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Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method


  • He, Jianqiang
  • Jones, James W.
  • Graham, Wendy D.
  • Dukes, Michael D.


Proper estimation of model parameters is required for ensuring accurate model predictions and good model-based decisions. The generalized likelihood uncertainty estimation (GLUE) method is a Bayesian Monte Carlo parameter estimation technique that makes use of a likelihood function to measure the closeness-of-fit of modeled and observed data. Various likelihood functions and methods of combining likelihood values have been used in previous studies. This research was conducted to determine the effects of using previously reported likelihood functions in a GLUE procedure for estimating parameters in a widely-used crop simulation model. A factorial computer experiment was conducted with synthetic measurement data to compare four likelihood functions and three methods of combining likelihood values using the CERES-Maize model of the Decision Support System for Agrotechnology Transfer (DSSAT). The procedure used an arbitrarily-selected parameter set as the known "true parameter set" and the CERES-Maize model to generate true output values. Then synthetic observations of crop variables were randomly generated (four replicates) by using the simulated true output values (dry yield, anthesis date, maturity date, leaf nitrogen concentration, soil nitrate concentration, and soil moisture) and adding a random observation error based on the variances of corresponding field measurements. The environmental conditions were obtained from a sweet corn (Zea mays L.) experiment conducted in 2005 in northern Florida. Results showed that the method of combining likelihood values had a strong influence on parameter estimates. The combination method based on the product of the likelihoods associated with each set of observations reduced the uncertainties in posterior distributions of parameter estimates most significantly. It was also found that the likelihood function based on Gaussian probability density function was the best among those tested. This combination accurately estimated the true parameter values, suggesting that it can be used when estimating CERES-Maize model parameters for real experiments.

Suggested Citation

  • He, Jianqiang & Jones, James W. & Graham, Wendy D. & Dukes, Michael D., 2010. "Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method," Agricultural Systems, Elsevier, vol. 103(5), pages 256-264, June.
  • Handle: RePEc:eee:agisys:v:103:y:2010:i:5:p:256-264

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    1. repec:eee:reensy:v:91:y:2006:i:10:p:1142-1147 is not listed on IDEAS
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    Cited by:

    1. Ahmadi, Mehdi & Ascough, James C. & DeJonge, Kendall C. & Arabi, Mazdak, 2014. "Multisite-multivariable sensitivity analysis of distributed watershed models: Enhancing the perceptions from computationally frugal methods," Ecological Modelling, Elsevier, vol. 279(C), pages 54-67.
    2. repec:eee:agiwat:v:193:y:2017:i:c:p:1-12 is not listed on IDEAS
    3. Enliang Guo & Jiquan Zhang & Yongfang Wang & Ha Si & Feng Zhang, 2016. "Dynamic risk assessment of waterlogging disaster for maize based on CERES-Maize model in Midwest of Jilin Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(3), pages 1747-1761, September.
    4. repec:eee:ecomod:v:290:y:2014:i:c:p:155-164 is not listed on IDEAS
    5. Shafiei, Mojtaba & Ghahraman, Bijan & Saghafian, Bahram & Davary, Kamran & Pande, Saket & Vazifedoust, Majid, 2014. "Uncertainty assessment of the agro-hydrological SWAP model application at field scale: A case study in a dry region," Agricultural Water Management, Elsevier, vol. 146(C), pages 324-334.
    6. Dzotsi, K.A. & Basso, B. & Jones, J.W., 2015. "Parameter and uncertainty estimation for maize, peanut and cotton using the SALUS crop model," Agricultural Systems, Elsevier, vol. 135(C), pages 31-47.
    7. He, Jianqiang & Dukes, Michael D. & Hochmuth, George J. & Jones, James W. & Graham, Wendy D., 2012. "Identifying irrigation and nitrogen best management practices for sweet corn production on sandy soils using CERES-Maize model," Agricultural Water Management, Elsevier, vol. 109(C), pages 61-70.
    8. Che-Chen Xu & Wen-Xiang Wu & Quan-Sheng Ge & Yang Zhou & Yu-Mei Lin & Ya-Mei Li, 2017. "Simulating climate change impacts and potential adaptations on rice yields in the Sichuan Basin, China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(4), pages 565-594, April.


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