Developing breeding objectives for radiata pinestructural wood production. I. Bioeconomic modeland economic weights

Miloš Ivkovi�, Harry X. Wu, Tony A. McRae, and Mike B. Powell

Abstract: Economic breeding objectives were developed for production of radiata pine (Pinus radiata D. Don) struc-tural timber in Australia. Production systems of eight companies, including plantation growers, sawmills, and integrated-system companies, were examined. A bioeconomic model linking the breeding-objective traits mean annual increment(MAI), stem sweep, average branch size, and modulus of elasticity (MoE) with production-system components wasconstructed using data obtained from industry and published sources. For a plantation grower the most important traitfor improvement was MAI (31% improvement of net present value after a 10% trait improvement). For a sawmill themost important trait was MoE (29% improvement of profit after a 10% trait improvement). For an integrated-systemcompany the two most important traits were MoE and MAI (24% and 21% improvement of net present value after a10% trait improvement, respectively). There was a high correlation between breeding objectives of plantation growerswithin a region (rG > 0.99), but a negative correlation between breeding objectives of plantation growers and sawmills(rGS = –0.32) and only an intermediate correlation (rGI < 0.65) between those of growers and integrated-system compa-nies.

Résumé : Des objectifs économiques d’amélioration ont été développés pour la production de bois de charpente avecle pin de Monterey (Pinus radiata D. Don) en Australie. Les systèmes de production ont été étudiés auprès de huitcompagnies, incluant des propriétaires de plantation, des industriels du sciage et des compagnies de systèmes intégrés.Un modèle bioéconomique reliant les caractères visés par les objectifs d’amélioration : l’accroissement annuel moyen(AAM), la courbure, la dimension des branches et le module d’élasticité (MoE), avec les composantes du système deproduction a été construit à l’aide de données obtenues de l’industrie et de publications. Pour un propriétaire de planta-tion, le caractère à améliorer le plus important était l’AAM (augmentation de la valeur actualisée nette de 31 % pourune amélioration du caractère de 10 %). Pour un industriel du sciage, le caractère le plus important était le MoE (aug-mentation des profits de 29 % pour une amélioration du caractère de 10 %). Pour une compagnie de systèmes intégrée,les deux caractères les plus importants étaient le MoE et l’AAM (augmentations respectives de 24 et 21 % de la valeuractualisée nette pour une amélioration des caractères de 10 %). Il y avait une corrélation forte entre les objectifsd’aménagement parmi les propriétaires de plantation dans une même région (rG > 0,99) mais négative entre les objec-tifs des propriétaires de plantation et ceux des industriels du sciage (rGS = –0,32) et seulement intermédiaire entre lespropriétaires de plantation et les compagnies intégrées (rGI < 0,65).

[Traduit par la Rédaction] Ivkovi� et al.: I. 2931

Introduction

Selection and breeding of radiata pine (Pinus radiata D.Don) in Australia have been very successful in terms ofgrowth traits (diameter and height) and form traits (stemstraightness and branch quality) over the first two genera-tions. The first crop from genetically improved radiata pineplantations showed a 30% improvement in growth rate overunselected seed stock (Wright and Eldridge 1985; Mathesonet al. 1986). However, breeding for growth rate and treeform may have reduced wood density, owing to a negativegenetic correlation between growth rate and wood density(Jayawickrama 2001). A positive genetic correlation was ob-

served between growth rate and branch size (Cotterill andZed 1980; Dean et al. 1983).

Index selection is an efficient method for improving sev-eral traits simultaneously (Cotterill and Dean 1990). The se-lection of individual trees for use in breeding is based on aselection index, and the vector (b) of index coefficients canbe obtained from

b = P–1Ga

where P is the phenotypic variance–covariance matrix forselection traits, G is the genetic variance–covariance matrixbetween selection traits and objective (target) traits, and a isthe vector of economic values for each breeding-objective

Can. J. For. Res. 36: 2920–2931 (2006) doi:10.1139/X06-161 © 2006 NRC Canada

2920

Received 13 November 2005. Accepted 20 June 2006. Published on the NRC Research Press Web site at http://cjfr.nrc.ca on5 January 2007.

M. Ivkovi�1 and H.X. Wu. Ensis Genetics, P.O. Box E4008, Kingston, ACT 2604, Australia.T.A. McRae and M.B. Powell. Southern Tree Breeding Association Inc., P.O. Box 1811, Mount Gambier, SA 5290, Australia.

1Corresponding author (e-mail: milosh.ivkovich@ensisjv.com).

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Ivkovi� et al.: I. 2921

trait. Developing economic breeding objectives involves de-fining breeding-objective traits and deriving the vector ofeconomic weights, a.

Economic weight is formally defined as the expectedchange in overall profitability of an enterprise that resultsfrom a unit increase in a given breeding-objective trait(Cotterill and Dean 1990; Aubry et al. 1998). Economicweights for breeding-objective traits reflect the potential im-pact of these traits on the overall profitability of a forestryenterprise. However, in deriving index coefficients, the realeconomic values for these traits are usually unknown. As aconsequence, alternative methods such as equal weight (Wuand Ying 1997), Monte Carlo simulation (Dean et al. 1988),or desired gain approach (Pesek and Baker 1969) have beenused. Although determination of real economic objectivesfor tree breeding programs is crucially important, it is rarelydone.

The development of economic breeding objectives for for-estry enterprises has been conducted for some eucalyptusand pine species (Borralho et al. 1993; Talbert 1995; Cham-bers et al. 1997; Chambers and Borralho 1999; Greaves1999; Apiolaza and Garrick 2000; Chambers 2000). Amodel has been developed for structural wood productionfrom exotic pine species grown in Queensland, Australia(Harding et al. 1999). However, the model was based on asingle forest grower and one forest processing company (asawmilling study). Only a preliminary study has been con-ducted, involving one integrated-system company (hereinaf-ter referred to as an integrated company), on radiata pinestructural timber in Australia (Greaves 1999).

To estimate the effects of breeding-objective traits on thevalue of harvested logs and final end-products, models link-ing breeding-objective traits with product values are needed.The only currently available model for a radiata pine pro-duction system is the forestry decision making packageSTANDPAK® developed by the New Zealand Forest Re-search Institute Ltd. (NZ-FRI) (Whiteside 1990). The pack-age is based on a series of models describing the productionprocess from stands to mills. The software uses clearly de-fined measures of some biological traits (e.g., branch index)that are used in field assessments and in mill recovery stud-ies. It has been used for evaluating tree-improvement options(Carson 1990; Witehira 1996; Greaves 1999) but cannot beapplied directly for estimating economic weights. Therefore,relationships in this study were developed using data fromthe Australian radiata pine industry (McKinley et al. 2003;Wu et al. 2005).

In this study we used data from eight major radiata pineproduction systems to derive economic weights for structuraltimber production in Australia. The objectives of the studywere to (i) develop bioeconomic models for the productionsystems and calculate the profitability of radiata pine enter-prises; (ii) model the impact of breeding-objective traits onthe production systems; (iii) derive the economic weights(breeding objectives); and (iv) derive correlations betweenbreeding objectives for the plantation growers, sawmills, andintegrated companies.

Methods

The general procedure for developing a breeding objective

and estimating economic weights was proposed by Ponzoniand Newman (1989) and adapted for forestry enterprises byChambers (2000). The four major steps are (1) specificationof the production and marketing systems; (2) identificationand definition of the wood flows and the sources of incomeand costs in a specified production system; (3) determinationof how breeding-objective traits influence wood flows, in-come, and costs in the production system; and (4) derivationof the economic value (or weight) of each trait.

Production systemsA production system for radiata pine structural timber in-

cludes two major components: growing the trees and pro-cessing the wood. These two production-system componentscan be fully or partially integrated. We obtained informationon production and marketing systems and identified woodflows and sources of income and costs for eight major Aus-tralian radiata pine growers and structural-timber producers.These eight companies together manage close to 330 000 haof plantations in southeast Australia (Victoria and SouthAustralia) and Tasmania and operate five sawmills with acombined annual processing capacity of 1 200 000 m3 ofsawlogs. Only two companies were considered to be com-pletely integrated.

The production and marketing systems were diverse. Oursurvey sheets were customised for each company and con-tained all major production-system components, wood flows,and cost–income structure. Because of the commercial con-fidentiality of the required survey information, data fromeach individual company were strictly protected and are notreported here. However, information from individual compa-nies was used to derive the generic breeding objectives andeconomic weights presented here.

The cost structure for a typical plantation grower includesland rental, growing costs, which include establishmentcosts and maintenance costs (e.g., fertilization, weed con-trol), and harvesting and transportation costs. The grower’sincome is derived from the sale of sawlogs and pulpwood(or forest chips). A range of geographic regions, site quali-ties, silviculture and thinning regimes, and rotation ageswere examined in this study. There were substantial differ-ences in cost and income structure among growers, but alsobetween regions within the estates of some growers.

The investigated sawmills produce mainly structural tim-ber. Sawmill operations include debarking, primary and sec-ondary sawing processes, edging, docking, drying, grading,and sorting. For sawmills, the most significant costs are forsawlogs delivered at the mill gate, and most income is de-rived from the sale of structural machine-graded pine (MGP)timber. Some of the studied sawmills belong to integratedenterprises that incur costs associated with growing trees andderive incomes from the sale of structural-grade timber,chips, and sawmill residue. In a completely integrated enter-prise all wood flows stay within the production system, but apartially integrated company may supply its sawmill fromexternal sources.

Biological traits designated as breeding-objective traitsFour biological traits were selected as breeding-objective

traits, based on a survey of eight radiata pine companies anda literature review (e.g., Shelbourne et al. 1997): mean an-

nual increment (MAI), stem sweep (SWE), average branchsize (BRS), and modulus of elasticity or stiffness (MoE).These traits have the most economic impact, particularly onthree major aspects of the production system: (1) growthrate and stem form determine the amount of merchantablevolume produced per hectare; (2) the degree of branchingand straightness determine sawlog quality; and (3) woodstiffness and knot properties determine timber quality (struc-tural timber grade outturn). The traits were also selected onthe basis of their potential for genetic improvement (as-sessed in other studies) and the cost of evaluation. To esti-mate the impacts of the four breeding-objective traits on theradiata pine production system and to link trait values withproduction-system parameters a bioeconomic model was es-tablished.

Bioeconomic modelIf a breeding objective can be described by a simple profit

function that takes genetic values as input and produces esti-mates of profit as outcome, then economic weights can bedetermined as partial derivatives of that function (Borralhoet al. 1993). However, production systems in forestry aregenerally complex and cannot be defined by a single profitequation. A bioeconomic model that simulates various bio-logical, technological, and economic components of a pro-duction system is more appropriate for these complexproduction systems (Goddard 1998). Such a model includesdetailed relationships of breeding-objective traits to differentproduction components and parameters. These linear andnonlinear relationships can reveal important and otherwiseundetectable effects of genetic improvement (Koots and Gib-son 1998).

Our bioeconomic model is a spreadsheet model linkingbreeding-objective traits with each component of a produc-tion system. A base model was first constructed in MicrosoftExcel® 2002. Various scenarios describing improvement inbreeding-objective traits were evaluated from the base modelusing Microsoft Visual Basic® version 6.3 macro processor.The bioeconomic model was constructed using consolidatedinformation (weighted averages based on current annualplanting area) from eight radiata pine companies. It includedwood flows along the system and costs and income associ-ated with each system component. Industry sawmillingstudies and published data were used to establish the rela-tionships linking the four breeding-objective traits with eachcomponent of the radiata pine structural wood productionsystem.

Table 1 summarizes the structure of the bioeconomicmodel, but it was much more detailed and had up to 250production-system components (rows). For example, for aplantation grower the wood-flow details included harvestedvolume by operation (two to four thinnings and clear fall),roundwood category (pulpwood, chips, sawlog, ply, D, E,and recovery log class), and 5 cm sawlog diameter class. Fora sawmill production system the wood-flow details includedsawlog volumes by 5 cm diameter class and bark percentage,green mill productivity and recovery by diameter and byheart-in (pith-in) and sapwood classs, green mill productoutput (volumes of green board, chips, fines, and shavings),kiln productivity and recovery (shrinkage volume), dry millproductivity and recovery (planing and docking volume

loss), finished-product volumes (structural grades MGP15,MGP12, MGP10, F8, and F7), boards standard and wide,flooring, linings, mouldings, scantlings, treated material, etc.

Variable costs for a forest grower included establishmentand maintenance costs (site preparation, plants and planting,fertilization, weed and wilding control, annual land andmaintenance costs), harvesting, chipping, and haulage costsby product type (pulplog, whole-tree chips, preservationsawlog, plylog, and recovery log). Variable costs for a saw-mill included sawlog procurement (mill gate), mill yardcosts, debarking, green and dry mill, kiln cost by boardclass, and chipping.

Income for a forest grower includes royalties for whole-tree chips (WTC), pulpwood, preservation, and sawlog by5 cm diameter class. Income for a sawmill is obtained fromthe sale of finished product (structural grades MGP15,MGP12, MGP10, F8, and F7), boards standard and wide,flooring, linings, mouldings, scantlings, treated material,etc., and the sale of sawmill residues.

On the base model structured as described above in theprevious section, the effects of four breeding-objective traitswere superimposed as in Table 1. These four traits had dif-ferent effects on each of the production-system components,and these effects are described in the following. A dis-counted cash-flow analysis was then applied to calculate thenet present value (NPV) of the system before and afterbreeding-objective traits were improved.

Effects of mean annual increment on production systemsMAI is defined as the average annual increment in volume

in cubic metres per hectare (m3·year–1·ha–1) evaluated at theend of a rotation. Some previous models have assumed thatthe increment in wood yield was constant throughout the ro-tation (Wright and Eldridge 1985; Greaves 1999; Chambers2000). However, MAI changes throughout the life of a plan-tation (Lewis et al. 1976). To model the effects of MAI onthe production system, an increase in MAI was treated in thesame way as an increase in site-quality (SQ) class. Thismethod has been used previously for evaluating genetic im-provement of Australian radiata pine plantations (Boardman1988). An increase in MAI affects not only total volume butalso log-diameter distribution. This currently affects reve-nue, since forest growers sell logs on the basis of size, withlarger logs returning a higher price per cubic metre. To esti-mate the effects of increasing MAI on total merchantablevolume and sawlog distribution obtained from thinnings andfinal felling, industry data and South Australian Yield Tables(Lewis et al. 1976) were used for interpolation. An increasein MAI also affects green-timber recovery, owing to changesin log-diameter distribution. Small-end diameter (SED) oflogs affects sawmill recovery of green timber (Cown 1992;Todoroki et al. 2001, 2002) and has a significant impact onprofitability for the mill processor. We obtained recoverydata for each SED class from two radiata pine sawmills. Onedata set from the optical log scanner included 95 000 saw-logs, which were sourced from two sites and were of twolengths (4.8 and 6.0 m). The data set was used to evaluate theeffect of SED-class distribution on green-timber recovery.

Effects of SWE on production systemsSWE was defined as the maximum deviation of a log’s

axis from a straight line over a given length of log (mm/m).

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It is considered an important trait because it increases han-dling costs and reduces recovery of milled products (Birk etal. 1989). Based on the literature and our data, butt logs usu-ally have a greater SWE than upper logs (Lavery 1986), andSWE is usually greater on first-rotation than on second-

rotation sites, with a difference of up to 50% in site meansobserved (Bail and Pederick 1989). SWE usually decreaseswith tree age, and so will be less in logs harvested at olderages (Maclaren 1995; Turner and Tombleson 1999). To ob-tain a reliable model describing the effects of SWE on the

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Ivkovi� et al.: I. 2923

Effect of 10% trait improvement

Base MAI SWE BRS MoE

Wood flow (m3/ha)Total harvested volume 736 810 736 736 736

WTC and (or) pulplogs 178 194 171 168 178Preservation wood 25 28 25 25 25

Sawlogs 513 564 522 532 513Small (<20 cm SED) 61 69 64 65 61Prime (20–45 cm SED) 432 473 438 445 432Large (>45 cm SED) 20 22 20 21 20

Green sawn timber 238 267 246 248 238Dry structural timber 133 148 138 139 142

MGP15 3 3 3 3 12MGP12 31 34 32 33 50MGP10 77 86 79 81 68F8 and F5 23 25 24 22 11

Other sawn products 41 48 43 44 38Sawmill residue (chips) 136 151 138 141 136

CostsEstablishment ($/ha) 1 912 1 912 1 912 1 912 1 912Annual maintenance ($/ha) 1 472 1 472 1 472 1 472 1 472Harvest ($/ha) 2 073 2 337 2 069 2 021 2 073Transport ($/ha) 1 201 1 346 1 199 1 201 1 201Wood procurement ($/ha) 236 269 241 246 236Green mill ($/ha) 2 972 3 350 3 037 3 109 2 972Dry mill ($/ha) 3 254 3 682 3 365 3 391 3 254

Total NPV costs ($/ha) 13 121 14 368 13 295 13 351 13 121∆NPV costs ($/ha) 1 247 174 230 0∆NPV costs (%) 9.5 1.3 1.8 0

IncomeTotal stumpage ($/ha) 9 191 10 280 9 219 9 296 9 191WTC and (or) pulplogs ($/ha) 2 663 2 916 2 534 2 448 2 169Preservation wood ($/ha) 549 627 549 549 549Sawlogs 6 355 7 170 6 443 6 545 6 355

Small (<20 cm SED) ($/ha) 584 645 608 606 584Prime (20–45 cm SED) ($/ha) 5 451 6 192 5 515 5 601 5 451Large (>45 cm SED) ($/ha) 320 333 320 338 320

Sawn timber 11 930 13 423 12 328 12 510 13 004MGP15 ($/ha) 204 226 210 250 1 016MGP12 ($/ha) 2 467 2 747 2 548 2 604 4 189MGP10 ($/ha) 6 005 6 720 6 210 6 300 5 341F8 and F5 ($/ha) 1 133 1 266 1 175 1 106 525

Other sawn products ($/ha) 144 158 148 133 89Sawmill residue (chips) ($/ha) 2 968 3 406 3 041 3 134 2 968

Total NPV income ($/ha) 17 561 19 873 18 079 18 292 18 793∆NPV income ($/ha) 2312 517 731 1 232∆NPV income (%) 13.2 2.9 4.2 7.0

Note: MAI, mean annual increment (present mean 22.6 m3·ha–1·year–1); SWE, stem sweep (present mean 10.3 mm/m); BRS,average branch size (present mean maximum branch size (maxBRS) 5.8 cm); MoE, modulus of elasticity (present mean11.2 GPa);WTC, whole-tree chips; SED, small-end diameter; MGP, machine-graded pine; NPV, net present value. Alldollar values are Australian dollars.

Table 1. Summary of wood flows, costs, and income per hectare for a generic integrated productionsystem at the base level and after improvements in four breeding-objective traits.

production system we studied logs of different harvest ages,diameters, and height classes. The effects of SWE are two-fold: on log grade and on recovery of green timber (Brownand Miller 1975; Todoroki et al. 2001). Based on our data,nearly 3% of logs harvested from stands of surveyed radiatapine companies do not meet sawlog grade specifications be-cause of SWE. To estimate the distribution of SWE valuesamong harvested logs, a log-normal distribution was fittedwithin each log-diameter class, assuming no correlation be-tween diameter and SWE (Whiteside 1990; Turner andTombleson 1999). To estimate the effect of SWE on loggrade, the fitted log-normal distribution was used to com-pute the proportion of log volume within each diameter classthat exceeded the SWE limits set by sawlog specifications(James 2001). To estimate the effect of SWE on green-timberrecovery, regression equations linking SWE with timber re-covery were fitted using optical log-scanner data for 95 000sawlogs. Analyses of variance showed that SWE and SWE ×SED factors had significant (p < 0.0001) effects on green-timber recovery. Because of the significant interaction be-tween SWE and SED the effect was evaluated separately foreach SED class.

Effects of branch size on production systemsBRS, maximum branch size on a log (maxBRS), or aver-

age maxBRS in four quadrants of a log (branch index, BIX)(Inglis and Cleland 1982) is commonly used to measurebranch size in radiata pine. BIX has been commonly used as avariable to correlate with sawn-timber recovery in sawmillingsimulation software (Whiteside 1990; Cown 1992; Todorokiet al. 2001, 2002). Branch size affects sawlog grade, harvest-ing cost, and structural-timber recovery. Branch size varieswith harvest age (thinning and clear fall) and log diameterand height classes. To estimate branch size for logs of differ-ent harvest ages and different diameter and height classes,regressions were fitted using industry and published data(McKinley et al. 2003).

The effect of branch size on log grade was estimated us-ing maxBRS. The current industry standard for maxBRS is75 mm for premium sawlogs (James 2001). To compute theproportion of logs that exceeded 75 mm maxBRS, a log-normal distribution was fitted for maxBRS using field datafrom the radiata pine industry. Goodness-of-fit tests (SASInsight®; SAS Institute Inc. 2004) indicated that the distribu-tion was reliable for predicting maxBRS of individual logswithin each diameter class. Using the fitted distribution, theproportions of logs with maxBRS over 75 mm were com-puted.

The effect of BRS on harvesting costs was estimated usinga linear relationship between harvesting costs and BRS. Har-vesting costs are similar for all logs with BRS less than45 mm. The industry data also indicate that a saving of ap-proximately 5% on harvesting cost can be achieved if largebranches (over 75 mm) are eliminated through selection.

To estimate the effect of BRS on structural-timber recov-ery, data from an industry sawmilling study on the effects ofBIX on MGP-grade timber recovery were used. In the study,a linear regression explained between 41% and 71% of thevariation in MGP-grade timber recovery. The analysesshowed that BIX reduced the recovery rate in a linear trendand a 1 cm increase in BIX decreased MoE by about

0.6 MPa. The regression effect was similar in magnitude tothe one reported by Greaves (1999). That author used simu-lation data from the SAWMOD® software (New ZealandForest Research Institute Ltd. (NZ-FRI) 1997) to predictrecovery of structural timber (F grades) for various BIXvalues (2–10 cm).

Effects of wood stiffness on production systemsThe stiffness of clear wood (measured as MoE), together

with knot (branch) characteristics, determines the basicworking strength of timber (Standards Australia Interna-tional Ltd. 1997). Both wood basic density and microfibrilangle play a significant role in determining MoE for clearwood (Cown et al. 1999). As a breeding-objective trait, MoEwas defined as that of whole-tree clear wood at harvest (ma-ture) age. Using recent reports on radiata pine (McKinley etal. 2003; Wu et al. 2006) that showed a high correlation be-tween the MoE values of outermost samples and the whole-disk static MoE value at a given height, we estimated thedistribution of MoE values within trees (SilviScan® esti-mates). In addition, we assumed a normal distribution ofMoE values within each log-diameter class for clear wood.MGP-grade timber recovery was then determined by evalu-ating the within-class normal distribution over the currentMGP-grade limits (Plantation Timber Association Australia2002). In a similar fashion we also estimated the effect ofdynamic MoE values using Director HM200® (Carter et al.2006) on MGP-grade timber recovery. Assuming knot sizeto be an independent effect, the model fitted the currentMGP production outturn well.

Discounted cash flow analysisEconomic evaluation was conducted for three types of

radiata pine production systems: plantation growers, struc-tural-timber processors (sawmills), and vertically integratedcompanies. Costs and incomes were evaluated on a per hect-are basis for the plantation growers and vertically integratedcompanies and per cubic metre of processed timber for thesawmills. The costs included were directly related to theirobjectives (i.e., product unit) and were variable (Food andAgriculture Organization 1985). The incomes were derivedfrom price lists for roundwood, sawn timber, and all by-products.

As costs are incurred and incomes derived at differenttimes in the production system, discounted cash flow analy-sis must be employed to accommodate differences in theirtiming. There is continuing discussion in the literature aboutthe appropriate discount rate for forest valuation (Cheungand Marsden 2002). In this paper, an intermediate discountrate of 6% was used for the base scenario. This rate repre-sents compounding free of inflation. We further assume thatthe cash flow occurs at the end of each year. Overall profit-ability for a plantation grower and an integrated enterprisewas measured by means of two economic indicators: NPVand the internal rate of return (IRR). Present value (PV) wascalculated as

PVV

=+

Vd t[ ( / )]1 100

where PV is the value at planting year of a cost or an in-come, V, incurred tV years after planting, and d is the dis-

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count rate. NPV was calculated as the difference betweenthe total present value income (IPV) and total present valuecosts (CPV) as

NPV = IPV – CPV

IRR was calculated as the break-even discount rate or thediscount rate at which the NPV equals zero (Klemperer1996).

Correlations among breeding objectivesEconomic weights for four breeding-objective traits were

calculated for each company and for three types of produc-tion systems (growing, sawmilling, and integrated). Eco-nomic weights (i.e., average breeding objective; Apiolazaand Garick 2000) were averaged across the radiata pine in-dustry and weighted by plantation size or by annual produc-tion of each company. To study the similarity amongcompanies and production systems, correlations between thebreeding objectives of forest growers, sawmillers, and inte-grated companies were calculated from the following equa-tion (Borralho et al. 1993):

rH H1 2

1 2

1 1 2 2

= ′′ ′

w ww w w w

GG G( )( )

where r is the correlation coefficient, w is a vector of theeconomic weights for objectives H1 and H2, and G is the ad-ditive genetic variance–covariance matrix between breeding-objective traits; G was constructed from parameters esti-mated by Wu et al. (2005).

Results and discussion

Effects of breeding-objective traits on productionsystems

The summary (base) bioeconomic model of integratedproduction of radiata pine structural timber is shown in thefirst column of Table 1. The table lists average (base) woodflows, with costs and incomes associated with each compo-nent of the production systems examined. Wood flows in-clude total harvested volume of whole-tree chips and (or)pulplogs, preservation wood, sawlogs, and green- and dry-timber recovery classified into MGP grades. Costs associ-

ated with the production system include establishment, an-nual maintenance, harvest, transport, wood procurement, andgreen- and dry-mill sawing. Incomes are derived fromstumpage and sales of sawn timber and residue. Incomefrom sawlogs is given according to three classes (small,prime, and large logs). Income from sawn timber is givenaccording to the grade and for residue. The effects of thefour breeding-objective traits on these wood flows and onincome and costs are described below.

Effects of MAIMAI improvement (free from concomitant changes in

other traits) affected total harvested volume, log diameterdistribution, and recovery of green timber. After a 10% in-crease in MAI, total roundwood volume for the first, second,and third thinnings and clear-felling increased by 14.6%,10.6%, 9.4%, and 9.0%, respectively. Log size distributionswere also changed, with a higher proportion of larger saw-logs (Table 2). Nonlinear interpolation was used to evaluatethe effect of genetic improvement in MAI by simulating theincrease in MAI as an increase in site-quality class(Boardman 1988).

The increase in MAI increased the total yield and alteredthe sawlog diameter distribution, which in turn increased therecovery of green sawn timber. An overall trend of increasein sawmill recovery of green timber with an increase in SEDwas observed (Table 3). There was an increase of about35 m3 in total volume of recovered green timber with a 10%improvement inof MAI. The absolute volume increase washighest (9.3 m3) for the prime sawlog SED classes (35–40and 40–45 cm). The percent volume increase varied from6% for diameter class 20–25 cm to 133% for diameter class45+ cm.

Effect of stem straightness or SWESWE affected log grade and green-timber recovery. The

impact of SWE on log grade is presented in Table 2. Forexample, a 10% reduction in SWE significantly reducedthe volume of degraded log (from 10.8 to 4.1 m3, a 61%reduction), and the absolute impact was greater on themedium-sized and small logs. The average effect of a 10%improvement of mean SWE on average green-timber recov-ery for sawlogs of each diameter class is given in Table 3.

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Centre-diameter class (cm)

<25 25–30 30–35 35–40 40–45 45+ Total

Total sawlog volume change due to improvement in MAI (m3/ha)Base 95.9 104.0 119.0 85.8 59.0 39.7 503.4After 10% increase in MAI 101.5 108.3 126.0 102.9 76.2 54.6 569.5Percent difference 5.8 4.1 5.9 19.9 29.2 37.5 13.1

Sawlog volume degraded because of SWE (m3/ha)Base 0.80 3.68 2.75 2.89 0.45 0.27 10.8After 10% reduction 0.02 1.58 0.99 1.04 0.12 0.07 4.1Percent difference 97.5 57.1 64.0 64.0 73.3 74.1 62

Sawlog volume degraded because of maxBRS (m3/ha)Base 5.15 4.52 4.00 4.14 1.43 0.05 19.3After 10% reduction 2.99 2.96 3.00 3.25 1.15 0.04 12.7Percent difference 41.9 34.5 25.0 21.5 19.6 20.0 34

Table 2. Change in volume of sawlogs harvested at clear fall for in each centre-diameter class before and after a 10% improvement inMAI, average SWE, and maxBRS.

Recovery increased by 0.2–0.7 m3 for the six diameterclasses. The percent increase in recovery after the 10% re-duction in SWE seemed to be higher for smaller logs (1.8%for 15–20 cm diameter logs to 0.8% for 45+ cm diameterlogs). This is similar to results from the literature (Cown1992; Todoroki et al. 2001, 2002), which all show that tim-ber recovery improved if SWE was reduced.

Effects of branch characteristicsA 33% decrease in degraded-sawlog volume was obtained

by decreasing maxBRS by 10% (Table 2). The 4.39% reduc-tion in harvesting costs was obtained after reducing BRS bythe same percentage. The effect of maxBRS on the percent-ages of MGP-grade structural timber was evaluated on thebasis of an industry sawmilling study. The model showedincreased recovery of high-grade (MGP15 and MGP12)boards and a reduction of lower grade (MGP10 and Fgrades) boards (Table 4). The total percentage of MGP-grade timber increased from 72.7% to 73.6% after a 10% re-duction in BRS. These changes after the the reduction inbranch size were not great, but their financial impact may beimportant for sawmills.

Other measures of knot size, such as the ratio of knot toboard cross-section area, also have the potential to correlatewith the MGP grade of boards. This ratio was found to be agood indicator of stiffness (MoE) and strength (Modulus ofRupture, MoR) of boards (Bier and Collins 1985; Xu 2002)and the accuracy of predictions of MoR based on MoE(Grant et al. 1984). Therefore, including branch angle to-gether with BRS as a breeding-objective trait could also leadto more improvement in board MGP grade (Xu 2002).

Effects of wood stiffnessThe effects of a 10% increase in clear-wood MoE and dy-

namic MoE on recovery of MPG-grade timber are presentedin Table 4. Two models produced similar results: a decreasein lower grade (F grades and MGP10) boards and an in-

crease in higher grade boards (MGP12 and MGP15). Thepercentage of F grades decreased from 27.3% to 14.2% and15.1% using clear-wood and acoustic MoE, respectively. Incontrast, the percentage of MGP15 timber increased from2.3% to 10.1% and 9.2% using clear-wood and dynamicMoE, respectively. Within our model we examined the ef-fects of both static (clear-wood bending) and acoustic mea-surements of MoE. However, acoustic MoE measurement isalso showing promise as a selection trait for overall treeMoE (Kumar et al. 2002). Acoustic measurements willlikely be used for decisions related to tree selection forbreeding and establishing new plantations.

Effect of trait improvement on wood flows, costs, andincomes

In the previous section we used the bioeconomic model toquantify the effects of four breeding-objective traits on pro-duction systems. These relationships were used to estimatethe economic weights of three types of production systemfor radiata pine: plantation growers, sawmillers, and inte-grated enterprises. The last four columns in Table 1 presentthe effects of a 10% improvement in four breeding-objectivetraits (a 10% incease in MAI and MoE and a 10% reductionin SWE and BRS) on wood flows, NPV costs, and NPV in-comes of the complete system for radiata pine structuralwood production. All values in the system were averagesweighted by plantation size for forest growers and by sawlogvolume for sawmillers. For a forest grower, the most impor-tant NPV expenditures are establishment costs at the begin-ning of a rotation, annual maintenance costs over a rotation,and harvesting and transportation costs at the end of a rota-tion. For a sawmill, the most important expenditures aresawlog costs and green and dry mill costs. The main incomefor a plantation grower is from the sale of round wood andchips, and for a sawmill or integrated company the main in-come is from the sale of sawn-timber products (Table 1).

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2926 Can. J. For. Res. Vol. 36, 2006

+10% MoE (%)

Structural grade Base (%) –10% BRS (%) Static Acoustic

F grades 27.3 26.4 14.2 15.1MGP 0 48.5 47.9 43.1 43.8MGP12 21.9 22.7 32.6 32MGP15 2.3 3.07 10.1 9.2Total MGP 72.7 73.6 85.8 84.9

Table 4. Base percentages of structural grades (MGP) and the effect of a 10% improvement inBRS and MoE based on static and acoustic measurements.

SED class (cm)

20–25 25–30 30–35 35–40 40–45 45+ Total

Base timber volume (m3/ha) 40.6 50.1 61.6 46.5 32.0 22.1 252.9After 10% improvement in MAI

New timber volume (m3/ha) 43.0 52.2 65.2 55.8 41.3 30.4 287.9Percent increase in timber volume 5.9 4.2 5.8 20.0 29.1 37.6 13.8

After 10% improvement in SWENew timber volume (m3/ha) 43.9 51.6 62.6 46.7 32.1 22.4 259.3Percent increase in timber volume 1.5 1.2 1.1 0.9 0.9 0.9 1.1

Table 3. Effects of a 10% improvement in MAI and SWE on green-timber recovery for each SED class.

The primary effect of increasing MAI was an increase inthe total volume of roundwood and an increase in the totalvolume of structural-timber recovery through a slight in-crease in average sawlog diameter (Table 1). However, in-creasing the growth rate resulted in increased harvesting,transportation, wood-procurement, and mill costs per hect-are. Decreasing SWE increased the volume of sawlogs andthe recovery of sawn structural timber, but decreased thevolume of sawmill residue. At the same time, the reductionin SWE decreased transportation costs per cubic metre. Theprimary effect of decreasing branch size was an increase inthe grade of sawlogs and in recovery of structural-grade tim-ber. Decreasing BRS also decreased harvesting costs per cu-bic metre of wood harvested. The primary effect ofincreasing MoE was an increase in the average value ofsawn timber through an increase in high-value MGP12 andMGP15 structural timber. For this complete production sys-tem, a 10% increase in MAI or MoE or a 10% reduction inSWE or BRS will increase net present costs by $1247, $0,$174, and $230 per hectare, respectively, while increasingnet present incomes by $2312, $1232, $517, and $732 perhectare, respectively (Table 1).

Effect of trait improvement on profitabilityBased on average wood flows, costs and incomes, and the

effect of a 10% improvement in the four breeding-objectivetraits on NPV, we computed overall NPVs and IRR for aplantation grower, a sawmiller, and an integrated productionsystem. Table 5 lists NPV, IRR, and percent improvement ona per-hectare basis for a base scenario and for scenarios aftera 10% improvement in MAI, SWE, BRS, and MoE. Changesin NPV and percent NPV for the sawmiller were also com-puted per cubic metre of timber processed. All three types ofproduction system (plantation, sawmill, and integrated com-pany) had positive NPVs at a discount rate of 6%, indicatingthat all three production systems of radiata pine enterpriseswere profitable. For a plantation, MAI, SWE, and BRS af-fect its profitability, but MAI is the main driver of profit(>31.2% change in NPV profit). For a sawmill, MoE was themost important trait (28.8% change in profit) and MAI had aminor negative effect (–1% change in profit). An increase inMAI and a shift in log-diameter distribution increased therecovery rate but did not increase NPV for sawmill opera-tions because higher royalties are paid for larger logs. Thisindicates a discrepancy in profits resulting from an increasein MAI for non-integrated production-system segments (i.e.,tree plantation versus sawmill). As a consequence, a bal-anced log price negotiated between the tree grower andsawmiller is needed for maximizing productivity and profitsin each segment of the production system. More balancedeconomic weights were obtained for an integrated produc-tion system, where MoE was the most profitable trait for im-provement (23.9% change in NPV) and MAI was the secondmost important trait (20.6% change in NPV).

Economic weights and general profit functionsThe economic weight of a breeding-objective trait is de-

fined as the change in profit after a unit improvement in thetrait value while all other traits are held constant. The eco-nomic weights for the plantation grower, sawmiller, and ver-tically integrated enterprises, averaged across the radiata

pine industry (i.e., weighted by plantation size or by annualsawmill production by different companies), are presented inTable 6. These can be used to weight breeding values pre-dicted for each individual genotype into a single index ofprofit (i.e., linear selection index) according to each of thethree production systems. For the plantation grower, theprofit ($/ha) resulting from unit improvement of the breed-ing-objective traits can be calculated as

PG = 291 × MAI – 58 × SWE – 248 × BRS

For a sawmill, profit per cubic metre of wood resultingfrom unit trait improvement is

PSM = –0.18 × MAI – 1.19 × SWE – 1.97

× BRS + 10.5 × MoE

For an integrated enterprise, profit ($/ha) resulting fromunit trait improvements is

PI = 416 × MAI – 194 × SWE – 620 × BRS

+ 977 × MoE

The previously calculated economic weights do not takeinto account the amount of exploitable genetic variation foreach breeding-objective trait. Economic weights can also bepresented on a genetic scale using additive genetic standarddeviation as a unit. The economic weights on a genetic scalewere derived by multiplying the economic weight of eachtrait by its additive genetic standard deviation obtained fromWu et al. (2005). For an integrated company, the economicweight of MoE was larger than that of MAI (Table 6), butMAI was much more important than MoE on a geneticscale. The economic weight of MAI on the genetic scale wasNPV = $3381, while the economic weight of MoE was onlyNPV = $1235. This is because there is more genetic varia-tion in MAI than in MoE. On the genetic scale, BRS andSWE had lower economic weights than MoE: NPV = $672and NPV = $336, respectively.

© 2006 NRC Canada

Ivkovi� et al.: I. 2927

MAI SWE BRS MoE

Trait improvement in: Base 10% –10% –10% 10%

PlantationNPV ($/ha) 2104 2760 2165 2249 2104∆NPV ($/ha) 654 61 145 0∆NPV (%) 31.2 2.9 6.9 0IRR (%) 8.4 9.1 8.5 8.6 8.4∆IRR (%) 7.6 0.6 1.4 0

SawmillPV ($/m3 log) 41 40 42 42 52∆PV ($/m3 log) –0.4 1.2 1.1 11∆PV (%) –1.0 3.1 2.8 28.8

Integrated companyNPV ($/ha) 4539 5449 4763 4921 5650∆NPV ($/ha) 940 204 362 1090∆NPV (%) 20.6 4.5 7.9 23.9IRR (%) 10.9 10.4 10.5 10.9∆IRR (%) 5.6 1.1 1.9 5.7

Note: IRR, internal rate of return. All dollar values are Australiandollars.

Table 5. Average NPV at discount rate of 6% of a 10% im-provement in breeding-objective traits.

Although, from a purely economic viewpoint, MoE andMAI were equally important as traits for a vertically inte-grated enterprise, from a breeder’s point of view there ismore available genetic variation in MAI for genetic manipu-lation. Pure economic weights do not take into account theamount of exploitable genetic variation in the trait of interest(Koots and Gibson 1998). In addition, the genetic correlationbetween MAI and MoE is unfavourable (Wu et al. 2005),which means that an improvement in either of the two traitsindependently will reduce the value of the other trait. Thisalso underlines the importance of using correct economicweights for these two most important traits.

Correlation between breeding objectives of productionsystems

The correlation between the objectives of plantation grow-ers within the one region (the Green Triangle region insoutheast Victoria and southwest South Australia) was, onaverage, very high (rG = 0.998). A correlation less than unityis not surprising given that production systems, includingsilvicultural regimes, rotation ages, and wood resources, var-ied among enterprises within a region. However, there was aregional (e.g., Green Triangle vs. Ballarat, central Victoria)difference in breeding objectives (rG = 0.730). This suggeststhat a breeding population selected for one region only canproduce about 73% of the potential gain in profit in anotherregion.

The correlation between a grower and a sawmill within aregion was negative (rGS = –0.320), indicating a significantdiscrepancy between the objectives of growers and sawmills.The correlations between the breeding objectives of a planta-tion grower and an integrated enterprise within a regionwere generally intermediate (rGI = 0.648), indicating that fora vertically integrated firm tree improvement would resultindirectly in only about 65% of the potential gain for a for-est grower. The correlation between sawmills within a regionwas high (rS = 0.995), but the correlation between a sawmilland an integrated enterprise within a region was lower (rSI =0.713). Finally, the correlation between the breeding objec-tives of integrated enterprises within a region was calculatedas rI = 0.875.

These results pose a question: Is the development of mul-tiple breeding populations for different production systemsor regions worthwhile? Regarding the negative correlationbetween the objectives of a grower and a sawmill, it is evi-dent that the development of acoustic tools will in the nearfuture remove this discrepancy (Carter et al. 2006). In gen-eral, the Southern Tree Breeding Association Inc. in Austra-lia has opted to develop customised deployment objectivesfor member companies and regions, but to keep a singlebreeding objective (White et al.1999). Average breeding ob-jectives and economic weights developed here are currently

used to make selections for the next breeding generation(Powell et al. 2004). This is a conservative option, whichmay result in loss of potential genetic gain, so the develop-ment of multiple breeding populations (Namkoong 1976),specialized breeds (Jayawickrama and Carson 2000), or elitepopulations (Byram et al. 2005) may be considered in thefuture. However, such a decision should be based on a fullcost–benefit analysis.

Conclusions

Derivation of a breeding objective is crucial to a breedingprogram, as it defines the profitability of the program for in-dustry. Several attempts at developing economic weights intree breeding used either a single production-system compo-nent or simulations. Here we have used actual industry data(wood-flow, financial, and sawmilling data) for a completeproduction system to develop “real” economic weights. Theresults should be directly and immediately applicable to se-lection and breeding in the Australian national radiata pinebreeding program managed by the Southern Tree BreedingAssociation Inc.

It was essential to include as many relationships as possi-ble in order to link biological traits to the profitability of theproduction systems. Usually, a series of component modelsis needed to connect tree biological traits to the productionsystem in a bio-economic model. We developed componentmodels to evaluate the effects of four biological traits (MAI,SWE, BRS, and MoE) on sawlog outturn and timber-recoveryrates. We were able to account for nonlinear relationships inestimating the economic impact of breeding-objective traitson the production systems, and this was a major improve-ment over earlier models (e.g., Greaves 1999). Using linearrelationships can lead to oversimplification and unrealisticestimates of the potential value of genetic improvement ofbreeding-objective traits.

For a plantation, MAI was the most profitable trait for im-provement. For a sawmill, MAI had little impact and MoEwas the most profitable trait. For an integrated enterprise,MoE and MAI were almost equally important. This demon-strates discrepancies in the economic weights, which cancause large losses in different segments of the industry.Some mills may pay a premium for logs of a particular ageor with a minimum threshold density or for logs from a par-ticular area (site class). These premiums are an attempt toincrease incomes from the higher quality sawlogs. Recentwood-quality initiatives have influenced forestry companiesto carry out dynamic evaluation of MoE for sawlog sorting(Wood Quality Initiative 2006). Thus, market signals are be-ing transmitted from processor to grower. It is likely thatwood stiffness will have an impact on log prices in the nearfuture. Therefore, the economic weights derived for a verti-

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2928 Can. J. For. Res. Vol. 36, 2006

Units MAI (m3/ha) SWE (mm/m) BRS (cm) MoE (GPa)

Grower $/ha 291 –58 –248 0Sawmill $/m3 –0.18 –1.19 –1.97 10.5Integrated company $/ha 416 –194 –620 977

Note: The values are computed as an industry-wide weighted average.

Table 6. Economic weights (NPV of a unit trait change per hectare or per cubic metre of wood) forMAI, SWE, maxBRS, and MoE.

cally integrated enterprise may be the best choice for radiatapine breeding in Australia.

Average breeding objectives and economic weights devel-oped here could be directly used to make selections for thenext breeding generation. Nevertheless, the breeding objec-tives would differ for different production systems and re-gions. Customised breeding and deployment objectives,multiple breeding populations, specialized breeds, or elitepopulations with different objectives for different compa-nies, production systems, and regions will also be developedusing the bioeconomic model.

We assumed that the production systems were optimal be-fore and after genetic improvement in breeding-objectivetraits. However, production and economic parameters maychange with technology development and under differentmarket conditions. It is important to test how the change inproduction-system parameters would affect the estimates ofeconomic weight. In Ivkovi� et al. (2006) we report on sen-sitivity analyses developed to verify some of our assump-tions relating to production-system parameters, such as costand income structures, and trait-effect parameters. Sensitiv-ity analyses also test the relative importance of genetic andeconomic parameters for deriving selection-index coeffi-cients.

Acknowledgements

The authors thank the following Australian radiata pineindustry representatives who provided us with either experi-mental data or valuable professional advice: Chris Berry,Stephen Elms, Rob Hanssen, Neil Harris, Sandra Hethering-ton, Phil Lloyd, Andrew Moore, Ken Nethercott, Lew Par-sons, Steve Roffey, Sue Shaw, and Hugh Stewart. We alsothank Dr. Washington Gapare, Dr. Colin Matheson, andthree anonymous reviewers for their comments and adviceduring preparation of this article. We thank the SouthernTree Breeding Association Inc. and the Forest and WoodProducts Research and Development Corporation, Australia,for their financial support.

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