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Author Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Ruane, A.C.; Boote, K.J.; Thorburn, P.J.; Rötter, R.P.; Cammarano, D.; Brisson, N.; Basso, B.; Martre, P.; Aggarwal, P.K.; Angulo, C.; Bertuzzi, P.; Biernath, C.; Challinor, A.J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Kersebaum, K.C.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J.W.; Williams, J.R.; Wolf, J.
Title Uncertainty in simulating wheat yields under climate change Type Journal Article
Year (up) 2013 Publication Nature Climate Change Abbreviated Journal Nat. Clim. Change
Volume 3 Issue 9 Pages 827-832
Keywords crop production; models; food; co2; temperature; projections; adaptation; scenarios; ensemble; impacts
Abstract Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.
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ISSN 1758-678x ISBN Medium Article
Area Expedition Conference
Notes CropM, ftnotmacsur, IPCC-AR5 Approved no
Call Number MA @ admin @ Serial 4599
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Author Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Ruane, A.C.; Boote, K.J.; Thorburn, P.; Antle, J.M.; Nelson, G.C.; Porter, C.; Janssen, S.; Asseng, S.; Basso, B.; Ewert, F.; Wallach, D.; Baigorria, G.; Winter, J.M.
Title The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies Type Journal Article
Year (up) 2013 Publication Agricultural and Forest Meteorology Abbreviated Journal Agricultural and Forest Meteorology
Volume 170 Issue Pages 166-182
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ISSN 0168-1923 ISBN Medium
Area Expedition Conference
Notes CropM, ftnotmacsur, IPCC-AR5 Approved no
Call Number MA @ admin @ Serial 4927
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Author Wallach, D.; Mearns, L.O.; Asseng, S.; Rötter, R.P.
Title Using ensembles of models in climate and crop modelling Type Conference Article
Year (up) 2014 Publication Abbreviated Journal
Volume Issue Pages
Keywords CropM
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Area Expedition Conference XIII ESA congress, Debrecen, Hungary, 2014-08-25 to 2014-08-29
Notes Approved no
Call Number MA @ admin @ Serial 2893
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Author Wallach, D.; Rivington, M.
Title A framework for assessing the uncertainty in crop model predictions Type Report
Year (up) 2014 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 3 Issue Pages D-C4.1.2
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Abstract It is of major importance in modeling to understand and quantify the uncertainty in model predictions, both in order to know how much confidence to have in those predictions, and as a first step toward model improvement. Here we show that there are basically three different approaches to evaluating uncertainty, and we explain the advantages and drawbacks of each. This is a necessary first step toward developing protocols for evaluation of uncertainty and so obtaining a clearer picture of the reliability of crop models. No Label
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Notes Approved no
Call Number MA @ admin @ Serial 2231
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Author Wallach, D.; Rivington, M.
Title A framework structure to integrate improved methods for uncertainty evaluation, and protocols for methods application Type Report
Year (up) 2014 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 3 Issue Pages D-C4.1.2
Keywords CropM
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Notes Approved no
Call Number MA @ admin @ Serial 2078
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