Tree Genetics & Genomes

, Volume 7, Issue 3, pp 563–571 | Cite as

Genetic control of very early compression and opposite wood in Pinus radiata and its implications for selection

  • Luis A. ApiolazaEmail author
  • Shakti S. Chauhan
  • John C. F. Walker
Original paper


The long time frame for evaluating selection candidates is a major barrier to the deployment of genetic gain from tree breeding programs. This situation is compounded in wood quality studies by constraints on the number of sampled individuals when trees are older and larger. This paper documents the degree of genetic control and genetic association for wood quality traits in 18-month-old leaning Pinus radiata seedlings. Trees were separately assessed for basic density, green and dry acoustic velocity, and longitudinal and volumetric shrinkage in opposite and compression wood. Heritability estimates were low to moderate for both compression and opposite wood (ranging from 0.15 to 0.38). Estimated genetic correlations were very high in opposite wood, where green velocity displayed the highest correlations with both longitudinal (−0.89) and volumetric (−0.64) shrinkage, closely followed by dry velocity. These correlations were substantially lower for compression wood. The estimated correlations between compression and opposite wood characteristics were high for most traits except for longitudinal shrinkage. We suggest how these results could be used for very early screening for wood stiffness and dimensional stability. We propose that information on early genetic control of wood quality and the methodologies used to elicit it should be integrated in breeding and deployment programs.


Wood quality Early selection Compression wood Radiata pine Bayesian models 



This project was funded by the New Zealand FRST Compromised Wood (P2080) Programme with participation of Forests New South Wales, Forest and Wood Products Australia, New Zealand Radiata Pine Breeding Company and Weyerhaeuser (USA). Genetic material and land for the trial was provided by Proseed Ltd. Many thanks to Dr Rowland Burdon and two anonymous reviewers for comments that contributed to greatly improving this article.


  1. Apiolaza LA (2009a) Very early selection for solid wood quality: screening for early winners. Ann For Sci 66:601CrossRefGoogle Scholar
  2. Apiolaza LA (2009b) Modeling wood quality using random regression splines. In: Australasian Forest Genetics Conference. 20–22 April, Perth, Western Australia, AustraliaGoogle Scholar
  3. Apiolaza LA, Garrick DJ (2001) Analysis of longitudinal data from progeny tests: some multivariate approaches. For Sci 47:129–140Google Scholar
  4. Apiolaza LA, Burdon RD, Garrick DJ (1999) Effect of univariate subsampling on the efficiency of bivariate parameter estimation and selection using half-sibs progeny tests. For Genetics 9:79–87Google Scholar
  5. Apiolaza LA, Walker JCF, Nair H, Butterfield B (2008) Very early screening of wood quality for radiata pine: pushing the envelope. In: Proceedings of the 51st international convention of the society of wood science and technology, Concepción, Chile. WQ-1.Google Scholar
  6. Apiolaza LA, Butterfield B, Chauhan S, Walker JCF (2011) Characterization of mechanically perturbed young stems: can it be used for wood quality screening? Ann For Sci In Press Google Scholar
  7. Bouffier L, Charlot C, Raffin A, Rozenberg P, Kremer A (2008) Can wood density be efficiently selected at early stage in maritime pine (Pinus pinaster Ait.)? Ann For Sci 65:106CrossRefGoogle Scholar
  8. 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 50:399–415Google Scholar
  9. Cappa EP, Cantet RJC (2006) Bayesian inference for normal multiple trait individual tree models with missing records via full conjugate Gibbs. Can J For Res 36:1276–1285CrossRefGoogle Scholar
  10. Cappa EP, Cantet RJC (2008) Direct and competition additive effects in tree breeding: Bayesian estimation from an individual tree mixed model. Silvae Genetica 57:45–49Google Scholar
  11. Chauhan S, Walker JCF (2006) Variations in acoustic velocity and density with age, and their interrelationships in radiata pine. For Ecol Manag 229:388–394CrossRefGoogle Scholar
  12. Damgaard LH (2007) Technical note: how to use Winbugs to draw inferences in animal models. J Anim Sci 85:1363–1368PubMedCrossRefGoogle Scholar
  13. Dungey HS, Matheson AC, Kain D, Evans R (2006) Genetics of wood stiffness and its component traits in Pinus radiata. Can J For Res 36:1165–1178CrossRefGoogle Scholar
  14. Eckard JT, Isik F, Bullock B, Li B, Gumpertz M (2010) Selection efficiency for solid wood traits in Pinus taeda using time-of-flight acoustic and micro-drill resistance methods. For Sci 56:233–241Google Scholar
  15. Gapare WJ, Ivković M, Baltunis BS, Matheson AC, Wu HX (2009) Genetic stability of wood density and diameter in Pinus radiata D. Don plantation estate across Australia. Tree Genetics & Genomes 6:113–125CrossRefGoogle Scholar
  16. Gomide JL (2009) Quality characteristics of elite Eucalyptus clones in Brazil. In: Apiolaza LA, Chauhan S, Walker JCF (eds) Revisiting eucalyptus 2009 workshop. Christchurch, New Zealand, pp 29–39Google Scholar
  17. Greaves BL (1999) The value of tree improvement: a case study in radiata pine grown for structural timber and liner-board. In: Nepveu G (ed) Proceedings of the Third IUFRO Workshop S5.01.04: Connection between silviculture and wood quality through modeling approaches and simulation software, La Londe-Les Maures, France, 5–12 SeptemberGoogle Scholar
  18. Hadfield J (2010) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Soft 33:2Google Scholar
  19. Hannrup B, Ekberg I (1998) Age-age correlations for tracheid length and wood density in Pinus sylvestris. Can J For Res 28:1373–1379CrossRefGoogle Scholar
  20. Harris JM, James RN, Collins MJ (1976) Case for improving wood density in radiata pine. NZ J For Sci 5:347–354Google Scholar
  21. Isik F, Gumpertz M, Li B, Goldfarb B, Sun X (2008) Analysis of cellulose microfibril angle using a linear mixed model in Pinus taeda clones. Can J For Res 38:1676–1689CrossRefGoogle Scholar
  22. 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 36:2920–2931CrossRefGoogle Scholar
  23. Jayawickrama KJS, Carson MJ (2000) A breeding strategy for the New Zealand Radiata pine breeding cooperative. Silvae Genetica 49:82–90Google Scholar
  24. Kumar S (2004) Genetic parameter estimates for wood stiffness, strength, internal checking, and resin bleeding for radiata pine. Can J For Res 34:2601–2610CrossRefGoogle Scholar
  25. Kumar S, Lee J (2002) Age-age correlations and early selection for end-of-rotation wood density in radiata pine. For Genetics 9:323–330Google Scholar
  26. Lachenbruch B, Droppelmann F, Balocchi C, Peredo M, Perez E (2010) Stem form and compression wood formation in young Pinus radiata trees. Can J For Res 40:26–36CrossRefGoogle Scholar
  27. 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 40:703–715CrossRefGoogle Scholar
  28. Loo JA, Tauer CG, van Buijtenen JP (1984) Juvenile–mature relationships and heritability estimates of several traits in loblolly pine (Pinus taeda). Can J For Res 14:822–825CrossRefGoogle Scholar
  29. Newman DH, Williams CG (1991) The incorporation of risk in optimal selection age determination. For Sci 37:1350–1364Google Scholar
  30. R Development Core Team (2008) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, AustriaGoogle Scholar
  31. Searle SR (1965) The value of indirect selection: I. Mass selection. Biometrics 21:682–707PubMedCrossRefGoogle Scholar
  32. Shelbourne CJA (1997) Genetics of adding value to the end-products of radiata pine. In: Burdon RD, Moore JM (eds) Proceedings of IUFRO ’97: Genetics of radiata pine. FRI Bulletin 203, Rotorua, New Zealand, pp 129–141Google Scholar
  33. Shelbourne CJA, Zobel BJ, Stonecypher RW (1969) The inheritance of compression wood and its genetic and phenotypic correlations with six others traits in five-year-old Loblolly pine. Silvae Genet 18:43–47Google Scholar
  34. Shelbourne CJA, Apiolaza LA, Jayawickrama KJS, Sorensson CT (1997) Developing breeding objectives for radiata pine in New Zealand. In: Burdon RD, Moore JM (eds) Proceedings of IUFRO ’97: Genetics of radiata pine. FRI Bulletin 203, Rotorua, New Zealand, pp 160–168Google Scholar
  35. Sierra de Grado R, Pando V, Martínez Surimendi P, Peñalvo A, Báscones E, Moulia B (2008) Biomechanical differences in the stem straightening process among Pinus pinaster provenances. A new approach for early selection of stem straightness. Tree Physiol 28:835–846PubMedGoogle Scholar
  36. Siripatanadilok S, Leney L (1985) Compression wood in western hemlock Tsuga heterophylla (Raf.) Sarg. Wood Fiber Sci 17:254–265Google Scholar
  37. Sorensen D, Gianola D (2002) Likelihood, Bayesian and MCMC methods in quantitative genetics. Springer, New YorkGoogle Scholar
  38. Soria F, Basurco F, Toval G, Silio L, Rodriguez MC, Toro M (1998) An application of Bayesian techniques to the genetic evaluation of growth traits in Eucalyptus globulus. Can J For Res 28:1286–1294CrossRefGoogle Scholar
  39. Vargas-Hernandez J, Adams WT (1992) Age-age correlations and early selection for wood density in young coastal Douglas-fir. For Sci 38:467–478Google Scholar
  40. Walker JCF (2006) Primary wood processing. Springer, DordrechtGoogle Scholar
  41. Walker JCF, Nakada R (1999) Understanding corewood in some softwoods: a selective review on stiffness and acoustics. Int For Rev 1:251–259Google Scholar
  42. Watt MS, Sorensson C, Cown DJ, Dungey HS, Evans R (2010) Determining the main and interactive effect of age and clone on wood density, microfibril angle, and modulus of elasticity for Pinus radiata. Can J For Res 40:1550–1557CrossRefGoogle Scholar
  43. White TL, Adams WT, Neale D (2007) Forest genetics. CAB International, WallingfordCrossRefGoogle Scholar
  44. Yamashita S, Yoshida M, Takayama S, Okuyama T (2007) Stem-righting mechanism in gymnosperm trees deduced from limitations in compression wood development. Ann Bot 99:487–493PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Luis A. Apiolaza
    • 1
    Email author
  • Shakti S. Chauhan
    • 1
  • John C. F. Walker
    • 1
  1. 1.School of ForestryUniversity of CanterburyChristchurchNew Zealand

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