Acoustic Velocity—Wood Fiber Attribute Relationships for Jack Pine Logs and Their Potential Utility

Abstract

This study presents an acoustic-based predictive modeling framework for estimating a suite of wood fiber attributes within jack pine (Pinus banksiana Lamb.) logs for informing in-forest log-segregation decision-making. Specifically, the relationships between acoustic velocity (longitudinal stress wave velocity; vl) and the dynamic modulus of elasticity (me), wood density (wd), microfibril angle (ma), tracheid wall thickness (wt), tracheid radial and tangential diameters (dr and dt, respectively), fiber coarseness (co), and specific surface area (sa), were parameterized deploying hierarchical mixed-effects model specifications and subsequently evaluated on their resultant goodness-of-fit, lack-of-fit, and predictive precision. Procedurally, the data acquisition phase involved: (1) randomly selecting 61 semi-mature sample trees within ten variable-sized plots established in unthinned and thinned compartments of four natural-origin stands situated in the central portion the Canadian Boreal Forest Region; (2) felling and sectioning each sample tree into four equal-length logs and obtaining twice-replicate vl measurements at the bottom and top cross-sectional faces of each log (n = 4) from which a log-specific mean vl value was calculated; and (3) sectioning each log at its midpoint and obtaining a cross-sectional sample disk from which a 2 × 2 cm bark-to-pith radial xylem sample was extracted and subsequently processed via SilviScan-3 to derive annual-ring-specific attribute values. The analytical phase involved: (1) stratifying the resultant attribute—acoustic velocity observational pairs for the 243 sample logs into approximately equal-sized calibration and validation data subsets; (2) parameterizing the attribute—acoustic relationships employing mixed-effects hierarchical linear regression specifications using the calibration data subset; and (3) evaluating the resultant models using the validation data subset via the deployment of suite of statistical-based metrics pertinent to the evaluation of the underlying assumptions and predictive performance. The results indicated that apart from tracheid diameters (dr and dt), the regression models were significant (p ≤ 0.05) and unbiased predictors which adhered to the underlying parameterization assumptions. However, the relationships varied widely in terms of explanatory power (index-of-fit ranking: wt (0.53) > me > sa > co > wd >> ma (0.08)) and predictive ability (sa > wt > wd > co >> me >>> ma). Likewise, based on simulations where an acoustic-based wd estimate is used as a surrogate measure for a Silviscan-equivalent value for a newly sampled log, predictive ability also varied by attribute: 95% of all future predictions for sa, wt, co, me, and ma would be within ±12%, ±14%, ±15%, ±27%, and ±55% and of the true values, respectively. Both the limitations and potential utility of these predictive relationships for use in log-segregation decision-making, are discussed. Future research initiatives, consisting of identifying and controlling extraneous sources of variation on acoustic velocity and establishing attribute-specific end-product-based design specifications, would be conducive to advancing the acoustic approach in boreal forest management.

Development of Spatiotemporal Whole-Stem Models for Estimating End-Product-Based Fibre Attribute Determinates for Jack Pine and Red Pine

“…For jack pine, as part of a dual objective investigation examining the potential of nondestructive attribute estimation methods (e.g., acoustic velocity; [15,16]) and development of fibre attribute models (e.g., [9] and this study), two geographically separated jack pine thinning experiments established in northeastern (Sewell) and northcentral (Tyrol) Ontario were selected for sampling. At the Sewell site, 31 jack pine sample trees were selected within six variable-size plots that were established in two even-aged natural-origin semi-mature (≈60 year) monospecific stands.…”

“…Furthermore, the distributions of each of the absolute variates for both species were assumed to be approximately normal at each stem height position as inferred from the approximate equivalence of the mean and median values. Additional details regarding the sites in terms of their silvicultural histories, site productivity and mensurational characteristics specific to the sampled stands and destructively sampled trees can be found in [15,16] for jack pine and in [17,18] for red pine. The cross-sectional fibre attribute determination was completed at the Silviscan-3 processing laboratory located at the FPInnovations headquarters situated at the University of British Columbia in Vancouver, British Columbia, Canada.…”

Acoustic-Based Prediction of End-Product-Based Fibre Determinates within Standing Jack Pine Trees

“…Table 1 provides a descriptive statistical summary of the mensurational characteristics and acoustic velocity measurements of the selected sample trees. The remaining log-based disks were used to examine longitudinal stress wave velocity-attribute relationships, as reported in a concurrent study by Newton [27]. The breast-height disks along with the corresponding 61 stump disks were also used to assess the Resistograph-based amplitude-wood density relationship.…”

“…With reference to the applicability of an alternative mixed-effects regression specification inclusive of random and fixed effects, a two-level hierarchical linear model specification was evaluated. This model consisted of simple linear formulations where the intercept parameter was treated as random (i.e., allowed to vary by tree) and the slope parameter was treated as fixed (sensu [26,27]). Following parameterization by deploying a hierarchical linear and nonlinear modeling software algorithm (HLM7, [41]), the statistical assessment of the resultant models indicated an absence of significant (p ≤ 0.05) random effects across all eight attributes, and hence negated further consideration of the mixed-effects specification.…”

“…), red pine and jack pine (Pinus banksiana Lamb.) and other species [14,[26][27][28][29][30][31][32], among attribute correlative relationships, have been used to formulate a more encompassing acoustic-based inferential framework. Specifically, deploying the bivariate relationships between m e and microfibril angle (MFA denoted m a in this study, • ), tracheid wall thickness (w t , µm), radial and tangential tracheid diameters (d r (µm) and d t (µm), respectively), fibre coarseness (c o , µg/m) and specific surface area (s a , m 2 /kg), yielded the following suite of empirical-based secondary relationships applicable to standing softwood trees (sensu [14]): (1) m a ∝ 1/m e ⇒m a ∝ w d v 2 .…”