A sensitivity analysis studies the "**sensitivity**" of the outputs of a system (target components) to changes in the parameters or initial conditions (source components). A sensitivity analysis can be performed locally or globally. A local analysis evaluates the target component sensitivities at a certain point in the source component space. These results can be used to **rank** the model components according to how much they influence the model output. Also, a local sensitivity analysis can be used to **identify** which model components can be estimated based on a given set of measurements. A global sensitivity analysis computes the **average** sensitivities of the target components to the source components over a larger part of the source component space.

- Local sensitivity analysis: Shows the dynamic sensitivity of your model variables to the model parameters, data variables or initial conditions.
- Global sensitivity analysis: Use the Morris screening and Extended FAST methods to perform a global sensitivity analysis.
- Ranking: Quickly spot the parameters, data variables or initial conditions that truely influence your model.
- Identifiability analysis: In one glance, find out which parameters or initial conditions can be estimated for a given set of measurements. You can even do the analysis before any data is collected.

The PhytoSim Sensitivity module can perform a local sensitivity analysis. The analysis is called 'local' because it is performed for a model with a given set of parameter values and initial conditions. For that specific reference situation, the Sensitivity module calculates how much the target components change when a source component is changed by only a small amount.

More details in the Sensitivity Analysis User Guide.

Analysing individual sensitivity functions can be a difficult task, especially when a large number of source and target components is involved. Therefore, the PhytoSim Sensitivity module also calculates a source component importance ranking, based on the so called sensitivity index, for each of the target components separately and a combined ranking for all target components.

More details in the Sensitivity Analysis User Guide.

A (practical) identifiability analysis is a powerful technique to determine which source components can be calibrated based on data gathered from an experiment, even before the experiment is performed! The experiment is defined by 3 characteristics: (1) which measurements will be performed, (2) what is the measurement accuracy and (3) what is the measurement interval. Based on these experimental degrees of freedom a "virtual" experiment is performed and the so-called collinearity index calculated. This collinearity index is a measure for the linear interdependence of the model parameters and serves as the basis for determining which parameter combinations will be identifiable once experimental data is available.

More details in the Sensitivity Analysis User Guide.

This method is a well known global sensitivity analysis screening technique which is able to detect, using a limited number of model simulations, which source components have (a) linear and additive or (b) non-linear and interaction effects. The method is also able to determine which source components interact with each other.

More details in the Sensitivity Analysis User Guide.

The Extended Fourier Amplitude Sensitivity Test is a variance based global sensitivity analysis technique. By decomposing the model output variance, this method is able to calculate first order sensitivity indices and total sensitivity indices. The total sensitivity indices, as the name suggests, quantify the total sensitivity of a source component, including the interactions with other source components.

More details in the Sensitivity Analysis User Guide.