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Prediction of Green and Dry Board Properties from Pre-Harvest Inventory and Resi Assessment Data
Conference paper   Peer reviewed

Prediction of Green and Dry Board Properties from Pre-Harvest Inventory and Resi Assessment Data

Jonathan J Harrington, Jan Rombouts, Geoffrey M Downes, Marco Lausberg and David Lee
Proceedings of the 2022 SWST International Convention, pp.189-196
International Society of Wood Science and Technology (SWST) International Convention, 65th (Kingscliff, Australia, 10-Jul-2022–15-Jul-2022)
International Society of Wood Science and Technology
2022
url
https://www.swst.org/wp/wp-content/uploads/2023/02/65th-Proceedings-FINAL-reduced.pdfView
Published Version Open

Abstract

Forestry sciences Forestry resi sawn board density stiffness
Pre-harvest inventory metrics (including Resi traces) were collected from 10 sites in the Green Triangle region of south-east South Australia. Logs were harvested and processed in a structural sawmill through to the drymill with stiffness data collected. There was a large range in site quality, log size, heartwood percentage, log velocity, dry board density and dry board MoE between sites. At the site mean level, Resi metrics were able to estimate tree/ log under bark diameters and to rank stands by log velocity, dry board density and dry board MoE. The inclusion of other factors (log SED, tree slenderness, etc) in the MoE model improved predictions of mill output, although it remains to be seen if this finding holds in general and if the improvement is sufficient to justify the extra effort. Resi predictions of stiffness in stands could be used to target the best stands for a mill producing structural timber, or alternatively, the Resi could be used to divert the lowest stiffness stands away from such a structural mill.

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