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Development of a segregation method to sort fast-grown Eucalyptus nitens (H. Deane & Maiden) Maiden plantation trees and logs for higher quality structural timber products

Citation

Balasso, M and Hunt, MA and Jacobs, A and O'Reilly-Wapstra, JM, Development of a segregation method to sort fast-grown Eucalyptus nitens (H. Deane & Maiden) Maiden plantation trees and logs for higher quality structural timber products, Annals of Forest Science, 79, (1) Article 1-15. ISSN 1297-966X (2022) [Refereed Article]


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Copyright 2022 The Authors Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1186/s13595-022-01122-2

Abstract

Key message: A method to segregate trees and logs of planted Eucalyptus nitens (H. Deane & Maiden) Maiden has been developed, showing that accounting for wood quality during the process of segregation and sorting of timber resources allows for the recovery of structural timber of the desired quality.

Context: Appropriate sorting of raw forest resources is necessary to allocate logs to different production streams, to ensure that the desired quality of timber is achieved. Acoustic wave velocity can be used to test the wood quality of trees and logs, and its use as a sorting tool needs to be investigated prior to the development of a segregation method to recover high-quality timber.

Aims: This study aimed to develop a segregation methodology for plantation E. nitens trees and logs to obtain high-quality structural boards.

Methods: Forty-nine logs of planted E. nitens were measured, assessed with acoustic wave velocity, and processed into 268 structural boards maintaining board, log, and tree identity. Board stiffness was determined via structural testing and boards were ranked in structural grades. Linear mixed effect models were used to predict board stiffness based on tree and log variables, and machine learning decision trees were used to create a segregation method for board grades. Different segregation options were compared through scenario simulation.

Results: The prediction of individual board stiffness with tree or log variables yielded low coefficients of variation due to large intra-log variability (R2 = 0.22 for tree variables and R2 = 0.28 for log variables). However, the decision tree identified acoustic wave velocity thresholds to segregate E. nitens trees and logs. When applied in scenario simulation, segregation based on log variables produced the best results, resulting in large shares of high-quality board grades, showing that a segregation method based on wood quality traits can yield larger higher recovery of higher quality timber, in respect to other scenarios.

Conclusion: Acoustic wave velocity can be used to segregate trees and logs for structural boards from plantation E. nitens, and machine learning decision trees can support the development of a segregation method to determine operational thresholds to increase the recovery of high-quality timber.

Item Details

Item Type:Refereed Article
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Forestry sciences
Research Field:Forestry product quality assessment
Objective Division:Manufacturing
Objective Group:Wood, wood products and paper
Objective Field:Wood products
UTAS Author:Balasso, M (Miss Michelle Balasso)
UTAS Author:Hunt, MA (Professor Mark Hunt)
UTAS Author:O'Reilly-Wapstra, JM (Associate Professor Julianne O'Reilly-Wapstra)
ID Code:149574
Year Published:2022
Funding Support:Australian Research Council (IC150100004)
Deposited By:Plant Science
Deposited On:2022-04-04
Last Modified:2022-05-12
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