacts of warming temperatures on yield risk.

Publications

Using insurance data to quantify the multidimensional impacts of warming temperatures on yield risk.

Similar Datasets

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