|
Rötter, R. P., Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., et al. (2013). Quantifying Uncertainties in Modeling Crop Water Use under Climate Change..
|
|
Nendel, C., Ewert, F., Rötter, R. P., Rosenzweig, C., Jones, J. W., Hatfield, J. L., et al. (2013). Addressing challenges and uncertainties for, the use of agro-ecosystem models to, assess climate change impact and food security across scales..
|
|
Boote, K. J., Porter, C., Jones, J. W., Thorburn, P. J., Kersebaum, K. C., Hoogenboom, G., et al. (2015). Sentinel site data for crop model improvement – definition and characterization. In J. L. Hatfield, & D. Fleisher (Eds.), (Vol. Advances in Agricultural Systems Modeling (7)). Madison, WI: ASA, CSSA, and SSSA.
|
Wallach, D., Thorburn, P., Asseng, S., Challinor, A. J., Ewert, F., Jones, J. W., et al. (2016). Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions (Vol. 8).
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)
|
|
Wallach, D., Thorburn, P., Asseng, S., Challinor, A. J., Ewert, F., Jones, J. W., et al. (2016). A framework for evaluating uncertainty in crop model predictions.. Berlin (Germany).
|
