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The Geometric Mean Regression Line: A Method for Developing Site Index Conversion Equations for Species in Mixed Stands

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Models that provide analysts with the ability to predict the site index of one species in a mixed stand from the site index of a cohort are useful for site quality evaluation, growth and yield modeling, and timber management planning. A simple compatible system is needed to perform site index conversions, and thus linear regression is an unsuitable technique. In this study, a single-equation compatible system is developed using the geometric mean regression (GMR) line. The properties of the GMR line make it well suited for site index conversion equations. The GMR methodology was applied to mixed coastal Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco)/western hemlock (Tsuga heterophylla [Raf.] Sarg.) and interior white spruce (Picea glauca [Moench] Voss)/lodgepole pine (Pinus contorta var. latifolia Dougl.) stands in British Columbia. Validation shows that these relationships are unbiased and expedient. Careful attention must be used when determining the suitability of Stands to which the relationship is applied. For. Sci. 41(1): 84-98.

Keywords: Douglas-fir; lodgepole pine; western hemlock; white spruce

Document Type: Journal Article

Affiliations: Biometrician, Growth and Yield, with the Research Branch, British Columbia Ministry of Forests, 31 Bastion Square, Victoria, British Columbia, Canada, V8W 3E7

Publication date: 1995-02-01

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
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