气候变化领域集成服务门户(CCPortal): No perfect storm for crop yield failure in Germany

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DOI	10.1088/1748-9326/aba2a4
	No perfect storm for crop yield failure in Germany
	Webber H.; Lischeid G.; Sommer M.; Finger R.; Nendel C.; Gaiser T.; Ewert F.
发表日期	2020
ISSN	17489318
卷号	15 期号:10
英文摘要	Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	crop yield failure; extreme events; Germany; process-based crop model; support vector machine
语种	英语
scopus关键词	Climate change; Climate models; Risk assessment; Data-driven methods; Extreme events; Food insecurity; Nutrient management; Process-based modelling; Process-based models; Related factors; Spatial patterns; Crops; agrometeorology; climate change; crop yield; machine learning; risk assessment; spatial analysis; Germany
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/153686
作者单位	Leibniz-Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany; Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany; Eth Zurich, Agricultural Economics and Policy Group, Zurich, Switzerland; Institute of Crop Science and Resources Conservation, University of Bonn, Bonn, Germany
推荐引用方式 GB/T 7714	Webber H.,Lischeid G.,Sommer M.,et al. No perfect storm for crop yield failure in Germany[J],2020,15(10).
APA	Webber H..,Lischeid G..,Sommer M..,Finger R..,Nendel C..,...&Ewert F..(2020).No perfect storm for crop yield failure in Germany.Environmental Research Letters,15(10).
MLA	Webber H.,et al."No perfect storm for crop yield failure in Germany".Environmental Research Letters 15.10(2020).