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      Diverging importance of drought stress for maize and winter wheat in Europe

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          Abstract

          Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984–2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO 2 offering no yield benefit in these years.

          Abstract

          Drivers of crop yield variability require quantification, and historical records can help in improving understanding. Here, Webber et al. report that drought stress will remain a key driver of yield losses in wheat and maize across Europe, and benefits from CO 2 will be limited in low-yielding years.

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          The representative concentration pathways: an overview

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            Climate trends and global crop production since 1980.

            Efforts to anticipate how climate change will affect future food availability can benefit from understanding the impacts of changes to date. We found that in the cropping regions and growing seasons of most countries, with the important exception of the United States, temperature trends from 1980 to 2008 exceeded one standard deviation of historic year-to-year variability. Models that link yields of the four largest commodity crops to weather indicate that global maize and wheat production declined by 3.8 and 5.5%, respectively, relative to a counterfactual without climate trends. For soybeans and rice, winners and losers largely balanced out. Climate trends were large enough in some countries to offset a significant portion of the increases in average yields that arose from technology, carbon dioxide fertilization, and other factors.
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              The use of the multi-model ensemble in probabilistic climate projections.

              Recent coordinated efforts, in which numerous climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multi-model ensembles sample initial condition, parameter as well as structural uncertainties in the model design, and they have prompted a variety of approaches to quantify uncertainty in future climate in a probabilistic way. This paper outlines the motivation for using multi-model ensembles, reviews the methodologies published so far and compares their results for regional temperature projections. The challenges in interpreting multi-model results, caused by the lack of verification of climate projections, the problem of model dependence, bias and tuning as well as the difficulty in making sense of an 'ensemble of opportunity', are discussed in detail.
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                Author and article information

                Contributors
                webber@zalf.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                12 October 2018
                12 October 2018
                2018
                : 9
                : 4249
                Affiliations
                [1 ]GRID grid.433014.1, Leibniz-Centre for Agricultural Landscape Research (ZALF), ; 15374 Müncheberg, Germany
                [2 ]ISNI 0000 0001 2240 3300, GRID grid.10388.32, Institute of Crop Science and Resources Conservation, , University of Bonn, ; Bonn, 53115 Germany
                [3 ]ISNI 0000 0001 1956 2722, GRID grid.7048.b, Department of Agroecology, , Aarhus University, ; Tjele, 8830 Denmark
                [4 ]ISNI 0000 0004 0493 9031, GRID grid.4556.2, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, ; Potsdam, 14473 Germany
                [5 ]ISNI 0000 0001 1019 1419, GRID grid.410381.f, Finnish Environment Institute, ; Helsinki, 00260 Finland
                [6 ]ISNI 0000 0001 2284 9855, GRID grid.419078.3, National Aeronautics and Space Administration Goddard Institute for Space Studies, ; New York, 10025 NY USA
                [7 ]ISNI 0000 0001 2156 6108, GRID grid.41891.35, Northern Ag Research Center, , Montana State University, ; 3710 Assinniboine Road, Havre, MT USA
                [8 ]ISNI 0000 0001 2172 5332, GRID grid.434209.8, LEPSE, Université Montpellier, INRA, , Montpellier SupAgro, ; 34060 Montpellier, France
                [9 ]ISNI 0000 0001 0671 9209, GRID grid.464033.6, Native Trait Research, , Limagrain Europe, ; 63720 Chappes, France
                [10 ]ISNI 0000 0000 9320 7537, GRID grid.1003.2, Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, , University of Queensland, ; 4069 Toowoomba, Australia
                [11 ]ISNI 0000 0004 1757 2304, GRID grid.8404.8, Department of Agri-food Production and Environmental Sciences, , University of Florence, ; P.le delle Cascine 18, 50144 Firenze, Italy
                [12 ]ETH Zurich, Agricultural Economics and Policy Group, Zürich, 8092 Switzerland
                [13 ]ISNI 0000 0001 2149 4407, GRID grid.5018.c, Agricultural Institute, Centre for Agricultural Research, , Hungarian Academy of Sciences, ; Martonvásár, 2462 Hungary
                [14 ]ISNI 0000 0001 2195 4653, GRID grid.425162.6, IFAPA-Centro Alameda del Obispo, ; P.O. Box 3092, 14080 Córdoba, Spain
                [15 ]ISNI 0000 0004 1936 8868, GRID grid.4563.4, School of Biosciences, , University of Nottingham, ; Loughborough, LE12 5RD UK
                [16 ]ISNI 0000 0001 2151 2978, GRID grid.5690.a, Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM), , Universidad Politécnica de Madrid, ; Madrid, 28040 Spain
                [17 ]CNR-IBIMET, Via Caproni 8, 50100 Firenze, Italy
                [18 ]ISNI 0000 0001 2194 2329, GRID grid.8048.4, Department of Economic Analysis and Finances, , Universidad de Castilla-La Mancha, ; 45071 Toledo, Spain
                [19 ]ISNI 0000 0001 2227 9389, GRID grid.418374.d, Department of Plant Science, , Rothamsted Research, ; Harpenden, AL5 2JQ UK
                [20 ]ISNI 0000 0001 2364 4210, GRID grid.7450.6, Department of Crop Sciences, , University of Göttingen, ; Göttingen, 37075 Germany
                [21 ]ISNI 0000 0001 2169 1988, GRID grid.414548.8, INRA, ; Castanet-Tolosan, 31326 France
                Author information
                http://orcid.org/0000-0001-8301-5424
                http://orcid.org/0000-0002-6639-1273
                http://orcid.org/0000-0002-9491-3550
                http://orcid.org/0000-0003-2478-8050
                http://orcid.org/0000-0002-7419-6558
                http://orcid.org/0000-0003-2447-3892
                http://orcid.org/0000-0002-8968-954X
                http://orcid.org/0000-0001-8236-7823
                http://orcid.org/0000-0002-3679-8427
                http://orcid.org/0000-0002-0833-9362
                http://orcid.org/0000-0002-8356-7517
                http://orcid.org/0000-0001-7608-9097
                http://orcid.org/0000-0001-7987-1623
                http://orcid.org/0000-0002-9998-0672
                Article
                6525
                10.1038/s41467-018-06525-2
                6185965
                30315168
                bcc2d1c4-bcc2-433f-8dff-ba3b2cc01d3a
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 March 2018
                : 7 September 2018
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