This study investigates wood density and anatomy of juvenile silver birch stems in Sweden, grown in mixed conifer stands. Our aim is to investigate if fertilization provides increased growth, as well as an eventual reduction in stem wood density. Measurements of basic density, ring width, cell wall thickness, and vessels are analyzed for 20 birch trees. Bark to pith radial sections are analyzed using a light microscope and the freeware ImageJ to compare treatments and ages. The results show that trees with fertilizer treatment have wider growth rings and thinner cell wall thickness compared to unfertilized trees. The fertilized trees also have a lower cambium age at the same height and the same diameter, and a slightly lower stem mean density (420 kg m−3) than the unfertilized stems (460 kg m−3). Fertilizer is a significant determinant of density and cell wall thickness in nonlinear models. The fertilized trees have increased growth and reached a fixed diameter earlier. The age difference between the trees likely explains some of the differences in cell wall thickness. This study supports the use of fertilizer as a silvicultural option for increasing the growth rate of silver birch for a relatively small reduction of wood density.

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This study investigated heritability of stem and wood traits to improve Swedish silver birch (Betula pendula Roth.) through breeding. Birch is 12% of Sweden’s forest area but mainly used for low value pulp or firewood. This paper applied non-destructive test (NDT) methods, and estimated traits’ heritability (h2), to help breed birch for high value solid wood products. Two trials of 22 families were assessed at age 19 for stem diameter (DBH), stem straightness, rough brown bark height (BH), grain angle (GA), Pilodyn penetration depth (Pilo) and acoustic velocity (AV). X-ray densitometry was performed on a subsample of radial cores taken at 1.3 m from the ground to get an average benchmark density. The h2 values were moderate for GA (0.20 and 0.21) and Pilo (0.53 and 0.48) at the two sites, but the h2 values for AV were low (0.05 and 0.30). There were moderate genotypic correlations between BH and DBH (0.51–0.54). There were low genotypic and phenotypic correlations between NDT measurements and other traits so including NDT in birch breeding efforts should not inadvertently reduce size, stem or wood quality. The high genetic correlations between sites suggest that GA, Pilo and AV values were determined more by genotype than by environment.

Background: Additivity has long been recognised as a desirable property of systems of equations to predict the biomass of components and the whole tree. However, most tree biomass studies report biomass equations fitted using traditional ordinary least-squares regression. Therefore, we aimed to develop models to estimate components, subtotals and above-ground total biomass for a Pinus radiata D.Don biomass dataset using traditional linear and nonlinear ordinary leastsquares regressions, and to contrast these equations with the additive procedures of biomass estimation.Methods: A total of 24 ten-year-old trees were felled to assess above-ground biomass. Two broad procedures were implemented for biomass modelling: (a) independent; and (b) additive. For the independent procedure, traditional linear models (LINOLS) with scaled power transformations and y-intercepts and nonlinear power models (NLINOLS) without y-intercepts were compared. The best linear (transformed) models from the independent procedure were further tested in three different additive structures (LINADD1, LINADD2, and LINADD3). All models were evaluated using goodness-of-fit statistics, standard errors of estimates, and residual plots.Results: The LINOLS with scaled power transformations and y-intercepts performed better for all components, subtotals and total above-ground biomass in contrast to NLINOLS that lacked y-intercepts. The additive model (LINADD3) in a joint generalised linear least-squares regression, also called seemingly unrelated regression (SUR), provided the best goodness-of-fit statistics and residual plots for four out of six components (stem, branch, new foliage and old foliage), two out of three subtotals (foliage and crown), and above-ground total biomass compared to other methods. However, bark, cone and bole biomass were better predicted by the LINOLS method.Conclusions: SUR was the best method to predict biomass for the 24-tree dataset because it provided the best goodness-of-fit statistics with unbiased estimates for 7 out of 10 biomass components. This study may assist silviculturists and forest managers to overcome one of the main problems when using biomass equations fitted independently for each tree component, which is that the sum of the biomasses of the predicted tree components does not necessarily add to the total biomass, as the additive biomass models do.