Skip to main content

Classification Model to Predict Fraser Fir Christmas Tree Grade

Buy Article:

$29.50 plus tax (Refund Policy)

Abstract:

Objective measures of Christmas tree grade that would better quantify tree responses to cultural treatments were sought. Fraser fir Christmas tree attributes in a 3-year-old plantation were measured and correlated with conventional three-class grade categories, and a discriminate classification model was developed to test the predictability of grade based on tree attributes. Tree attributes best correlated with grade were basal diameter, terminal leader length and total bud and lateral bud frequencies on the terminal leader. Grade prediction based on five easily measured growth attributes produced a discriminate classification success rate of 72%. For. Sci. 36(1):45-53.

Keywords: Christmas tree production; Tree morphology; discriminate analysis

Document Type: Journal Article

Affiliations: Professor of Silviculture and Forest Soils, Virginia Polytechnic Institute and State University, Blacksburg, VA

Publication date: 1990-03-01

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.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 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
X
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