SILVA FENNICA - Helsinki

SILVA FENNICA

Vol. 31(1), 1997

SILVA FENNICA

a quarterly journal of forest science

Publishers The Finnish Society of Forest ScienceFinnish Forest Research Institute

Editors Editor-in-chief Eeva KorpilahtiProduction editors Tommi Salonen, Seppo Oja

Editorial Office Unioninkatu 40 A, FIN-00170 Helsinki, FinlandPhone +358 9 857 051, Fax +358 9 625 308, E-mail [email protected],WWW http://www.metla.fi/publish/silva/

Managing Erkki Annila (Finnish Forest Research Institute), Jyrki Kangas (Finnish Forest Research Insti-Board tute), Esko Mikkonen (University of Helsinki), Lauri Valsta (Finnish Forest Research Institute),

Harri Vasander (University of Helsinki), and Seppo Vehkamäki (University of Helsinki)

Editorial Per Angelstam (Grimsö Wildlife Research Station, Sweden)Board Julius Boutelje (Swedish University of Agricultural Sciences, Sweden)

Finn H. Brskke (Swedish University of Agricultural Sciences, Sweden)J. Douglas Brodie (Oregon State University, USA)Raymond L. Czaplewski (USDA Forest Service, USA)George Gertner (University of Illinois, USA)Martin Hubbes (University of Toronto, Canada)

William F. Hyde (Virginia Polytechnic Institute and State University, USA)Jochen Kleinschmit (Lower Saxony Forest Research Institute, Germany)Michael Kohl (Swiss Federal Institute for Forest, Snow and Landscape Research, Switzerland)Noel Lust (University of Gent, Belgium)Bo Längström (Swedish University of Agricultural Sciences, Sweden)William J. Mattson (USDA Forest Service, USA)Robert Mendelsohn (Yale University, USA)Hugh G. Miller (University of Aberdeen, United Kingdom)John Pastor (University of Minnesota, USA)John Sessions (Oregon State University, USA)Jadwiga Sienkiewicz (Environment Protection Institute, Poland)Richard Stephan (Federal Research Centre for Forestry and Forest Products, Germany)Elon S. Verry (USDA Forest Service, USA)A. Graham D. Whyte (University of Canterbury, New Zealand)Claire G. Williams (Texas A&M University, USA)

Aim and Scope Silva Fennica publishes original research articles, critical review articles, research notes report-ing preliminary or tentative results, and discussion papers. The journal covers all aspects offorest research, both basic and applied subjects. The scope includes forest environment andsilviculture, physiology, ecology, soil science, entomology, pathology, and genetics related toforests, forest operations and techniques, inventory, growth, yield, quantitative and managementsciences, forest products, as well as forestry-related social, economic, information and policysciences.

SILVA FENNICAa quarterly journal of forest science

Vol. 31(1), 1997

The Finnish Society of Forest Science

The Finnish Forest Research Institute

Silva Fennica 31(1) research articles

Progeny Trial Estimates of GeneticParameters for Growth and QualityTraits in Scots Pine

Matti Haapanen, Pirkko Veiling & Marja-Leena Annala

Haapanen, M., Veiling, P. & Annala, M.-L 1997. Progeny trial estimates of genetic param-eters for growth and quality traits in Scots pine. Silva Fennica 31(1): 3-12.

Estimates of individual heritability and genetic correlation are presented for a set of 10growth and quality traits based on data from 16 Scots pine (Pinus sylvestris L.) progenytrials in Finland. Seven of the traits (tree height, stem diameter, crown width, Pilodynvalue, branch diameter, branch angle and branch number) were objectively measured,whereas three traits (stem straightness, branching score and overall score) were assessedvisually. The genetic correlations were mostly moderate or low, and favorable from thetree breeder's point of view. All variables related to tree size correlated relativelystrongly and positively. Tree height exhibited a more favorable genetic relationship withthe crown form traits than diameter, the latter showing positive correlation with branchdiameter. Except for the slight negative correlation between branch angle and branchdiameter, the branching traits were not notably correlated. The pilodyn value waspositively correlated with stem diameter, reflecting negative correlation between diam-eter growth and wood density. The highest genetic correlations occurred among the twovisually evaluated quality scores and branch diameter. All of the heritabilities were lessthan 0.4. Overall score, Pilodyn, branch angle, branching score and tree height showedthe highest heritability.

Keywords heritability, genetic correlation, progeny testing, Scots pine, wood qualityAuthors' address Finnish Forest Research Institute, Vantaa Research Centre, Box 18,FIN-01301 Vantaa, Finland Fax +358 9 8570 5711 E-mail [email protected] 15 January 1997

1 Introduction In Scandinavia, the number, size and quality ofknots are the key factors determining the value

Together with total yield, wood quality is con- of sawn goods (Nordic timber... 1995). Conseq-sidered an important target of genetic improve- uently, crown form and branch characteristicsment in tree species used commercially as sawn have received the greatest attention in breedingtimber, such as Scots pine (Pinus sylvestris L.). for quality in Scots pine. The first-generation


plus trees in Finland, for instance, were selectedgiving a strong emphasis on branch diameterand form of the living crown (Sarvas 1953). Inprogeny testing, the evaluation of form traits isusually postponed to the age of 10-20 years. Atthis age, trees are assumed to express a sufficientdegree of genetic variation in traits affecting thefinal product value, for instance in branch diam-eter and angle, number of branches and wooddensity.

Selection for multiple goals is normally basedon a linear model which combines genetic andeconomic information on a number of traits intoan optimal index value (e.g. Hazel 1943, Falcon-er 1981, Adams and Morgenstern 1991). Theconstruction of selection indices requires esti-mates of heritability and genetic correlation.However, genetic parameters are often not knownor poorly estimated. In Scots pine, genetic pa-rameter estimates for various traits have beenreported in a number of studies, e.g. Ehrenberg(1963), Werner and Ericsson (1980), Pöykkö(1982), Veiling (1982), Veiling and Tigerstedt(1984), Andersson (1986), Eriksson et ai. (1987),Veiling (1988) and Haapanen and Pöykkö (1993).In the majority of studies, however, the esti-mates originate from just one or at the most onlya few of experiments. This easily results in poorprecision; estimates of genetic correlation espe-cially are sensitive to a small number of geneticentries included in the analysis (Klein et al. 1973,Namkoong and Roberds 1974, Hodge and White1992). Secondly, single-site heritabilities are usu-ally biased (overestimated) due to the confound-ing of variance components for genotype-by-siteinteraction and the additive genetic variance (Zo-bel and Talbert 1984). Finally, applying the esti-mates obtained in one study to other situations isusually difficult since the same traits are oftenassessed in slightly different ways, and the refer-ence populations examined may also not be thesame. To address these problems, the aim of thisstudy was to obtain reliable estimates of geneticparameters for the normal growth and qualitytraits utilising comprehensive experimental datafrom 16 progeny trials of Scots pine. The mainemphasis was placed on between-trait geneticcorrelations as they are much less well known inScots pine than the heritabilities of individualtraits.

2 Material and Methods

2.1 Material

Sixteen Scots pine progeny trials located in south-ern Finland were assessed for a varying numberof growth and quality traits. Most of the assess-ments were made late in the 1980's. The age ofthe trees ranged from 11 to 24 years at the timeof assessment (Table 1). The main body of thematerial consisted of open-pollinated families offirst-generation Scots pine plus trees. The familystructure was unique in each progeny trial, ex-cept in the two pairs of replicated trials (329/1—2, and 428/1-2). A few parent trees were repre-sented in more than one trial. All the trials werelaid out using a randomized complete block de-sign. The typical plot configuration was a plotwith 25 trees arranged in 5 x 5 contiguous plots.

The selection of trees for assessment was car-ried out systematically. The sampling practiceand the number of trees selected per plot variedin the different trials but a couple of main ruleswere followed. If the trees had been planted ineasily identifiable rows, trees were sampled alongthe diagonal axis of the plot (i.e. 5 trees on 25-tree square plots); otherwise, every fifth tree ona plot was assessed. Damaged or exceptionallysmall trees were disregarded as potential out-liers.

2.2 Traits

Table 1 lists the traits assessed in each trial. Ofthe traits, only tree height, stem diameter, branchdiameter, branch angle, branch number and Pilo-dyn value were assessed in almost all of thetrials. Branch diameter and angle were recordedon the thickest branch in two whorls that locatedapprox. at height of 1.3 m. The branch numberwas counted on the same two whorls. Wooddensity was assessed on the northern and south-ern sides of the stem at breast height, using Pilo-dyn Wood Tester. The two observations obtainedfor each of the branching traits and the Pilodynvalue, were averaged and treated as a singleobservation.

Stem straightness, branching and overall qual-ity were assessed visually in 3-6 trials (Table 1).

Haapanen, Veiling and Annala Progeny Trial Estimates of Genetic Parameters...

Tabl

e 1.

Sum

mar

y of

the

prog

eny

tria

ls a

nd th

e as

sess

ed tr

aits

. T

he n

umbe

r of

blo

cks

is g

iven

as

the

harm

onic

mea

n pe

r en

try.

The

cod

es f

or th

e tr

aits

are

as fo

llow

s: H

= T

ree

heig

ht (

dm),

D =

Ste

m d

iam

eter

(m

m),

CW

= M

axim

um c

row

n w

idth

(dm

), B

D =

Bra

nch

diam

eter

(m

m),

BA

= B

ranc

h an

gle

(deg

rees

), N

B =

Num

ber

of b

ranc

hes

in a

who

rl, B

R =

Bra

nchi

ng (

1-5

scor

e),

ST =

Str

aigh

tnes

s (1

-5 s

core

), O

S =

Ove

rall

scor

e (1

-10

scor

e).

PV =

Pilo

dyn

valu

e (m

m).

5


Straightness and branching were recorded usinga five-point subjective (1-5) scoring system inwhich class 1 represents "straight bole" or "verysmall number of branches" and class 5 "verycrooked bole" or "very many branches", respec-tively. The variable 'overall score' had 10 class-es (1 for excellent and 10 for the most inferiorphenotype). The categorical traits were analysedin the same way as the other traits.

2.3 Methods

Prior to further data processing, all unimprovedcheck seedlots and full-sib families were delet-ed. The remaining families raised from open-pollinated seed orchard seed were assumed tohave a half-sib structure with a coefficient ofrelationship of 0.25.

Individual heritabilities were estimated for alltrials and traits using the formula

0.25(< 'ft,(1)

The three variance components denote family(G2

f), family x block (o^,) and within-plot (cl)effects. The standard errors of the heritabilityestimates were calculated using the formula ofBecker (1984, page 48).

The formula used for calculating genetic cor-relations (rG) between all pairs of traits, / and j ,was:

(2)

where <J^+/) denotes the family variance com-ponent estimated for the sum of the two varia-bles (Williams and Matheson 1994). The MIXEDprocedure of the SAS statistical package (SASInstitute Inc. 1992) was used to derive REMLestimates of all the variance components.

Since single-site estimates of heritability areupward biased, the estimates were pooled in orderto obtain a more reliable single estimate. No inter-dependence was observed between the geneticparameter estimates and the age of the trials.Hence, the pooling was done across the trials irre-spective of the age. The varying precision of the

trials was taken into account by weighting eachsingle-site heritability by the inverse of its vari-ance as outlined by Borralho et al. (1992). Thegenetic correlations were also averaged across thetrials. In this case, the inverse of the number offamilies analysed per trial was used as the weight-ing factor. Before averaging, individual correla-tions having a value beyond the theoretical bound-aries, were set to either -1.00 or +1.00. In addi-tion, if the standard error of a single-site familyvariance component (<r}(,-) or CTy(7)) exceeded thevalue of the variance component itself, the vari-ance component was assigned a missing value inorder to eliminate its biasing effect on the pooledestimates.

3 Results

The genetic correlations and the individual levelheritabilities are presented in Tables 2 and 3,respectively. In general, the heritabilities, as wellas most of the averaged correlations, were eithermoderate or low. The highest genetic correlationwas found between the branching and overallscores (rG = 0.90). Both of these traits reflectedvariation in branch diameter (rG = 0.79 and rG =0.83, respectively) and tree height (rG = -0.57and -0.63).

Of the other quality traits, the branch diameterand angle were loosely associated (rc = -0.27).Branch number and stem straightness, in turn,exhibited low genetic correlations with all of theother traits. The Pilodyn value was also ratherindependent of the other traits, showing notablecorrelation only with stem (rc = 0.45) and branchdiameter (rG = 0.40).

The tree size variables (height, stem diameter,branch diameter and maximum crown diameter)were all positively correlated, rG ranging from0.08 to 0.77. The two standard growth traitsassessed in the progeny trials, namely tree heightand stem diameter, showed markedly dissimilargenetic relationships with the individual qualitytraits. For instance, stem and branch diametershowed a fairly high correlation (rG = 0.43),whereas tree height and branch diameter had agenetic correlation of only 0.11. Moreover, con-trary to height, diameter was not highly correlat-


ed with the visual branching and overall scores.The weighted averages of the heritability esti-

mates fell by between 0.06 and 0.33, being con-sistently smaller than the respective unweightedaverages. The variation among the single-siteestimates of heritability was considerable (Table3). The traits with the highest heritabilities wereoverall score (0.33), Pilodyn value (0.28), branchangle (0.27), branching score (0.26) and treeheight (0.24). Stem and branch diameter, branchnumber and straightness score, in turn, were theleast heritable traits. Standard errors of the sin-gle-site heritabilities were mostly between 0.10and 0.20, whereas those for the weighted meanswere in the range 0.01 to 0.12 (Table 3). Testorchard trial No. 553/1 showed exceptionallyhigh heritabilities for almost all the traits, other-wise there was no sign of any consistent differ-ence in the magnitude of the heritability esti-mates between the test orchards and the conven-tional forestry trials.

4 Discussion

The additive genetic relationships between thetraits were mostly neutral or favorable for treebreeding, suggesting relatively straightforwardselection for multiple objectives. The pooled her-itability estimates were between 0.1 and 0.3,which is in perfect consistency with the resultsof Cornelius (1994), who reviewed heritabilityvalues from 67 separate studies. Of the growthtraits, tree height had a relatively high heritabili-ty and was also more advantageously associatedwith stem quality than diameter. Among the qual-ity traits, the Pilodyn value and branch angleshowed the highest heritability, as noted in manyearlier studies (Ehrenberg 1963, Veiling and Ti-gerstedt 1984, Veiling 1988). Thus, tree heighttogether with a couple of additional traits, suchas branching score and Pilodyn value could prob-ably be incorporated to form an effective selec-tion index. Unrestricted selection for stem diam-eter or volume alone, in turn, would most likelyresult in severe deterioration of branch qualityand consequent loss of economic gain (e.g. Kinget al. 1992, Haapanen and Pöykkö 1993).

Visual assessment of stem quality has lately

received much attention in Finland as a cost-effective alternative to laborious measurementof a large number of individual branching andform traits (Venäläinen et al. 1995). Visual grad-ing covers many more traits than can easily bemeasured and incorporated into a selection in-dex. In this study, the overall score and branch-ing score had somewhat higher heritability thanthe measured traits. Moreover, in spite of theirclose interrelationship with branch diameter, thescore traits showed negative, and thus favorable,correlation with tree height and stem diameter,whereas the respective correlation betweenbranch diameter and growth traits was stronglypositive. These results, although based on threetrials only, encourage continued study of the useof visual scoring in connection with advancedgeneration selection.

Stem straightness exhibited notably lower her-itability than the other two score traits. Possiblereasons for this are low phenotypic variation oran insufficient number of categories used forscoring, a question discussed by Raymond andCotterill (1990). The former explanation is moreprobable, since the mean score value in the sixtrials assessed for straightness varied between1.1 and 1.5 (Table 1). In other words, the scorershad placed most of the trees in the best twocategories. It is important to note that all scoretraits were assessed using an absolute rather thana site-specific scale. Williams and Lambeth(1989) discussed the pros and cons of both sys-tems, and concluded that an absolute score ismore effective than a relative one, when geneticexpression of the assessed trait varies widelyacross test sites. Using a site-specific scale, assupported by Cotterill and Dean (1990), couldquite possibly have resulted in higher heritabili-ty for stem straightness.

The pooled estimates of genetic parameterspresented here were based on by far the largestnumber of Scots pine trials and families ana-lysed for this purpose so far. Thus, we believethey are much more precise than the sort ofsingle-site estimates commonly published. Evenso, many of the pooled estimates that were basedon a small number of observations (trials) arelikely to be relatively imprecise. This especiallyconcerns the estimates of genetic correlation thatshowed much variation among the trials. In ad-


Tabl

e 2.

Gen

etic

cor

rela

tions

bet

wee

n gr

owth

and

qua

lity

tra

its i

n 16

Sco

ts p

ine

prog

eny

tria

ls.

The

mea

ns a

nd s

tand

ard

devi

atio

ns w

ere

wei

ghte

d by

the

num

bei

of fa

mili

es i

n ea

ch tr

ial.

Irra

tiona

l est

imat

es (

-1 >

TQ

> 1

) w

ere

set t

o -1

ja

1 be

fore

ave

ragi

ng.

The

cod

es f

or th

e tr

aits

are

the

sam

e as

in

Tab

le 1

.

8


Tabl

e 2

cont

d.

9


Tabl

e 3.

Est

imat

es o

f ind

ivid

ual n

arro

w s

ense

her

itabi

lity

and

thei

r st

anda

rd e

rror

in th

e 16

Sco

ts p

ine

prog

eny

tria

ls. L

iste

d on

the

botto

m li

nes

are:

Unw

eigh

ted

mea

n (/*

2 Unwe

ight

ed) a

nd s

tand

ard

devi

atio

n (s

td)

of th

e si

ngle

-site

her

itabi

litie

s, w

eigh

ted

mea

n of

the

sing

le-s

ite h

erita

bilit

ies

(/i2 we

ighted

) and

its

stan

dard

err

or(s.

e. (h

2)).

10


dition, the fact that the trials were assessed atdifferent ages, may result in some bias. We arenot aware of any published results on age trendsof genetic parameters for quality traits in Scotspine. However, as no age trends were observedin this study, the bias was assumed to be negligi-ble.

We suggest that these estimates should assisttree breeders in planning a multiple-goal selec-tion strategy for Scots pine in Finland. They canalso be used directly to derive the genetic vari-ances and between-trait covariances that are re-quired as input values in traditional selectionindex matrices (Becker 1984). Computation ofspecific indices was considered to be beyond thescope of this study, since they commonly de-pend not only on genetic parameters, but also onthe relative contribution of individual juveniletraits to the net economic value of the end prod-uct, about which rather little is known.

References

Adams, T. & Morgenstern, E. 1991. Multiple-traitselection in jack pine. Canadian Journal of ForestResearch 21: 439^45.

Andersson, B. 1986. Possibilities with selection forquality in young progeny trials of Scots pine (Pi-nus sylvestris L.). Institute for Forest Improve-ment, Yearbook 1985. p. 58-80. (In Swedish withEnglish summary).

Becker, W. 1984. Manual of quantitative genetics.Academic Enterprises, Pullman, Wa. 188 p.

Borralho, N., Cotterill, P. & Kanowski, P. 1992. Ge-netic control of growth of Eucalyptus globulus inPortugal. II. Efficiencies of early selection. SilvaeGenetica41(2):70-77.

Cornelius, J. 1994. Heritabilities and additive geneticcoefficients of variation in forest trees. CanadianJournal of Forest Research 24: 372-379.

Cotterill, P. & Dean, C. 1990. Succesful tree breedingwith index selection. CSIRO Australia. 80 p.

Ehrenberg, C. 1963. Genetic variation in progeny testsof Scots pine (Pinus silvestris L.). Studia Foresta-HaSuecicalO. 118 p.

Eriksson, G., Ilstedt, B., Nilsson, C. & Ryttman, H.1987. Within- and between-population variationof growth and stem quality in a 30-year-old Pinus

sylvestris trial. Scandinavian Journal of ForestResearch 2: 301-314.

Falconer, D. 1981. Introduction to quantitative genet-ics. 2nd. ed. Longman Group Ltd. 340 p.

Haapanen, M. & Pöykkö, T. 1993. Genetic relation-ships between growth and quality traits in an 8-year-old half-sib progeny trial of Scots pine. Scan-dinavian Journal of Forest Research 8: 305-312.

Hazel, L. 1943. The genetic basis for construction ofselection indices. Genetics 28: 476-490.

Hodge, G. & White, T. 1992. Genetic parameter esti-mates for growth traits at different ages in Slashpine and some implications for breeding. SilvaeGenetica41 (4-5): 252-262.

Klein, T., DeFries, J. & Finkbeiner, C. 1973. Herita-bility and genetic correlation: standard error ofestimates and sample size. Behavior Genetics 3(4):355-364.

King, J., yeh, F., Heaman, J. & Dancik, B. 1992.Selection of crown form traits in controlled cross-es of coastal Douglas-fir. Silvae Genetica 41(6):362-370.

Namkoong, G. & Roberds, J. 1974. Choosing matingdesigns to efficiently estimate genetic variancecomponents for trees. I. Sampling errors of stand-ard analysis of variance estimators. Silvae Geneti-ca 23(1-3): 43-53.

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Pöykkö, T. 1982. Genetic variation in quality charac-ters of Scots pine. An evaluation by means of theheritability concept. Silva Fennica 16: 135-140.

Raymond, C. & Cotterill, P. 1990. Methods of assess-ing crown form of Pinus radiata. Silvae Genetica39(2): 67-71.

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SAS/STAT software: Changes and enhancements.Release 6.07.1992. SAS Institute Inc., SAS Tech-nical Report P-229. p. 289-366.

Veiling, P. 1982. Genetic variation in quality charac-teristics of Scots pine. Silva Fennica 16: 129-134.

— 1988. The relationships between yield componentsin the breeding of Scots pine. Summary of academ-ic dissertation. University of Helsinki. 59 p.

11


— & Tigerstedt, P. 1984. Harvest index in a progenytest of Scots pine with reference to the model ofselection. Silva Fennica 18: 21-32.

Venäläinen, M., Pöykkö, T. & Hahl, J. 1995. Thequality of young Scots pine stems in predictingthe breeding value. Proceedings of the 25th meet-ing of the Canadian Tree Improvement Associa-tion, p. 66.

Werner, M. & Ericsson, T. 1980. Wood quality stud-ies in progenies from a Scots pine seed orchard.Institute for Forest Improvement, Yearbook 1979.p. 40^9 . (In Swedish with English summary).

White, T. & Hodge, G. 1989. Predicting breedingvalues with applications in forest tree improve-ment. Kluwer Academic Pub., Dordrech, The Neth-erlands. 367 p.

Williams, C. & Lambeth, C. 1989. Bole straightnessmeasurement for advanced generation loblolly pinegenetic tests. Silvae Genetica 38 (5-6): 212-217.

Williams, E. & Matheson, A. 1994. Experimental de-sign and analysis for use in tree improvement.CSIRO. 174 p.

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