Modelling the influence of radiata pine log variables on structural lumber production

Abstract

We run logit models to explain the variability of Pinus radiata structural lumber in 71 second and third unpruned logs. The response variable was the proportion of lumber with a static modulus of elasticity greater or equal than 8000 MPa, pMSG8+, and the explanatory variables were log volume, branch index, largest branch, log internode index, wood basic density, and acoustic velocity. The average pMSG8+ volume was 44,30 % and 36,18 % in the second and third log respectively. Ten models were selected based on meeting statistical assumptions, their goodness of fit, and the statistical significance of their parameters. The best models (R 2 -adj. > 0,75) included acoustic velocity (AV) as explanatory variable, which explained 56,25 % of the variability of pMSG8+. Models without AV presented goodness of fit ranging from 0,60 to 0,75 (R 2 -adj.), and variables with the highest weight to explain the variability of pMSG8+ were volume, followed by wood basic density, branch index, and largest branch. It is possible to model pMSG8+ from log variables even when acoustic velocity is not available; however, this requires wood basic density models calibrated for the Pinus radiata growing zone.

Selection history affects very early expression of wood properties in Pinus radiata

“…The value of improving corewood quality in radiata pine affects decision-making on stocking, early and intermediate silviculture, and has a strategic impact on economic returns and rotation age. As an example, Gavilán et al (2023) showed that an average increase of 1% in corewood acoustic velocity implies an average increase of structural wood in a log between 8-9% for second and third logs.…”