Breeding for Wood Quality; Acoustic Tools and Technology 2007 AFG - - PowerPoint PPT Presentation

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

Contents

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

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

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

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

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

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

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

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 density x velocity ≈

2

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) HM200 on log (Director) (ft/s)

Sitka spruce Western hemlock Jack pine White birch Ponderosa pine R

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

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

50 100 150 200 250 300 350 400 450 500 500 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 Time of Flight (micro-sec)

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

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 R2 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

Standing tree sampling – single trees

• Sawlog study –

radiata pine

• Correlation

between standing tree and log velocity improves as sample intensity increases

Standing Harvesting Stem Log Log Deck Lumber or Tree Processor to Mill Veneer ST300 PH330 HM200 HM200 LM600 Grader >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>>

Correlation vs number of samples

0.00 0.20 0.40 0.60 0.80 1.00 10 20 30 40 50 Number of samples Correlation (R

2)

Rx 0031 Rx 0035

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

• scilloscope

based dataset

McVicars Validation HM vs ST

y = 1.4316x - 0.2893 R2 = 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) ST velocity (km/sec)

Rx0031 Rx0035 Generic relationship Version 1 ST300 (cap) Linear (Rx0035) Linear (Rx0031) Linear (Generic relationship) Linear (Version 1 ST300 (cap))

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

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

• f 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

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

• 20
• 15
• 10
• 5

Board Temperature (C) Acoustic Wave Velocity (m/s)

V = 2365 - 17.69T (T ? 0 °C) V = 2365 - 41.42T (T ? 0 °C)

Density (MC ) adjusted acoustic speed

• 25
• 20
• 15
• 1
• 5

Source: L Bjorklund, VMR, SDC Source: P Harris, IRL Source: X Wang, University of Minnesota

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

R adiata Pine - Log velocity within stem

2.50 3.00 3.50 4.00 5 1 1 5 20 25 30 Distance up stem (m) Velocity (km/ sec) Average 3.2 km/ sec Average + 2 x SD Average - 2 x SD Stand Mean 3.2

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

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

Log age (years) Stand Linear (Stand)

Velocity vs Stand A ge

Age (years) Velocity (km/sec) Mean Velocity (50% oldest age) = 3.43 Mean Velocity (50% highest V) = 3.37 Benefit = 0.06km/sec

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