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Uncertainty of CERES-Maize Calibration under Different Irrigation Strategies Using PEST Optimization Algorithm

State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, CAS & MWR, Yangling 712100, China
Agronomy College, Qingdao Agricultural University, Qingdao 266109, China
Rangeland Resources and Systems Research Unit, USDA-ARS, Fort Collins, CO 80526, USA
Center for Agricultural Resources Research, USDA-ARS, Fort Collins, CO 80526, USA
Duke University, Durham, NC 27701, USA
National Laboratory for Agriculture and the Environment, USDA-ARS, Ames, IA 50011, USA
National water Quality Program, U.S. Geological Survey, 413 National Center Reston, VA 20192
Natural Resource Management Branch, Queensland Department of Primary Industries; Adelaide 5001, Australia
Author to whom correspondence should be addressed.
Agronomy 2019, 9(5), 241;
Received: 29 March 2019 / Revised: 4 May 2019 / Accepted: 7 May 2019 / Published: 10 May 2019
(This article belongs to the Special Issue Model Application for Sustainable Agricultural Water)
PDF [2748 KB, uploaded 10 May 2019]


An important but rarely studied aspect of crop modeling is the uncertainty associated with model calibration and its effect on model prediction. Biomass and grain yield data from a four-year maize experiment (2008–2011) with six irrigation treatments were divided into subsets by either treatments (Calibration-by-Treatment) or years (Calibration-by-Year). These subsets were then used to calibrate crop cultivar parameters in CERES (Crop Environment Resource Synthesis)-Maize implemented within RZWQM2 (Root Zone Water Quality Model 2) using the automatic Parameter ESTimation (PEST) algorithm to explore model calibration uncertainties. After calibration for each subset, PEST also generated 300 cultivar parameter sets by assuming a normal distribution of each parameter within their reported values in the literature, using the Latin hypercube sampling (LHS) method. The parameter sets that produced similar goodness of fit (11–164 depending on subset used for calibration) were then used to predict all the treatments and years of the entire dataset. Our results showed that the selection of calibration datasets greatly affected the calibrated crop parameters and their uncertainty, as well as prediction uncertainty of grain yield and biomass. The high variability in model prediction of grain yield and biomass among the six (Calibration-by-Treatment) or the four (Calibration-by-Year) scenarios indicated that parameter uncertainty should be considered in calibrating CERES-Maize with grain yield and biomass data from different irrigation treatments, and model predictions should be provided with confidence intervals. View Full-Text
Keywords: CERES-Maize; crop cultivar parameters; model uncertainty; irrigation treatment; PEST CERES-Maize; crop cultivar parameters; model uncertainty; irrigation treatment; PEST

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Fang, Q.; Ma, L.; Harmel, R.D.; Yu, Q.; Sima, M.W.; Bartling, P.N.S.; Malone, R.W.; Nolan, B.T.; Doherty, J. Uncertainty of CERES-Maize Calibration under Different Irrigation Strategies Using PEST Optimization Algorithm. Agronomy 2019, 9, 241.

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