Multivariate Regression Model for Predicting Yields of Grade Lumber from Yellow Birch Sawlogs

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and circular head-rigs were accounted for in the model. Lumber yields predicted using the model compared favorably with guidelines for expected yields from factory-grade logs. The coefficients presented here can be used in computer programs for the purpose of sawmill simulations, economic analyses, or log year inventory systems.

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