Abstract
Sensitivity Analysis (SA) is applied to a hierarchical qualitative model built to assess the sustainability of cropping systems. Three approaches were tested to perform a first-order SA on such a model, assuming a fixed model structure and no correlation among input variables: (i) factorial designs combined with analysis of variance (ANOVA), (ii) conditional probabilities, (iii) Monte Carlo sampling (MC). If the complete factorial design is too large to be computed, MC and conditional probabilities represent efficient alternatives to perform an analysis of the overall qualitative model. Conditional probabilities exploit the hierarchical structure of the model to give exact first-order indices, while MC could be a more flexible approach for the introduction of correlations among variables. We discuss how such SA results can guide modellers and end-users in modelling and application phases.
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