IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0172861.html
   My bibliography  Save this article

Use of inverse modeling to evaluate CENTURY-predictions for soil carbon sequestration in US rain-fed corn production systems

Author

Listed:
  • Hoyoung Kwon
  • Carmen M Ugarte
  • Stephen M Ogle
  • Stephen A Williams
  • Michelle M Wander

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.

Suggested Citation

  • Hoyoung Kwon & Carmen M Ugarte & Stephen M Ogle & Stephen A Williams & Michelle M Wander, 2017. "Use of inverse modeling to evaluate CENTURY-predictions for soil carbon sequestration in US rain-fed corn production systems," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-18, February.
  • Handle: RePEc:plo:pone00:0172861
    DOI: 10.1371/journal.pone.0172861
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0172861
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0172861&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0172861?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ogle, Stephen M. & Breidt, F. Jay & Easter, Mark & Williams, Steve & Paustian, Keith, 2007. "An empirically based approach for estimating uncertainty associated with modelling carbon sequestration in soils," Ecological Modelling, Elsevier, vol. 205(3), pages 453-463.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ramakhanna, Selebalo Joseph & Mapeshoane, Botle Esther & Omuto, Christian Thine, 2022. "Carbon sequestration potential in croplands in Lesotho," Ecological Modelling, Elsevier, vol. 471(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Gangsheng & Chen, Shulin, 2013. "Evaluation of a soil greenhouse gas emission model based on Bayesian inference and MCMC: Model uncertainty," Ecological Modelling, Elsevier, vol. 253(C), pages 97-106.
    2. Monte, Luigi, 2009. "Multi-model approach and evaluation of the uncertainty of model results. Rationale and applications to predict the behaviour of contaminants in the abiotic components of the fresh water environment," Ecological Modelling, Elsevier, vol. 220(12), pages 1469-1480.
    3. Shuang Gao & Patrick L. Gurian & Paul R. Adler & Sabrina Spatari & Ram Gurung & Saurajyoti Kar & Stephen M. Ogle & William J. Parton & Stephen J. Grosso, 2018. "Framework for improved confidence in modeled nitrous oxide estimates for biofuel regulatory standards," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(8), pages 1281-1301, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0172861. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.