TY - JOUR A1 - Webber, Heidi A1 - Lischeid, Gunnar A1 - Sommer, Michael A1 - Finger, Robert A1 - Nendel, Claas A1 - Gaiser, Thomas A1 - Ewert, Frank T1 - No perfect storm for crop yield failure in Germany T2 - Environmental research letters N2 - 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. KW - crop yield failure KW - extreme events KW - support vector machine KW - process-based crop model KW - Germany Y1 - 2020 UR - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/56144 SN - 1748-9326 VL - 15 IS - 10 PB - IOP Publ. Ltd. CY - Bristol ER -