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| DOI | 10.1088/1748-9326/ab154b |
| The effects of climate extremes on global agricultural yields | |
| Vogel E.; Donat M.G.; Alexander L.V.; Meinshausen M.; Ray D.K.; Karoly D.; Meinshausen N.; Frieler K. | |
| 发表日期 | 2019 |
| ISSN | 17489318 |
| 卷号 | 14期号:5 |
| 英文摘要 | Climate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system. This study analyses, to our knowledge for the first time, the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using sub-national yield data and applying a machine-learning algorithm. We find that growing season climate factors - including mean climate as well as climate extremes - explain 20%-49% of the variance of yield anomalies (the range describes the differences between crop types), with 18%-43% of the explained variance attributable to climate extremes, depending on crop type. Temperature-related extremes show a stronger association with yield anomalies than precipitation-related factors, while irrigation partly mitigates negative effects of high temperature extremes. We developed a composite indicator to identify hotspot regions that are critical for global production and particularly susceptible to the effects of climate extremes. These regions include North America for maize, spring wheat and soy production, Asia in the case of maize and rice production as well as Europe for spring wheat production. Our study highlights the importance of considering climate extremes for agricultural predictions and adaptation planning and provides an overview of critical regions that are most susceptible to variations in growing season climate and climate extremes. © 2019 The Author(s). Published by IOP Publishing Ltd. |
| 英文关键词 | agriculture; crop yields; extreme weather events; machine learning; random forest |
| 语种 | 英语 |
| scopus关键词 | Agriculture; Chemical contamination; Crops; Decision trees; Food supply; Learning algorithms; Learning systems; Agricultural yields; Climate extremes; Composite indicators; Crop yield; Extreme weather events; Global production; High temperature; Random forests; Machine learning; agricultural application; agricultural production; algorithm; biological weathering; crop yield; extreme event; food security; global change; growing season; livelihood; machine learning; maize; rice; soybean; weather; wheat; Glycine max; Triticum aestivum; Zea mays |
| 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/154585 |
| 作者单位 | Australian-German Climate and Energy College, University of Melbourne, Parkville, VIC 3010, Australia; School of Earth Sciences, University of Melbourne, Parkville, VIC 3010, Australia; Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia; Barcelona Supercomputing Center (BSC), Barcelona, Spain; ARC Centre of Excellence for Climate Extremes, Australia; Potsdam Institute for Climate Impact Research, Potsdam, D-14473, Germany; Institute on the Environment, University of Minnesota, Saint Paul, MN 55108, United States; ARC Centre of Excellence for Climate System Science, Australia; Seminar for Statistics, ETH Zurich, Zurich, Switzerland |
| 推荐引用方式 GB/T 7714 | Vogel E.,Donat M.G.,Alexander L.V.,et al. The effects of climate extremes on global agricultural yields[J],2019,14(5). |
| APA | Vogel E..,Donat M.G..,Alexander L.V..,Meinshausen M..,Ray D.K..,...&Frieler K..(2019).The effects of climate extremes on global agricultural yields.Environmental Research Letters,14(5). |
| MLA | Vogel E.,et al."The effects of climate extremes on global agricultural yields".Environmental Research Letters 14.5(2019). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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