Canadian Forest Service Publications
Annual ring density for lodgepole pine as derived from models for earlywood density, latewood density and latewood proportion. 2015. Sattler, D.F.; Finlay, C.; Stewart, J.D. Forestry 88(5):622-632.
Available from: Northern Forestry Centre
Catalog ID: 36376
CFS Availability: PDF (request by e-mail)
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Model-based prediction of annual ring density (RD) is necessary to manage forests for wood quality objectives. However, annual RD in lodgepole pine (Pinus contorta Dougl. ex Loud.) exhibits a high degree of variability making it a challenge to model. We compared two methods of predicting annual RD including (1) a ring component approach and (2) a direct approach. The former approach uses model-based estimates of earlywood density (EWD), latewood density (LWD) and latewood proportion (LWP) to calculate annual RD. The latter approach uses a single model with annual RD as the dependent variable. The two approaches were tested using a dataset which included sites on the western and eastern slopes of the Rocky Mountains, within the provinces of British Columbia and Alberta, Canada. The best models for EWD, LWD and LWP included ring number and ring width, while site-specific parameters indicated that sites on the western slopes differed from those on the eastern slopes. Component-based estimates of annual RD using only fixed effects explained 25 per cent of the variability, increasing to 63 per cent with random effects. The best model for a direct estimate of annual RD explained only 5 per cent of the variability using fixed effects, increasing to 55 per cent with random effects.
Plain Language Summary
Wood density plays a major role in determining the quality of wood for industrial purposes. Density is related to strength and flexibility characteristics in solid wood products. In addition, estimation of pulp yield, carbon, and biomass from measurements of tree volume requires some knowledge of wood density. The density of the wood produced in the growth rings laid down by the tree each year is affected by the growing conditions. However, because density varies greatly from year to year (annually) and within each year (seasonally), it is difficult to develop mathematical models for predicting wood density. In addition, wood density exhibits developmental trends from the pith (core of the tree) out to the bark. We tried a new approach to capturing this variation by first modeling three components of wood density separately, then combining them to calculate the density of an annual ring. We used data from six lodgepole pine sites to develop models for earlywood density, latewood density, and latewood proportion. We found that this approach gave better results than models that predicted annual ring density directly. Although individual models developed for each site worked best, models developed from the pooled data set should be appropriate for the entire area of this study (Alberta foothills and East Kootenays in British Columbia). Regional models for the different components may be needed in other parts of the lodgepole pine range.
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