Differences in parameter estimates derived from various methods for the ORYZA (v3) Model
TAN Jun-wei1, DUAN Qing-yun2, GONG Wei3, DI Zhen-hua3
1 College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, P.R.China 2 College of Water Resources and Hydrology, Hohai University, Nanjing 210098, P.R.China 3 Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, P.R.China
Abstract Parameter estimation is always a difficult issue for crop model users, and inaccurate parameter values will result in deceptive model predictions. Parameter values may vary with different inversion methods due to equifinality and differences in the estimating processes. Therefore, it is of great importance to evaluate the factors which may influence parameter estimates and to make a comparison of the current widely-used methods. In this study, three popular frequentist methods (SCE-UA, GA and PEST) and two Bayesian-based methods (GLUE and MCMC-AM) were applied to estimate nine cultivar parameters using the ORYZA (v3) Model. The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. Among the frequentist methods, the SCE-UA was recommended due to the balance between stable convergence and high efficiency. All the parameter estimates remarkably improved the goodness of model-fit, and the parameter estimates derived from the Bayesian-based methods had relatively worse performance compared to the frequentist methods. In particular, the parameter estimates with the highest probability density of posterior distributions derived from the MCMC-AM method (MCMC_Pmax) led to results equivalent to those derived from the frequentist methods, and even better in some situations. Additionally, model accuracy was greatly influenced by the values of phenology parameters in validation.
TAN Jun-wei, DUAN Qing-yun, GONG Wei, DI Zhen-hua.
2022.
Differences in parameter estimates derived from various methods for the ORYZA (v3) Model. Journal of Integrative Agriculture, 21(2): 375-388.
Beven K, Binley A. 1992. The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes, 6, 279–298.
Botev Z I, Grotowski J F, Kroese D P. 2010. Kernel density estimation via diffusion. The Annals of Statistics, 38, 2916–2957.
Bouman B A M, kropff M J, Tuong T P, Wopereis M C S, Berge H F M T, Laar H H V. 2001. ORYZA2000: Modelling Lowland Rice. International Rice Reaserch Institute, Wageningen University and Research Centre, Los Banos, Philippines, Wageningen, Netherlands. p. 235.
Deb K, Goyal M. 1996. A combined genetic adaptive search (GeneAS) for engineering design. Science and Informatics, 26, 30–45.
Doherty J. 2004. PEST: Model-Independent Parameter Estimation, User Manual. 5th ed. Brisbane, Queensland, Watermark Numeric Computating, Australia.
Doherty J E. 2010. PEST: Model Independent Parameter Estimation. Watermark Computing, Corinda, Australia.
Duan Q, Sorooshian S, Gupta V. 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research, 28, 1015–1031.
Duan Q Y, Gupta V K, Sorooshian S. 1993. Shuffled complex evolution approach for effective and efficient global minimization. Journal of Optimization Theory and Applications, 76, 501–521.
Dumont B, Leemans V, Mansouri M, Bodson B, Destain J P, Destain M F. 2014. Parameter identification of the STICS crop model, using an accelerated formal MCMC approach. Environmental Modelling & Software, 52, 121–135.
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, 135, 31–47.
Fonseca A, Ames D P, Yang P, Botelho C, Boaventura R, Vilar V. 2014. Watershed model parameter estimation and uncertainty in data-limited environments. Environmental Modelling & Software, 51, 84–93.
Goldberg D E. 1989. Genetic algorithm in search, optimization, and machine learning. Addison–Wesley, Reading, MA.
Guillaume S, Bergez J E, Wallach D, Eric J. 2011. Methodological comparison of calibration procedures for durum wheat parameters in the STICS model. European Journal of Agronomy, 35, 115–126.
Haario H, Saksman E, Tamminen J. 2001. An adaptive metropolis algorithm. Bernoulli, 7, 223–242.
He J, Gouis J L, Stratonovitch P, Allard V, Gaju O, Heumez E, Orford S, Griffiths S, Snape J W, Foulkes M J. 2012. Simulation of environmental and genotypic variations of final leaf number and anthesis date for wheat. European Journal of Agronomy, 42, 22–33.
Jiang Y, Liu C, Li X, Liu L, Wang H. 2015. Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment. Environmental Modelling & Software, 67, 72–88.
Jones J W, He J, Boote K J, Wilkens P, Porter C H, Hu Z. 2011. Estimating DSSAT cropping system cultivar-specific parameters using Bayesian techniques. In: Ahuja L R, Ma L. eds., Methods of Introducing System Models into Aricultural Research. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, WI. pp. 365–394.
Li T, Angeles O, Rd M M, Manalo E, Manalili M P, Radanielson A, Mohanty S. 2017. From ORYZA2000 to ORYZA (v3): An improved simulation model for rice in drought and nitrogen-deficient environments. Agricultural and Forest Meteorology, 237–238, 246.
Li T, Hasegawa T, Yin X, Zhu Y, Boote K, Adam M, Bregaglio S, Buis S, Confalonieri R, Fumoto T. 2015. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Global Change Biology, 21, 1328–1341.
Lv Z, Liu X, Tang L, Liu L, Cao W, Zhu Y. 2016. Estimation of ecotype-specific cultivar parameters in a wheat phenology model and uncertainty analysis. Agricultural and Forest Meteorology, 221, 219–229.
Makowski D, Naud C, Jeuffroy M H, Barbottin A, Monod H. 2006. Global sensitivity analysis for calculating the contribution of genetic parameters to the variance of crop model prediction. Reliability Engineering & System Safety, 91, 1142–1147.
Nelder J A, Mead R. 1965. A simplex method for function minimization. Computer Journal, 7, 308–313.
Sexton J, Everingham Y, Inman-Bamber G. 2016. A theoretical and real world evaluation of two Bayesian techniques for the calibration of variety parameters in a sugarcane crop model. Environmental Modelling & Software, 83, 126–142.
Smith T J, Marshall L A. 2009. Bayesian methods in hydrologic modeling: A study of recent advancements in Markov chain Monte Carlo techniques. Water Resources Research, 44, 67–76.
Tan J, Cao J, Cui Y, Duan Q, Gong W. 2019. Comparison of the generalized likelihood uncertainty estimation and markov chain monte carlo methods for uncertainty analysis of the ORYZA_V3 model. Agronomy Journal, 111, 555–564.
Tan J, Cui Y, Luo Y. 2016. Global sensitivity analysis of outputs over rice-growth process in ORYZA model. Environmental Modelling & Software, 83, 36–46.
Tan J, Cui Y, Luo Y. 2017. Assessment of uncertainty and sensitivity analyses for ORYZA model under different ranges of parameter variation. European Journal of Agronomy, 91, 54–62.
Tremblay M, Wallach D. 2004. Comparison of parameter estimation methods for crop models. Agronomie, 24, 351–365.
Wallach D, Buis S, Lecharpentier P, Bourges J, Clastre P, Launay M, Bergez J E, Guerif M, Soudais J, Justes E. 2011. A package of parameter estimation methods and implementation for the STICS crop–soil model. Environmental Modelling & Software, 26, 386–394.
Wallach D, Makowski D, Jones J W, Brun F. 2014. Chapter 6 - Parameter estimation with classical methods (model calibration). Working with Dynamic Crop Models. 2nd ed. Academic Press, San Diego. pp. 205–276.
Wallach D, Thorburn P, Asseng S, Challinor A J, Ewert F, Jones J W, Rotter R, Ruane A. 2016. Estimating model prediction error: Should you treat predictions as fixed or random? Environmental Modelling & Software, 84, 529–539.
Wu J, Fukuhara M, Takeda T. 2005. Parameter estimation of an ecological system by a neural network with residual minimization training. Ecological Modelling, 189, 289–304.
