Are we focusing on the right parameters? Insights from Global Sensitivity Analysis of a Functional-Structural Plant Model

Abstract

Performing global sensitivity analysis on functional-structural plant models (FSP models) can greatly benefit both model development and analysis by identifying the relevance of parameters for specific model outputs. Setting unimportant parameters to a fixed value decreases dimensionality of the typically large model parameter space. Efforts can then be concentrated on accurately estimating the most important input parameters. In this work we apply the Elementary Effects method for dimensional models with arbitrary input types, adapting the method to models with inherent randomness. Our FSP model simulated a maize stand for 160 days of growth, considering three outputs: yield, peak biomass and peak leaf area index (LAI). Of 52 input parameters, 12 were identified as important for yield and peak biomass and 14 for LAI. Over 70% of parameters were deemed unimportant for the outputs under consideration, including most parameters relating to crop architecture. Parameters governing shade avoidance response and leaf appearance rate (phyllochron) were also unimportant; variations in these physiological and developmental parameters do lead to visible changes in plant architecture, but not to significant changes in yield, biomass or LAI. Some inputs identified as unimportant due to their low sensitivity index have a relatively high standard deviation of effects, with high fluctuations around a low mean, which could indicate non-linearity or interaction effects. Consequently, parameters with low sensitivity index but high standard deviation should be investigated further. Our study demonstrates that global sensitivity analysis can reveal which parameter values have the most influence on key outputs, predicting specific parameter estimates that need to be carefully characterised.