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A Review of Progress and Applications in Wood Quality Modelling

  • Modelling Productivity and Function (A Almeida, Section Editor)
  • Published:
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Abstract

Purpose of Review

Producing wood of the right quality is an important part of forest management. In the same way that forest growth models are valuable decision support tools for producing desired yields, models that predict wood quality in standing trees should assist forest managers to make quality-influenced decisions. A challenge for wood quality (WQ) models is to predict the properties of potential products from standing trees, given multiple possible growing environments and silvicultural adjustments. While much research has been undertaken to model forest growth, much less work has focussed on producing wood quality models. As a result, many opportunities exist to expand our knowledge.

Recent Findings

There has been an increase in the availability and use of non-destructive methods for wood quality assessment in standing trees. In parallel, a range of new models have been proposed in the last two decades, predicting wood property variation, and as a result wood quality, using both fully empirical (statistical) and process-based (mechanistic) approaches.

Summary

We review here models that predict wood quality in standing trees. Although other research is mentioned where applicable, the focus is on research done within the last 20 years. We propose a simple classification of WQ models, first into two broad groupings: fully empirical and process-based. Comprehensive, although not exhaustive, summaries of a wide range of published models in both categories are given. The question of scale is addressed with relevance to the range of possibilities which these different types of models present. We distinguish between empirical models which predict stand or tree-level wood quality and those which predict within-tree wood quality variability. In this latter group are branching models (variation up the stem) and models predicting pith-to-bark clear-wood wood property variability. In the case of process-based models, simulation of within-tree variability, and specifically, how that variability arose over time, is always necessary. We discuss how wood quality models are, or should increasingly be, part of decision support systems that aid forest managers and give some perspectives on ways to increase model impact for forest management for wood quality.

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Notes

  1. Analysis undertaken on 9th March 2022: https://www.webofscience.com/wos/woscc/basic-search

  2. https://www.webofscience.com

  3. https://www.hyleccontrols.com.au/product/pilodyn-wood-density-meter/

  4. https://new.abb.com/products/7TCA083160R0059/st300

  5. https://www.scionresearch.com/about-us/about-scion/corporate-publications/scion-connections/past-issues-list/issue-17,-september-2015/meet-discbot,-our-new-quality-detective

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Acknowledgements

This study contributes to the Strategic Research Areas BECC and MERGE. The authors are grateful for the suggestions and comments by the anonymous reviewers which improved the manuscript.

Funding

AHES is supported by the European Research Council under the European Union Horizon 2020 programme (Grant 758873, TreeMort). GD and DD acknowledge support from Forest and Wood Products Australia in various projects to understand wood quality in radiata pine in Australia. DD is currently funded by the Hans Merensky Foundation as holder of the Hans Merensky Chair in Advanced Modelling of Eucalypt Wood Formation at Stellenbosch University.

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Authors and Affiliations

  1. Department of Forest and Wood Science, Stellenbosch University, Private Bag X1, Matieland, 7600, South Africa

    David M. Drew & Thomas Seifert

  2. Forest Quality, PO Box 293, Huonville, TAS, 7109, Australia

    Geoffrey M. Downes

  3. Chair of Forest Growth and Dendroecology, Freiburg University, Tennenbacher Str.4, 79106, Freiburg, Germany

    Thomas Seifert

  4. Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 223 62, Lund, Sweden

    Annemarie Eckes-Shepard

  5. Centre de Recherche Sur Les Matériaux Renouvelables, Département Des Sciences du Bois Et de La Forêt, Université Laval, Québec, QC, G1V 0A6, Canada

    Alexis Achim

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Correspondence to David M. Drew.

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Drew, D.M., Downes, G.M., Seifert, T. et al. A Review of Progress and Applications in Wood Quality Modelling. Curr Forestry Rep 8, 317–332 (2022). https://doi.org/10.1007/s40725-022-00171-0

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