The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil NO 3 − compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO 3 − and NH 4 + . Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent. DayCent model W. J. Parton, S. J. Del Grosso, S. Ogle, K. Paustian, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA (970) 491-2195 century@colostate.edu 1998 PC with at least 512 K of RAM. A graphics adapter (CGA, EGA, VGA, or Hercules monographic) and 2 Mb of disk space are recommended. Windows Available on request; Free PEST version 13.0 John Doherty Watermark Numerical Computing, 336 Cliveden Avenue, Corinda 4075, Australia 07 3379 1664 johndoherty@ozemail.com.au 1994 Desktop or Laptop Windows or Linux down load from: http://www.pesthomepage.org/Downloads.php; Free. 
Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling