Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change
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...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
IOP Publishing
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/146079 |
| _version_ | 1855533616048635904 |
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| author | Parkes, Ben Higginbottom, Thomas P. Hufkens, Koen Ceballos, Francisco Kramer, Berber Foster, Timothy |
| author_browse | Ceballos, Francisco Foster, Timothy Higginbottom, Thomas P. Hufkens, Koen Kramer, Berber Parkes, Ben |
| author_facet | Parkes, Ben Higginbottom, Thomas P. Hufkens, Koen Ceballos, Francisco Kramer, Berber Foster, Timothy |
| author_sort | Parkes, Ben |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace146079 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | IOP Publishing |
| publisherStr | IOP Publishing |
| record_format | dspace |
| spelling | CGSpace1460792025-12-08T10:11:39Z Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change Parkes, Ben Higginbottom, Thomas P. Hufkens, Koen Ceballos, Francisco Kramer, Berber Foster, Timothy insurance climate rice crop yield crop performance yields food security weather data weather wheat crop modelling climate change 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. 2019-12-31 2024-06-21T09:05:46Z 2024-06-21T09:05:46Z Journal Article https://hdl.handle.net/10568/146079 en https://doi.org/10.2499/p15738coll2.133428 https://doi.org/10.2499/p15738coll2.134046 https://doi.org/10.1016/j.deveng.2019.100042 https://doi.org/10.1016/j.agrformet.2018.11.002 https://hdl.handle.net/10568/106171 Open Access IOP Publishing Parkes, Ben; Higginbottom, Thomas P.; Hufkens, Koen; Ceballos, Francisco; Kramer, Berber; and Foster, Tim. 2019. Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change. Environmental Research Letters 14(12): 124089. https://doi.org/10.1088/1748-9326/ab5ebb |
| spellingShingle | insurance climate rice crop yield crop performance yields food security weather data weather wheat crop modelling climate change Parkes, Ben Higginbottom, Thomas P. Hufkens, Koen Ceballos, Francisco Kramer, Berber Foster, Timothy Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change |
| title | Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change |
| title_full | Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change |
| title_fullStr | Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change |
| title_full_unstemmed | Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change |
| title_short | Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change |
| title_sort | weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change |
| topic | insurance climate rice crop yield crop performance yields food security weather data weather wheat crop modelling climate change |
| url | https://hdl.handle.net/10568/146079 |
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