1. business and industrial
2. agriculture and forestry
3. forestry

Economics of Stand, Stem
and Log Segregation
Based on Wood Properties
Why Segregate?
100%
Volume Recovery
90%
80%
4%
70%
2%
4%
60%
38%
11%
50%
34%
9%
G3
15%
45%
40%
30%
39%
G2
35%
20%
31%
39%
$$$
32%
10%
19%
10%
9%
A
B
C
G1
6%
0%
D
E
F
Variability between Washington Douglas Fir sites
Glen Murphy
Professor, Waiariki Institute of Technology
Growing Confidence in Future Forests - TST meeting - April 2015
Growing Confidence in Future Forests - TST meeting - April 2015
Veneer Recovery
Veneer Recovery
Return to Log Value vs Acoustic Velocity
G1/G2 Veneer Recovery vs Acoustic Velocity
1200
Total Net Revenue ($/mbf)
G1/G2 Volume Recovery (%)
60
50
40
30
20
G1G2% = 48.9V ‐ 143.3
R² = 0.91
10
0
3.5
3.6
3.7
3.8
3.9
4.0
4.1
Average acoustic velocity (km/sec)
Amishev & Murphy 2008
Growing Confidence in Future Forests - TST meeting - April 2015
First Look May 2014
● Segregation will occur when the financial benefits
outweigh the costs.
● When, where, and who segregates stems and logs
will affect what is segregated and how segregation
occurs.
● What qualities need to be segregated is likely to be
customer dependent.
● New sensor technologies are providing a range of
new tools for determining how segregation is done.
● The earlier in the supply chain segregation is carried
out the greater is the need for standardised tools
and procedures – one or a few tools will have to do it
all.
Growing Confidence in Future Forests - TST meeting - April 2015
1150
1100
1050
$/MBF = 327.17x ‐ 188.42
R² = 0.62
1000
950
3.50
3.60
3.70
3.80
3.90
4.00
4.10
Average acoustic velocity (km/sec)
Amishev & Murphy 2009
Growing Confidence in Future Forests - TST meeting - April 2015
Goals for First 3 years
 International review of log segregation
practices and economics.
 Technical review of tree and log attributes
collected by on-board computers on
harvesters/processors
 Strengths/weaknesses of harvester/ALS/ TLS
data for assessment of external and internal
wood properties
 Design and build an economics of
segregation model
 Test the model with three case studies
 Communicate results through 3 manuscripts,
a technical note, workshop presentations and
participation in cluster group meetings
Growing Confidence in Future Forests - TST meeting - April 2015
1
International Literature Review
Murphy G, Cown D. (in review) Economics of stand, stem
and log segregation based on wood properties: a review.
Scandinavian Journal of Forestry Research.
Looked at
● International trends generating an interest in
segregation based on wood properties
International Literature Review
Findings
 Regional or stand level attribute models will
facilitate a coarse level of segregation but not
account well for the between and within stem
variation.
● Costs and benefits from the view points of different
participants in the value chain
 Many tools and techniques are available for
segregating wood based on internal
properties but few (e.g. CT scanning,
acoustics) have been implemented
commercially.
● Models that have been used to evaluate the
economics of segregation
 Some are better suited for application in
mills (e.g. CT scanning) than in forests.
● Technologies that can be used to segregate stands,
stems and logs at various points in the value chain
Growing Confidence in Future Forests - TST meeting - April 2015
International Literature Review
Findings
 The benefits of segregation are not clear
due to
 high variability of wood properties
 poor market signals (in terms of price) for wood
with superior properties
 poor understanding of the costs across the value
chain.
 Most of the existing economic models tend
to look at the economics of segregation from
the perspective of a single participant in the
value chain. Only a few models look across
the value chain and these have limitations.
Growing Confidence in Future Forests - TST meeting - April 2015
Harvester Data Technical Review
Tree Attributes
 User input – species, stand location, grade changes,
stem form
 Measured – stand location, time, diameter OB,
merchantable height,
 Predicted – DBH, diameter UB, taper, sound knot
zone, density, stem value
 Emerging - TLS - 3D sweep profile, OB taper, stem
location, reaction wood zones; RFID - stem ID
Log Attributes




User input – grade, sweep
Measured – length, diameter OB, time, location
Predicted – volume, value, density, weight
Emerging – acoustic stiffness; NIR density; TLS
reaction wood, clearwood; CV knot size and spacing
Growing Confidence in Future Forests - TST meeting - April 2015
Forest Measurement Methods
 Methods assessed: traditional, ALS, TLS,
harvester data
 Attributes assessed: area, TRV, stocking,
DBH, tree height, taper, log product mix,
external features, internal features
Growing Confidence in Future Forests - TST meeting - April 2015
Growing Confidence in Future Forests - TST meeting - April 2015
Forest Measurement Methods
External Features
Traditional
Form (e.g. double leaders) Good
Branches - OK (dependent on
cruiser)
Sweep – OK for 2D with
experience
Other defects – Good.
Species – Good
Health – Good.
Aerial laser scanning Form, branches, sweep, other
defects dependent on
representative ground plots.
Species – emerging, manual
Health – emerging, manual
Internal Features
Density – DBH cores
Stiffness – acoustics at
stand level OK
Resin pockets - poor
Internal checking – poor
All above may be
geospatially predicted at
regional level
Density - ??
Stiffness - ?? based on
crown and height
features
Resin pockets - No
Internal checking - No
All above may be
geospatially predicted at
regional level
Growing Confidence in Future Forests - TST meeting - April 2015
2
Forest Measurement Methods
External Features
Terrestrial laser Form - not automated.
Branches
– possible but only for
scanning
Harvester data
bottom portion due to occlusion.
Sweep – OK for 3D on bottom
portion –depends on occlusion.
Other defects – fluting OK
otherwise requires manual input
Species – manual input
Health – manual input
Form ?? based off number of saw
cuts or operator input
Sweep – poor – operator
dependent
Branches – operator dependent
Other external – operator
dependent
LOGECS Model
Internal Features
Density - No
Stiffness - ?? possibly linked
to sweep
Resin pockets - No
Internal checking – No
All above may be
geospatially predicted at
regional level
Density – ?? possible using
NIR and saw chips
Stiffness – acoustics on
harvester head
Resin pockets - ?? possible
with NIR
Internal checking – possible
with NIR
Growing Confidence in Future Forests - TST meeting - April 2015
LOGECS Model
 Properties of interest
 Structural - stiffness and stability
 Appearance – clearwood, defects and
stability
 Fibre - density
 Technologies of interest
 Acoustics
 Laser scanning
Growing Confidence in Future Forests - TST meeting - April 2015
Optimal Acoustic Bucking
Growing Confidence in Future Forests - TST meeting - April 2015
Optimal Acoustic Bucking
Murphy G. 2014. Priority list bucking on a mechanized
harvester considering external properties and stiffness of
Douglas-fir. International Journal of Forest Engineering.
Challenges
 Buck-to-value versus buck-to-demand
 Optimal versus near-optimal
 External only versus external and internal
 Downgrades for incorrect forecasts
 Time-of-flight versus resonance acoustics
VALMAX modified to include
priority list bucking based on
acoustic velocity forecasts
and measurements
Growing Confidence in Future Forests - TST meeting - April 2015
What Next – 2015-2016?
 Complete design and build of LOGECS
 Test LOGECS with three case studies –
Nelson/Marlborough, Hawkes Bay, BOP ??
 Communicate results through two
manuscripts, a technical note, workshop and
conference presentations, and participation in
cluster group meetings
5% increase in minimum acoustic velocity
 50% reduction in number of veneer logs
 3 to 5% drop in value recovery
Growing Confidence in Future Forests - TST meeting - April 2015
Growing Confidence in Future Forests - TST meeting - April 2015
3
What Next – 2017-2019?
 Refine LOGECS and add more wood
properties if necessary
Economics of Stand, Stem
and Log Segregation
Based on Wood Properties
 Identify cost effective and profitable
segregation strategies using LOGECS and
GxExS analyses
 Identify and test technologies for tagging and
tracking logs that have been segregated
based on internal wood properties
 Identify the most appropriate intervention
points and technologies for structural lumber,
appearance lumber and fibre markets.
Growing Confidence in Future Forests - TST meeting - April 2015
Glen Murphy
Professor, Waiariki Institute of Technology
Growing Confidence in Future Forests - TST meeting - April 2015
4