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173M. Ivkovic, H.X. Wu, D.J. Spencer and T.A. McRae

Australian Forestry 2007 Vol. 70 No. 3 pp. 173184

Modelling the effects of stem sweep, branch size and wood stiffness

of radiata pine on structural timber production

M. Ivkovic1,2, H.X. Wu1,D.J. Spencer1 and T.A. McRae3

1Ensis-Genetics, PO Box E4008, Kingston, ACT 2604, Australia2Email: [email protected]

3Southern Tree Breeding Association Inc., PO Box 1811, Mount Gambier, SA 5290, Australia

Revised manuscript received 28 May 2007

Summary

The effects of changing three important biological traits stem

sweep (SWE), branch size (BRS) and modulus of elasticity

(MoE) on the radiata pine production system were examined

using data obtained from the Australian radiata pine industry

and from scientific experiments. Significant improvements in

sawlog grade, structural timber grade recovery and the

proportion of higher-grade timber can be obtained by reducing

SWE and BRS and by increasing MoE. A 10% reduction in sweep

reduced sawlog degrade by 17.1% and increased green timber

recovery by about 0.5%. A 10% reduction in BRS decreased

the volume of degraded sawlog by 68% and increased structural

timber recovery by 0.61.6%. An increase of 10% in MoE

increased structural timber recovery by 12.313.1%. The mainadvantage of modelling the effects of biological traits using data

from industry is greater reliability relative to models based on

assumptions. The modelling provides quantitative information

that the timber industry can use to increase its productivity and

profitability.

Keywords: models; traits; wood properties; production; structural timbers;

profitability; Pinus radiata

Introduction

Four biological traits have been identified as the most important

traits affecting the profitability of the radiata pine structural

timber production system in Australia (Ivkovic et al. 2006a).

These traits were:

tree volume growth or mean annual increment (MAI)

stem straightness or sweep (SWE)

branch size (BRS)

stiffness or modulus of elasticity (MoE) of wood.

This is because:

production of merchantable volume is influenced by growth

rate and stem form

log quality is determined by log size, straightness andbranching

board volume recovery is affected by small-end diameter and

shape of logs

board quality and the structural timber grade outturn is

determined by wood stiffness.

These four traits are targeted in the third generation of radiata

pine breeding in Australia to improve the profitability of the

radiata pine production system (Wu et al. 2005).

The effects of growth rate on volume and assortment yield under

different production conditions are included in growth and yield

models (e.g. Strandgard et al. 2002). However, the effects of

stem and wood quality traits such as stem sweep, branch size

and modulus of elasticity are not incorporated in such models.

Stem sweep, diameter, taper, eccentricity and the characteristics

of branch nodes describe log shape. The log-shape variables

interact with processing variables and determine timber volume

recovery. In radiata pine logs, a moderate sweep can reduce log

conversion percentage significantly (Brown and Miller 1975;

Cown et al. 1984; Todoroki et al. 2001). Log sweep also affects

the stiffness of boards (Downes et al. 2002).

Branch size determines knot size in boards, which affects timber

strength (e.g. Bier 1986; Todoroki et al. 2001, 2002). Accuracy

of prediction of timber strength using MoE can be improved by

considering knot size, knot area and position of the knot across

the face of the board (Grant et al. 1984). The influence of sweep

and knots on timber volume and quality is reflected in current

standards for visual stress-grading of softwoods (AS 28582004).

Wood stiffness determines the mechanical performance of

structural timber. Australian standards AS/NZS 4063 (1992) and

AS/NZS 4490 (1997) provide a means for evaluating the

structural design properties of a reference population of graded

timber and for ongoing monitoring of production. More recently,

acoustic measurements of the MoE of logs have been used as

predictors of timber MoE (e.g. Matheson et al. 2002; Dickson

et al. 2004).

To evaluate the relative economic impacts of sweep, branching

characteristics and wood stiffness on a structural wood produc-tion system, links between measurements of these traits and

the value of structural timber are necessary. Models such as

SAWMOD (Whiteside et al. 1997) within the decision-

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174 Effects of stem sweep, branch size and wood stiffness on structural timber production


making package ATLAS Forecaster (ATLAS Technology 2006),

and AUTOSAW simulation software (Todoroki 1997), have

been used to assess the relative importance of different traits

of radiata pine. However, these models have been mostly

developed under production conditions in New Zealand, and it

is uncertain whether they are applicable for evaluating radiata

pine grown in Australian plantations with high stocking and earlyproduction thinning regimes (Lavery 1986).

Recent studies such as Resource evaluation for future profit

(McKinley et al. 2003) and Breeding radiata pine to maximise

profits from structural products (Wu et al. 2005) have provided

some excellent Australian data for models of radiata pine solid

wood production. We previously developed a bio-economic

model (Ivkovic et al. 2006a) based on various biological

(e.g. within-stem patterns of traits), technological (e.g.

silviculture, processing technique) and economic (e.g. measures

of profitability) parameters that simulates the effects of tree

traits on the profitability of production systems. The model

connected different traits and production system componentsthrough linear and nonlinear relationships. The objective of this

article is to present more detailed models of trait effects in

particular, how SWE, BRS and MoE influence structural wood

quantity and product quality (grade) in the radiata pine structural

timber production system.

Materials and methods

Data sets

For this study we obtained information and data sets on

production wood-flows from industry, on form and branchingfrom measurements in plantations and progeny tests, on log

shape from optical scanner data and on timber grade recovery

from sawmill studies. The data sets and sources were:

1. Information on production flows of radiata pine wood,

from growing and processing industry participants including

Hancock Victorian Plantations Pty Ltd (HVP), South

Australian Forestry Corporation (ForestrySA), Auspine Ltd,

Green Triangle Forest Products (GTFP), Norske Skog Paper

Mills (Australia) Ltd, Midway Plantations Pty Ltd, Treecorp

Pty Ltd and Associated Kiln Driers Pty Ltd (AKD) (Ivkovic

et al. 2006a)

2. Branch size distribution data, from ForestrySA (Dr JanRombouts, ForestrySA, Mt Gambier, 2004, pers. comm.).

Two sites in the Green Triangle region, planted in 1972 and

1973, were assessed in 2000. Silvicultural treatments

included three thinning regimes:

optimum thinning guide (OTG) (stems after thinning

T1: 660 ha1; T2: 460 ha1)

OTG + 25% (T1: 850 ha1; T2: 590 ha1)

OTG 45% (T1: 400 ha1; T2: 260 ha1),

and four fertiliser regimes: 0, 150, 300 and 2 150 kg N ha 1.

Maximum branch size was recorded for each of two sub-logs,

in each of five log-height classes (4.5 m, 10.5 m, 16.5, 22.5

and 28.5 m); altogether 1576 (sub) logs were assessed.

3. Form and branching data, from assessment of Progeny Test

PT53 (David Spencer, Ensis Canberra, 2004,pers. comm.).

PT53 was planted in 1972 at Bondo (Buccleuch State Forest,

New South Wales (NSW)) and included progeny from both

control-crossed (including reciprocals) and open-pollinated

families, and unimproved control material. The trial was

assessed for growth and form in 1981. In 1986 the trial was

thinned on an out-row basis: the middle tree in each row plot

of five trees was felled. The thinned trees were assessed forbranching, in particular: height of each whorl; branch diameter

and angle at each whorl for one branch on one side of the

tree; and all branch diameters and angles in the first base

whorl above 6 m. Altogether 90 trees were assessed.

4. Optical log scanner data, from Tarpeena Sawmill, SA (Rob

Hansen, Auspine Mt Gambier, 2004, pers. comm.). The

sawmill is targeting the house framing market, and it has a

high-technology saw line associated with kiln drying, auto-

grading, stacking and moulding operations. Shape-scanning

technology has improved timber recovery from logs of given

specifications. The data sets included logs scanned in October

and November 2003, and contained pattern-sorted logs andassociated recovery, from logs of two lengths: 4.8 m and

6.0 m. All logs originated from clear-fall operations at two

sites: Byjuke and Kongorong. Logs with excessive sweep

(>100 mm) were rejected. Sweep was measured as the

largest deviation (mm) of the log from a centre line between

the log ends. The values were divided by log length to obtain

sweep in mm m1. Green board volume divided by the log

volume was used as a measure of the recovery.

5. Sawmilling study results, from the Lakeside Sawmill (Tony

Haslett and Alan Selleck1, GTFP Mt Gambier, 2003, pers.

comm.). The sample included 50 logs from 3738-y-old

clear-fall, with 2530% of the harvested logs from highersite qualities (SQ1SQ3) and 7075% from lower site

qualities (SQ4SQ7). The logs were 4.2 m long and limited

to a small-end diameter range of 1842 cm which can

produce structural lumber. Logs were segregated by log

position (butt log, and 2nd, 3rd, etc. upper log). Logs were

sorted and colour coded and the following information

recorded: log position, diameter, sound-wave velocity and

branch index. Sawlogs were sawn with current patterns,

lumber sorted, dried in standard high-temperature kiln

schedules, planed and machine graded.

6. Resource evaluation data, from a recent collaborative

project: Resource evaluation for future profit (McKinley

et al. 2003). A sub-sample of trees was selected from ten

previously, intensively, sampled sites. Selection criteria used

were diameter at breast height and outer-wood basic density,

and selections were representative of the range of the two

traits for each site. Tree selection favoured those trees that

could provide 5-m logs with a minimum 20-cm small-end

diameter, as regular disc sampling at 5-m intervals was

required for at least six stems per site. Malformed or strongly

swept stems were avoided. Between nine and sixteen stems

were selected per site, with some stems making five logs,

while others provided only two or three logs. The overall

1Haslett, T. and Selleck, A. (2003) Return to sawlog: clearfall analysis. Green

Triangle Forest Products (GTFP) Unpublished.

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objective was to obtain at least 40 logs per site, which could

be subsequently split into three batches each of 1315 logs

for sonic sorting.

In all 407 logs were sawn into samples 100 mm 40 mm at

Whitehead Timber Sales sawmill in Mt Gambier. The lumber

was segregated into heart-in (containing pith) and sapwood

packets for separate kiln drying schedules in order tooptimise the drying process at Auspines Tarpeena Sawmill.

The rough-sawn kiln-dry lumber was gauged to 90 mm 35 mm

at CHHs Mount Gambier sawmill, sorted and subsequently

tested on a stress grading machine according to Australian

standard AS/NZS 4063 (1992). As this was a batch sawing

study, boards could not be tracked back to individual trees or

logs. Therefore, it was not possible to examine the relation-

ships between SilviScan data measured on discs from

individual trees and the boards produced.

7. Sawmilling study data, using logs from CSIRO Progeny Test

PT52 (Matheson et al.1997; Matheson 1998). PT52 was

planted in 1971 at Tallaganda State Forest, NSW, andcontained 306 control-pollinated crosses of selected radiata

pine. Growth and form traits were measured in 1979, 1982

and 1995. In 1996, 11 crosses (families from a 4 4 diallel)

were used for the analysis of wood properties and a saw-

milling study. Two 3.6-m bottom logs from each tree from

above 1.3 m were sawn for optimum volume recovery and

produced a total of 1254 boards. The boards were then kiln

dried to 12% moisture content, dressed and machine stress-

graded in a commercial sawmill operation. A total of 293

boards, two for each tree sampled, were used for small-clear

mechanical testing (MoE, MoR, stability and microfibril

angle testing). Knot number, type, diameter, distance fromedge, and stress grade of the knot section were recorded for

each board.

Method to assess the effects of stem sweep

Stem sweep (SWE) was defined as the maximum deviation of

the log axis from a straight line over a length of log in units of

millimetres per metre (mm m1). The two main effects of sweep

on a production system are through log grade and structural

timber recovery:

Effect of sweep on log grade

Data set 6 by McKinley et al. (2003) was used to examine the

effect of sweep on log degrade, that is the ability of logs to

meet sawlog-grade specifications (James 2001). Data were

available on sweep of logs only after extremely deformed logs

were eliminated because most modern harvesting machines

remove swept butts and other deformities after tree felling and

before log making. According to information from industry,

about 3% of total harvested log volume does not meet sawlog

specifications due to excessive sweep (Lew Parsons, ForestrySA,

Mt Gambier, 2003, pers. comm.). To describe the distribution

of sweep, a log-normal distribution is usually assumed

(Whiteside 1990; Turner and Tombleson 1999), but such adistribution fitted to data set 6 would not represent the log sweep

initially present at harvesting. To approximate the distribution

of the logs before initial grading by the harvester, a right-

censored log-normal distribution (with 3% of extreme sweep

values assumed missing) was fitted to the individual sweep

observations using procedure LIFEREG in SAS (SAS Institute

2005). The LIFEREG procedure uses an iterative algorithm

developed by Turnbull (1976) to compute a nonparametric

maximum likelihood estimate of the cumulative distribution

function for the data. Goodness-of-fit of such a distribution to

the data was confirmed using the Kolmogorov D statistics(P > 0.10). The mean and variance of the log-normally

distributed random variable SWE were obtained as described

by Johnson et al. (1995).

Data sets 4 and 6 were used to analyse the significance of age

(confounded with site), diameter and log-height class using

ANOVA (SAS Institute 2005). To estimate the effect of sweep

on sawlog degrade, mean sweep values were assigned to harvest

age, diameter and log-height classes. The proportion of degraded

logs for each age, log-diameter and height class was derived

from the log-normal distribution using the sweep limits

currently set by ForestrySA (James 2001). The models were

then used together with volume allocation to calculate overallaverages, as suggested by Downes et al. (1997).

Effect of sweep on timber recovery

Cown et al. (1984) established that an increase of 0.1 in the

ratio of sweep to SED results in a decrease of about 5% in timber

recovery (Cowns rule of thumb). Todoroki (1995), using 100

radiata pine pruned and unpruned logs and the sawmill simulation

software AUTOSAW, confirmed Cowns result. Another

simulation using SAWMOD (Whiteside et al. 1987) revealed

that an increase in sweep of 1 mm m1 of log length reduced

green timber recovery by about 0.5%, although the relationshipwas generally nonlinear and differed among SED classes

(Greaves2).

The effect of sweep on green timber recovery in the current

study was modelled using the optical log-scanner data set 4

provided by industry, although we recognised that the sawlogs

entering sawmills have already been selected for straightness

in the forest. The effects of sweep and its interactions with site,

log-length or SED on green timber recover were assessed by

analyses of variance. Sweep was linked with timber recovery by

regression equations. The effects of sweep on green timber

recovery from each SED class were estimated by multiple

regression, and the results were compared with results fromsimulations with AUTOSAW(Todoroki 1995) and SAWMOD

(Greaves2).

Method to assess effects of branch characteristics

Traits related to branching habit include branch size, branch

angle, whorl frequency and number of branches within a whorl.

Branch characteristics may be assessed by different measures:

the most commonly used are average branch size (BRS),

maximum branch size on a log (MaxBRS), and average of

2Greaves, B.L. (1999) Radiata: picking the winners: the estimation and applica-

tion of economic weights for unpruned radiata pine grown for structural timber

and liner-board. Restricted report for STBA, Mt Gambier, South Australia.

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maximum branch sizes in four quadrants of a log or branch index

(BIX) (Inglis and Cleland 1982; Woollons et al. 2002). BIX

has been commonly used in New Zealand as a variable to

correlate with sawn timber recovery (Cown 1992; Todoroki et

al. 2001, 2002), and it is also used in SAWMOD (Whiteside

et al. 1987). The effects of BRS, MaxBRS and BIX on log grade

and structural timber recovery were examined to study theeffects of branching characteristics on production systems.

Effect of maximum branch size (MaxBRS) on log grade

The effect of MaxBRS was evaluated using the ForestrySA

standard limit (75 mm) allowable for premium sawlogs (James

2001). Data on branch size distribution obtained from industry

and our data (data sets 2, 3 and 6) were used to fit regressions

(procedure GENMOD SAS Institute 2005) for allocating branch

size distribution among age, diameter and log-height classes.

The effects of harvest age (thinning and clearfall), log diameter

and log-height class on maximum branch size were all accounted

for in that way, and the models were then used together with

volume allocation to calculate overall averages as suggested by

Downes et al. (1997).

Using data sets 2 and 3, the goodness-of-fit tests (SAS Insight,

SAS Institute 2005) for log-normal andWeibull distributions

were evaluated. The two distributions were used to predict

MaxBS for individual logs within different sawlog classes. The

simplified method of moments (Garcia 1981) was used to

calculate Weibull distribution parameters based on observed

means and coefficients of variation. The fitted distributions

were used to compute the proportion of logs with MaxBRS

>75 mm. Where the data contained only BIX, MaxBRS wasestimated by a formula relating the two variables (Whiteside et

al. 1987):

MaxBRS = 1.315BIX 0.48

or BIX = 0.6426MaxBRS + 1.036.(1)

Effec t of branch size (BRS) on structural timber grade

recovery

The effects of BRS were evaluated using three sets of data:

Data from an industry sawmilling study on the effects of

branch size on machine-graded pine (MGP) grade recovery

(data set 5, Haslett and Selleck1) were used to relate MGP

grade recovery to the branch index (BIX) of logs by linear

regression.

Data from a previous CSIRO sawmilling study (data set 7,

Matheson 1998) were used to evaluate the effects of knot

size on machine grade recovery. A logistic regression was

used to relate recovery percentage data to knot size (Hbert

and Cown 1999). In that model knot size was assumed to be

the same as branch size.

Simulated data from SAWMOD (Whiteside et al. 1987)

were used to predict recovery of structural timber grades for

various BIX values (210 cm) as reported by Greaves2. To

convert F grades into MoE grades, a cumulative normal distri-

bution of MoE was fitted to the grade recovery percentages.

Method to assess effects of modulus of elasticity

To provide an objective trait for genetic improvement, MoE

was defined as the whole-tree clear-wood MoE at harvesting

age. High correlations have been shown between outermost

samples and whole-tree MoE values of radiata pine at a given

height (e.g. McKinley et al. 2003; Wu et al. 2006). To estimatethe effect of whole-tree MoE on structural timber recovery,

however, estimates of the MoE distribution within log age,

diameter and height classes were necessary (Downes et al.

1997). SilviScan and Director HM200 data (set 6) from a

recent resource evaluation study in the Green Triangle region

(McKinley et al. 2003) were used to allocate mean MoE values

to age, diameter and log-height classes by ordinal regression

analyses (Procedure GENMOD, SAS Institute 2005).

We assumed that the distribution of clear-wood MoE within

each log class was normal. The MGP grade recovery was

determined by evaluating (i.e. shifting) the within-class normal

distribution of MoE over the current MGP grade limits (PTAA2002) (Fig. 1). The model assumed knot size to be an indepen-

dent effect relative to clear-wood stiffness (Carter et al. 2006).

The model fitted well the current MGP production outturn. The

effect of dynamic modulus of elasticity (MoE = nominal density

(stress wave velocity)2) on MGP grade recovery was also

estimated using the same resource evaluation study data.

Results

Effects of stem sweep

Effect of sweep on log grade

Summary statistics for sweep (SWE) in plantation-grown logs,

estimated via a censored log-normal distribution, are given in

Table 1a. First logs had more sweep (7.1 mm vs. 3.5 mm), a

higher coefficient of variation (1.7 vs. 1.4) and a less skewed

7 8 9 10 11 12 13 14 15 16 17 18 19 20

MoE (GPa)

MGP15MGP10 MGP12

Frequency

Figure1. Hypothetical representation of the effect of shifting the normal

distribution of MoE over grade limits for machine-graded pine (MGP)

(PTAA 2002). Unbroken line = a base distribution; broken line = a

distribution after trait improvement.

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distribution (1.1 vs. 2.3) than upper logs. For log-height classes,

only the contrast involving first logs was significant(P < 0.001),

and therefore in subsequent models only two classes (butt and

upper) were included. The significance of factors affecting

sweep was examined by ANOVA using untransformed data set 6

(McKinley et al. 2003) (Table 1b). Although harvest age was

a (marginally) significant (P < 0.038) factor affecting sweep, itwas difficult to model because there was no obvious trend with

age. (In data set 6 the effect of age is confounded with the effects

of site and silvicultural regime.) Small-end diameter appeared

not to be a statistically significant factor influencing sweep

(P > 0.079).

The log-normal distribution fitted the within-class data best, a

result consistent with that of Turner and Tombleson (1999), who

showed that the log-normal distribution fits sweep data better

than the exponential or the Weibull distribution. Kolmogorov

D statistics for log-normal and exponential distributions were

0.086 (P > 0.01) and 0.243 (P < 0.001) respectively for butt

logs; and 0.128 (P < 0.01) and 0.186 (P < 0.001) respectivelyfor upper logs. Based on these results, a log-normal distribution

was a more reliable predictor of log sweep. Figure 2 is a

histogram of observed frequencies and fitted distributions for

upper logs.

1.2 2.4 3.6 4.8 6 7.2 8.4 9.6 10.8 12 13.2

Sweep (mm m1)

Frequency(%)

95

80

25

20

15

10

5

0

Figure2.Histogram of observed sweep distribution for upper logs (data set 6),

and log-normal (unbroken line) and exponential (broken line) approximations

Table 1.(a)Summary of statistics for the variable sweep (mm m1), and (b) results of analysis of variance of sweep

(data set 6, McKinley et al. 2003). LH = Log-height class (i.e. 1st or upper), SED = Small-end diameter class.

(a) Summary of statistics for sweep

Log height class Mean Min. Max.Standard

deviation

Coefficient of

variationSkewness Kurtosis

First log 7.1 0 16.3 12.1 1.7 1.1 0.6

Upper logs 3.5 0 13.3 5.0 1.4 2.3 2.4

(b) Results of analysis of variance for sweep

Source DF Pr > F

Age1 009

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Using the log-normal fit based on original data, the volume ofeach sawlog expected to be degraded by sweep was calculated.

Exclusion of extremely deformed logs resulted in slightly

downward-biased position and dispersion statistics. The

calculated sawlog degrade was somewhat lower than that

suggested by the plantation managers (2.5% vs. 3.0%), but this

was expected because the estimates of the mean and SD were

downwardly biased. Using an upper-censored distribution, we

adjusted the mean and CV in our model so that the resulting

degrade was about 3%. The results indicated that the volume of

degraded log could be much reduced (by 17.1%) by a 10%

reduction in sweep, and the reduction would be greater in the

larger centre-diameter classes (Table2a).

Effect of sweep on green timber recovery

Sweep, sweep log length and sweep SED had statistically

significant (P < 0.0001) effects on green timber recovery

(Table3). The interaction of sweep with log length is only a

consequence of sweep being measured over a greater length in

longer logs. However, the interaction of sweep and SED

indicated that the effects of sweep on recovery were dependent

on sawlog diameter. Analyses of sawmill data indicated that

sweep had a strong negative relationship with green timber

recovery (Fig.3). The regression coefficients of green timber

recovery on sweep within each SED class reflected the SWE SED interaction, showing that sweep more strongly affected

timber recovery from sawlogs with smaller diameters.

The average effect of a 10% reduction in mean sweep on the

average green timber recovery based on industry data is

presented in Table2b(i). The effect of the same 10% reduction

in mean sweep based on results from Cown et al. (1984) and

Todoroki (1995), and from SAWMODsimulations were also

calculated and are given in Table2b (ii) and(iii). The increases

of recovery were similar among the three models. Expressed

as percentages, the recovery increase was larger for smaller

log classes (e.g. 0.75% for 1525 cm logs, but only 0.43% for

4050 cm logs).

Effects of branch characteristics

The maximum branch size (MaxBS) did not differ significantly

(P = 0.68) between the four fertiliser treatments of data set 3.

However, significant differences (P < 0.001) were detected

between thinning regimes (for Optimum Thinning Guide (OTG)

mean MaxBRS = 3.7 cm; for OTG + 25%, MaxBRS = 3.4 cm,

and for OTG 45%, MaxBRS = 4.4 cm). There were also

significant differences (P = 0.01) between five log-height

classes, using either Bonferroni (experiment wise) or Duncan

(comparison wise) tests (Table 4). Significant overall

differences (P < 0.001) for both MaxBRS and BIX between log-

height classes were also found in data set 6 (McKinley et al.

2003), except between classes 1 and 2 (P < 0.05). In this latterdata set, trees belonging to different age classes were pooled.

Because of these significant differences our model had to

account for harvest age, and log-diameter and log-height classes.

The regression model for allocating branch size distribution

among the age, diameter and log-height classes is given in

Table 5a, and approximations of the distribution of within-class

maximum branch size are given in Figure4. The log-normal

distribution fitted data slightly better than the Weibull

distribution.

Table 2. (a) The reduction in volume of degraded sawlog at clear-fall in each diameter class after a 10% decrease in mean

sweep, and (b)the average increase in green timber recovery after a 10% decrease in mean sweep, estimated by three different

methods

Diameter class (cm)Attribute

F

Log length 1

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Recovery(

%)

SED Class 1

70

60

50

40

30

20

0 2 4 6 8 10 12 14 16

Sweep (mm m1)

Recovery(%)

SED Class 3

70

60

50

40

30

20

0 2 4 6 8 10 12 14 16

Sweep (mm m1)

Rec

overy(%)

SED Class 5

60

55

50

45

40

35

0 2 4 6 8 10 12 14

Sweep (mm m1)

Recovery(

%)

SED Class 2

70

60

50

40

30

20

0 2 4 6 8 10 12 14 16

Sweep (mm m1)

Recovery(%)

SED Class 4

65

60

50

40

30

20

0 2 4 6 8 10 12 14 16

Sweep (mm m1)

55

45

35

Figure3. Effect of sweep on green timber recovery in five small-end diameter (SED) classes with 5-cm increments from 15 cm to 40 cm (data set 4)

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0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 5 10 15 20 25 30 35 40 45 50 55 60 65

Max branch size at 6 m (mm)

Cumulativep

robability

Actual

LogNormal

Weibull

Figure4. Approximations of the distribution of maximum branch size at

whorls closest to 6 m height (data set 3)

Table 5. Regression coefficients used to allocate to small-end diameter (SED1) and log-height (LH2) classes(a) maximum

branch size (MaxBRS) (data set 2); and (b) clear-wood MoE value based on SilviScan prediction (data set 6, McKinley et

al. 2003). No between-class interaction coefficients were statistically significant.

(a) Maximum branch size (b) Clear-wood MoE

Parameter Estimate Std dev. Pr>ChiSq Estimate Std dev. Pr>ChiSq

Intercept 6.2988 0.4967

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The following are our results related to the effects of branching:

Effect of maximum branch size (MaxBRS) on log grade

The log-normal distribution was used to estimate the percentage

of sawlogs with branch size above the cut-off point of 75 mm

MaxBRS. Using the fitted distribution, the percentage ofdegraded sawlog was calculated before and after a 10% reduction

in MaxBRS. An overall 68% decrease in the volume of degraded

sawlogs was obtained by decreasing MaxBRS by 10%. The

decrease was slightly higher in larger diameter classes (>40 cm)

than in smaller diameter classes (< 30 cm) (i.e. 70% vs. 66%).

Effect of branch size (BRS) on structural timber recovery

The percentages of MGP structural grades were evaluated using

three models:

Linear regressions from an industry sawmilling study (data

set 5, Haslett and Selleck

1

). In the study the total MGP yieldswere high, with values of 87% for upper logs and 90% for

butt logs. The branch index (BIX) together with SED

accounted for 4171% of the variation in MGP grade

recovery. The linear regression coefficients of BIX on MGP

grade recovery were used within each SED class within our

base model (Table 6a).

Ordinal regression of CSIRO sawmilling study data (Matheson

1998). The minimum machine grade of sections including

knots was correlated with overall board F grade (minimum of

all sections within a board), and the Spearman correlation

coefficient was 0.76 (Prob > |r| = 0.001). Hence,in 76% of

293 boards the minimum grade of knot section determined

overall board grade, and in only 24% of boards the overall

board grade was determined by the minimum strength of clear

sections. Ordinal logistic regression was used to predict board

grade based on average knot diameter, and the regression

coefficients were statistically significant. Maximum likeli-

hood estimates of logistic regression coefficients were used

to evaluate the effect of knot diameter on timber grade

recovery (Table 6b). However, the accuracy of classification

was not high, with less than 60% concordant. Given the

assumption that the minimum grade of knotty section is

uncorrelated with the minimum grade of clear section, then

knot number was a significant factor (P = 0.0001) correlated

with overall board grade, even more closely than knot diameter

(P = 0.0230). When number of knots was introduced as a

second independent variable, the fraction of correctly

classified observations increased to more than 65%.

SAWMOD simulations as presented by Greaves (1999). The

results showed that BIX reduced recovery rate in a linear trend

and an increase of BIX by 1 cm decreased the MoE by about0.74 GPa (Table 6c).

Effects of modulus of elasticity

Clearwood MoE was determined by harvest age, diameter and

log-height class. The distributions of MoE for different ages

(continuous), and diameter and log-height classes were

estimated by multiple linear regressions (procedure GENMOD,

SAS institute 2005, data set 6); the results are in Table 5b. These

results were based on SilviScan predictions of clearwood

MoE. Dynamic MoE data obtained by using Director HM200

were also analysed, and the within-tree distribution of MoE wasfound to be similar (results not shown).

The effect of an increase in 10% MoE (or a shift of MoE

distribution mean over set limits, Fig. 1) on MPG recovery was

estimated using the SilviScan and Director M200 measure-

ments. Both data sets produced similar results: a decrease in

lower-grade boards and an increase of higher-grade boards. The

fraction of visual F5 grade decreased from 27.3% to 14.2%

and 15.1% using SilviScan predicted and dynamic MoE,

respectively. In contrast, the fraction of MGP15 grade increased

from 2.3% to 10.1% and 9.2% using SilviScan predicted and

dynamic MoE, respectively(Table 6d, e).

Discussion

Significance of industry-based models

In this study, a series of models was constructed using data from

industry and scientific studies. These models, linking tree traits

with product value, were constructed to estimate effects of tree

traits on the value of harvested log and end-products. A well-

constructed production system model based on data and

component models obtained directly from industry is highly

desirable, because it is realistic and reliable.

Table 6.Base percentage of MGP structural grades and changes after a 10% reduction in: (a) branch index (BIX), the values based

on industry sawmilling study (data set 5, Haslett and Selleck1); (b) branch size (BRS), based on the CSIRO sawmilling study (data

set 7, Matheson 1998); (c)BRS based on SAWMOD simulations (Greaves2); and a 10% increase in (d) modulus of elasticity

MoE based on SilviScan predictionsand (e) MoE based on Director HM200 readings (data set 6, McKinley et al. 2003)

Change in MPG (%)Structural grade Base MPG (%)

(a) BIX 10% (b) BRS 10% (c) BRS 10% (d) MoE + 10% (e) MoE + 10%

F5 and F7 27.30 26.40 25.70 26.70 14.2 15.1

MGP10 48.50 47.90 49.80 48.60 43.1 43.8MGP12 21.90 22.70 22.20 22.30 32.6 32.0

MGP15 02.30 03.07 02.32 02.34 10.1 09.2

Total MGP 72.70 73.60 74.30 73.30 85.8 85.0

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Bio-economic modelling has been widely used in animal

breeding programs (Tess et al. 1983; Hirooka et al. 1998, Koots

and Gibson 1998a,b; Wolfovaet al. 2005). Such models combine

biological and economic factors (inputs and outputs) within a

production system. Using such a model, the effects on the

production system of changes in any of the relevant factors can

be investigated. Such models provide a very good tool forestimating the economic value of genetic changes in various

traits, and can also be used to investigate the robustness of these

values to changes in management and market factors.

Besides our model (Ivkovic et al. 2006a,b) there have been

other recent attempts to include measures of tree form and wood

quality into value evaluation models in Australia (Strandgard et

al. 2002; Catchpoole and Nester 2002). Models are also being

developed linking tree or log characteristics to radiata pine

growing and processing in New Zealand (e.g. ATLAS

Forecaster or AUTOSAW). These packages are based on a

series of models describing the production process from stands

to mills, and use measures of biological traits to aid decision-making (Carson 1990). A significant body of work was also

developed by the IUFRO Working Party 5.01.04 (Wood Quality

Modelling) and Wood Quality Initiative Ltd, a research and

development company in New Zealand. Such work is likely to

have a significant effect on productivity and wood product value.

Reliability of the models

A bio-economic model usually needs a series of component

models to connect tree biological traits to the production system

Ivkovic et al. 2006a,b). In this study, we used industry and other

data to develop individual models to evaluate the effects of threebiological traits (SWE, BRS and MoE) on sawlog outturn and

timber recovery.

Our models accommodated non-linear relationships to estimate

the effect of tree traits on the production system (i.e. shifting

of trait distributions). Simple linear relationships alone may

over-simplify reality, and lead to unrealistic estimates of the

potential value of the traits examined (Greaves et al. 1997;

Koots and Gibson 1998a). However, models based on non-linear

relationships (e.g.evaluating trait distributions over set limits)

are highly dependent on the set limits and the estimates of the

trait means. Resource evaluation studies such as by McKinley

et al. (2003) produced more reliable means and distributionsfor tree traits.

We used only linear relationships (regressions) for mean trait

value allocation to log age, diameter and height classes.

Development of non-linear trait value allocation models could

improve our allocations (e.g. Tian and Cown 1997). The current

model does allow the importance of non-linearities to be

evaluated, and another study (Ivkovic et al. 2006b) did include

sensitivity analyses to verify some of our assumptions, including

production system parameters and trait distribution parameters.

Data set 6 (McKinley et al. 2003), frequently used in our study,

was based on 400 logs; data set 2 on maximum branch sizeincluded 1576 logs; and the model for sweep effects was based

on the large data set 4, which included 95 000 logs. The use of

large data sets should produce more reliable predictions of the

effects of tree traits on sawmill production than those from

previously reported Australian models (Greaves 1999).

Nevertheless, models developed in this project and those from

literature (Cown 1992; Todoroki et al. 2001, 2002) showed

that by decreasing SWE and BRS (or BIX), timber grade

recovery improved. Although the percentage of change was in

some cases relatively small, the financial impact may beimportant for producers. Any new data obtained in the future

will contribute to the refinement and improvement of the

precision of existing models.

Other measures of knot size such as branch angle, knot number

and the ratio of knot to board cross-section area (KAR) also

have potential to correlate with the MGP grade of boards (Bier

1986; Xu 2002). Static (clear-wood), SilviScan-predicted and

dynamic measurements of MoE were examined in the current

study. Dynamic MoE measurements are showing promise as a

selection trait for overall tree MoE, but they should not be

confounded by the effects of knots (Carter et al. 2006). Some

companies are now successfully using acoustic evaluation ofMoE for sawlog sorting (Carter Holt Harvey 2004). Although

acoustic MoE shows promise as a selection trait for overall

tree MoE, more detailed statistical analyses and comparisons

should be made between different methods for MoE evaluation

(Cown et al. 1999; Kumar 2004; Matheson et al. 2007). The

definition of breeding and silvicultural objectives, and the

evaluation of the effects different traits on production systems,

is a continuing task.

Conclusion

We estimated that a 10% improvement in stem straightnessdecreased sawlog degrade due to sweep by 17.1% and increased

green timber recovery by about 0.5%. A 10% reduction in BRS

decreased degraded sawlog volume due to branch size by 68%,

and increased structural timber recovery by 0.61.6%. An

increase of 10% in MoE increased structural timber recovery

by 12.313.1%.

These figures indicate that MoE had the most significant effect

on the radiata pine structural timber production system.

However, although the concept of changing the mean values of

objective traits by 10%, and keeping the trait coefficients of

variation (standard deviation to mean ratio) constant, is very

useful for purposes of discussion, different traits do have

different coefficients of variation. Wu et al. (2005) estimated

phenotypic coefficients of variation of 33.3% for SWE and

32.2% for BIX, but only 12.6% for MoE. Additive genetic

coefficients of variation were 16.7% for SWE, 16.6% for BIX

but only 8.4% for MoE. Therefore the actual potential to change

the mean of the trait MoE through genetics and/or silviculture

may be less than for the other traits.

Quantifying the effects of form, branching and wood quality on

the production system is especially important in radiata pine,

because there are trade-offs between those quality traits and

the quantity of wood produced (i.e. growth rate). Bio-economicmodeling can be used to evaluate such trade-offs and to explore

possible future scenarios to decide the best type of trees to be

grown in plantations. An application of bio-economic modelling

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in tree breeding is presented in Ivkovic et al. (2006a,b), where

the model assumptions are discussed and the results of

sensitivity analyses reported.

We strongly suggest that the bio-economic modelling

methodology is also applicable to the planning of silvicultural

operations, plantation management and forestry decision-making in general. The modelling provides quantitative

information which the timber industry can use to increase its

productivity and profitability.

Acknowledgements

The authors of this paper would like to thank the following

people who provided us with either the experimental data or

valuable professional advice: Chris Berry, Stephen Elms, Rob

Hanssen, Neil Harris, Sandra Hetherington, Phil Lloyd, Dr Colin

Matheson, Andrew Moore, Ken Nethercott, Dr Jim OHehir,

Lew Parsons, Steve Roffey, Dr Jan Rombouts, Sue Shaw and

Hugh Stewart. We also thank Dr Brian Baltunis and Dr Chris

Harwood for their comments during preparation of this article.

We thank the Southern Tree Breeding Association and Forest

and Wood Products Research and Development Corporation

for their financial support.

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