Experimental and simulation uncertainties have not been included in many of the statistics used in assessing agricultural model performance. The objectives of this study were to develop an F-test that can be used to evaluate model performance considering experimental and simulation uncertainties, and identify the best datasets to use for model calibration using different water stress functions in a cropping system model. Data on irrigated maize in Colorado, USA, and the Root Zone Water Quality Model (RZWQM) were used as an example to demonstrate model calibration using the modified F-test along with other commonly used statistics. Compared to the d-index, the F-test provided a statistical test under a certain confidence level that better distinguished the goodness of model prediction for both biomass and yield while considering uncertainty. To obtain robust model parameters, we recommend using multiple treatments across multiple years for model calibration, regardless of water stress functions used.
Sima, N. Q., Harmel, R. D., Fang, Q. X., Ma, L., & Andales, A. A. (2018). A modified F-test for evaluating model performance by including both experimental and simulation uncertainties. Environmental Modelling and Software, 104, 236–248. https://doi.org/10.1016/j.envsoft.2018.03.011