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Author (down) Webber, H.; Kahiluoto, H.; Rötter, R.P.; Ewert, F.
Title Enhancing climate resilience of cropping systems Type Book Chapter
Year 2014 Publication Abbreviated Journal
Volume Issue Pages 167-185
Keywords CropM
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Publisher CAB International Place of Publication Wallingford Editor Fuhrer, J.; Gregory, P.J.
Language Summary Language Original Title
Series Editor Series Title Climate Change Impact and Adaptation in Agricultural Systems Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number MA @ admin @ Serial 2897
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Author (down) Wang, E.; et, A.
Title Causes for uncertainty in simulating wheat response to temperature Type Conference Article
Year 2014 Publication Abbreviated Journal
Volume Issue Pages
Keywords CropM
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12
Notes Approved no
Call Number MA @ admin @ Serial 2894
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Author (down) Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A.
Title Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions Type Report
Year 2016 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 8 Issue Pages C4.1-D
Keywords MACSUR_ACK; CropM
Abstract Crop models are important tools for impact assessment of climate change, as well as for  exploring management options under current climate. It is essential to evaluate the  uncertainty associated with predictions of these models. Several ways of quantifying  prediction uncertainty have been explored in the literature, but there have been no  studies of how the different approaches are related to one another, and how they are  related to some overall measure of prediction uncertainty. Here we show that all the  different approaches can be related to two different viewpoints about the model; either  the model is treated as a fixed predictor with some average error, or the model can be  treated as a random variable with uncertainty in one or more of model structure, model  inputs and model parameters. We discuss the differences, and show how mean squared  error of prediction can be estimated in both cases. The results can be used to put  uncertainty estimates into a more general framework and to relate different uncertainty  estimates to one another and to overall prediction uncertainty. This should lead to a  better understanding of crop model prediction uncertainty and the underlying causes of  that uncertainty. This study was published as (Wallach et al. 2016)
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Notes Approved no
Call Number MA @ office @ Serial 2954
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Author (down) Wallach, D.; Rivington, M.
Title A framework structure to integrate improved methods for uncertainty evaluation, and protocols for methods application Type Report
Year 2014 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 3 Issue Pages D-C4.1.2
Keywords CropM
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes Approved no
Call Number MA @ admin @ Serial 2078
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Author (down) Wallach, D.; Mearns, L.O.; Asseng, S.; Rötter, R.P.
Title Using ensembles of models in climate and crop modelling Type Conference Article
Year 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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