Modelling microfibril angle variation in New Zealand-grown radiata pine

Moore, John R.; Cown, D. J.; McKinley, Russell B.

doi:10.1186/s40490-014-0025-4

Cited by 24 publications

(25 citation statements)

References 49 publications

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“…Total tree height was generally not measured in these studies, which has presented some problems in previous analyses (e.g., Moore et al 2014;Watt and Zoric 2010). However, it was possible to predict the total height of trees in many studies using the relationship between disc diameter and height up the stem at which the disc was sampled.…”

Section: Description Of Datasets Used In the Analysesmentioning

confidence: 99%

Modelling variation in wood density within and among trees in stands of New Zealand-grown radiata pine

Kimberley

Cown

McKinley

et al. 2015

N.Z. j. of For. Sci.

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Background: Density is an important wood property due to its correlation with other wood properties such as stiffness and pulp yield, as well as being central to the accounting of carbon sequestration in forests. It is influenced by site, silviculture, and genetics, and models that predict the variation in wood density within and among trees are required by forest managers so that they can develop strategies to achieve certain wood density targets. The aim of the study presented here was to develop a wood density model for radiata pine (Pinus radiata D. Don) growing in New Zealand. Methods: The model was developed using an extensive historical dataset containing wood density values from increment cores and stem discs that were obtained from almost 10,000 trees at over 300 sites. The model consists of two sub-models: (1) a sub-model for predicting the radial variation in breast-height wood density and (2) a sub-model for predicting the distribution of density vertically within the stem. Results: The radial variation in breast-height wood density was predicted as a function of either ring number or both ring number and ring width, with the latter model better accounting for the effects of stand spacing. Additional model components were also developed in order to convert from annual ring density values to a whole-disc density, predict log density from disc densities, and account for the variation in wood density among individual trees within in a stand. The model can be used to predict the density of discs or logs cut from any position within a tree and can utilise measured outerwood density values to predict the density by log height for a particular stand. It can be used in conjunction with outerwood density to predict wood density distributions by logs for stands of any specified geographic location and management regime and is designed to be able to incorporate genetic adjustments at a later stage. Conclusions: The analysis has confirmed and quantified much of the previous knowledge on the factors that affect the variation in wood density in radiata pine, particularly the influences of site factors and silviculture. It has also quantified the extent and patterns of variation in wood density within and among trees.

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Section: Description Of Datasets Used In the Analysesmentioning

confidence: 99%

Modelling variation in wood density within and among trees in stands of New Zealand-grown radiata pine

Kimberley

Cown

McKinley

et al. 2015

N.Z. j. of For. Sci.

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“…Especially in older stands, the impact of MFA might be less than density. An example is the strong variation of wood density and wood stiffness among site but little evidence of variation in MFA (Moore et al 2014). Therefore, a combined use of Pilodyn and acoustic measurement would be an ideal method to predict MOE in standing trees.…”

Section: Predictability Of Wood Density Mfa and Moe Using Pilodyn Pmentioning

confidence: 99%

Estimating solid wood properties using Pilodyn and acoustic velocity on standing trees of Norway spruce

Chen

Karlsson

Lundqvist³

et al. 2015

Annals of Forest Science

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“…Spiral grain is apparently more variable and more difficult to model than other important radiata pine wood properties such as density (Kimberley et al in review) or microfibril angle (Moore et al 2014). However, the amount of variation in radiata pine SGA that was explained by the models developed here was slightly higher than by the models developed by Gjerdrum et al (2002) and Fonweban et al (2013) for Norway spruce and Sitka spruce SGA, respectively.…”

Section: Discussionmentioning

confidence: 44%

“…growth rings within discs, within trees, within stands), a mixed modelling approach was adopted similar to that used in other studies that have developed ring level wood properties models (e.g. Auty et al 2013;Jordan et al 2005;Moore et al 2014). A variance components analysis was undertaken to determine how much of the variation in spiral grain angle was attributable to each stratum (i.e.…”

Section: Discussionmentioning

confidence: 99%

“…1 Location of the 57 sites where data had previously been collected on spiral grain angle dataset and the need to be able to predict the intrastem patterns in SGA for a wide range of tree sizes, it is useful to know the relative height that each sample was taken from as well as the absolute height. To overcome this problem, total height was predicted for each tree using the same approach that was taken for the dataset used to develop a model of radiata pine microfibril angle (Moore et al 2014). This involved using a compatible volume and taper function (Katz et al 1984) along with data on the diameter and heights of each disc sampled from the tree.…”

Section: Data Sourcesmentioning

confidence: 99%

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Modelling spiral grain angle variation in New Zealand-grown radiata pine

Moore

Cown

McKinley

2015

N.Z. j. of For. Sci.

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Background: Spiral grain angle (SGA) is a wood property that has a strong influence on end-product quality, particularly for solid timber, and most commercial log and timber grading rules restrict the amount of visible surface sloping grain. The aim of this study was to develop parametric models that can be used to predict the intra-and inter-stem variation in SGA in radiata pine (Pinus radiata D.Don) trees growing in New Zealand. Methods: Empirical models were developed using a dataset that contained records from over 1,100 trees that had SGA measured in both the radial direction and at different heights up the stem. Linear and nonlinear model forms were evaluated for their ability to predict the radial variation in SGA.Results: Most values of spiral grain angle were between −5°and +10°, with a few extreme values (up to 20°) observed. A simple linear model based on cambial age was able to account for the radial variation in SGA outside of the innermost growth rings, but only explained 26 % of the overall variation in SGA. Including a relative height term in this linear model increased the proportion of variation in SGA explained to 30 %. A variance components analysis showed that 78 % of the variation in SGA occurred within individual stems, with only 7 % of the variation due to differences between sites. Conclusions: Our results confirmed that SGA decreased from the inner growth rings to the bark and increased with height up the stem, with low values of SGA only found in the lower peripheral part of the stem. Given that the data came from a wide range of sites, the relatively small amount of inter-site variation was unexpected. The models developed here can be incorporated into a growth and yield simulation system to enable forest managers to compare the potential impacts of different factors on the size of the corewood zone in a tree containing wood with high SGA.

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Modelling microfibril angle variation in New Zealand-grown radiata pine

Cited by 24 publications

References 49 publications

Modelling variation in wood density within and among trees in stands of New Zealand-grown radiata pine

Modelling variation in wood density within and among trees in stands of New Zealand-grown radiata pine

Estimating solid wood properties using Pilodyn and acoustic velocity on standing trees of Norway spruce

Modelling spiral grain angle variation in New Zealand-grown radiata pine

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