Modeling lumber yield of white spruce in Alberta, Canada: a comparative approach

Abstract

Estimation of lumber yield from a forest inventory is important in determining the optimal utilization of available regional wood supply. In this study, we review existing approaches of lumber yield estimation, including knowledge-based empirical board-foot log rules used in the United States and some regions of Canada, survey-based wood conversion factors, and optimization technology-based computer simulations. Lumber yields estimated from different approaches are then compared using six datasets from a white spruce commercial thinning experiment in Alberta, Canada. Our results indicated that (1) estimated lumber yield can be significantly different when different methods are employed; (2) board-foot log rules often underestimate lumber yield; (3) wood conversion factors represent regional average of lumber yield as a constant and thus are unsuitable for forest inventory-based lumber yield estimation; and (4) optimization technology-based computer simulations can provide the best estimate of lumber yield for a given forest inventory as long as the mill conditions and lumber dimensions from market demand are specified. Forestry investment in applying computer simulation methods should be encouraged in sawmill operations to improve lumber yield and enhance environmental protection, because, for a given amount of lumber, improving lumber yield means reduced demand for harvest operations.

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