Modeling biophysical processes is a complex endeavor because of large data requirements and uncertainty in model parameters. Model predictions should incorporate, when possible, analyses of their uncertainty and sensitivity. The study incorporated uncertainty analysis on EPIC (Environmental Policy Impact Calculator) predictions of corn (Zea mays L.) yield and soil organic carbon (SOC) using generalized likelihood uncertainty estimation (GLUE). An automatic parameter optimization procedure was developed at the conclusion of sensitivity analysis, which was conducted using the extended Fourier amplitude sensitivity test (FAST). The analyses were based on an experimental field under 34-year continuous corn with five N treatments at the Arlington Agricultural Research Station in Wisconsin. The observed average annual yields per treatment during 1958 to 1991 fell well within the 90% confidence interval (CI) of the annually averaged predictions. The width of the 90% CI bands of predicted average yields ranged from 0.31 to 1.6 Mg ha-1. The predicted means per treatment over simulations were 3.26 to 6.37 Mg ha-1, with observations from 3.28 to 6.4 Mg ha-1. The predicted means of yearly yield over simulations were 1.77 to 9.22 Mg ha-1, with observations from 1.35 to 10.22 Mg ha-1. The 90% confidence width for predicted yearly SOC in the top 0.2 m soil was 285 to 625 g C m-2, while predicted means were 5122 to 6564 g C m-2 and observations were 5645 to 6733 g C m-2. The optimal parameter set identified through the automatic parameter optimization procedure gave an R2 of 0.96 for average corn yield predictions and 0.89 for yearly SOC. EPIC was dependable, from a statistical point of view, in predicting average yield and SOC dynamics.
