气候变化领域集成服务门户(CCPortal): Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change

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DOI	10.1088/1748-9326/ab5ebb
	Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change
	Parkes B.; Higginbottom T.P.; Hufkens K.; Ceballos F.; Kramer B.; Foster T.
发表日期	2019
ISSN	17489318
卷号	14 期号:12
英文摘要	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 The Author(s). Published by IOP Publishing Ltd.
英文关键词	Crop yields; Fixed effects model; India; Rice; Weather data; Wheat
语种	英语
scopus关键词	Climate models; Crops; Developing countries; Input output programs; Remote sensing; Uncertainty analysis; Crop yield; Fixed effects; India; Rice; Weather data; Wheat; Climate change; accuracy assessment; climate change; crop yield; data set; developing world; growing season; rice; uncertainty analysis; weather forecasting; wheat; India; Triticum aestivum
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154228
作者单位	Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom; International Food Policy Research Institute, 1201 Eye St, Washington, DC 20005-3915, United States; Computational and Applied Vegetation Ecology Lab, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Ghent, B-9000, Belgium; Centre for Crisis Studies and Mitigation University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
推荐引用方式 GB/T 7714	Parkes B.,Higginbottom T.P.,Hufkens K.,et al. Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change[J],2019,14(12).
APA	Parkes B.,Higginbottom T.P.,Hufkens K.,Ceballos F.,Kramer B.,&Foster T..(2019).Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change.Environmental Research Letters,14(12).
MLA	Parkes B.,et al."Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change".Environmental Research Letters 14.12(2019).