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

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