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Forest Systems
Article . 2010 . Peer-reviewed
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Forest Systems
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Simulating wood quality in forest management models

Authors: Mäkelä, A.; Grace, J.C.; Deckmyn, G.; Kantola, A.; Campioli, M.;

Simulating wood quality in forest management models

Abstract

The raw material properties of wood develop as the tree grows, laying down wood cells with specific properties, and forming the stem structure that is focal for timber quality. This development is influenced by genetic and environmental factors and forest management practices. It is desirable in growth and yield models intended for the economic assessment of management practices to include some indication of wood quality and how it is affected by genetics, environmental factors and silvicultural measures. This paper reviews approaches and models that allow us to consider the development of wood quality in combination with tree growth, and thus to include wood quality in the assessment of the value of the yield. We present such models as classified into three categories based on their complexity and information needs: quality indicators, static quality models, and dynamic quality models. We illustrate three advanced dynamic quality models and their applications with example case studies. These include empirical, hybrid, and mechanistic models applied to predictions of both sawn timber and fibre properties. Finally, we consider the current challenges for wood quality modelling in connection with growth models.

Countries
Belgium, Finland
Keywords

metsikkömallit, model, wood quality, model, simulation, timber, fibre, timber, forest management, silviculture, calidad de madera, puuaineen laatu, modelos híbridos., wood quality, simulation, orest products and technology, fibre, calidad de madera; modelos mixtos; modelos empiricos; modelos híbridos., silviculture;forest management;orest products and technology, Biology, modelos empiricos, modelos mixtos

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    25
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
25
Top 10%
Top 10%
Top 10%
Green
gold