Optimal timing of early genetic selection for sawn timber traits in Picea abies

Abstract

In breeding Norway spruce, selection for improved growth and survival is performed at age 10-15 years in order to optimize genetic gain per year. We investigated whether a selection based on wood traits such as density and grain angle, measured under bark in the field at the same age would be informative enough with respect to structural quality traits of sawn boards. To achieve this objective, a sawing study was conducted on the butt logs of 401 trees from a 34-year-old Norway spruce progeny trial situated in southern Sweden. Stem discs were excised from the top of the logs and radial profile data of grain angle, and wood density was recorded for specific annual rings. The sawn and dried boards were assessed for structural traits such as twist, board density, bending stiffness (static modulus of elasticity, sMoE) and bending strength (modulus of rupture, MoR). Additive genetic correlations (r a) between single annual ring density measurements and board density, sMoE and MoR were consistently strong (r a > 0.7) for annual rings 5-13. Genetic correlations of similar magnitude between grain angle and board twist were estimated for all investigated annual rings (from 2 to around 26 under bark). Consequently, it was found that indirect selection for wood density and grain angle at the tree age 10-16 years would result in more genetic gain per year than selection at later ages. This makes it feasible to perform simultaneous selection of progeny in the field for both growth and wood traits at similar ages.

Genetic Improvement of Sawn-Board Stiffness and Strength in Scots Pine (Pinus sylvestris L.)

“…Phenotypic and additive genetic correlations of DBH with the benchmark structural traits (MOE S.local , MOE S.global and MOR, Table 3) and other wood traits measured on sawn boards (MOE BOARD , VEL MTG and DEN VOL , Table 5) were weakly to moderately negative (−0.03 to −0.65). These results are in congruence with those reported in a number of other studies: most of the genetic correlations between DBH and MOEs were negative, either weak [9,41,56], moderate [7,61] or varying by DBH measurement age [40,55]. In exception, weak positive correlations between DBH and MOEs were observed by [8].…”

“…This would correspond with the fact that trees with a lower diameter have a higher proportion of juvenile wood [63] and with the positive correlations estimated in this study between DBH and wood traits assessed on standing trees. Nevertheless, due to the relatively high standard errors associated with additive genetic correlation estimates, which were also reported in other studies [7][8][9]40,41], the results should be interpreted with caution.…”

“…Individual-tree narrow-sense heritability estimates for VEL (0.05-0.24) and MOE (0.08-0.26), reported in Table 2, were weak but in most cases still appreciable (>0.10). Low heritabilities for benchmark local and global static MOE (0.11 and 0.08, respectively) and MOR (0.14) were a little lower than those reported for Norway spruce sawn boards (0.23 for MOE S.local and 0.21 for MOR) [40]. On the other hand, heritabilities for static MOE and MOR calculated based on destructive testing of small clear specimens were found to be moderate in a number of conifer species, e.g., 0.53 and 0.54 for radiata pine [7] or 0.44 and 0.60 for hybrid larch [9], respectively.…”

Genetic Parameters of Stem and Wood Traits in Full-Sib Silver Birch Families

“…A 5 mm diameter core was taken at breast height from the south through to the north face of these trees. In total, 58 cores were collected from Hyssna and 72 cores from Brunsberg, and prepared using the method outlined by Hallingbäck et al [67].…”

“…The program provided average light intensity (LI) values for a user defined area, which included a single annual ring. LI was then converted to wood density by using a function that included temperature, relative humidity, LI values for a calibration sample, and sample thickness [67]. Average wood density (DENS Itrax ) was used for the remaining analysis, and is the average density value of all rings identified in LIGNOVISION™ for a given stem.…”