Analyzing effects of intra- and interspecific competition on timber quality attributes of Fagus sylvatica L.—from quality assessments on standing trees to sawn boards

Abstract

Timber quality is the main driver of timber prices and is strongly influenced by the competition a tree experiences until its day of harvest. Regulating competition is an integral part of silviculture, and therefore, deeper understanding of the competitor’s influence on timber quality is important. Since mixed forest stands and the share of broadleaved tree species have increased in the recent past because of a changed forest policy in several countries, effects of mixture types on timber quality are of increasing importance. In this study, we investigated the effects of intra- and interspecific competition on the internal timber quality of European beech (Fagus sylvatica L.). To analyze the effects of competition intensity and competitor species identity on the timber quality of 82 target beech trees, three different approaches were used: terrestrial laser scanning (TLS), a quality assessment on the standing tree by local district foresters, and a quality assessment of the sawn wood (boards) after harvesting. We investigated the relationship between external and internal quality features and additionally compared the different approaches to assess quality. We found that the present competitive situation was partly related to internal timber quality, with increasing competition leading to increased internal timber quality. We further observed more discoloration in timber of beech trees growing in mixture with other broadleaved tree species. We also showed that predicting discoloration is possible through the number of bark anomalies on the stem surface. Also, the external quality assessment of local foresters on standing trees predicted the internal timber features well. Finally, TLS appeared to be a valuable addition for assessing timber quality in situ.

Appendix

See Table 5 and Figs. 8, 9, 10, 11.

Table 5 Summary of the generalized additive model (GAM) with statistical significance (F test) of the smoothing term (p_smooth), deviance explained by the model (DE), effective degrees of freedom as an indicator for linearity (EDF), and adjusted R² (R² (adj.))

Fig. 8

Exemplary images of boards from sample trees mixed with other broadleaved tree species (ash and maple) showing highest discoloration surfaces with a 35.33%, b 33.01%, c 28.73%, and d 20.18% mean discoloration surface per target tree (created using Adobe Photoshop CS3 Extended version 10.0)

Fig. 9

Differences of competitor tree attributes (a number of competitor trees, b mean distance [m], c mean DBH [cm], and d mean height [m]) per sample tree within the four different mixture types (spruce, other broadleaved tree species (OB), Douglas-fir, and beech). Letters (a and b) indicate significant differences between the mixture types at p < 0.05 (nonparametric, Kruskal–Wallis test)

Fig. 10

Range of the calculated competitor’s crown width [m] within each mixture type (spruce, other broadleaved tree species (OB), Douglas-fir, and beech) for all competitor with a DBH ≥ 30 cm. Letters (a, b, c, and d) indicate significant differences between the groups at p < 0.05 (nonparametric, Kruskal–Wallis test)

Fig. 11

Range of the accumulated knot surface [cm²] for all sample trees within each mixture type (spruce, other broadleaved tree species (OB), Douglas-fir, and beech). Letters (a and b) indicate significant differences between the mixture types at p < 0.05 (nonparametric, Kruskal–Wallis test)