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Height Growth Rate of Douglas-Fir: A Comparison of Model Forms

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Five model forms were evaluated for their ability to predict height growth rate of individual Douglas-firs (Pseudotsuga menziesii [Mirb.] Franco) growing in even or uneven-aged stands of southwest Oregon. Three models had been previously used for Douglas-fir; the fourth was a simple modification of one of these, and the fifth was developed in this study by means of multidimensional graphing and modeling. Two forms were age-dependent, in that they used transformations of stand age, and three were age-independent. The model developed by multidimensional techniques provided the lowest mean squared error for both an even-aged data set (1,763 observations) and a combined even- and uneven-aged data set (2,242 observations). The five models were verified on a randomly drawn subset of the data consisting of 241 observations. Again the model developed with multidimensional techniques had the lowest mean residual and the lowest mean squared error. Independent variables in this model are stand crown closure at the top of the tree, crown ratio, and potential height growth rate as predicted by an existing dominant height equation. The resulting model is age-independent and can be applied to trees in both even- and uneven-aged stands. Multidimensional techniques were most useful in modeling this complex nonlinear surface. For. Sci. 34(1):165-175.

Keywords: Crown closure; crown ratio; modifier function; nonlinear regression; potential height growth rate

Document Type: Journal Article

Affiliations: Mathematical Statistician, Pacific Southwest Forest and Range Experiment Station, Redding, CA 96001

Publication date: 1988-03-01

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  • 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
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