PNNL

Abstract

Recent international efforts have brought renewed emphasis on the comparison of different agricultural systems models. Thus far, analysis of model-ensemble simulated results has not clearly differentiated between ensemble prediction uncertainties due to model structural differences per se and those due to parameter value uncertainties. Additionally, despite increasing use of Bayesian parameter estimation approaches that permit uncertainty quantification, inadequate attention has been given to the full posterior distributions for estimated parameters. The objectives of this study were to quantify the impact of parameter value uncertainty on prediction uncertainty for modeling spring wheat phenology using Bayesian analysis and to assess the relative contributions of model-structure-driven and parameter-value-driven uncertainty to overall prediction uncertainty. This study used a random walk Metropolis algorithm to estimate parameters for 30 spring wheat genotypes using three phenology models based on multi-location trial data for days to heading and days to maturity. For days to heading, the largest source of predictive uncertainty was parameter-value-driven uncertainty, while for days to maturity uncertainty due to model structure was the largest source of prediction uncertainty. Across all cases, parameter-driven uncertainty accounted for between 25 and 65% of predictive uncertainty, while model-structure-driven uncertainty accounted for between 22 and 70%. This study demonstrated the importance of quantifying both model-structure- and parameter-value-driven uncertainty when assessing overall prediction uncertainty in modeling spring wheat phenology. More generally, Bayesian parameter estimation provided a useful framework for quantifying and analyzing sources of prediction uncertainty.