Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis
- Oklahoma State Univ., Stillwater, OK (United States); International Maize and Wheat Improvement Center (Mexico)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Commonwealth Scientific and Industrial Research Organization (CSIRO), QLD (Australia)
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 with field-scale crop models, 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 nine phenology models based on multi-location trial data for days to heading and days to maturity. Across all cases, parameter-driven uncertainty accounted for between 19 and 52% of predictive uncertainty, while model-structure-driven uncertainty accounted for between 12 and 64%. Here, 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.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- OIA-1301789; OCI1126330; AC05-76RL01830
- OSTI ID:
- 1335863
- Report Number(s):
- PNNL-SA-116887; PII: S1161030116301800
- Journal Information:
- European Journal of Agronomy, Vol. 88; ISSN 1161-0301
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Role of Modelling in International Crop Research: Overview and Some Case Studies
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journal | December 2018 |
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