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Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogsAuthor(s): Daniel A. Yaussy; Robert L. Brisbin
Source: Res. Pap. NE-536. Broomall, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiement Station. 11p.
Publication Series: Research Paper (RP)
Station: Northeastern Research Station
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DescriptionA 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.
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CitationYaussy, 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.
KeywordsLog quality, log grades, end product yields
- Lumber grade-yields for factory-grade northern red oak sawlogs
- Product recovery from tree grade 1 northern red oak on Menominee tribal lands
- Decay not serious in northern red oak
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