In this study, biomass equations for the above-and below-ground tree components of Scots pine (Pinus sylvestris) and Norway spruce (Picea abies [L.] Karst.) were developed. The models were based on 908 pine trees and 613 spruce trees collected in 77 stands located on mineral soil, and represented a wide range of stand and site conditions in Finland. The whole data set consisted of three sub data sets: 33 temporary sample plots, five thinning experiments, and the control plots of 39 fertilization experiments. The biomass equations were estimated for the individual tree components: stem wood, stem bark, living and dead branches, needles, stump, and roots. In the data analysis, a multivariate procedure was applied in order to take into account the statistical dependence among the equations. Three multivariate models for above-ground biomass and one for below-ground biomass were constructed. The multivariate model (1) was mainly based on tree diameter and height, and additional commonly measured tree variables were used in the multivariate models (2) and (3). Despite the unbalanced data in terms of the response variables, the statistical method generated equations that enable more flexible application of the equations, and ensure better biomass additivity compared to the independently estimated equations. The equations provided logical biomass predictions for a number of tree components, and were comparable with other functions used in Finland and Sweden even though the study material was not an objective, representative sample of the tree stands in Finland.

Biomass equations were compiled for the above-and below-ground tree components of birch (Betula pendula Roth and Betula pubescens Ehrh.). The equations were based on 127 sample trees in 24 birch stands located on mineral soil sites. The study material consisted of 20 temporary plots and ten plots from four thinning experiments with different thinning intensities (unthinned, moderately and heavily thinned plots).The equations were estimated for the following individual tree components: stem wood, stem bark, living and dead branches, foliage, stump, and roots. In the data analysis, a multivariate procedure was applied in order to take into account the statistical dependency among the equations. Three multivariate variance component models were constructed for the above-ground biomass components, and one for the below-ground biomass components. The multivariate model (1) was mainly based on tree diameter and height, and in the multivariate models (2) and (3) additional commonly measured tree variables were used.The equations provided logical biomass predictions for a number of tree components, and were comparable with other functions used in Finland and Sweden. The applied statistical method generated equations that gave more reliable biomass estimates than the equations presented earlier. Furthermore, the structure of the multivariate models enables more flexible application of the equations, especially for research purposes.

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 40(4): 673-685.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.

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