Uncertainty analysis of DSSAT plant model parameters in estimating cotton yield using GLUE

Document Type : Original Article

Authors

1 Department of Water Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran.

2 Department of Water Engineering, Faculty of Agriculture, Birjand University, Birjand, Iran.

3 Department of Irrigation and Drainage, Joint Cooperation Program, Imam Khomeini International University (IKIU) and Wageningen University and Research (WUR), Imam Khomeini University, Qazvin, Iran.

4 Department of Ecology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran.

Abstract

Introduction
Crop growth simulation models are extensively used for various agricultural studies, including optimal crop selection, irrigation management, and assessing climate change impacts. Among these models, the DSSAT (Decision Support System for Agrotechnology Transfer) is particularly prominent for its ability to simulate growth, yield, and other dynamics for 34 different crops. The DSSAT model integrates various components such as soil, weather, crop management, and genetic factors to provide comprehensive insights into crop performance (Jones et al., 2003). Accurate parameter calibration in this model is crucial for reliable simulations. However, the inherent variability and uncertainty in parameter values pose significant challenges. Uncertainty can arise from various sources, including measurement errors, spatial and temporal variability, and model structure. Addressing these uncertainties is essential to enhance the reliability and accuracy of the model predictions. The Generalized Likelihood Uncertainty Estimation (GLUE) algorithm offers a robust framework for quantifying and incorporating parameter uncertainty into model simulations (Beven & Binley, 1992).In this study, we focus on the application of the GLUE algorithm to the DSSAT model for cotton, aiming to improve the model's predictive accuracy by accounting for parameter uncertainty. We utilize observational data from different irrigation treatments to calibrate the model and evaluate the posterior probability distributions of the parameters.

Materials and Methods
The study used data from a 2009 experiment conducted at the Birjand University research farm. The DSSAT v4.5 model was employed, requiring inputs such as weather, soil properties, and crop management data. Four irrigation treatments (50%, 75%, 100%, and 125% of crop water requirement) were tested to evaluate the GLUE algorithm’s performance in estimating model parameters.

Results and discussion
The results demonstrated that the GLUE algorithm effectively estimated the probability distributions of the DSSAT model parameters for cotton. The algorithm’s performance was compared with previous models lacking uncertainty assessments, showing significant improvements in simulation accuracy (Qasemi et al., 2019). The findings highlighted the importance of considering parameter uncertainty for better predictive accuracy and model reliability.

Conclusions
The GLUE algorithm, through Monte Carlo simulations, provides a robust method for assessing and incorporating parameter uncertainty in crop growth models like DSSAT. This approach enhances the model's reliability in predicting crop performance under varying conditions, which is crucial for agricultural planning and management.

Keywords

Main Subjects

References

Bannayan Aval, M., Nezami, A., Ganjeali, A., & Bagheri, A. (2012). Simulation of Chickpea Production in Khorasan Provinces Using a Simple Crop Model and Markov Chain Monte Carlo technique. Iranian Journal of Field Crops Research, 10(4), 634-642. https://doi.org/10.22067/gsc.v10i4.20334[In Persian]
Beven, K. (2007). Towards integrated environmental models of everywhere: uncertainty, data and modelling as a learning process. Hydrology and Earth System Sciences, 11(1), 460–467. https://doi.org/10.5194/hess-11-460-2007
Blasone, R.-S., Vrugt, J. A., Madsen, H., Rosbjerg, D., Robinson, B. A., & Zyvoloski, G. A. (2008). Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling. Advances in Water Resources, 31(4), 630–648. https://doi.org/10.1016/j.advwatres.2007.12.003
Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., Bussière, F., Cabidoche, Y., Cellier, P., Debaeke, P., Gaudillère, J., Hénault, C., Maraux, F., Seguin, B., & Sinoquet, H. (2003). An overview of the crop model stics. European Journal of Agronomy, 18(3–4), 309–332. https://doi.org/10.1016/S1161-0301(02)00110-7
Buddhaboon, C., Jintrawet, A., & Hoogenboom, G. (2018). Methodology to estimate rice genetic coefficients for the CSM-CERES-Rice model using GENCALC and GLUE genetic coefficient estimators. The Journal of Agricultural Science, 156(4), 482–492. https://doi.org/10.1017/S0021859618000527
Farshi, A. A., Feyen, J., Belmans, C., & De Wijngaert, K. (1987). Modelling of yield of winter wheat as a function of soil water availability. Agricultural Water Management, 12(4), 323–339. https://doi.org/10.1016/0378-3774(87)90007-2
Gardner, R. H., Dale, V. H., & O’Neill, R. V. (1990). Error Propagation and uncertainty in process modeling. In Dixon, R. K., Meldahl, R. S., Ruark, G. A., & Warren, W. G. editors. Forest Growth: Process Modeling of Response to Environmental Stress. Timber Press, Portland, 208-219. https://link.springer.com/chapter/10.1007/1-4020-4663-4_3
Garibay, V. M., Kothari, K., Ale, S., Gitz, D. C., Morgan, G. D., & Munster, C. L. (2019). Determining water-use-efficient irrigation strategies for cotton using the DSSAT CSM CROPGRO-cotton model evaluated with in-season data. Agricultural Water Management, 223, 105695. https://doi.org/10.1016/j.agwat.2019.105695
Ghasemi, M., Naseri, A., & Moazed, H. (2019). Parameterization and Evaluation of the DSSAT-CANEGRO Model for Sugarcane CP57-614 in Khuzestan Climate Condition. Iranian Journal of Soil and Water Research, 50(6), 1331-1340. https://doi.org/10.22059/ijswr.2018.259986.667944 [In Persian]
Ghorbani Baravati, F., Najafi Mood, M. H., Ramezani, Y., & Khashei, A. (2023). Impact of climate change on cotton growth and yield (case study: Birjand Plain). Iranian Journal of Soil and Water Research, 54(8), 1131-1145. https://doi.org/10.22059/ijswr.2023.321555.668931[In Persian]
He, J., Dukes, M. D., Jones, J. W., Graham, W. D., & Judge, J. (2009). Applying glue for estimating ceres‐maize genetic and soil parameters for sweet corn production. American Society of Agricultural and Biological Engineers, 52(6), 1907–1921. https://elibrary.asabe.org/abstract.asp?aid=29218
Hopkins, J. C., & Leipold, R. J. (1996). On the dangers of adjusting the parameter values of mechanism-based mathematical models. Journal of Theoretical Biology, 183(4), 417–427. https://doi.org/10.1006/jtbi.1996.0232
Jones, J., Hoogenboom, G., Porter, C., Boote, K., Batchelor, W., Hunt, L., Wilkens, P., Singh, U., Gijsman, A., & Ritchie, J. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3–4), 235–265. https://doi.org/10.1016/S1161-0301(02)00107-7
Jones, J. W., He, J., Boote, K. J., Wilkens, P., Porter, C. H., & Hu, Z. (2015). Estimating DSSAT cropping system cultivar-specific parameters using Bayesian techniques. In Methods of introducing system models into agricultural research, 365-393. https://doi.org/10.2134/advagricsystmodel2.c13
Khatua, R., Panneerselvam, S., Geethalakshmi, V., & Jeyakumar, P. (2023). Calibration and Validation of DSSAT (CROPGRO) Model for Winter Irrigated Cotton in Coimbatore, Tamil Nadu, India. International Journal of Plant & Soil Science, 35(22), 752-760. https://journalijpss.com/index.php/IJPSS/article/view/4187
Keating, B., Carberry, P., Hammer, G., Probert, M., Robertson, M., Holzworth, D., Huth, N., Hargreaves, J. N., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J., Silburn, M., Wang, E., Brown, S., Bristow, K., Asseng, S., … & Smith, C. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18(3–4), 267–288. https://doi.org/10.1016/S1161-0301(02)00108-9
Kumar, R., Mishra, S. K., Singh, K., Al-Ashkar, I., Iqbal, M. A., Muzamil, M. N., ... & El Sabagh, A. (2023). Impact analysis of moisture stress on growth and yield of cotton using DSSAT-CROPGRO-cotton model under semi-arid climate. PeerJ, 11. https://doi.org/10.7717/peerj.16329
Landivar, J. A., Baker, D. N., & Jenkins, J. N. (1983). Application of GOSSYM to genetic feasibility studies. I. Analyses of fruit abscission and yield in Okra‐Leaf Cottons 1. Crop Science, 23(3), 497–504. https://doi.org/10.2135/cropsci1983.0011183X002300030014x
Li, Z., He, J., Xu, X., Jin, X., Huang, W., Clark, B., Yang, G., & Li, Z. (2018). Estimating genetic parameters of DSSAT-CERES model with the GLUE method for winter wheat (Triticum aestivum L.) production. Computers and Electronics in Agriculture, 154, 213–221. https://doi.org/10.1016/j.compag.2018.09.009
Makowski, D., Hillier, J., Wallach, D., Andrieu, B., & M.H. Jeuffroy. (2006). Parameter estimation for crop models. In D. Wallach, D. Makowski, & J. Jones (Eds.) , Working with Dynamic Crop Models: Evaluating, Analyzing, Parameterizing and Using Them. Elsevier, Amsterdam, 101–150. https://www.researchgate.net/publication/233858512_Parameter_estimation_for_crop_models
Marin, F. R., Jones, J. W., Royce, F., Suguitani, C., Donzeli, J. L., Filho, W. J. P., & Nassif, D. S. P. (2011). Parameterization and evaluation of predictions of DSSAT/CANEGRO for Brazilian sugarcane. Agronomy Journal, 103(2), 304–315. https://doi.org/10.2134/agronj2010.0302
McKay, M. D., Beckman, R. J., & Conover, W. J. (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239–245. http://dx.doi.org/10.1080/00401706.1979.10489755
Pang, X., & Letey, J. (1998). Development and evaluation of ENIRO-GRO, an integrated water, salinity, and nitrogen model. Soil Science Society of America Journal, 62, 1418–1427. https://www.scirp.org/reference/referencespapers?referenceid=3122307
Pourreza-Bilondi, M., Samadi, S. Z., Akhoond-Ali, A. M., & Ghahraman, B. (2017). Reliability of semiarid flash flood modeling using Bayesian framework. Journal of Hydrologic Engineering, 22(4), 05016039. http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0001482
Shafiei, M., Ghahraman, B., Saghafian, B., Davary, K., Pande, S., & Vazifedoust, M. (2014). Uncertainty assessment of the agro-hydrological SWAP model application at field scale: A case study in a dry region. Agricultural Water Management, 146, 324-334. https://doi.org/10.1016/j.agwat.2014.09.008
Sievänen, R., & Burk, T. E. (1994). Fitting process-based models with stand growth data: problems and experiences. Forest Ecology and Management, 69(1–3), 145–156. https://doi.org/10.1016/0378-1127(94)90225-9
Soltani, A., Torabi, B., Zeinali, E., & Sarparast, R. (2004). Response of Chickpea to photoperiod as a qualitative long-day plant. Asian Journal of Plant Sciences, 3(6), 705–708. https://doi.org/10.3923/ajps.2004.705.708
Turley, M. C., & Ford, E. D. (2009). Definition and calculation of uncertainty in ecological process models. Ecological Modelling, 220(17), 1968–1983. https://doi.org/10.1016/j.ecolmodel.2009.04.046
Van Oijen, M. (2002). On the use of specific publication criteria for papers on process-based modelling in plant science. Field Crops Research, 74(2–3), 197–205. https://doi.org/10.1016/S0378-4290(01)00211-8
Vanuytrecht, E., Raes, D., Steduto, P., Hsiao, T. C., Fereres, E., Heng, L. K., Garcia Vila, M., & Mejias Moreno, P. (2014). AquaCrop: FAO’s crop water productivity and yield response model. Environmental Modelling & Software, 62, 351–360. https://doi.org/10.1016/j.envsoft.2014.08.005
Vrugt, J. a., ter Braak, C. J. F., Clark, M. P., Hyman, J. M., & Robinson, B. a. (2008). Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation. Water Resources Research, 44, 1–52. https://doi.org/10.1029/2007WR006720
Whisler, F., Acock, B., Baker, D., Fye, R., Hodges, H., Lambert, J., & Lemmon, H. (1986). Crop simulation models in agronomic systems. Advances in Agronomy, 40, 141–208. http://books.google.com/books?hl=en&lr=&id=HVGOVkGDouUC&oi=fnd&pg=PA141&dq=Crop+simulation+model+in+agronomic+systems&ots=5F6iiJ8pZL&sig=Tyghz8iX5JRcFYfEeOnZs0TSGgk
Zand-Parsa, S., Sepaskhah, A. R., & Ronaghi, A. (2006). Development and evaluation of integrated water and nitrogen model for maize. Agricultural Water Management, 81(3), 227–256. https://doi.org/10.1016/j.agwat.2005.03.010
Zhiwei, J., Zhongxin, C., Qingbo, Z., & Jianqiang, R. (2011). Global sensitivity analysis of CERES-Wheat model parameters. Transactions of the CSAE, 27(1), 236–242. http://dx.doi.org/10.3969/j.issn.1002-6819.2011.01.038
Advanced Technologies in Water Efficiency
Volume 4, Issue 4
December 2024
Pages 19-34

Files

History

  • Receive Date: 07 July 2024
  • Revise Date: 12 September 2024
  • Accept Date: 01 October 2024

Share

How to cite

Statistics