Predicting probability of A-quality lumber of Scots pine (Pinus sylvestris L.) prior to or concurrently with logging operation

Jori Uusitalo (Corresponding Author), Olli Ylhäisi, Hannu Rummukainen, Marika Makkonen

    Research output: Contribution to journalArticleScientificpeer-review

    1 Citation (Scopus)

    Abstract

    Knot properties have a profound influence on the suitability of wood for many wood products leading to significant value differences between different quality grades. It would therefore be rather advantageous to maximise the volume of good quality timber attained from the logs. The objective of this study was to assess how well A-quality lumber of Scots pine derived from log tomography features can be predicted with characteristics measured prior to or concurrently with the logging operation. The study is based on field experiments and X-ray scanning of 204 stems from southern Finland in 2014. We employed mixed logistic regression techniques to model the relationship between the main stem characteristics and probability of A-quality lumber. From the tree characteristics that can be measured or detected from standing trees, the height from the ground level to the lowest dead branch was found to be the best predictor of A-quality lumber. From the characteristics that could, at least in theory, be detected and measured at the moment of harvest, early growth rate and size of tree were found to be the best combination for predicting the probability of A-class quality.

    Original languageEnglish
    Pages (from-to)475-483
    Number of pages9
    JournalScandinavian Journal of Forest Research
    Volume33
    Issue number5
    DOIs
    Publication statusPublished - 4 Jul 2018
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    lumber
    Pinus sylvestris
    logging
    wood quality
    stems
    wood products
    tomography
    knots
    branches
    Finland
    timber
    logistics
    X-radiation
    stem
    methodology

    Keywords

    • Bucking
    • cross-cutting
    • knots
    • wood quality
    • wood supply chain management

    Cite this

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    abstract = "Knot properties have a profound influence on the suitability of wood for many wood products leading to significant value differences between different quality grades. It would therefore be rather advantageous to maximise the volume of good quality timber attained from the logs. The objective of this study was to assess how well A-quality lumber of Scots pine derived from log tomography features can be predicted with characteristics measured prior to or concurrently with the logging operation. The study is based on field experiments and X-ray scanning of 204 stems from southern Finland in 2014. We employed mixed logistic regression techniques to model the relationship between the main stem characteristics and probability of A-quality lumber. From the tree characteristics that can be measured or detected from standing trees, the height from the ground level to the lowest dead branch was found to be the best predictor of A-quality lumber. From the characteristics that could, at least in theory, be detected and measured at the moment of harvest, early growth rate and size of tree were found to be the best combination for predicting the probability of A-class quality.",
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    Predicting probability of A-quality lumber of Scots pine (Pinus sylvestris L.) prior to or concurrently with logging operation. / Uusitalo, Jori (Corresponding Author); Ylhäisi, Olli; Rummukainen, Hannu; Makkonen, Marika.

    In: Scandinavian Journal of Forest Research, Vol. 33, No. 5, 04.07.2018, p. 475-483.

    Research output: Contribution to journalArticleScientificpeer-review

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    AU - Uusitalo, Jori

    AU - Ylhäisi, Olli

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    AU - Makkonen, Marika

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