Skip to main content

Base–Age Invariant Site Index Models from a Generalized Algebraic Parameter Prediction Approach

Buy Article:

$21.50 plus tax (Refund Policy)

A simple idea is proposed to develop polymorphic base–age invariant models with multiple asymptotes: the asymptotic parameter is taken as the site-specific parameter () and one of the other parameters is taken as dependent on  as a simple power function. This approach is a constrained form of the generalized algebraic difference approach (GADA) and eliminates the requirement in previous GADA models to obtain an explicit solution for :  just has to be estimated along with other parameters of the model. The approach may be used to adapt base–age specific models, developed in terms of the “parameter prediction” method, to be “base–age invariant.” The new approach and the models it produces are evaluated on data sets for Chinese fir, red alder, and birch, with models being fitted using the “dummy variable” method in which each observed data series has its own site-specific  parameter. The base–age invariant models developed are superior to their comparable base–age specific models.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: algebraic difference approach (ADA); base–age invariance; base–age specific; generalized algebraic difference approach (GADA); site index model

Document Type: Research Article

Publication date: 2008-10-01

More about this publication?
  • Important Notice: SAF's journals are now published through partnership with the Oxford University Press. Access to archived material will be available here on the Ingenta website until March 31, 2018. For new material, please access the journals via OUP's website. Note that access via Ingenta will be permanently discontinued after March 31, 2018. Members requiring support to access SAF's journals via OUP's site should contact SAF's membership department for assistance.

    Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
  • Submit a Paper
  • Membership Information
  • Author Guidelines
  • Podcasts
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more