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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 2013 Publication Nature Climate Change Abbreviated Journal Nat. Clim. Change
Volume (down) 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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Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
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 Rötter, R.P.; Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Basso, B.; Ruane, A.; Boote, K.J.; Thorburn, P.; Brisson, N.; Martre, P.; Aggarwal, P.K.; Angulo, C.; Pertuzzi; Biernath, C.; Challinor, A.J.; Doltra, J.; Gayler, S.; Goldberg, R.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Kersebaum, K.-C.; Müller, C.; Kumar, S.N.; 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 Quantifying Uncertainties in Modeling Crop Water Use under Climate Change Type Conference Article
Year 2013 Publication Abbreviated Journal
Volume (down) Issue Pages
Keywords CropM
Abstract
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference Impacts World 2013, International Conference on Climate Change Effects, Potsdam, Germany, 2013-05-27 to 2013-05-30
Notes Approved no
Call Number MA @ admin @ Serial 2767
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Author Nendel, C.; Ewert, F.; Rötter, R.P.; Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Asseng, S.; Ruane, A.C.; Banse, M.; Tiffin, R.; Brouwer, F.; Sinabell, F.; Scollan, N.; Meijs, J.; Angulo, C.; Antle, J.M.; Baigorria, G.; Basso, B.; Bindi, M.; Boote, K.J.; Gaiser, T.; Janssen, S.; Kersebaum, K.C.; Nelson, G.; Olesen, J.E.; Palosuo, T.; Porter, C.H.; Porter, J.R.; Rivington, M.; Semenov, M.; Stewart, D.; Thorburn, P.; Trnka, M.; van Ittersum, M.K.; Verhagen, J.; Wallach, D.; Winter, J.M.
Title Addressing challenges and uncertainties for, the use of agro-ecosystem models to, assess climate change impact and food security across scales Type Conference Article
Year 2013 Publication Abbreviated Journal
Volume (down) Issue Pages
Keywords CropM
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Publisher Place of Publication Editor
Language Summary Language Original Title
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Area Expedition Conference Climate Change and Regional Responses Conference, Dresden, 2013-05-27 to 2013-05-27
Notes Approved no
Call Number MA @ admin @ Serial 2679
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Author Wallach, D.; Nissanka, S.P.; Karunaratne, A.S.; Weerakoon, W.M.W.; Thorburn, P.J.; Boote, K.J.; Jones, J.W.
Title Accounting for both parameter and model structure uncertainty in crop model predictions of phenology: A case study on rice Type Journal Article
Year 2016 Publication European Journal of Agronomy Abbreviated Journal European Journal of Agronomy
Volume (down) Issue Pages
Keywords Uncertainty; Phenology; Parameter uncertainty; Multi-model ensemble; Generalized least squares; Rice; Crop model; APSIM; DSSAT
Abstract We consider predictions of the impact of climate warming on rice development times in Sri Lanka. The major emphasis is on the uncertainty of the predictions, and in particular on the estimation of mean squared error of prediction. Three contributions to mean squared error are considered. The first is parameter uncertainty that results from model calibration. To take proper account of the complex data structure, generalized least squares is used to estimate the parameters and the variance-covariance matrix of the parameter estimators. The second contribution is model structure uncertainty, which we estimate using two different models. An ANOVA analysis is used to separate the contributions of parameter and model uncertainty to mean squared error. The third contribution is model error, which is estimated using hindcasts. Mean squared error of prediction of time from emergence to maturity, for baseline +2 °C, is estimated as 108 days2, with model error contributing 86 days2, followed by model structure uncertainty which contributes 15 days2 and parameter uncertainty which contributes 7 days2. We also show how prediction uncertainty is reduced if prediction concerns development time averaged over years, or the difference in development time between baseline and warmer temperatures.
Address 2016-09-13
Corporate Author Thesis
Publisher Place of Publication Editor
Language Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1161-0301 ISBN Medium Article
Area CropM Expedition Conference
Notes CropM; wos; ftnotmacsur; wsnotyet; Approved no
Call Number MA @ admin @ Serial 4777
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Author Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.P.; Ruane, A.
Title A framework for evaluating uncertainty in crop model predictions Type Conference Article
Year 2016 Publication Abbreviated Journal
Volume (down) Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Berlin (Germany) Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference International Crop Modelling Symposium iCROPM 2016, 2016-05-15 to 2016-05-17, Berlin, Germany
Notes Approved no
Call Number MA @ admin @ Serial 4925
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