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

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dc.creatorParkes, Ben
dc.creatorHigginbottom, Thomas P.
dc.creatorHufkens, Koen
dc.creatorCeballos, Francisco
dc.creatorKramer, Berber
dc.creatorFoster, Timothy
dc.date2019-10-03
dc.date2024-06-21T09:05:46Z
dc.date2024-06-21T09:05:46Z
dc.date.accessioned2026-06-27T15:43:51Z
dc.descriptionExtreme weather events, 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 extreme weather 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 in 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 extreme weather events, which highlight the need for improved consideration of input data uncertainty in statistical studies that explore impacts of climate variability and change on agriculture.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/146078
dc.identifier.urihttp://hdl.handle.net/123456789/113009
dc.languageen
dc.publisherInternational Food Policy Research Institute
dc.relationhttps://hdl.handle.net/10568/145739
dc.relationhttps://doi.org/10.2499/p15738coll2.133104
dc.relationhttps://doi.org/10.22004/ag.econ.275926
dc.rightsOpen Access
dc.sourceParkes, Ben; Higginbottom, Thomas P.; Hufken, Koen; Ceballos, Francisco; Kramer, Berber; and Foster, Timothy. 2019. Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change. IFPRI Discussion Paper 1870. Washington, DC: International Food Policy Research Institute (IFPRI). https://hdl.handle.net/10568/146078
dc.subjectinsurance
dc.subjectyields
dc.subjectweather
dc.subjectcrop modelling
dc.subjectclimate change
dc.titleWeather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change
dc.typeWorking Paper

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Instituto Internacional de Investigación sobre Políticas Alimentarias (IFPRI)