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Title: Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis

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 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 quantifyingmore » and analyzing sources of prediction uncertainty.« less

Authors:
 [1];  [2]
  1. Oklahoma State Univ., Stillwater, OK (United States); International Maize and Wheat Improvement Center (Mexico)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Commonwealth Scientific and Industrial Research Organization (CSIRO), QLD (Australia)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1335863
Report Number(s):
PNNL-SA-116887
Journal ID: ISSN 1161-0301; PII: S1161030116301800
Grant/Contract Number:  
OIA-1301789; OCI1126330; AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
European Journal of Agronomy
Additional Journal Information:
Journal Volume: 88; Journal ID: ISSN 1161-0301
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; Bayesian parameter estimation; prediction uncertainty; crop modeling; agricultural systems modeling; wheat phenology

Citation Formats

Alderman, Phillip D., and Stanfill, Bryan. Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis. United States: N. p., 2016. Web. doi:10.1016/J.EJA.2016.09.016.
Alderman, Phillip D., & Stanfill, Bryan. Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis. United States. doi:10.1016/J.EJA.2016.09.016.
Alderman, Phillip D., and Stanfill, Bryan. Thu . "Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis". United States. doi:10.1016/J.EJA.2016.09.016. https://www.osti.gov/servlets/purl/1335863.
@article{osti_1335863,
title = {Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis},
author = {Alderman, Phillip D. and Stanfill, Bryan},
abstractNote = {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.},
doi = {10.1016/J.EJA.2016.09.016},
journal = {European Journal of Agronomy},
number = ,
volume = 88,
place = {United States},
year = {2016},
month = {10}
}

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