Acoustic evaluation of loblolly pine tree- and lumber-length logs allows for segregation of lumber modulus of elasticity, not for modulus of rupture

Self Cite

Abstract:Loblolly pine (Pinus taeda L.) is increasingly grown on intensively managed plantations that yield high growth rates. Wood properties, including specific gravity (SG), change with cambial age, and thus intensively managed trees contain a high proportion of low density corewood when harvested because of reduced rotation lengths. This study was undertaken to develop models of ring-level properties (SG and width) in intensively managed loblolly pine plantations. Ninety-three trees from five stands aged from 24 to 33 years were harvested, and 490 disks were obtained from in between the 5.2-m logs that were cut, and at the merchantable top. The disks were cut into pith-to-bark radial strips that were scanned on an X-ray densitometer, and the resultant data analyzed using non-linear mixed-effects models. The fixed effects of the models, which included cambial age and for some models disk height and ring width, were able to explain 56, 46, 54, 16, and 46 percent of the within-tree variation for ring SG, ring width, latewood SG, earlywood SG, and latewood percent, respectively. To assess implications for wood utilization, a modeled tree was built by using height, diameter, and taper equations and these models were linked with the developed ring SG model to produce a tree properties map. The linked information was also used to generate tree and log SG and proportion of corewood values for different rotation ages. The results from this study are a step towards integrating wood quality models into growth-and-yield modeling systems that are important for loblolly pine plantation management.

Section: Sample Originmentioning

confidence: 99%

Models for Predicting Specific Gravity and Ring Width for Loblolly Pine from Intensively Managed Plantations, and Implications for Wood Utilization

Dahlen

Auty

Eberhardt

2018

Self Cite

“…Above the fiber saturation point AV will decrease with increasing moisture content but at a slower rate than below the fiber saturation point [54,55]. Many studies have demonstrated good relationships between tree or log AV and MOE of small defect-free wood specimens [56], and moderate relationships between tree or log AV and MOE of structural products [47,49,50,53,[57][58][59][60]. For example, Ikeda and Arima [47] reported significant correlations between tree AV and MOE of logs and square sawn timbers in sugi (Cryptomeria japonica D. Don), while Huang [57] demonstrated that loblolly pine trees with potential to produce high and low stiffness lumber can be identified by tree AV alone.…”

Section: Acousticsmentioning

confidence: 99%

“…Carr. ), and Butler et al [53], working with loblolly pine, set threshold values of AV measured from logs to correspond with thresholds of MOE of lumber cut from logs. Attempts to use standing tree AV measurements to assess fiber attributes (measured using SilviScan) and pulp yield have provided mixed results [61,62].…”

Section: Acousticsmentioning

confidence: 99%

“…Attempts to use standing tree AV measurements to assess fiber attributes (measured using SilviScan) and pulp yield have provided mixed results [61,62]. The density term in the MOE dyn equation is generally assumed to be constant when assessing standing trees and logs, although measuring the actual green density can improve the accuracy of the acoustic velocity models if relating to static properties is of interest [51][52][53]. Measuring the basic density is not a suitable technique for improving the accuracy of the MOE dyn equation [53] because AV changes with moisture content [54,55].…”

Section: Acousticsmentioning

confidence: 99%

See 1 more Smart Citation

Non-Destructive Evaluation Techniques and What They Tell Us about Wood Property Variation

Schimleck

Dahlen

Apiolaza

et al. 2019

Self Cite

To maximize utilization of our forest resources, detailed knowledge of wood property variation and the impacts this has on end-product performance is required at multiple scales (within and among trees, regionally). As many wood properties are difficult and time-consuming to measure our knowledge regarding their variation is often inadequate as is our understanding of their responses to genetic and silvicultural manipulation. The emergence of many non-destructive evaluation (NDE) methodologies offers the potential to greatly enhance our understanding of the forest resource; however, it is critical to recognize that any technique has its limitations and it is important to select the appropriate technique for a given application. In this review, we will discuss the following technologies for assessing wood properties both in the field: acoustics, Pilodyn, Resistograph and Rigidimeter and the lab: computer tomography (CT) scanning, DiscBot, near infrared (NIR) spectroscopy, radial sample acoustics and SilviScan. We will discuss these techniques, explore their utilization, and list applications that best suit each methodology. As an end goal, NDE technologies will help researchers worldwide characterize wood properties, develop accurate models for prediction, and utilize field equipment that can validate the predictions. The continued advancement of NDE technologies will also allow researchers to better understand the impact on wood properties on product performance.

“…Further research into analytical advancements that increase the amount of variability explained and decrease the standard error of estimate, possibly by including covariates within the acoustic-attribute model specification as suggested by Bérubé-Deschênes et al [35] and Butler et al [36], may be warranted. Acoustic velocity is known to be affected by a multitude of internal and external factors which includes knot distributions, embedded voids, environmental conditions during acoustic sampling (temperature and moisture conditions), tree size, local competition, and overall site conditions (e.g., [35,37,38]).…”

Section: Potential Utility Of the Expanded Acoustic-based Inferentialmentioning

confidence: 99%

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

Newton

2018

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.