Compared with existing studies, our study performed the sensitivity analysis of APSIM-Oryza under more environmental conditions, thereby providing more comprehensive insights into the model and its parameters. The range of parameter variation affected the stability of sensitivity analysis results, but the main conclusions were consistent between the results obtained from the ☓0% perturbation and those obtained the ±50% perturbation in this study. The CO2 concentration did not have much influence on the results of sensitivity analysis. Differences also exist for parameter sensitivity of early and late rice in the same site. In particular, the sensitivity index of RGRLMX was larger under cold climate than under warm climate. Climate conditions had obvious influence on the sensitivity index of several parameters (e.g. Results showed that the influential parameters were the same under different environmental conditions, but their orders were often different. The ☓0% and ±50% perturbations of base values were used as the ranges of parameter variation, and local fertilization and irrigation managements were considered. total aboveground dry matter WAGT and dry weight of storage organs WSO) and twenty parameters were analyzed. By targeting the most influential phenological parameters for calibration first and then the yield component parameters, the calibration of APSIM can be streamlined.This study conducted the global sensitivity analysis of the APSIM-Oryza rice growth model under eight climate conditions and two CO2 levels using the extended Fourier Amplitude Sensitivity Test method. We conclude that to minimize cultivar-related uncertainty, cultivar parameters should be carefully calibrated when applying the APSIM-wheat model to a new cultivar in a new environment. Fertilization of 100 kg N ha −1 increased the maximum yield to 9157 kg ha −1 and biomass to 22,057 kg ha −1. Under 0 kg N ha −1, with the variation of cultivar parameters simulated yield varied from 64 to 3559 kg ha −1 (minimum and maximum), biomass from 693 to 12,864 kg ha −1. Fertilization influenced the rank order of parameter sensitivities more strongly than climate–soil conditions for yield and biomass outputs. All ten cultivar parameters affected biomass, amongst which the parameters of vernalization sensitivity and thermal time from floral initiation to flowering were the most influential. We found that yield was most sensitive to the cultivar parameters that determine the yield component (grains per gram stem, max grain size, and potential grain filling rate) and the phenology parameters that determine length of the reproductive stages (thermal time from floral initiation to flowing and thermal time from start grain filling to maturity). Uncertainties for the four outputs with varying cultivar parameters, climate–soil conditions and management practices were evaluated. We explored the effects of changing climate, soil, and management practices on parameter sensitivity by analyzing two fertilization rates (0 and 100 kg N ha −1), across five sites in Australia's cereal-growing regions.
Apsim global sensitivity analysis simulator#
We applied the variance-based global sensitivity analysis to the wheat module of the Agricultural Production Systems sIMulator (APSIM) for the first time and calculated the sensitivity of four outputs including yield, biomass, flowering day, and maturity day to ten cultivar parameters including both the main and total effects sensitivity indices. Sensitivity analysis can quantify the influence of model input parameters on model outputs. Determining the relative importance of the cultivar parameters to the specific outputs could streamline the calibration of crop models for new cultivars. We applied the variance-based global sensitivity analysis to the wheat module of the Agricultural Production Systems sIMulator (APSIM) for the first time and calculated the sensitivity of four outputs including yield, biomass, flowering day, and maturity day to ten cultivar parameters including both the main and total effects sensitivity indices. Usually, these parameters cannot be directly measured and need to be calibrated when the crop model is applied to a new environment or a new cultivar. Process-based crop models use many cultivar parameters to simulate crop growth.
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