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Assessing timber quality Dan Ridley-Ellis Centre for Wood Science & Technology, Edinburgh Napier University RFS Future Foresters Technology Day 1 st National Forestry & Arboriculture Student Conference blogs.napier.ac.uk/cwst 20/10/2017

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Assessing timber quality Dan Ridley-Ellis

Centre for Wood Science & Technology, Edinburgh Napier University

RFS Future Foresters Technology Day

1st National Forestry & Arboriculture Student Conference

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blogs.napier.ac.uk/cwst 20/10/2017 2 Image : wood for good

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What is wood quality?

Wood quality depends on the application
For construction

– Strength (aka MOR) – Stiffness (aka MOE) – Density – Variation in the above Appearance and knots Distortion Durability Sizes Ease of processing Etc…

But be aware: It is not all about density!

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Density – not always a good thing

If only we were a bit heavier!

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Reducing wastage

Fail quality control Visual assessment? Segregation to other markets Log / stem data (forest data) Logs at the forest Young trees Harvest age trees Green sawn timber Dry sawn timber Logs at the sawmill Green cants Pass grading Fail grading Timber data (log / stem data, forest data) Fail pre-grading Timber data  predictive of dry (log / stem data, forest data) Cost Cost More cost incurred in processing Higher penalty for incorrect decision Cost Cost Secondary processor/end user Waste Co-products Species choice Effects of tree breeding

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Simple

Site
Spacing
Thinning
Age
DBH
Crown ratio
Slenderness
Branches

Models for forest growth (yield) can estimate timber quality (so long as we have the underpinning research)

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Increment coring
Pin penetration
Resistance drilling
Acoustics

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Standing tree acoustics

SIRT benchmarking data With a perfect grading machine

Moore, J. R., Lyon, A. J., Searles, G. J., Lehneke, S. A., Ridley-Ellis, D. J. Within- and between-stand variation in selected properties of Sitka spruce sawn timber in the United Kingdom: implications for segregation and grade

recovery. Annals of Forest Science

(February 2013)

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Felled logs

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Future forests

Different species

– New planting – Using more of what we already have

Familiar species but changed

– By climate change, pests and diseases – By forest management – By seed selection and tree breeding

Especially now with genomic selection
e.g. “Sitka spruced” project https://www.forestry.gov.uk/fr/sitkaspruced

We will need ways of dealing with more variety and less information from past experience and extensive testing

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GB volume forecast (FC)

WPCS “British spruce” WPNN “British pine” WLAD “larches” Douglas fir Lodgepole pine

000 m3

Birch Ash Sycamore Beech Oak Alder

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New technology in markets

e.g. Biorefinery

– Extractives – http://ited.iidi.org.uk/

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Summary

Existing & emerging tech allows assessment
f wood quality earlier in the chain
Doesn’t need to work tree-by-tree to be

useful

What is “quality” depends on the end use
It is not all about density!
And foresters are the ones with most