Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions

Daniel Wallach, Peter Thorburn, Senthold Asseng, Andrew J. Challinor, Frank Ewert, James W. Jones, Reimund Rötter, Alex Ruane


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)

Full Text:



Asseng S, Ewert F, Rosenzweig C, et al (2013) Uncertainty in simulating wheat yields under

climate change. Nat Clim Chang 3:827–832. doi: 10.1038/nclimate1916

Wallach D, Thorburn P, Asseng S, et al (2016) Estimating model prediction error: Should

you treat predictions as fixed or random? Environ Model Softw 84:529–539. doi:


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