Canadian Forest Service Publications
Acoustic Velocity—Wood Fiber Attribute Relationships for Jack Pine Logs and Their Potential Utility. 2018. Newton, P.F. Forests 9: 749.
Issued by: Great Lakes Forestry Centre
Catalog ID: 39646
Availability: PDF (download)
Available from the Journal's Web site. †
† This site may require a fee
Plain Language Summary
Given the wide spread occurrence of jack pine across the boreal landscape combined with the vast array of potential end-products it can produce, inclusive of solid wood products (e.g., dimensional lumber) and associated mill-work derivatives (window frames, doors, shelving, moldings, and paneling, and composite lumber products such as glulam-based beams, headers and heavy trusses and finger-jointed joists and rafters), and pulp-derived products such as paperboards, newsprint, facial tissues and specialized coated papers (), the species has become the dominant feedstock species for numerous industrial conversion facilities. However, this diversity of end-products complicates in-forest segregation, allocation, and merchandizing decision-making. Hence the provision of enhanced operational intelligence arising from in-forest forecasts of end-product potential of harvested logs through non-destructive acoustic-based methods, may yield increased efficiencies within the upper portion of the jack pine forest products supply chain. Consequentially, the development and evaluation of a suite of acoustic-based models for predicting the principal attributes governing end-product potential for jack pine as presented in this study, represents an incremental contribution towards more informed decision-making. Specifically, deploying a mixed-effects linear modeling approach combined with cross-validation techniques, viable forecasting models for predicting the dynamic modulus of elasticity, wood density, microfibril angle, cell wall thickness, fiber coarseness and specific surface area were developed. Although these positive results confer additional empirical support for the proposed acoustic-based inferential framework, further research in the areas of accounting for environmentally induced wave variation, specifying end-product-based design thresholds, and explicitly establishing linkages between log-based attribute estimates and those within recoverable end-products, would be beneficial. Collectively, the results presented here for jack pine not only provides the prerequisite parameterized relationships for improving in-forest segregation and allocation decision-making but also contributes to solidifying the empirical foundation of the expanded acoustic-based inferential framework.