Uncertainty and Sensitivity Analysis

By Daniel Wallach, David Makowski, James W. Jones, F. Brun. Working with Dynamic Crop Models, 2019

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Uncertainty analysis consists of quantitatively evaluating uncertainty in model components (input variables, parameters, equations) for a given situation, and deducing an uncertainty distribution for each output variable rather than a single value. Uncertainty analysis is a key component of model-based risk analysis and decision making because it provides risk assessors and decision makers with information about the accuracy of model outputs. The aim of sensitivity analysis is to determine how sensitive the output of a model is with respect to elements of the model which are subject to uncertainty. Sensitivity analysis may be used to study relationships between model outputs and model inputs, and to identify which input factors have a small or a large influence on the output. This chapter presents the main steps of uncertainty and sensitivity analysis, and then describes in detail several methods that can be easily implemented with R. All methods are illustrated with several case studies using epidemiological and weed population models.

Subfields: Ecology, Evolution, Behavior and Systematics, Plant Science, Statistics and Probability

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