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Predicting Ponderosa Pine Plantation Yields Using Unmanaged Stand Data

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A method has been developed to predict the yield of intensively managed forest plantations when no managed stand data exists. Using individual tree data obtained from unmanaged stands, prediction equations were developed for ponderosa pine (Pinus ponderosa Laws.) plantations in the Blackfoot River drainage in western Montana. Volume yields were predicted for plantations up to 80 years old, ranging in initial spacing from 2 X 2 m to 5 X 5 m, as a function of age, initial density, and site index. The initial spacing of individual dominant trees in unmanaged stands was estimated as a function of the trees' past radial growth rates and diameters, and the location of the trees' nearest neighbors. Because of the sigmoidly shaped pattern of plotted data points, nonlinear regression techniques were used to predict mean tree dbh and volume, and current density, each as a function of age, initial density, and site index. For both models, predicted values increase substantially as initial spacing increases from 2 m to 3 m, with smaller increases as initial spacing further increases from 3 m to 5 m. Yield is inversely related to initial spacing for all site indexes. Forest Sci. 29:503-514.
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Keywords: Pinus ponderosa; growing space polygon; initial spacing

Document Type: Journal Article

Affiliations: Associate Professor, School of Forestry, University of Montana, Missoula, MT 59812

Publication date: 1983-09-01

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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.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

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    June 1, 2016 to Feb. 28, 2017

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