Hydraulic Engineering Commons^™

Global Sensitivity Analysis Of Key Parameters In A Process-Based Sugarcane Growth Model - A Bayesian Approach, Justin Sexton, Yvette Everingham

International Congress on Environmental Modelling and Software

While several statistical methods are available to analyse model sensitivity, their application to complex process-based models is often impractical due to the large number of simulation runs required. A Bayesian approach to global sensitivity analysis can greatly reduce the number of simulation runs required by building an emulator of the model which is less computationally demanding. A Gaussian Emulation Machine (GEM) was used to efficiently assess the sensitivity of key agronomic outputs from the APSIM-Sugar crop model to influential input parameters. The sensitivity of simulated biomass and sucrose at harvest was assessed on 14 parameters representing varietal differences and growth …