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Title: Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs
Author: Yaussy, Daniel A.; Brisbin, Robert L.;
Source: Res. Pap. NE-536. Broomall, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiement Station. 11p.
Publication Series: Research Paper (RP)
Description: A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model can be modified to predict various combinations of lumber grades.
Keywords: Log quality, log grades, end product yields
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Yaussy, Daniel A.; Brisbin, Robert L. 1983. Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs. Res. Pap. NE-536. Broomall, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiement Station. 11p.
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