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Height Growth and Site Index Curves for Inland Douglas-fir Based on Stem Analysis Data and Forest Habitat Type

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Results of a stem analysis site index study for inland Douglas-fir (Pseudotsuga menziesii [Mirb] Franco var. glauca) growing in both even- and uneven-aged stands in the northern Rocky Mountains are reported. Separate height growth and site index equations were fit: a generalized logistic adequately described the height growth data, and a linear model arrived at by simplifying the results of Dahms' method adequately described the site index data. Forest habitat type proved to be a useful concomitant variable in all models, for curve shape differed between three habitat series groupings. The Richards function was not flexible enough to fit either the height growth or site index data, and Dahms' method produced models that contained twice as many parameters as needed. Furthermore, Dahms' method did not produce an acceptable height growth model. Weighted regression was used to fit all models. A first-order autoregressive variance component was also estimated, to account for autocorrelation; it was subsequently found that ignoring the autocorrelation problem did not result in biased parameter estimates. Precision curves illustrating the expected relationship between sample size, standard error, and age should help users achieve a specified error in estimating height growth and site index models. Forest Sci. 30:943-965.
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Keywords: Dahms' method; Heger's method; Pseudotsuga menziesii; Richards function; autocorrelation; logistic; nonlinear regression; site productivity

Document Type: Journal Article

Affiliations: Principal Mensurationist, Intermountain Forest and Range Experiment Station, Forestry Sciences Laboratory, Moscow, ID 83843

Publication date: 1984-12-01

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
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