ARMOSA is a process-based model that has been developed to quantify the effects of crop management practices on soil nitrogen and carbon cycles, groundwater nitrate pollution, and soil carbon sequestration. The model simulates crop growth and development by including soil water dynamics, carbon and nitrogen cycling and evapotranspiration. The processes tak- ing place in the soil-crop continuum are described by algorithms, which are characterized by a wide set of parameters. To identify the most important processes affecting annual yield and nitrogen leaching at 1 m depth, a global sensitivity analysis of the model was carried out on the model parameters with a two-step approach. The analysis was performed on winter wheat (Triticum aestivum L.) in four soil profiles in Marchfeld (Austria) from 2010 to 2018. Among the many parameters of the model, our analysis focuses on seventy parameters which were more related to the two target variables. First, the screening method of Morris (with the improve- ment proposed by Campolongo and Saltelli, 2007) was adopted to obtain a first qualitative ranking of the parameters without extensive computations. Then, the more accurate Sobol method is applied to the top parameters resulting from the first screening (specifically, 11 parameters affecting the annual yield and 13 parameters influencing the nitrogen leaching at 1 m depth). Our analysis highlights that the most affecting parameters on the yield are the threshold characterizing the critical nitrogen concentration from emergence to flowering for the aboveground partition of the plant (acrit) and the potential carbon assimilation rate (PCO2), the drought sensitivity parameter (WSPar) and the maximum depth of roots. As for nitrogen leaching, the parameter related to the microbial efficiency in decomposing litter was the most impacting. Hydrological properties resulted in little impact on output variability. Given the link between parameters and processes, this analysis highlights the drivers which strongly affect yield and leaching being the best indicators of productivity and environmental impact, respectively.
A two-step global sensitivity analysis of the ARMOSA model
Annachiara Colombi;Angelo Basile;Marialaura Bancheri;
2022
Abstract
ARMOSA is a process-based model that has been developed to quantify the effects of crop management practices on soil nitrogen and carbon cycles, groundwater nitrate pollution, and soil carbon sequestration. The model simulates crop growth and development by including soil water dynamics, carbon and nitrogen cycling and evapotranspiration. The processes tak- ing place in the soil-crop continuum are described by algorithms, which are characterized by a wide set of parameters. To identify the most important processes affecting annual yield and nitrogen leaching at 1 m depth, a global sensitivity analysis of the model was carried out on the model parameters with a two-step approach. The analysis was performed on winter wheat (Triticum aestivum L.) in four soil profiles in Marchfeld (Austria) from 2010 to 2018. Among the many parameters of the model, our analysis focuses on seventy parameters which were more related to the two target variables. First, the screening method of Morris (with the improve- ment proposed by Campolongo and Saltelli, 2007) was adopted to obtain a first qualitative ranking of the parameters without extensive computations. Then, the more accurate Sobol method is applied to the top parameters resulting from the first screening (specifically, 11 parameters affecting the annual yield and 13 parameters influencing the nitrogen leaching at 1 m depth). Our analysis highlights that the most affecting parameters on the yield are the threshold characterizing the critical nitrogen concentration from emergence to flowering for the aboveground partition of the plant (acrit) and the potential carbon assimilation rate (PCO2), the drought sensitivity parameter (WSPar) and the maximum depth of roots. As for nitrogen leaching, the parameter related to the microbial efficiency in decomposing litter was the most impacting. Hydrological properties resulted in little impact on output variability. Given the link between parameters and processes, this analysis highlights the drivers which strongly affect yield and leaching being the best indicators of productivity and environmental impact, respectively.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
