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Title:   Author  item
                                            LIU, SHUGUANG - Collaboratoritem
                                            McCarty, Gregoryitem
                                            Mirsky, Stevenitem
                                            OCHSNER, TYSON - Oklahoma State Universityitem
                                            BAUMGART-GETZ, A - Oklahoma State Universityitem
                                            PROKOPY, L - Purdue Universityitem
                                            SHAO, G - Purdue Universityitem
                                            CHAN, S - Jet Propulsion Laboratoryitem
                                            NJOKU, E - Jet Propulsion Laboratoryitem
                                            KERR, Y - Collaboratoritem
                                            ALLEN, R - University Of Idahoitem
                                            MORSE, A - Collaboratoritem
                                            SHI, J - University Of California
  Submitted to: Book Chapter
                                    Publication Type: Book / Chapter
                                    Publication Acceptance Date: 10/1/2011
                                    Publication  Date: 6/1/2012
                                    Citation: Liu, S., Mccarty, G.W., Mirsky, S.B., Ochsner, T., Baumgart-Getz, A., Prokopy, L.S., Shao, G., Chan, S., Njoku, E., Kerr, Y., Allen, R.G., Morse, A., Shi, J. 2012. The general ensemble biogeochemical modeling system (GEMS) and its applications to agriculture systems in the United States. Managing Agricultural Greenhouse Gases. Amsterdam:Elsevier p. 309-323.Interpretive Summary:
                                Technical Abstract: The General Ensemble Biogeochemical Modeling System (GEMS) was developed for a proper integration of well-established ecosystem biogeochemical models with various spatial databases to simulate biogeochemical cycles over large areas. Major driving variables include land cover and land use, climate, soils, disturbances, and various management activities. GEMS uses two approaches to quantify the uncertainty of model outputs.  First, to reduce biases in individual models, it uses multiple site-scale biogeochemical models to perform model simulations.  Second, it adopts Monte Carlo ensemble simulations of each simulation unit (one site/pixel or group of sites/pixels with similar biophysical conditions) to incorporate uncertainties and variability (as measured by variances and covariance) of input variables into model simulations. In this paper, we illustrated the applications of GEMS at the site and regional scales with an emphasis on incorporating agricultural practices. Challenges in modeling soil carbon dynamics and greenhouse emissions are also discussed.


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