Canadian Forest Service Publications
Development, validation, and application of a model of intra- and inter-tree variability of wood density for lodgepole pine in western Canada. 2013. Peng, M.; Stewart, J.D. Canadian Journal of Forest Research 43(12):1172-1180.
Issued by: Northern Forestry Centre
Catalog ID: 36883
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Wood density is an important characteristic for evaluating wood quality and estimating tree biomass. The ring-level focus of most density models requires inputs and produces predictions at a scale at which industry does not currently operate. Our objective was to develop a model at a scale intermediate between ring and tree levels that would capture the significant, operationally relevant variability that exists within lodgepole pine (Pinus contorta Douglas ex Loudon) trees. We developed a model for estimating mean wood density for groups of five growth rings using a nonlinear mixed-effects modelling approach. The data were collected from six long-term silviculture research trials in Alberta and British Columbia, Canada. An independent data set from west-central Alberta was used for validation. Our results indicated that the density variation pattern can be reproduced using ring width and distance from pith as fixed effects and including site- and tree-level attributes as random effects. The stochastic variation of density among trees was large. Based on the estimated tree-level random effect distribution, prediction bands conditional on simulated random effects were proposed to assess intertree variability of density within a site. Estimation of site-level random effects may reduce bias for sites with low or high wood density.
Plain Language Summary
The properties of the wood within a tree and between trees vary and can affect the quality and value of solid wood products. We studied wood density because it has a strong effect on wood quality. We developed and tested a model for estimating wood density using lodgepole pine stands from western Alberta and southeastern British Columbia. When wood density for a whole tree is predicted, the estimate does not reflect the variation in density within that tree. This kind of prediction would help manufacturers because higher value wood products can be made from denser wood. To predict density, we looked at annual growth rings in trees. We developed a mathematical model that describes the variation in wood density from measurements of the width and distance from the centre of the tree of annual growth rings. Average values are used for groups of 5 annual growth rings, which correspond to the re-measurement interval often used in research in which growth is monitored. This allowed us to determine some of the variability of density within the tree, and improved the performance of the model. Our computer model also simulates the variability in density as well as predicting the mean value of trees. This computer model will help companies that produce forest products forecast the quality of wood and its potential value before it arrives at the mill.