Volume and Value Prediction for Young-Growth True Fir Trees

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Statistical models were developed for predicting the lumber volume and value of young-growth red, white, and grand fir trees. Equations were derived to predict gross tree volume and cubic recovery percent and then combined to predict lumber volume. Two methods were used to predict lumber value; one predicts the lumber volume in each of three lumber grades using nonlinear regression, and the other predicts an indexed value using linear regression. Field data from recovery studies in Idaho, Oregon, and California were used to develop regression equations. Forest Sci. 30:871-882.

Keywords: Abies concolor; Abies grandis; Abies magnifica; lumber

Document Type: Journal Article

Affiliations: Assistant Professor of Forest Biometrics, Oregon State University, Corvallis, OR 97331

Publication date: December 1, 1984

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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.
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