Skip to main content
SpringerLink
Log in

Inheritance of growth and solid wood quality traits in a large Norway spruce population tested at two locations in southern Sweden

  • Original Paper
  • Published:
Tree Genetics & Genomes Aims and scope Submit manuscript

Abstract

Unfavorable genetic correlations between growth and wood quality traits are one of the biggest challenges in advanced conifer breeding programs. To examine and deal with such correlation, increment cores were sampled at breast height from 5,618 trees in 524 open-pollinated families in two 21-year-old Norway spruce progeny trials in southern Sweden, and age trends of genetic variation, genetic correlation, and efficiency of selection were investigated. Wood quality traits were measured on 12-mm increment cores using SilviScan. Heritability was moderate (~0.4–0.5) for wood density and modulus of elasticity (MOE) but low (~0.2) for microfibril angle (MFA). Different age trends were observed for wood density, MFA, and MOE, and the lower heritability of MFA relative to wood density and MOE in Norway spruce contrasted with general trends of the three wood quality traits in pine. Genetic correlations among growth, wood density, MFA, and MOE increased to a considerably high value from pith to bark with unfavorable genetic correlations (−0.6 between growth and wood density, −0.74 between growth and MOE). Age–age genetic correlations reached 0.9 after ring 4 for diameter at breast height (DBH), wood density, MFA, and MOE traits. Early selections at ring 10 for diameter and at ring 6 or 7 for wood quality traits had similar effectiveness as selection conducted at reference ring 15. Selection based on diameter alone produced 19.0 % genetic gain in diameter but resulted in 4.8 % decrease in wood density, 9.4 % decrease in MOE, and 8.0 % increase in MFA. Index selection with a restriction of no change in wood density, MOE, and MFA, respectively, produced relatively lower genetic gains in diameter (16.4, 12.2, and 14.1 %, respectively), indicating such index selection could be implemented to maintain current wood density. Index selection using economic weights is, however, recommended for maximum economic efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Baltunis BS, Wu HX, Powell MB (2007) Inheritance of density, microfibril angle, and modulus of elasticity in juvenile wood of Pinus radiata at two locations in Australia. Can J For Res 37(11):2164–2174. doi:10.1139/x07-061

    Article  Google Scholar 

  • Baltunis BS, Gapare WG, Wu HX (2010) Genetic parameters and genotype by environment interaction in radiata pine for growth and wood quality traits in Australia. Silvae Genet 59:113–124

    Google Scholar 

  • Berlin M, Lönnstedt L, Jansson G, Danell Ö, Ericsson T (2010) Developing a Scots pine breeding objective: a case study involving a Swedish sawmill. Silva Fenn 44(4):643–656

    Article  Google Scholar 

  • Bouffier L, Raffin A, Rozenberg P, Meredieu C, Kremer A (2009) What are the consequences of growth selection on wood density in the French maritime pine breeding programme? Tree Gene Genom 5:11–25. doi:10.1007/s11295-008-0165-x

    Article  Google Scholar 

  • Bridgwater FE, Talbert JT, Jahromi S (1983) Index selection for increased dry weight in a young Loblolly pine population. Silvae Genet 32:157–161

    Google Scholar 

  • Burdon R (1977) Genetic correlation as a concept for studying genotype-environment interaction in forest tree breeding. Silvae Genet 26:168–175

    Google Scholar 

  • Burdon RD, Kibblewhite RP, Walker JCF, Megraw RA, Evans R, Cown DJ (2004) Juvenile versus mature wood: a new concept, orthogonal to corewood versus outerwood, with special reference to Pinus radiata and P. taeda. For Sci 50(4):399–415

    Google Scholar 

  • Dadswell HE, Fielding JM, Nicholls JWP, Brown AG (1961) Tree-to-tree variations and the gross heritability of wood characteristics of Pinus radiata. TAPPI 44:174–179

    CAS  Google Scholar 

  • Evans R (1999) A variance approach to the X-ray diffractometric estimation of microfibril angle in wood. Appita J 52(4):283–289

    Google Scholar 

  • Evans R, Ilic J (2001) Rapid prediction of wood stiffness from micrfibril angle and density. For Prod J 51:53–57

  • Evans R (2006) Wood stiffness by X-ray diffractometry. In: Stokke DD, Groom HL (eds) Characterization of the cellulosic cell wall. Wiley, Hoboken, pp 138–146

    Chapter  Google Scholar 

  • Falconer D, Mackay T (1996) Introduction to quantitative genetics, 4th edn. Longman, New York

    Google Scholar 

  • Fielding JM (1953) Variation in Monterey pine. Forestry and Timber Bureau, Australia Bulletin No.31. 43 p

  • Fielding JM, Brown AG (1960–61) Variations in the density of the wood of Monterey pine from tree to tree. For Timber Bur Leafl 77:1–28

  • Gapare WJ, Wu HX, Abarquez A (2006) Genetic control of the time of transition from juvenile to mature wood in Pinus radiata D. Don. Ann For Sci 63(8):871–878

    Article  Google Scholar 

  • Gapare WJ, Matheson AC, Ivković M, Baltunis BS, Wu HX (2010) Genetic stability of wood density in Pinus radiata D. Don plantation estate across Australia. Tree Gene Genom 6:113–125

    Article  Google Scholar 

  • Gilmour AR, Gogel B, Cullis B, Thompson R (2009) ASReml user guide release 3.0. VSN International Ltd, Hemel Hempstead

    Google Scholar 

  • Gräns D, Hannrup B, Isik F, Lundqvist S-O, McKeand S (2009) Genetic variation and relationships to growth traits for microfibril angle, wood density and modulus of elasticity in a Picea abies clonal trial in southern Sweden. Scand J For Res 24(6):494–503. doi:10.1080/02827580903280061

    Article  Google Scholar 

  • Gwaze DP, Bridgwater FE, Byram TD, Lowe WJ (2001) Genetic parameter estimates for growth and wood density in loblolly pine (Pinus taeda L). For Genet 8(1):47–55

    Google Scholar 

  • Gwaze D, Harding K, Purnell R, Bridgwater F (2002) Optimum selection age for wood density in loblolly pine. Can J For Res 32(8):1393–1399

    Article  Google Scholar 

  • Hallingbäck HR, Sánchez L, Wu HX (2014) Single versus subdivided population strategies in breeding against an adverse genetic correlation. Tree Gene Genom. doi:10.1007/s11295-014-0707-3

    Google Scholar 

  • Hannrup B, Cahalan C, Chantre G, Grabner M, Karlsson B, Bayon IL, Jones GL, Müller U, Pereira H, Rodrigues JC, Rosner S, Rozenberg P, Wilhelmsson L, Wimmer R (2004) Genetic parameters of growth and wood quality traits in Picea abies. Scand J For Res 19(1):14–29. doi:10.1080/02827580310019536

    Article  Google Scholar 

  • Hong Z, Fries A, Wu HX (2014) High negative genetic correlations between growth traits and wood properties suggest incorporating multiple traits selection including economic weights for the future Scots pine breeding programs. Ann For Sci 71:463–472. doi:10.1007/s13595-014-0359-3

    Article  Google Scholar 

  • Hylen G (1997) Genetic variation of wood density and its relationship with growth traits in young Norway spruce. Silvae Genet 46(1):55–60

    Google Scholar 

  • Ivković M, Wu HX, McRae TA, Powell MB (2006) Developing breeding objectives for radiata pine structural wood production. I. Bioeconomic model and economic weights. Can J For Res 36(11):2920–2931

    Article  Google Scholar 

  • Ivković M, Wu HX, Kumar S (2010) Bio-economic modeling as a method for determining economic weights for optimal multiple-trait tree selection. Silvae Genet 59:77–90

    Google Scholar 

  • Karlsson B, Rosvall O (1993) Progeny testing and breeding strategies. Proceedings of the Nordic group for tree breeding. Edinburgh

  • Kempthorne O, Nordskog AW (1959) Restricted selection indices. Biometrics 15(1):10–19. doi:10.2307/2527598

    Article  Google Scholar 

  • Klein TW (1974) Heritability and genetic correlation: statistical power, population comparisons, and sample size. Behav Genet 4(2):171–189. doi:10.1007/bf01065758

    PubMed  CAS  Google Scholar 

  • Lenz P, Cloutier A, MacKay J, Beaulieu J (2010) Genetic control of wood properties in Picea glauca—an analysis of trends with cambial age. Can J For Res 40(4):703–715. doi:10.1139/X10-014

    Article  Google Scholar 

  • Lenz P, MacKay J, Rainville A, Cloutier A, Beaulieu J (2011) The influence of cambial age on breeding for wood properties in Picea glauca. Tree Gene Genom 7(3):641–653. doi:10.1007/s11295-011-0364-8

    Article  Google Scholar 

  • Lenz P, Auty D, Achim A, Beaulieu J, Mackay J (2013) Genetic improvement of white spruce mechanical wood traits—early screening by means of acoustic velocity. Forests 4(3):575–594

    Article  Google Scholar 

  • Li L, Wu HX (2005) Efficiency of early selection for rotation-aged growth and wood density traits in Pinus radiata. Can J For Res 35:2019–2029

    Article  Google Scholar 

  • Louzada JLPC (2003) Genetic correlations between wood density components in Pinus pinaster Ait. Ann For Sci 60:285–294

    Article  Google Scholar 

  • Mrode R, Thompson R (2005) Linear models for the prediction of animal breeding values. CABI, UK

    Book  Google Scholar 

  • Panshin AJ, De Zeeuw C (1980) Textbook of wood technology: structure, identification, uses, and properties of the commercial woods of the United States and Canada, 4th edn. New York, McGraw-Hill

    Google Scholar 

  • Pot D, Chantre G, Rozenberg P, Rodrigues JC, Jones GL, Pereira H, Hannrup B, Cahalan C, Plomion C (2002) Genetic control of pulp and timber properties in maritime pine (Pinus pinaster Ait). Ann For Sci 59:563–575

    Article  Google Scholar 

  • Rosvall O, Ståhl P, Almqvist C, Anderson B, Berlin M, Ericsson T, Eriksson M, Gregorsson B, Hajek J, Hallander J, Högberg K, Jansson G, Karlsson B, Kroon J, Lindgren D, Mullin T, Stener L. 2011. Review of the Swedish tree breeding programme. Skogforsk Internal Report

  • Rozenberg P, Cahalan C (1997) Spruce and wood quality: genetic aspects (a review). Silvae Genet 46:270–279

    Google Scholar 

  • Skrøppa T, Hylen G, Dietrichson J (1999) Relationships between wood density components and juvenile height growth and growth rhythm traits for Norway spruce provenances and families. Silvae Genet 48(5):235–238

    Google Scholar 

  • Steffenrem A, Kvaalen H, Høibø OA, Edvardsen ØM, Skrøppa T (2009) Genetic variation of wood quality traits and relationships with growth in Picea abies. Scand J For Res 24(1):15–27

    Article  Google Scholar 

  • Vargas-Hernandez J, Adams WT (1991) Genetic variation of wood density components in young coastal Douglas-fir: implications for tree breeding. Can J For Res 21(12):1801–1807. doi:10.1139/x91-248

    Article  Google Scholar 

  • White TL, Adams WT, Neale DB (2007) Forest genetics. CABI, Wallingford

    Book  Google Scholar 

  • Wu HX, Sanchez L (2011) Effect of selection method on genetic correlation and gain in a two-trait selection scheme. Aust For 74(1):36–42. doi:10.1080/00049158.2011.10676344

    Article  Google Scholar 

  • Wu HX, Powell MB, Yang JL, Ivković M, McRae TA (2007) Efficiency of early selection for rotation-aged wood quality traits in radiata pine. Ann For Sci 64(1):1–9

    Article  Google Scholar 

  • Wu HX, Ivković M, Gapare WJ, Baltunis BS, Powell MB, McRae TA (2008) Breeding for wood quality and profit in radiata pine: a review of genetic parameters. N Z J For Sci 38(1):56–87

    Google Scholar 

  • Yanchuk A, Sanchez L (2011) Multivariate selection under adverse genetic correlations: impacts of population sizes and selection strategies on gains and coancestry in forest tree breeding. Tree Gene Genom 7(6):1169–1183. doi:10.1007/s11295-011-0404-4

    Article  Google Scholar 

  • Zhang SY, Morgenstern EK (1995) Genetic variation and inheritance of wood density in black spruce (Picea mariana) and its relationship with growth: implications for tree breeding. Wood Sci Technol 30(1):63–75. doi:10.1007/bf00195269

    Article  Google Scholar 

  • Zobel BJ, Jett J (1995) Genetics of wood production. Springer, Berlin

    Book  Google Scholar 

  • Zobel BJ, Van Buijtenen JP (1989) Wood variation: its causes and control. Springer, Berlin

    Book  Google Scholar 

Download references

Acknowledgments

The samples were collected in Skogforsk’s operative breeding experiments and managed by Johan Malm and other technical staff with funding from the Swedish Spruce Genome Sequencing project. The comprehensive set of wood quality data was produced at Innventia within the strategic research program Bio4Energy, funded by the Swedish government. The samples were prepared and analyzed by Åke Hansson, Thomas Trost, and other researchers, and Thomas Grahn organized the data in the Bio4Energy Trait Database for the later steps of the evaluation.

Data Archiving Statement

The raw quantitative traits data from SilviScan are currently archived in Innventia AB database, and the accession numbers will be supplied for further use of the data for collaboration.

Author information

Authors and Affiliations

  1. Umeå Plant Science Centre, Department Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-90183, Umeå, Sweden

    Zhi-Qiang Chen, María Rosario García Gil & Harry X. Wu

  2. Skogforsk, Ekebo 2250, SE-268 90, Svalöv, Sweden

    Bo Karlsson

  3. INNVENTIA AB, Drottning Kristinas väg 61, SE-114 86, Stockholm, Sweden

    Sven-Olof Lundqvist & Lars Olsson

  4. Black Mountain Laboratory, CSIRO Plant Industry, Canberra, ACT, 2601, Australia

    Harry X. Wu

Authors
  1. Zhi-Qiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. María Rosario García Gil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Karlsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sven-Olof Lundqvist
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lars Olsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Harry X. Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Harry X. Wu.

Additional information

Editorial Responsibility J. Beaulieu

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, ZQ., Gil, M.R.G., Karlsson, B. et al. Inheritance of growth and solid wood quality traits in a large Norway spruce population tested at two locations in southern Sweden. Tree Genetics & Genomes 10, 1291–1303 (2014). https://doi.org/10.1007/s11295-014-0761-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11295-014-0761-x

Keywords

Search

Navigation