Global Sensitivity Analysis of WOFOST Model Parameters for Maize and Wheat Yield Simulation

Document Type : Research Paper

Authors

1 Eastern Water and Environment Research Institute

2 Ferdowsi University of Mashhad

3 Sciences and Researches University of Tehran

4 University of Guilan

Abstract

The dynamical simulation model of WOFOST is widely used for yield estimation at farm and regional scales as well as different climate conditions. In modelling processes, there are lots of parameters which have to be estimated (calibrated) and also in the other hand there are limitations for providing enough observational data. Therefor it is required to choose sensitive parameters for model calibration. In this study, a global sensitivity analysis has presented for maize and wheat simulation in WOFOST model. Global sensitivity analysis methods are useful tools to rank the model parameters based on their influence on model outputs and considering the entire range of parameters. In other words, these methods consider the influence of a unique parameter as well as the influence of its combinations with the other parameters. In this paper, Regional Sensitivity Analysis (RSA) method is applied as a global method and its results are discussed. The variations of sensitivity index for the two crops are obtained from minimum 0.006 (insensitive) to 0.37 (high sensitive). Furthermore, Results for maize crop showed that the parameters which are related to temperature process (TSUMAM, TSUMEA) and absorbed radiation (SLA, AMAX, EFF) are among the most influential parameters in simulation of maize crop yield. In case of wheat crop, only the parameters which are related to absorbed radiation process (SLA, RGRLAI, AMAX, and EFF) are identified as most influential parameters.

Keywords

Main Subjects

References

Amiri, A., Rezaie, M., Motamed, M. and Emami, S. (2011). Evaluation of WOFOST crop model in irrigation management. Agronomy Journal. 90: 9-18. (In farsi)
Bafkar, A., Borumandnasab, S., Behzad, M., Farhadi Bansule, B. (2013). Potential production prediction of maize in Mahidasht region with WOFOST. Iranian Journal of Field Crop Science. 42(4):484-494. (In farsi)
Boons-Prins E. R., De Koning G.H.J., Van Diepen C.A. and Penning de Vries F.W.T. (1993). Crop specific simulation parameters for yield forecasting across the European community. Simulation Reports CABO-TT No. 32, CABO and Department of Theoretical Production Ecology, Wageningen, the Netherlands.
Confatonieri R., Bellocchi G. and Donatell M. (2009). Multi-metric evaluation of the model WARM, CropSyst, and WOFOST for rice. Ecological Modeling. 220: 1395-1410.
Droogers P., (2000). Estimating actual evapotranspiration using a detailed agro-hydrological model. Journal of Hydrology, 229: 50–58.
Eitzinger J., Trnka J. Hösch Z. and Dubrovský M. (2004). Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions. Ecological Modeling, 171:223–246.
Fodor N., Kovács G.J. (2003). Sensitivity of 4M maize model to the inaccuracy of weather and soil input data. Applied Ecology and Environmental Research. 1: 75-85.
Freer J., Beven K. and Ambroise B. (1996). Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach, Water Resources Research, doi: 10.1029/95WR03723.
Hornberger G., and Spear R. (1981). An approach to the preliminary analysis of environmental systems. Journal of Environmental Management. 12 (1): 7–18.
Kroes J.G., and van Dam J.C. (2003). Reference Manual SWAP version 3.0.3, Alterra-rapport 773, ISSN 1566–7197. 367, 93–103.
Kuchaki, A., Nasiri, M., Badagh Jamali, J., and Marashi H. (2006). Evaluation of the effects of climate change on growth chracterstics and yield of rainfed wheat in Iran. Water and Soil Journal. 20(7):83-94. (In farsi)
Kucherenko, S., Rodriguez-Fernandez, M. Pantelides, C., and Shah N. (2009). Monte Carlo evaluation of derivative-based global sensitivity measures, Reliability Engineering & System Safety, 94 (7): 1135–1148.
Ma, G., Huang J., Wu W., Fan J., Zou J., and Wu S. (2013). Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield. Mathematical and Computer Modelling, 58(3), 634-643.
McKay, M.D., Beckman, R.J., Conover, W.J., 1979. Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239–245.
Makowski, D., Hillier J., Wallach D., Andrieu B., Jeuffroy M. H., (2006). Parameter estimation for crop models. In Wallach, D., Makowski, D. Jones, J. W., (eds.), (2006). Working with dynamic crop models. Evaluation, analysis, parameterization and applications. Elsevier, Amsterdam, 447 pp.
Martorana, F., Bellocchi G. (1999). A review of methodologies to evaluate agro-ecosystem simulation models. Italian Journal of Agronomy, 3: 19-39.
Mertens, J., Madsen, H., Kristensen, M., Jacques D. and Feyen J. (2005). Sensitivity of soil parameters in unsaturated zone modelling and the relation between effective, laboratory and in situ estimates. Hydrological processes, 19(8): 1611-1633.
Morris, M.D. (1991). Factorial sampling plans for preliminary computational experiments, Technometrics, 33 (2): 161–174.
Rivington M., Matthews K.B., Bellocchi G., Buchan K. 2006. Evaluating uncertainty introduced to process-based simulation model estimates by alternative sources of meteorological data. Agricultural Systems, 88: 451-471.
Saltelli A., Tarantola S., Campolongo F., and Ratto M. 2004. Sensitivity analysis in practice: A guide to assessing scientific models. John Wiley and Sons, Chichester, UK.
Sobol′ I. M. 2001. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and computers in simulation, 55(1-3): 271-280.
Supit I., Hooyer A.A., Van Diepen C.A. (Eds.). (1994). System Description of the WOFOST 6.0 Crop Simulation Model Implemented in CGMS, vol. 1: Theory and Algorithms. EUR publication 15956, Agricultural series, Luxembourg, 146 pp.
Tung, Y. K., & Yen, B. C. (2005). Hydrosystems engineering uncertainty analysis. McGraw-Hill Civil Engineering. 298 pp.
Vazifedoust M., 2007. Development of an agricultural drought assessment system: integration of agro-hydrological modelling, remote sensing and geographical information. Dissertation PhD thesis. Wegeningen University.
Wolf J. and van Diepen C.A. 1994. Effects of climate change on silage maize production potential in the European Community. Agricultural Forest Meteorology, 71: 33-60.
Zhou J., Cheng G., Li X., Hu B.X., and Wang G. 2012. Numerical modeling of wheat irrigation using coupled HYDRUS and WOFOST models. Soil Science Society of America Journal, 76(2): 648-662.
 
 
 
 
 
Iranian Journal of Soil and Water Research
Volume 49, Issue 4
July and August 2018
Pages 831-839

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