title: Use of inverse modeling to evaluate CENTURY-predictions for soil carbon sequestration in US rain-fed corn production systems.
abstract: We evaluated the accuracy and precision of the CENTURY soil organic matter model for predicting soil organic carbon (SOC) sequestration under rainfed corn-based cropping systems in the US. This was achieved by inversely modeling long-term SOC data obtained from 10 experimental sites where corn, soybean, or wheat were grown with a range of tillage, fertilization, and organic matter additions. Inverse modeling was accomplished using a surrogate model for CENTURY's SOC dynamics sub-model wherein mass balance and decomposition kinetics equations from CENTURY are coded and solved by using a nonlinear regression routine of a standard statistical software package. With this approach we generated statistics of CENTURY parameters that are associated with the effects of N fertilization and organic amendment on SOC decay, which are not as well quantified as those of tillage, and initial status of SOC. The results showed that the fit between simulated and observed SOC prior to inverse modeling (R2 = 0.41) can be improved to R2 = 0.84 mainly by increasing the rate of SOC decay up to 1.5 fold for the year in which N fertilizer application rates are over 200 kg N ha-1. We also observed positive relationships between C inputs and the rate of SOC decay, indicating that the structure of CENTURY, and therefore model accuracy, could be improved by representing SOC decay as Michaelis-Menten kinetics rather than first-order kinetics. Finally, calibration of initial status of SOC against observed levels allowed us to account for site history, confirming that values should be adjusted to account for soil condition during model initialization. Future research should apply this inverse modeling approach to explore how C input rates and N abundance interact to alter SOC decay rates using C inputs made in various forms over a wider range of rates.
authors.affiliation: Environment and Production Technology Division, International Food Policy Research Institute, Washington, DC, United States of America.
authors.forename: Hoyoung
authors.identifier: http://orcid.org/0000-0002-3421-9292
authors.initials: H
authors.lastname: Kwon
authors.affiliation: Department of Natural Resources and Environmental Sciences, University of Illinois, Urbana, IL, United States of America.
authors.forename: Carmen M
authors.initials: CM
authors.lastname: Ugarte
authors.affiliation: Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, United States of America.
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Ogle
authors.affiliation: Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, United States of America.
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Williams
authors.affiliation: Department of Natural Resources and Environmental Sciences, University of Illinois, Urbana, IL, United States of America.
authors.forename: Michelle M
authors.initials: MM
authors.lastname: Wander
mesh_terms: D000383:Agriculture / Q000379:methods; D000465:Algorithms; D002244:Carbon / Q000737:chemistry*; D057965:Carbon Sequestration; D018556:Crops, Agricultural; D005308:Fertilizers; D007700:Kinetics; D015233:Models, Statistical; D009010:Monte Carlo Method; D009584:Nitrogen / Q000737:chemistry; D017711:Nonlinear Dynamics; D011891:Rain; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D012984:Software; D012987:Soil / Q000737:chemistry*; D013025:Glycine max; D014908:Triticum; D014481:United States; D003313:Zea mays
chemical_list: D005308:Fertilizers; D012987:Soil; D002244:Carbon; D009584:Nitrogen