Variation in Wood Quality in White Spruce (Picea Glauca (Moench) Voss). Part I. Defining the Juvenile–Mature Wood Transition Based on Tracheid Length

Abstract

Estimations of transition age (TA) and juvenile wood proportion (JWP) are important for wood industries due to their impact on end-product quality. However, the relationships between analytical determination of TA based on tracheid length (TL) and recognized thresholds for adequate end products have not yet been established. In this study, we used three different statistical models to estimate TA in white spruce (Picea glauca (Moench) Voss) based on TL radial variation. We compared the results with technological maturity. A two-millimeter threshold, previously suggested for good paper tear strength, was used. Tracheid length increased from pith to bark and from breast height to upper height. Juvenile wood (JW) was conical with the three models. At breast height, TA ranged from 11 to 27 years and JWP ranged from 15.3% to 47.5% across the three models. The linear mixed OPEN ACCESSForests 2015, 6 184 model produced more conservative estimates than the maximum-quadratic-linear (M_Q_L) model. Both the linear mixed model and the M_Q_L model produced more conservative TA estimates than the piecewise model. TA estimates by the MIXED model, and to a lesser extent by the M_Q_L model, were equivalent to those for real mature wood, whereas TA estimates by the piecewise model were considerably lower, falling into the transition wood area.

Effect of Seed Transfer on Selected Wood Quality Attributes of Jack Pine (Pinus banksiana Lamb.)

“…In a previous study, we found that a polynomial model that accounts for the autocorrelation among successive growth rings is a better choice than the piecewise model in determining TA based on TL [11]. However, the age span of the material used in this study did not allow using the polynomial model with TL.…”

“…where Y ij = measured value of the WQA, µ = overall mean of the provenance, P i = random effect of provenance i, and ε ij = random error term associated with the j th tree of the i th provenance, ε ij~N (0, σ 2 residuals ). Intraclass correlation coefficients (ICC) were calculated from variance parameter estimates computed with the MIXED model using Equation (11). The intraclass correlation coefficient informs us about the proportion of the total variance explained by provenances [31,32].…”

“…However, many extraneous factors (e.g., silvicultural management, tree-level genetic, edaphic conditions, herbivory and disease impacts, extreme climatic phenomena, and extreme natural disturbance) could alter the complex temperature-growth relationship induced by planting of seed sources in a given test location [4,5].Together with ring density (RD), tracheid length (TL) is among the most important WQA in pulp [8] and solid wood [9] applications. TL allows a clear distinction between juvenile wood (JW) and mature wood (MW) through TA determination [10,11]. JW is found near the pith and shifts to MW toward the bark of older trees.…”

“…Together with ring density (RD), tracheid length (TL) is among the most important WQA in pulp [8] and solid wood [9] applications. TL allows a clear distinction between juvenile wood (JW) and mature wood (MW) through TA determination [10,11]. JW is found near the pith and shifts to MW toward the bark of older trees.…”

Cell geometry across the ring structure of Sitka spruce

“…One way of generating these data is the time-consuming process of manually measuring cells from physically and chemically macerated wood under a light microscope [Fujiwara and Yang, 2000]. Alternatively, optical fibre analysers are used, which allow a rapid and automated analysis of samples, and this technique has been used to build up a picture of fibre geometry across different species [Li et al, 2011, Mvolo et al, 2015 and throughout a complete tree [Yemele et al, 2015]. These studies show that cell length varies from the centre of the tree, increasing through the juvenile wood and becoming relatively constant in mature wood.…”