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A multiple linear regression equation was developed to predict bark factor for jack pine in Michigan as a function of tree height. The equation was validated on independent data sets. The prediction equation yielded average relative errors from -2.9 to 0.4% for all tree heights above stump height. At stump height the average relative errors varied from -5.3 to -2.3%. The jack pine equation was compared with red pine and aspen bark factor equations. The new equation can be used to more accurately estimate tree and log wood volumes than when using a constant bark factor determined at breast height, which, in general leads to underestimates of wood volume. North. J. Appl. For. 10(2):86-89.
Document Type: Journal Article
School of Natural Resources, The University of Michigan, Ann Arbor, MI 48109-1115
Publication date: June 1, 1993
More about this publication?
Each regional journal of applied forestry focuses on research, practice, and techniques targeted to foresters and allied professionals in specific regions of the United States and Canada. The Northern Journal of Applied Forestry covers northeastern, midwestern, and boreal forests in the United States and Canada.