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 »
- Authors:
-
- 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)
- 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. https://doi.org/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. https://doi.org/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 = {Thu Oct 06 00:00:00 EDT 2016},
month = {Thu Oct 06 00:00:00 EDT 2016}
}
Web of Science
Works referenced in this record:
Adapting the CSM-CROPGRO model for pigeonpea using sequential parameter estimation
journal, September 2015
- Alderman, Phillip D.; Boote, Kenneth J.; Jones, James W.
- Field Crops Research, Vol. 181
A methodology and an optimization tool to calibrate phenology of short-day species included in the APSIM PLANT model: Application to soybean
journal, December 2014
- Archontoulis, Sotirios V.; Miguez, Fernando E.; Moore, Kenneth J.
- Environmental Modelling & Software, Vol. 62
Uncertainty in simulating wheat yields under climate change
journal, June 2013
- Asseng, S.; Ewert, F.; Rosenzweig, C.
- Nature Climate Change, Vol. 3, Issue 9
Rising temperatures reduce global wheat production
journal, December 2014
- Asseng, S.; Ewert, F.; Martre, P.
- Nature Climate Change, Vol. 5, Issue 2
Adapting the CROPGRO Legume Model to Simulate Growth of Faba Bean
journal, July 2002
- Boote, Kenneth J.; Mínguez, María Inés; Sau, Federico
- Agronomy Journal, Vol. 94, Issue 4
An augmented Arabidopsis phenology model reveals seasonal temperature control of flowering time
journal, February 2012
- Chew, Yin Hoon; Wilczek, Amity M.; Williams, Mathew
- New Phytologist, Vol. 194, Issue 3
Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice
journal, June 2009
- Confalonieri, Roberto; Acutis, Marco; Bellocchi, Gianni
- Ecological Modelling, Vol. 220, Issue 11
Parameter and uncertainty estimation for maize, peanut and cotton using the SALUS crop model
journal, May 2015
- Dzotsi, K. A.; Basso, B.; Jones, J. W.
- Agricultural Systems, Vol. 135
Inference from Iterative Simulation Using Multiple Sequences
journal, November 1992
- Gelman, Andrew; Rubin, Donald B.
- Statistical Science, Vol. 7, Issue 4
Using a mathematical framework to examine physiological changes in winter wheat after livestock grazing
journal, September 2012
- Harrison, Matthew T.; Evans, John R.; Moore, Andrew D.
- Field Crops Research, Vol. 136
A model driven by crop water use and nitrogen supply for simulating changes in the regional yield of rain-fed lowland rice in Northeast Thailand
journal, January 2008
- Hasegawa, Toshihiro; Sawano, Shinji; Goto, Shinkichi
- Paddy and Water Environment, Vol. 6, Issue 1
Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method
journal, June 2010
- He, Jianqiang; Jones, James W.; Graham, Wendy D.
- Agricultural Systems, Vol. 103, Issue 5
GENCALC: Software to Facilitate the Use of Crop Models for Analyzing Field Experiments
journal, September 1993
- Hunt, L. A.; Pararajasingham, S.; Jones, J. W.
- Agronomy Journal, Vol. 85, Issue 5
Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach
journal, February 2009
- Iizumi, T.; Yokozawa, M.; Nishimori, M.
- Agricultural and Forest Meteorology, Vol. 149, Issue 2
A comparison of the models AFRCWHEAT2, CERES-Wheat, Sirius, SUCROS2 and SWHEAT with measurements from wheat grown under drought
journal, January 1998
- Jamieson, P. D.; Porter, J. R.; Goudriaan, J.
- Field Crops Research, Vol. 55, Issue 1-2
Reconciling alternative models of phenological development in winter wheat
journal, July 2007
- Jamieson, P. D.; Brooking, I. R.; Semenov, M. A.
- Field Crops Research, Vol. 103, Issue 1
Using a Bayesian approach to parameter estimation; comparison of the GLUE and MCMC methods
journal, March 2002
- Makowski, David; Wallach, Daniel; Tremblay, Marie
- Agronomie, Vol. 22, Issue 2
Multimodel ensembles of wheat growth: many models are better than one
journal, December 2014
- Martre, Pierre; Wallach, Daniel; Asseng, Senthold
- Global Change Biology, Vol. 21, Issue 2
Photoperiod and Vernalization Effect on Anthesis Date in Winter‐Sown Spring Wheat Regions
journal, July 2013
- Ottman, Michael J.; Anthony Hunt, L.; White, Jeffrey W.
- Agronomy Journal, Vol. 105, Issue 4
Comparison of the wheat simulation models Afrcwheat2, Ceres-wheat and Swheat for non-limiting conditions of crop growth
journal, April 1993
- Porter, J. R.; Jamieson, P. D.; Wilson, D. R.
- Field Crops Research, Vol. 33, Issue 1-2
Temperatures and the growth and development of wheat: a review
journal, January 1999
- Porter, John R.; Gawith, Megan
- European Journal of Agronomy, Vol. 10, Issue 1
Weak convergence and optimal scaling of random walk Metropolis algorithms
journal, February 1997
- Roberts, G. O.; Gelman, A.; Gilks, W. R.
- The Annals of Applied Probability, Vol. 7, Issue 1
The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies
journal, March 2013
- Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.
- Agricultural and Forest Meteorology, Vol. 170
Crop Model Calibration: A Statistical Perspective
journal, July 2011
- Wallach, Daniel
- Agronomy Journal, Vol. 103, Issue 4
Estimating model prediction error: Should you treat predictions as fixed or random?
journal, October 2016
- Wallach, Daniel; Thorburn, Peter; Asseng, Senthold
- Environmental Modelling & Software, Vol. 84
Simulation of phenological development of wheat crops
journal, September 1998
- Wang, Enli; Engel, Thomas
- Agricultural Systems, Vol. 58, Issue 1
Simulation-Based Analysis of Effects of Vrn and Ppd Loci on Flowering in Wheat
journal, March 2008
- White, Jeffrey W.; Herndl, Markus; Hunt, L. A.
- Crop Science, Vol. 48, Issue 2
Responses of time of anthesis and maturity to sowing dates and infrared warming in spring wheat
journal, November 2011
- White, Jeffrey W.; Kimball, Bruce A.; Wall, Gerard W.
- Field Crops Research, Vol. 124, Issue 2
Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments
journal, July 2013
- Zheng, Bangyou; Biddulph, Ben; Li, Dora
- Journal of Experimental Botany, Vol. 64, Issue 12
Works referencing / citing this record:
Role of Modelling in International Crop Research: Overview and Some Case Studies
journal, December 2018
- Reynolds, Matthew; Kropff, Martin; Crossa, Jose
- Agronomy, Vol. 8, Issue 12