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

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Uncertainty and sensitivity assessments of an agricultural–hydrological model (RZWQM2) using the GLUE method (2016, methodology): “Likelihood functions of GLUE method can be conceived as functions of a synthesis of variance of observations and errors (Beven and Freer, 2001), such as mean square error (MSE), and Nash and Sutcliffe efficiency (NE), based on univariate or multi variables (He et al., 2010).”
Maize Cultivar Specific Parameters for Decision Support System for Agrotechnology Transfer (DSSAT) Application in Tanzania (2014, methodology): “[14] used generalized likelihood uncertainty estimation (GLUE) method to estimate maize cultivar coefficients.”
DEALINGWITHUNCERTAINTY INAGENT-BASED SIMULATION:FARM-LEVELMODELING OFADAPTATION TOCLIMATECHANGE INSOUTHWESTGERMANY (2015, methodology): “Alternative approaches such as GLUE (Beven and Freer 2001), which do not rely on a formal derivation of the likelihood but use standard goodness-of-fit measures to calculate informal likelihood functions, are somewhat subjective and do not necessarily increase transparency (He et al. 2010).”
Process-based simple model for simulating sugarcane growth and production (2014, methodology): “From the point of view of Monte Carlo sampling in the GLUE method, more parameter sets lead to more stable results (He et al., 2010), and here, we used 6000 samples that were considered to be independent.”