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Author  |
Webber, H.; Kahiluoto, H.; Rötter, R.P.; Ewert, F. |
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Title |
Enhancing climate resilience of cropping systems |
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Book Chapter |
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Year |
2014 |
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167-185 |
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Keywords |
CropM |
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CAB International |
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Wallingford |
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Fuhrer, J.; Gregory, P.J. |
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Climate Change Impact and Adaptation in Agricultural Systems |
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MA @ admin @ |
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2897 |
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Author |
Wang, E.; et, A. |
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Title |
Causes for uncertainty in simulating wheat response to temperature |
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Conference Article |
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Year |
2014 |
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CropM |
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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 |
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Call Number |
MA @ admin @ |
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2894 |
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Author |
Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. |
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Title |
Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions |
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Report |
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2016 |
Publication |
FACCE MACSUR Reports |
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8 |
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Pages |
C4.1-D |
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Keywords |
MACSUR_ACK; CropM |
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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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MA @ office @ |
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2954 |
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Author |
Wallach, D.; Rivington, M. |
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Title |
A framework structure to integrate improved methods for uncertainty evaluation, and protocols for methods application |
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Report |
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2014 |
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FACCE MACSUR Reports |
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3 |
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Pages |
D-C4.1.2 |
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CropM |
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MA @ admin @ |
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2078 |
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Author |
Wallach, D.; Mearns, L.O.; Asseng, S.; Rötter, R.P. |
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Title |
Using ensembles of models in climate and crop modelling |
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Year |
2014 |
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CropM |
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XIII ESA congress, Debrecen, Hungary, 2014-08-25 to 2014-08-29 |
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MA @ admin @ |
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2893 |
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