Modeling the effect of initial planting density on within tree variation of stiffness in loblolly pine

Abstract

& Context Modulus of elasticity (MOE) is an important mechanical property determining the end-use and value of loblolly pine (Pinus taeda L.) lumber. & Aim In this study, a model was developed to predict the within tree variation of MOE, from pith-to-bark and stumpto-tip, using data collected from a 21-year-old unthinned stand where trees were planted under seven initial stand density levels (746-2,243 trees/hectare). & Methods The study was laid out in a randomized complete block design, with seven levels of initial planting density, replicated three times. Seven trees were destructively sampled from each plot, and bolts were cut from each tree at heights of 2.4, 7.3, and 12.2 m. Static bending samples (of dimension 2.5×2.5×40.6 cm) representing pith-to-bark variation were cut from each bolt and MOE measured. A three-parameter logistic function was used to model the pith-to-bark variation in stiffness with distance from pith as an explanatory variable. & Results Based on the final fitted model, it was found that the asymptotic parameter (maximum outerwood MOE013.48 GPa) was not influenced by sampling height, initial planting density, and stem slenderness. However, the inflection and scale parameters were significantly influenced by these variables. & Conclusions In summary, we found that initial planting density had a significant influence on the amount of corewood produced with higher initial planting densities producing a lower proportion of corewood as indicated by a linear decrease in inflection point with an increase in planting density.

Wood and Fiber Quality of Plantation-Grown Conifers: A Summary of Research with an Emphasis on Loblolly and Radiata Pine

“…Hence, trees planted with wider initial spacing contain a greater proportion of corewood [53][54][55], and have larger-diameter knots [54,[56][57][58]. The presence of the large corewood zone, and more knots in these short-rotation trees results in a large decline in lumber quality, and thus, in lumber recovery [9,59,60].…”

“…Models explaining height variation in SG were also developed, based on data from discs collected at various heights of trees, sampled from conventionally managed loblolly pine plantations [112][113][114]. Other than prediction of SG, models to predict radial and longitudinal variation in MFA [38,112,115,116], MOE, and MOR were developed for loblolly pine [37,55]. A schematic representation of predicted within-tree variation in SG and MFA, with ring number from pith, is presented in Figure 3.…”

“…In loblolly pine, Antony et al [126] modeled responses in SG of LW following mid-rotation fertilization, as a function of the rate of fertilizer applied, and time since fertilization. In another study, Antony et al [55] modeled the effect of initial stocking on variation in wood stiffness within trees (Figure 4). Maps showing within-tree variation in stiffness (GPa) of loblolly pine for a tree with average diameter at breast height, and total height, planted at three initial planting densities.…”

Growth and wood properties of genetically improved loblolly pine: propagation type comparison and genetic parameters

“…We observed a weak genetic correlation for DBH with wood traits but a higher and positive genetic correlation for THT with wood properties. This might be due to the positive effect of stem slenderness (ratio of THT with DBH) on wood properties that has been reported for radiata pine (Watt and Zoric 2010) and loblolly pine (Antony et al 2012b). Further, we have explored the empirical best linear unbiased predictors (EBLUPs) of clonal lines estimated from the fitted bivariate models (Fig.…”

Empirical statistical model for predicting wood properties of Paulownia fortunie: Part 1: Physical and biometrical properties

“…However, the cost of providing relevant input data is a crucial issue for the application of prediction models in forestry planning and harvester assessing systems. Models based on existing or easily measured input data are clearly preferable if they are to be of practical use (Cown et al 1999, Wilhelmsson et al 2002, Gardiner et al 2011, Antony et al 2012.…”