Search Results (12)

To explore the radial variation in wood properties of slash pine (Pinus elliottii Engelm.) during its growth process and to achieve the early prediction of these properties, our study was carried out in three slash pine harvest-age plantations in Ganzhou, Jian, and Jingdezhen, Jiangxi province of South China. Wood core samples were collected from 360 sample trees from the three plantations. SilviScan technology was utilized to acquire wood property parameters, such as tangential fiber widths (TFWs), radial fiber widths (RFWs), fiber wall thickness (FWT), fiber coarseness (FC), microfibril angle (MFA), modulus of elasticity (MOE), wood density (WD) and ring width (RD). Subsequent systematic analysis focused on the phenotypic and radial variation patterns of wood properties, aiming to establish a clear boundary between juvenile and mature wood. Based on determining the boundary between juvenile and mature wood, a regression equation was used to establish the relationship between the properties of juvenile wood and the ring ages. This relationship was then extended to the mature wood section to predict the properties of mature wood. Our results indicated significant differences in wood properties across different locations. The coefficients of variation for RD and MOE were higher than other properties, suggesting a significant potential for selective breeding. Distinct radial variation patterns in wood properties from the pith to the bark were observed. The boundary between juvenile and mature wood was reached at the age of 22. The prediction models developed for each wood property showed high accuracy, with determination coefficients exceeding 0.87. Additionally, the relative and standard errors between the measured and predicted values were kept below 10.15%, indicating robust predictability. Mature wood exhibited greater strength compared to juvenile wood. The approach of using juvenile wood properties to predict those of mature wood is validated. This method provides a feasible avenue for the early prediction of wood properties in slash pine. Full article

The objective of this study was to develop spatiotemporal whole-stem wood quality prediction models for a suite of end-product-based fibre attribute determinates for jack pine (Pinus banksiana Lamb.) and red pine (Pinus resinosa Aiton): specifically, for wood density (W_d), microfibril angle (M_a), modulus of elasticity (M_e), fibre coarseness (C_o), tracheid wall thickness (W_t), tracheid radial diameter (D_r), tracheid tangential diameter (D_t), and specific surface area (S_a). Procedurally, these attributes were determined for each annual ring within pith-to-bark xylem sequences extracted from 610 jack pine and 223 red pine cross-sectional disks positioned throughout the main stem of 61 jack pine and 54 red pine sample trees growing within even-aged monospecific stands in central Canada. Deploying a block cross-validation-like approach in order to reduce serial data dependency and enable predictive performance assessments, species-specific calibration and validation data subsets consisting of cumulative moving average values were systematically generated from the 27,820 jack pine and 11,291 red pine attribute-specific annual ring values. Graphical, correlation, regression and validation analyses were used to specify, parameterize and assess the predictive performance of tertiary-level (ring-disk-tree) hierarchical mixed-effects whole-stem equations for each attribute by species. As a result, the jack pine equations explained 46, 66, 74, 63, 59, 72, 42 and 48% of the variation in W_d, M_a, M_e, C_o, W_t, D_r, D_t and S_a, respectively. The red pine equations explained slightly higher levels of variation except for M_e: 50, 71, 31, 83, 72, 78, 56 and 71% of the variation in W_d, M_a, M_e, C_o, W_t, D_r, D_t and S_a, respectively. Graphical assessments and statistical metrics related to attribute and species-specific residual error patterns and goodness-of-fit, lack-of-fit and predictive error metrics, revealed an absence of systematic bias, misspecification or aberrant predictive performance. Consequently, the resultant parameterized models were acknowledged as acceptable functional descriptors of the intrinsic spatiotemporal cumulative developmental patterns of the studied end-product fibre attribute determinates, for these two pine species. Although predicted development patterns were similar between the species with the greatest degree of nonlinearity occurring before a cambial age of approximately 30 years, irrespective of attribute, jack pine exhibited a greater degree of nonlinearity in the W_d and D_t developmental trajectories, whereas red pine exhibited a greater degree of nonlinearity in the M_a, M_e, C_o, W_t, D_r and S_a developmental trajectories. Potential biomechanical linkages underlying the observed attribute distribution patterns, as well as the potential utility of the models in forest management, are also discussed. Full article

Mature age Pinus radiata D. Don trees were sampled across nine sites in northern New South Wales, Australia, that were expected, based on site quality and inventory metrics, to exhibit significant variation in productivity and wood quality. Twenty trees per site were harvested and 13 mm diameter, pith-to-bark increment cores were extracted from three trees per site from eight of the nine sites for SilviScan analysis. Outerwood increment cores were collected from all trees for basic density measurement. The same trees were also sampled using an IML PD400 (Resi) instrument. Radial mean properties of wood basic density derived from Resi traces were found to correlate strongly with the mean density data derived from SilviScan analyses and from increment cores. The Resi-derived basic density of 10 mm radial segments was strongly correlated with SilviScan measures of basic density averaged at similar intervals. Full article

Research Highlights: This research presents a novel approach for comparing structural carbon allocation to tree growth and to climate in a dendrochronological analysis. Increasing temperatures reduced the carbon proportion of wood in some cases. Background and Objectives: Our goal was to estimate the structural carbon content of wood within hybrid white spruce (Picea glauca (Moench) × engelmannii (Parry) grown in British Columbia, Canada, and compare the percent carbon content to wood properties and climate conditions of the region. Specific objectives included: (i) the determination of average incremental percent carbon, ring widths (RW), earlywood (EW) and latewood (LW) widths, cell wall thickness, and density over time; (ii) the determination of differences between percent carbon in individual forest stands and between regions; and (iii) the evaluation of the relationships between percent carbon and climate variation over time. Methods: Trees were sampled from twelve sites in northern British Columbia. Wood cores were analyzed with standard dendrochronology techniques and SilviScan analysis. Percent structural carbon was determined using acetone extraction and elemental analysis for 5 year increments. Individual chronologies of wood properties and percent carbon, and chronologies grouped by region were compared by difference of means. Temperature and precipitation values from the regions were compared to the carbon chronologies using correlation, regression, and visual interpretation. Results: Significant differences were found between the percent structural carbon of wood in individual natural and planted stands; none in regional aggregates. Some significant relationships were found between percent carbon, RW, EW, LW, and the cell wall thickness and density values. Percent carbon accumulation in planted stands and natural stands was found in some cases to correlate with increasing temperatures. Natural stand percent carbon values truncated to the last 30 years of growth was shown as more sensitive to climate variation compared to the entire time series. Conclusions: Differences between the stands in terms of structural carbon proportion vary by site-specific climate characteristics in areas of central interior British Columbia. Wood properties can be good indicators of variation in sequestered carbon in some stands. Carbon accumulation was reduced with increasing temperatures; however, warmer late-season conditions appear to enhance growth and carbon accumulation. Full article

Loblolly pine (Pinus taeda L.) is one of the most important commercial timber species in the world. While the species is native to the southeastern United States of America (USA), it has been widely planted in southern Brazil, where it is the most commonly planted exotic species. Interest exists in utilizing nondestructive testing methods for wood property assessment to aid in improving the wood quality of Brazilian grown loblolly pine. We used near-infrared hyperspectral imaging (NIR-HSI) on increment cores to provide data representative of the radial variation of families sampled from a 10-year-old progeny test located in Rio Negrinho municipality, Santa Catarina, Brazil. Hyperspectral images were averaged to provide an individual NIR spectrum per tree for cluster analysis (hierarchical complete linkage with square Euclidean distance) to identify trees with similar wood properties. Four clusters (0, 1, 2, 3) were identified, and based on SilviScan data for air-dry density, microfibril angle (MFA), and stiffness, clusters differed in average wood properties. Average ring data demonstrated that trees in Cluster 0 had the highest average ring densities, and those in Cluster 3 the lowest. Cluster 3 trees also had the lowest ring MFAs. NIR-HSI provides a rapid approach for collecting wood property data and, when coupled with cluster analysis, potentially, allows screening for desirable wood properties amongst families in tree improvement programs. Full article

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. Full article

The objective of this study was to specify, parameterize, and evaluate an acoustic-based inferential framework for estimating commercially-relevant wood attributes within standing jack pine (Pinus banksiana Lamb) trees. The analytical framework consisted of a suite of models for predicting the dynamic modulus of elasticity (m_e), microfibril angle (m_a), oven-dried wood density (w_d), tracheid wall thickness (w_t), radial and tangential tracheid diameters (d_r and d_t, respectively), fibre coarseness (c_o), and specific surface area (s_a), from dilatational stress wave velocity (v_d). Data acquisition consisted of (1) in-forest collection of acoustic velocity measurements on 61 sample trees situated within 10 variable-sized plots that were established in four mature jack pine stands situated in boreal Canada followed by the removal of breast-height cross-sectional disk samples, and (2) given (1), in-laboratory extraction of radial-based transverse xylem samples from the 61 disks and subsequent attribute determination via Silviscan-3. Statistically, attribute-specific acoustic prediction models were specified, parameterized, and, subsequently, evaluated on their goodness-of-fit, lack-of-fit, and predictive ability. The results indicated that significant (p ≤ 0.05) and unbiased relationships could be established for all attributes but d_t. The models explained 71%, 66%, 61%, 42%, 30%, 19%, and 13% of the variation in m_e, w_t, s_a, c_o, w_d, m_a, and d_r, respectively. Simulated model performance when deploying an acoustic-based wood density estimate indicated that the expected magnitude of the error arising from predicting d_t, c_o, s_a, w_t, m_e, and m_a prediction would be in the order of ±8%, ±12%, ±12%, ±13%, ±20%, and ±39% of their true values, respectively. Assessment of the utility of predicting the prerequisite w_d estimate using micro-drill resistance measures revealed that the amplitude-based w_d estimate was inconsequentially more precise than that obtained from v_d (≈ <2%). A discourse regarding the potential utility and limitations of the acoustic-based computational suite for forecasting jack pine end-product potential was also articulated. Full article

Wood stiffness is an important wood mechanical property that predetermines the suitability of sawn timber for construction purposes. Negative genetic correlations between wood stiffness and growth traits have, however, been reported for many conifer species including Scots pine. It is, therefore, important that breeding programs consider wood stiffness and growth traits simultaneously. The study aims to (1) evaluate different approaches of calculating the dynamic modulus of elasticity (MOE, non-destructively assessed stiffness) using data from X-ray analysis (SilviScan) as a benchmark, (2) estimate genetic parameters, and (3) apply index selection. In total, we non-destructively measured 622 standing trees from 175 full-sib families for acoustic velocity (VEL) using Hitman and for wood density (DEN) using Resistograph and Pilodyn. We combined VEL with different wood densities, raw (DEN_RES) and adjusted (DEN_RES.TB) Resistograph density, Pilodyn density measured with (DEN_PIL) and without bark (DEN_PIL.B), constant of 1000 kg·m⁻³ (DEN_CONST), and SilviScan density (DEN_SILV), to calculate MOEs and compare them with the benchmark SilviScan MOE (MOE_SILV). We also derived Smith–Hazel indices for simultaneous improvement of stem diameter (DBH) and wood stiffness. The highest additive genetic and phenotypic correlations of the benchmark MOE_SILV with the alternative MOE measures (tested) were attained by MOE_DENSILV (0.95 and 0.75, respectively) and were closely followed by MOE_DENRES.TB (0.91 and 0.70, respectively) and MOE_DENCONST and VEL (0.91 and 0.65, respectively for both). Correlations with MOE_DENPIL, MOE_DENPIL.B, and MOE_DENRES were lower. Narrow-sense heritabilities were moderate, ranging from 0.39 (MOE_SILV) to 0.46 (MOE_DENSILV). All indices revealed an opportunity for joint improvement of DBH and MOE. Conclusions: MOE_DENRES.TB appears to be the most efficient approach for indirect selection for wood stiffness in Scots pine, although VEL alone and MOE_DENCONST have provided very good results too. An index combining DBH and MOE_DENRES.TB seems to offer the best compromise for simultaneous improvement of growth, fiber, and wood quality traits. Full article

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; v_l) and the dynamic modulus of elasticity (m_e), wood density (w_d), microfibril angle (m_a), tracheid wall thickness (w_t), tracheid radial and tangential diameters (d_r and d_t, respectively), fiber coarseness (c_o), and specific surface area (s_a), 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 v_l measurements at the bottom and top cross-sectional faces of each log (n = 4) from which a log-specific mean v_l 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 (d_r and d_t), 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: w_t (0.53) > m_e > s_a > c_o > w_d >> m_a (0.08)) and predictive ability (s_a > w_t > w_d > c_o >> m_e >>> m_a). Likewise, based on simulations where an acoustic-based w_d 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 s_a, w_t, c_o, m_e, and m_a 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. Full article

Maps developed using Akima’s interpolation method were used to compare patterns of within-tree variation for Pinus taeda L. (loblolly pine) wood properties in plantation-grown trees aged 13 and 22 years. Air-dry density, microfibril angle (MFA) and modulus of elasticity (MOE) maps represented the average of 18 sampled trees in each age class. Near infrared (NIR) spectroscopy models calibrated using SilviScan provided data for the analysis. Zones of high density, low MFA and high MOE wood increased markedly in size in maps of the older trees. The proportion of wood meeting the visually graded No. 1 (11 GPa) and No. 2 (9.7 GPa) MOE design values for southern pine lumber increased from 44 to 74% and from 58 to 83% respectively demonstrating the impact of age on end-product quality. Air-dry density increased from pith to bark at all heights but lacked a significant trend vertically, while radial and longitudinal trends were observed for MFA and MOE. Changes were consistent with the asymptotic progression of properties associated with full maturity in older trees. Full article

The relationship between acoustic velocity (v_d) and the dynamic modulus of elasticity (m_e), wood density (w_d), microfibril angle, tracheid wall thickness (w_t,), radial and tangential diameters, fibre coarseness (c_o) and specific surface area (s_a), within standing red pine (Pinus resinosa Ait.) trees, was investigated. The data acquisition phase involved 3 basic steps: (1) random selection of 54 sample trees from 2 intensively-managed 80-year-old plantations in central Canada; (2) attainment of cardinal-based v_d measurements transecting the breast-height position on each sample tree; and (3) felling, sectioning and obtaining cross-sectional samples from the first 5.3 m sawlog from which Silviscan-based area-weighted mean attribute estimates were determined. The data analysis phase consisted of applying graphical and correlation analyses to specify regression models for each of the 8 attribute-acoustic velocity relationships. Results indicated that viable relationships were obtained for m_e, w_d, w_t, c_o and s_a based on a set of statistical measures: goodness-of-fit (42%, 14%, 45%, 27% and 43% of the variability explained, respectively), lack-of-fit (unbiasedness) and predictive precision (±12%, ±8%, ±7%, ±8% and ±6% error tolerance intervals, respectively). Non-destructive approaches for estimating the prerequisite w_d value when deploying the analytical framework were also empirically evaluated. Collectively, the proposed approach and associated results provide the foundation for the development of a comprehensive and precise end-product segregation strategy for use in red pine management. Full article

Our objective was to model the average wood density in black spruce trees in representative stands across a boreal forest landscape based on relationships with predictor variables extracted from airborne light detection and ranging (LiDAR) point cloud data. Increment core samples were collected from dominant or co-dominant black spruce trees in a network of 400 m² plots distributed among forest stands representing the full range of species composition and stand development across a 1,231,707 ha forest management unit in northeastern Ontario, Canada. Wood quality data were generated from optical microscopy, image analysis, X-ray densitometry and diffractometry as employed in SilviScan™. Each increment core was associated with a set of field measurements at the plot level as well as a suite of LiDAR-derived variables calculated on a 20 × 20 m raster from a wall-to-wall coverage at a resolution of ~1 point m⁻². We used a multiple linear regression approach to identify important predictor variables and describe relationships between stand structure and wood density for average black spruce trees in the stands we observed. A hierarchical classification model was then fitted using random forests to make spatial predictions of mean wood density for average trees in black spruce stands. The model explained 39 percent of the variance in the response variable, with an estimated root mean square error of 38.8 (kg·m⁻³). Among the predictor variables, P20 (second decile LiDAR height in m) and quadratic mean diameter were most important. Other predictors describing canopy depth and cover were of secondary importance and differed according to the modeling approach. LiDAR-derived variables appear to capture differences in stand structure that reflect different constraints on growth rates, determining the proportion of thin-walled earlywood cells in black spruce stems, and ultimately influencing the pattern of variation in important wood quality attributes such as wood density. A spatial characterization of variation in a desirable wood quality attribute, such as density, enhances the possibility for value chain optimization, which could allow the forest industry to be more competitive through efficient planning for black spruce management by including an indication of suitability for specific products as a modeled variable derived from standard inventory data. Full article