Uncertainty in the environmental modelling process - A framework and guidance
Environmental Modelling & Software
Environmental Modelling & Software
A simple index for assessing fire danger rating
Environmental Modelling & Software
Environmental Modelling & Software
How to avoid a perfunctory sensitivity analysis
Environmental Modelling & Software
Identification and classification of uncertainties in the application of environmental models
Environmental Modelling & Software
Sobol' sensitivity analysis of a complex environmental model
Environmental Modelling & Software
Improved historical solar radiation gridded data for Australia
Environmental Modelling & Software
Reducing the impact of model scale on simulated, gridded switchgrass yields
Environmental Modelling & Software
Relative yield decomposition: A method for understanding the behaviour of complex crop models
Environmental Modelling & Software
Global sensitivity analysis of yield output from the water productivity model
Environmental Modelling & Software
Hi-index | 0.00 |
Crop growth models are increasingly used as part of research into areas such as climate change and bioenergy, so it is particularly important to understand the effects of environmental inputs on model results. Rather than investigating the effects of separate input parameters, we assess results obtained from a crop growth model using a selection of entire meteorological and soil input datasets, since these define modelled conditions. Yields are found to vary significantly only where the combination of inputs makes the crop vulnerable to drought, rather than being especially sensitive to any single input. Results highlight the significance of soil water parameters, which are likely to become increasingly critical in areas affected by climate change. Differences between datasets demonstrate the need to consider the dataset-dependence of parameterised model terms, both for model validation and predictions based on alternative datasets.