How global sensitive is the AquaCrop model to input parameters? A case study of silage maize yield on a regional scale

Abstract

AquaCrop is a water-driven crop growth model that simulates aboveground biomass production in croplands. This study aimed to identify the driving parameters of the AquaCrop model for the model calibration and simplification to fill the research gap in intermediate environmental conditions between sub-tropical sub-humid and temperate sub-humid climates for silage maize.To this end, we applied global sensitivity analysis (GSA) by combining the Morris method and the Extended Fourier Amplitude Sensitivity Test (EFAST) on crop yield output. The process involved a field sampling of soil and crop of silage maize carried out in the agricultural fields of Ghale-Nou, southern Tehran, Iran, in the summer of 2019 in order to measure certain model parameters.In compliance with the Morris method, 30 parameters were identified as the least sensitive, while results from the EFAST test showed 9 parameters as contributing to the highest sensitivities in the model. The results clearly point to the capacity of employing a combination of both methods to attain a more efficient model calibration. Particular root, soil, canopy development, and biomass production parameters were influential and merit attention during calibration. Instead, parameters describing crop responses to water stress were acting rather insensitive in this study condition. The insights gained from this study, i.e., assessing parameter ranges and distinguishing between less sensitive and more sensitive parameters based on environmental and crop conditions, have the potential to be applied to other crop growth models with caution.