Jaakko Repola

Models for vertical wood density of Scots pine, Norway spruce and birch stems, and their application to determine average wood density

Repola J. (2006). Models for vertical wood density of Scots pine, Norway spruce and birch stems, and their application to determine average wood density. Silva Fennica vol. 40 no. 4 article id 322. https://doi.org/10.14214/sf.322

Abstract

The purpose of this study was to investigate the vertical dependence of the basic density of Scots pine, Norway spruce, and birch stems, and how such dependence could be applied for determining the average stem wood density. The study material consisted of 38 Scots pine (Pinus sylvestris), 39 Norway spruce (Picea abies [L.] Karst.) and 15 birch (Betula pendula and Betula pubescens) stands located on mineral soil sites in southern Finland. The stem material mainly represented thinning removal from stands at different stages of development. The linear mixed model technique, with both fixed and random effects, was used to estimate the model. According to the fixed part of the model, wood density was dependent on the vertical location along the stem in all three tree species. Wood density in pine decreased from the butt to the top, and the gradient in wood density was steep at the butt but decreased in the upper part of the stem. The vertical dependence was similar in birch, but the density gradient was much smaller. For spruce the vertical dependence of the basic density was moderate. The model can be calibrated for a tree stem when one or more sample disks are measured at freely selected heights. Using treewise calibrated predictions of the vertical density dependence and measured stem diameters, almost unbiased estimates, and lower prediction errors than with traditional methods, were obtained for the average stem wood density. The advantages of the method were greater for pine with a strong vertical dependence in basic density, than for spruce and birch.

Keywords
Norway spruce; Scots pine; silver birch; wood density; downy birch; mixed models

Received 14 March 2006 Accepted 7 July 2006 Published 31 December 2006

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