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A Review of Progress and Applications in Wood Quality Modelling

A Review of Progress and Applications in Wood Quality Modelling Purpose of ReviewProducing 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 FindingsThere 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.SummaryWe 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Current Forestry Reports Springer Journals

A Review of Progress and Applications in Wood Quality Modelling

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References (194)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
eISSN
2198-6436
DOI
10.1007/s40725-022-00171-0
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Abstract

Purpose of ReviewProducing 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 FindingsThere 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.SummaryWe 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.

Journal

Current Forestry Reports – Springer Journals

Published: Dec 1, 2022

Keywords: Cambial model; Xylogenesis; Projection model; Model ensembles; LiDAR; Within-tree variability

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