IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0140095.html
   My bibliography  Save this article

A Mixed-Effects Model with Different Strategies for Modeling Volume in Cunninghamia lanceolata Plantations

Author

Listed:
  • Mei Guangyi
  • Sun Yujun
  • Xu Hao
  • Sergio de-Miguel

Abstract

A systematic evaluation of nonlinear mixed-effect taper models for volume prediction was performed. Of 21 taper equations with fewer than 5 parameters each, the best 4-parameter fixed-effect model according to fitting statistics was then modified by comparing its values for the parameters total height (H), diameter at breast height (DBH), and aboveground height (h) to modeling data. Seven alternative prediction strategies were compared using the best new equation in the absence of calibration data, which is often unavailable in forestry practice. The results of this study suggest that because calibration may sometimes be a realistic option, though it is rarely used in practical applications, one of the best strategies for improving the accuracy of volume prediction is the strategy with 7 calculated total heights of 3, 6 and 9 trees in the largest, smallest and medium-size categories, respectively. We cannot use the average trees or dominant trees for calculating the random parameter for further predictions. The method described here will allow the user to make the best choices of taper type and the best random-effect calculated strategy for each practical application and situation at tree level.

Suggested Citation

  • Mei Guangyi & Sun Yujun & Xu Hao & Sergio de-Miguel, 2015. "A Mixed-Effects Model with Different Strategies for Modeling Volume in Cunninghamia lanceolata Plantations," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-14, October.
  • Handle: RePEc:plo:pone00:0140095
    DOI: 10.1371/journal.pone.0140095
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140095
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0140095&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0140095?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. Timothy G. Gregoire & Oliver Schabenberger & Fanzhi Kong, 2000. "Prediction from an Integrated Regression Equation: A Forestry Application," Biometrics, The International Biometric Society, vol. 56(2), pages 414-419, June.
    Full references (including those not matched with items on IDEAS)

    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. Manuel Arias-Rodil & Fernando Castedo-Dorado & Asunción Cámara-Obregón & Ulises Diéguez-Aranda, 2015. "Fitting and Calibrating a Multilevel Mixed-Effects Stem Taper Model for Maritime Pine in NW Spain," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-20, December.
    2. Chenggang Wang & Danli Du & Tiansen Liu & Yue Zhu & Dongxue Yang & Yuan Huang & Fan Meng, 2024. "Impact of Green Technology Innovation on Green Economy: Evidence from China," Sustainability, MDPI, vol. 16(19), pages 1-23, October.

    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:pone00:0140095. 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.