No perfect storm for crop yield failure in Germany
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2020-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/aba2a4 |
| _version_ | 1797747867225423872 |
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| author | Heidi Webber Gunnar Lischeid Michael Sommer Robert Finger Claas Nendel Thomas Gaiser Frank Ewert |
| author_facet | Heidi Webber Gunnar Lischeid Michael Sommer Robert Finger Claas Nendel Thomas Gaiser Frank Ewert |
| author_sort | Heidi Webber |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T15:56:46Z |
| format | Article |
| id | doaj.art-781e64ab02cb42f5b106f1c4b0334d7f |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:56:46Z |
| publishDate | 2020-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-781e64ab02cb42f5b106f1c4b0334d7f2023-08-09T14:52:50ZengIOP PublishingEnvironmental Research Letters1748-93262020-01-01151010401210.1088/1748-9326/aba2a4No perfect storm for crop yield failure in GermanyHeidi Webber0https://orcid.org/0000-0001-8301-5424Gunnar Lischeid1Michael Sommer2Robert Finger3https://orcid.org/0000-0002-0634-5742Claas Nendel4Thomas Gaiser5Frank Ewert6Leibniz-Centre for Agricultural Landscape Research (ZALF) , Müncheberg, GermanyLeibniz-Centre for Agricultural Landscape Research (ZALF) , Müncheberg, Germany; Institute of Environmental Science and Geography, University of Potsdam , Potsdam, GermanyLeibniz-Centre for Agricultural Landscape Research (ZALF) , Müncheberg, Germany; Institute of Environmental Science and Geography, University of Potsdam , Potsdam, GermanyETH Zurich, Agricultural Economics and Policy Group , Zürich, SwitzerlandLeibniz-Centre for Agricultural Landscape Research (ZALF) , Müncheberg, Germany; Institute of Biochemistry and Biology, University of Potsdam , Potsdam, GermanyInstitute of Crop Science and Resources Conservation, University of Bonn , Bonn, GermanyLeibniz-Centre for Agricultural Landscape Research (ZALF) , Müncheberg, Germany; Institute of Crop Science and Resources Conservation, University of Bonn , Bonn, GermanyLarge-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.https://doi.org/10.1088/1748-9326/aba2a4crop yield failureextreme eventssupport vector machineprocess-based crop modelGermany |
| spellingShingle | Heidi Webber Gunnar Lischeid Michael Sommer Robert Finger Claas Nendel Thomas Gaiser Frank Ewert No perfect storm for crop yield failure in Germany Environmental Research Letters crop yield failure extreme events support vector machine process-based crop model Germany |
| title | No perfect storm for crop yield failure in Germany |
| title_full | No perfect storm for crop yield failure in Germany |
| title_fullStr | No perfect storm for crop yield failure in Germany |
| title_full_unstemmed | No perfect storm for crop yield failure in Germany |
| title_short | No perfect storm for crop yield failure in Germany |
| title_sort | no perfect storm for crop yield failure in germany |
| topic | crop yield failure extreme events support vector machine process-based crop model Germany |
| url | https://doi.org/10.1088/1748-9326/aba2a4 |
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