Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change

Bibliographic Data

ID15549593
AuthorsBen Parkes (0000-0002-5936-8331, University of Manchester, corresponding author), Thomas Higginbottom (0000-0003-1150-1444, University of Manchester), Koen Hufkens (0000-0002-5070-8109, Ghent University), Francisco Ceballos (0000-0001-8699-5114, International Food Policy Research Institute), Berber Kramer (0000-0001-7644-6613, International Food Policy Research Institute), Timothy Foster (0000-0001-9594-1556, University of Manchester)
Year2019
Volume14
Issue12
Pages124089-124089
Publication date2019-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ab5ebb
OpenAlexW2977401313
LanguageEN
Citations received1
References cited67

Weather shocks, such as heatwaves, droughts, and excess rainfall, are a major cause of crop yield losses and food insecurity worldwide. Statistical or process-based crop models can be used to quantify how yields will respond to these events and future climate change. However, the accuracy of weather-yield relationships derived from crop models, whether statistical or process-based, is dependent on the quality of the underlying input data used to run these models. In this context, a major challenge in many developing countries is the lack of accessible and reliable meteorological datasets. Gridded weather datasets, derived from combinations of in situ gauges, remote sensing, and climate models, provide a solution to fill this gap, and have been widely used to evaluate climate impacts on agriculture in data-scarce regions worldwide. However, these reference datasets are also known to contain important biases and uncertainties. To date, there has been little research to assess how the choice of reference datasets influences projected sensitivity of crop yields to weather. We compare multiple freely available gridded datasets that provide daily weather data over the Indian sub-continent over the period 1983–2005, and explore their implications for estimates of yield responses to weather variability for key crops grown in the region (wheat and rice). Our results show that individual gridded weather datasets vary in their representation of historic spatial and temporal temperature and precipitation patterns across India. We show that these differences create large uncertainties in estimated crop yield responses and exposure to variability in growing season weather, which in turn, highlights the need for improved consideration of input data uncertainty in statistical studies that explore impacts of climate variability and change on agriculture

Agriculture · Climate change · Climate model · Climatology · Context (archaeology · Crop yield · Geography · Growing season · Meteorology · Precipitation · Yield (engineering · Climate change impacts on agriculture · Climate variability and models · Environmental Science · Hydrology and Drought Analysis · Ecology

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