Skip to main content

Projection Variance Partitioning of a Conceptual Forest Growth Model with Orthogonal Polynomials

Buy Article:

$29.50 plus tax (Refund Policy)

An uncertainty analysis was performed for a conceptual forest growth model for red pine (Pinus resinosa Ait.). Low order orthogonal response surface models were developed to predict the variance of projections made with the conceptual model. Based on these orthogonal response surface models, it was possible to partition the variance of the projections. Approximate error budgets were developed, and the power of hypotheses tests based on model predictions were assessed. For. Sci. 42(4):474-486.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Keywords: Dynamic growth model; error budget; pipe model; uncertainty assessment

Document Type: Journal Article

Affiliations: Associate Professor of Forest Biometrics, Department of Forestry, National Taiwan University, 1, Sec, 4, Roosevelt Rd., Taipei, Taiwan 10764 R.O.C

Publication date: 1996-11-01

More about this publication?
  • 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