Breeding for Wood Quality; Acoustic Tools and Technology 2007 AFG & IUFRO SPWG Joint Conference Hobart, Tasmania – April 2007 Peter Carter – Chief Executive, Fibre-gen

1. 1
Breeding for Wood Quality;
Acoustic Tools and
Technology
2007 AFG & IUFRO SPWG Joint Conference
Hobart, Tasmania – April 2007
Peter Carter – Chief Executive, Fibre-gen

2. 2
Contents
• Why acoustics?
• How acoustics work
• Results, tricks and traps
• Who’s doing it?
• Conclusions

3. 3
Why? Global developments
• Resource wood quality is changing, target of value improvement
– Global emphasis on structural and appearance qualities
– Age of clearfall declining, log quality more variable
– Tree breeding has improved volume more than quality
• Increased attention to quality standards eg NZ Standard 3622
– Development of ‘verified visual’ grading (sample proof tested)
– Price differential in lumber and engineered wood markets
– Mills sensitive to stiffness of smaller diameter young wood
• New tools – Structural and LVL mills can now measure stiffness
Breeding for stiffness will enhance business returns

4. 4
Why? Financial values
What is stiffness worth – a couple of examples
• Verified visual grading – batch pass/fail
– VSG8 lumber premium is NZ$100/m3 ($450 vs $350)
– At 55% conversion, 80% structural, equates to $36/m3 log
– At 600m3/ha, 70% sawlog, 27 yrs, 8%, equates to $1,893/ha
• MSG lumber – incremental benefit
– MGP8 lumber premium is NZ$250/m3
– 0.1km/sec gives 5% more MGP8, worth $12.50/m3
– At 600m3/ha, 70% sawlog, 27 yrs, 8%, equates to $657/ha
Breeding for stiffness will enhance business returns

5. 5
Why? Financial values
What is stiffness worth – more examples
• Sitka Spruce – United Kingdom
– Structural £150, Industrial £100
• Spruce – Sweden
– MSR 1,450kr, Visual structural 1,350kr
• Douglas fir – Oregon, USA
– MSR $350, Visual structural $310
– LVL $350, Ply $230
• Southern Yellow Pine – Arkansas
– MSR $195, Visual structural $178
Absolute differences vary with market conditions – premiums remain
Breeding for stiffness will enhance business returns

6. 6
Why? Financial values
Other values are significant too
• Microfibril angle
– R2 in range 0.8 – 0.9
– MFA is key predictor of solid wood stability and fibre stiffness
• Pulp & Paper properties
– Fibre length and paper strength
– Coarseness and sheet quality
– Energy consumption and yield
• Eucalypt stiffness
• Ash group Eucalypt internal collapse
Breeding for stiffness will enhance business returns

7. 7
Why? Feasibility
Hitman ST300
• New tools are quick, non-destructive, easy and efficient
– Less than 1 minute/tree for testing
– Wireless, with no cables to tangle or fail
– Quick and easy insertion and removal of probes
– No cores needed
– No significant damage to young trees
• Mechanical and software enhancements improve precision
• Variability and heritability are high
• Breeding program on 10,000ha/annum could deliver >$10m/annum
Sonic speed provides an attractive breeding opportunity

8. 8
Why? Feasible and valuable
Hitman ST300
• Variability and heritability are
high
• Example mean 3.2 km/sec with
SD 0.2
• Top 10% mean is 3.5 km/sec
• Top 2% mean is 3.63km/sec
• With heritability of 60%,
delivered gain is 0.18 and 0.26
respectively
• MSG example values this at
$1,180 and $1,700/ha NPV at
time of planting
Normal Distribution
0%
2%
4%
6%
8%
10%
12%
14%
2.6
2.7
2.8
2.9
3
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
Velocity (km/sec)

9. 9
HM200, LM600 – how they work
• Stiffness = density x (velocity)2
• Velocity is derived from resonant
frequency (2nd harmonic) and length
• Sensor/microphone detects
frequency from hammer blow
• Green density is relatively constant
3.3
length
velocity = 2 x length / time
stiffness densityx velocity≈ 2

10. 10
Hitman ST300, PH330 – how they work
• ‘Time of flight’ outerwood velocity measure – higher than
log measure
• Ruggedised, waterproof, wireless, auto-distance, audible
and visual output, interface to PDA
• Velocity correlates strongly with log velocity at stand
level
Acoustic speed - standing tree vs log
6000
7000
8000
9000
10000
11000
12000
13000
14000
6000 8000 10000 12000 14000 16000
ST300 prototype on tree (ft/s)
HM200onlog(Director)(ft/s)
Sitka spruce
Western hemlock
Jack pine
White birch
Ponderosa pine
R2
= 0.925
Source: X Wang et al, University of Minnesota
Juvenile Wood
15 yrs 25 yrs 35 yrs
Juvenile Wood
15 yrs 25 yrs 35 yrs

11. 11
Improved Precision
Hitman ST300
• Mechanical and software enhancements improve
precision
– Calibration against absolute standard
– Filters enhance precision
TOF vs Distance (Brass Bar)
y = 0.2941x + 0.2476
R2
= 0.9997
0
50
100
150
200
250
300
350
400
450
500
0 500 1000 1500
Distance (mm)
TOF(us)
Recorded Time of Flight Variation
(SD 3.5 vs 7.5)
300
320
340
360
380
400
420
440
460
480
500
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Sample number
TimeofFlight(micro-sec)

12. 12
Standing tree sampling – single trees
• Measure is a single sample of outerwood velocity
• Sampling procedure and intensity must match need
• Single tree - intensive sampling
– Variation around stem
– Knot location
– Transverse
– Compression wood
– Hit variability
• 1-3 sets of 10 hits, in each of 2-4 locations around stem
• High productivity (>60 sample sets/hour) – faster than
density coring

13. 13
Standing tree sampling – single trees
• Eyrewell study – radiata pine, age 28
• Correlation between standing tree and log velocity
improves as sample intensity increases
Location/s on tree taps R
2
Upper side 3 0.44
Upper side 3 0.48
Upper side 3 0.43
Upper side (A) 9 0.50
Lower side (B) 9 0.45
Random side (D) 9 0.60
Mean A+B 18 0.61
Mean A+D 18 0.62
Mean A+B+D 27 0.67

14. 14
Standing tree sampling – single trees
• Sawlog study –
radiata pine
• Correlation
between standing
tree and log
velocity improves
as sample
intensity
increases
Correlation vs number of samples
0.00
0.20
0.40
0.60
0.80
1.00
0 10 20 30 40 50
Number of samples
Correlation(R
2
)
Rx 0031
Rx 0035

15. 15
Standing tree sampling – single trees
• Sawlog studies –
radiata pine
• ST vs HM
relationship is
stable, new vs old
• ST velocity is
higher than
‘generic’ field
oscilloscope
based dataset
McVicars Validation HM vs ST
y = 1.4316x - 0.2893
R
2
= 0.5121
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
2 2.5 3 3.5 4
HM velocity (km/sec)
STvelocity(km/sec)
Rx0031 Rx0035 Generic relationship
Version 1 ST300 (cap) Linear (Rx0035) Linear (Rx0031)
Linear (Generic relationship) Linear (Version 1 ST300 (cap))

16. 16
Standing tree sampling - stands
• More extensive sampling – large block genetic gain
trials
• Stand average measure
– Cover the stand – plots of 5+ trees
– Cover diameter range
– Variability between trees > within
– Sample as many trees as possible in least time
• 1 set of 10 hits/tree on 50+ trees/stand
• Productivity dependent upon terrain and vegetation

17. 17
Target Velocities – NZ example
• Dynamic MOE of 8GPa is indicative of VSG8 production and
would require
– Average log velocity 2.8km/sec (allowing 0.1km/sec for SE
of mean)
– Green density 1000kg/m3
• 8GPa target velocity could vary 2.70 - 3.00 km/sec average
• Equivalent standing tree velocities 3.6
- 4.0 km/sec average at harvest
• Towards end of juvenile wood
formation, target 2.8 km/sec although
2.6 may be adequate for structural
minimum (5.6 GPa)

18. 18
Results – effect of temperature on velocity
In general
• Acoustic velocity is higher at lower temperatures
But
• Rate of change is most significant around freezing
• Moisture content changes may compensate on logs, but not in trees
Temperature Effect on Acoustic Velocity of Green Board
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
3000
3200
3400
3600
3800
4000
-20 -15 -10 -5 0 5 10 15 20
Board Temperature (C)
AcousticWaveVelocity(m/s)
Stack 6 (50 boards)
Stack 2 (50 boards)
V = 2365 - 17.69T (T ? 0 °C)
V = 2365 - 41.42T (T ? 0 °C)
Density(MC) adjustedacousticspeed
2
2.5
3
3.5
4
4.5
5
-25 -20 -15 -10 -5 0 5 10 15 20 25
Series1
Series2
Series3
Series4
Series5
Series6
Series7
Series8
Series9
Series10
Series11
Series12
Source: L Bjorklund, VMR, SDCSource: P Harris, IRLSource: X Wang, University of Minnesota

19. 19
Results –velocity within stem – butt to top
• Acoustic velocity varies from butt to top although
greatest variation is between stems
• Highest velocity logs are in mid section of stem
• Variation follows pattern of microfibril angle
Source: X Wang et al, University of Minnesota
Radiata Pine - Log velocity within stem
2.50
3.00
3.50
4.00
0 5 10 15 20 25 30
Distance upstem(m)
Velocity(km/sec)
Average 3.2 km/ sec
Average + 2 x SD
Average -2 x SD
Stand Mean 3.2

20. 20
Location of boards in the log
Average
stiffness of
wood in
boards up
the stems
Average stiffness of lumber cut from
some 60 trees. Note the low stiffness at
the base of the tree, in the butt logs.
Why not cut a short, 2.5 m butt log?
1st log 2nd log 3rd log
Ping Xu, 2002
Results – log velocity within stem – pith to bark
Source: J Walker, University of Canterbury

21. 21
Results – velocity and MoE correlate with age
In general
• Acoustic velocity increases with increasing age
But
• Other factors affect velocity and MoE
• Wide range of velocities within stands
• Strategy – set appropriate breeding targets for different ages
Log age vs. average acoustic velocity
R
2
= 0.66
2.50
2.60
2.70
2.80
2.90
3.00
3.10
3.20
3.30
3.40
3.50
18 20 22 24 26 28 30 32 34
Log age (years)
Stand
Linear (Stand)
VelocityvsStand Age
2.80
2.90
3.00
3.10
3.20
3.30
3.40
3.50
3.60
3.70
20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
Age (years)
Velocity(km/sec)
Mean Velocity (50% oldest age) = 3.43
Mean Velocity (50% highest V) = 3.37
Benefit = 0.06km/sec

22. 22
Conclusions
• Highly significant values are at stake
• Variation and heritability are high
• New tools are available that are easy
to use, efficient, and precise
• Breeding applications include clonal
ranking, progeny trials, and genetic
gain studies
• For supporting information
peter.carter@fibre-gen.com
www.fibre-gen.com