Annals of Forest Science

, Volume 66, Issue 6, pp 601–601 | Cite as

Very early selection for solid wood quality: screening for early winners

Open Access
Review Article

Abstract

  • • This article reviews the theoretical basis for indirect selection — where early selection is an example — and how correlated response is calculated.

  • • The review is followed by a description of issues as to the choice of selection criteria that could explain the lack of substantial progress on breeding for wood quality. These include: the autoregressive nature of selection criteria, overemphasizing the importance of basic density as selection criterion, ignoring age-related trends of wood properties, using rotation age rather than technical thresholds as objective traits and ignoring that not all grades have identical marginal economic value.

  • • Three data sets are either analyzed for the first time or reanalyzed under different assumptions, to explore the importance of these criticisms.

  • • Finally, the use of critical value thresholds as very early selection criteria is suggested and discussed in the context of improving intrinsic corewood quality.

Keywords

wood stiffness early selection threshold trait Pinus radiata 

Sélection très précoce pour les propriétés du bois massif : détection précoce des individus les plus performants

Résumé

  • • Cet article passe en revue les bases théoriques des méthodes de sélection indirecte — parmi lesquelles figure la sélection précoce — et la manière dont le gain corrélé est calculé.

  • • Cette revue est suivie d’une description des problèmes liés au choix des critères de sélection et qui pourraient expliquer le manque de progrès substantiel suite à l’amélioration pour les propriétés du bois. Ceux-ci incluent l’auto-corrélation des critères de sélection, l’exagération de l’importance de la densité comme critère de sélection, l’oubli de l’évolution des propriétés du bois avec l’âge cambial, l’utilisation de l’âge de révolution comme caractère-cible plutôt que des seuils techniques et l’oubli que toutes les classes de produits n’ont pas la même valeur économique marginale.

  • • Trois jeux de données ont été soit analysés pour la première fois soit ré-analysés sous différentes hypothèses pour explorer l’importance de ces critiques.

  • • Enfin, l’utilisation de seuils critiques comme critères de sélection très précoce est suggérée et discutée dans le cadre de l’amélioration intrinsèque de la qualité du bois juvénile.

Mots-clés

rigidité sélection précoce caractère seuil Pinus radiata 

References

  1. Apiolaza L.A., 2008. Improvement objectives for short rotation forestry. New Zeal. J. For. 52: 28–30.Google Scholar
  2. Apiolaza L.A. and Garrick D.J., 2001. Analysis of longitudinal data from progeny tests: some multivariate approaches. For. Sci. 47: 129–140.Google Scholar
  3. Apiolaza L.A. and Greaves B.L., 2001. Why are most breeders not using economic breeding objectives? In: IUFRO Conference developing the Eucalypt of the Future, Valdivia, Chile.Google Scholar
  4. Apiolaza L.A., Raymond C.A., and Yeo B.J., 2005. Genetic variation of physical and chemical wood properties of Eucalyptus globulus. Silvae Genet. 54: 160–166.Google Scholar
  5. Apiolaza L.A., Walker J.C.F., Nair H., and Butterfield B.G., 2008. Very early screening of wood quality for radiata pine: pushing the envelope. In: Proceedings of the 51st international convention of society of wood science and technology, WQ-1.Google Scholar
  6. Balocchi C.E., Bridgwater F.E., Zobel B.J., and Jahromi S., 1993. Age trends in genetic parameters for tree height in a nonselected population of loblolly pine. For. Sci. 39: 231–251.Google Scholar
  7. Binet F.E., 1965. On the construction of an index for indirect selection. Biometrics 21: 291–299.PubMedCrossRefGoogle Scholar
  8. Burdon R.D., Kibblewhite R.P., Walker J.C.F., Megraw R.A., Evans R., and Cown D.J., 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: 399–415.Google Scholar
  9. Calus M.P.L., Meuwissen T.H.E., de Roos A.P.W., and Veerkamp R.F., 2008. Accuracy of genomic selection using different methods to define haplotypes. Genetics 178: 553–561.PubMedCrossRefGoogle Scholar
  10. Cave I.D., 1968. The anisotropic elasticity of the plant cell wall. Wood Sci. Technol. 2: 269–278.CrossRefGoogle Scholar
  11. Chauhan S.S., 2008. Pairing test and longitudinal growth strain: establishing the association. In: proceedings of the 51st international convention of society of wood science and technology, WQ-2.Google Scholar
  12. Chauhan S.S. and Walker J.C.F., 2006. Variations in acoustic velocity and density with age, and their relationship in radiata pine. For. Ecol. Manage. 229: 388–394.CrossRefGoogle Scholar
  13. Dungey H.S., Matheson A.C., Kain D., and Evans R., 2006. Genetics of wood stiffness and its component traits in Pinus radiata. Can. J. For. Res. 36: 1165–1178.CrossRefGoogle Scholar
  14. Evans R. and Ilic J., 2001. Rapid prediction of wood stiffness from microfibril, angle and density. For. Prod. J. 51: 53–57.Google Scholar
  15. Fernando R.L. and Grossman M., 1989. Marker assisted selection using best linear unbiased prediction. Genet. Sel. Evol. 21: 467–477.CrossRefGoogle Scholar
  16. Fernando R.L., Habier D., Stricker C., Dekkers J.C.M., and Totir L.R., 2007. Genomic selection. Acta Agr. Scand. A — An. 57: 192–195.Google Scholar
  17. Floyd S.L. and Stanish M.A., 2004. Methods for quantitatively determining lengthwise shrinkage in wood products. US Patent Application 10814767.Google Scholar
  18. Gaunt D., 1998. If you are not winning change the rules. New Zeal. For. Res. Wood Proc. Newsletter 23: 3.Google Scholar
  19. Gianola D., Fernando R.L., and Stella A., 2006. Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173: 1761–1776.PubMedCrossRefGoogle Scholar
  20. Gibson J.P., 1999. Molecular and quantitative genetics: a useful flirtation. In: from Jay Lush to Genomics: Visions for animal breeding and genetics, Iowa State University, Ames, Iowa, USA, pp. 77–84.Google Scholar
  21. Gilmour A.R., Cullis B.R., Welham S.J., and Thompson R., 2002. ASReml reference manual. New South Wales Agriculture, Orange, NSW, Australia.Google Scholar
  22. Grattapaglia D. and Kirst M., 2008. Eucalyptus applied genomics: from gene sequences to breeding tools. New Phytol. 179: 911–929.PubMedCrossRefGoogle Scholar
  23. Hazel L.N., 1943. The genetic basis for constructing selection indexes. Genetics 28: 476–490.PubMedGoogle Scholar
  24. Huang C.L., Lindström H., Nakada R., and Ralston J., 2003. Cell wall structure and wood properties determined by acoustics — a selective review. Holz Roh. Werkst. 61: 321–335.CrossRefGoogle Scholar
  25. Isik F., Gumpertz M., Li B.L., Goldfarb B., and Sun X., 2008. Analysis of cellulose microfibril angle using a linear mixed model in Pinus taeda clones. Can. J. For. Res. 38: 1676–1689.CrossRefGoogle Scholar
  26. Isik F. and Li B., 2003. Rapid assessment of wood density of live trees using the resistograph for selection in tree improvement programs. Can. J. For. Res. 33: 2426–2435.CrossRefGoogle Scholar
  27. Kumar S., Dungey H.S., and Matheson A.C., 2006. Genetic parameters and strategies for genetic improvement of stiffness in radiata pine. Silvae Genet. 55: 77–84.Google Scholar
  28. Lima J.T., Breese M.C., and Cahalan C.M., 2004. Variation in microfibril angle in Eucalyptus clones. Holzforschung 58: 160–166.CrossRefGoogle Scholar
  29. Lin C.Y., 1978. Index selection for genetic improvement of quantitative characters. Theor. Appl. Genet. 52: 49–56.Google Scholar
  30. Lindström H., Harris P., and Nakada R., 2002. Methods for measuring stiffness of young trees. Holz Roh. Werkst. 60: 165–174.CrossRefGoogle Scholar
  31. Lopez G.A., Potts B.M., Vaillancourt R.E., and Apiolaza L.A., 2003. Maternal and carry over effects on early growth of Eucalyptus globulus. Can. J. For. Res. 33: 2108–2115.CrossRefGoogle Scholar
  32. Megraw R.A., Bremer D., Leaf G., and Roers J., 1999. Stiffness in loblolly pine as a function of ring position and height, and its relationship to microfibril angle and specific gravity. In: IUFRO workshop connection between silviculture and wood quality through modelling approaches and simulation software, La Londe-Les-Maures, France, pp. 341–349.Google Scholar
  33. Meuwissen T.H.E., Hayes B.J., and Goddard M.E., 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: 1819–1829.PubMedGoogle Scholar
  34. Myszewski J.H., Bridgwater F.E., Lowe W.J., Byram T.D., and Megraw R.A., 2004. Genetic variation in the microfibril angle of loblolly pine from two test sites. Southern J. Appl. For. 28: 196–204.Google Scholar
  35. Nakada R., 2007. Within-tree variation of wood characteristics in conifers and the anatomical characteristics specific to very young trees. In: J.C.F. Walker (Ed.), The compromised wood workshop, Christchurch, New Zealand, 51–67.Google Scholar
  36. Newman D.H. and Williams C.G., 1991. The incorporation of risk in optimal selection age determination. For. Sci. 37: 1350–1364.Google Scholar
  37. O’Malley D.M. and McKeand S.E., 1994. Marker assisted selection for breeding value in forest trees. For. Genet. 1: 207–218.Google Scholar
  38. R Development Core Team, 2008. R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria.Google Scholar
  39. Raymond C.A., 2002. Genetics of Eucalyptus wood properties. Ann. For. Sci. 59: 525–531.CrossRefGoogle Scholar
  40. Raymond C.A. and Schimleck L.R., 2002. Development of near infrared reflectance analysis calibrations for estimating genetic parameters for cellulose content in Eucalyptus globulus. Can. J. For. Res. 32: 170–176.CrossRefGoogle Scholar
  41. Raymond C.A., Schimleck L.R., Muneri A., and Michell A.J., 2001. Genetic parameters and genotype-by-environment interaction for pulp yield predicted using near infrared reflectance analysis and pulp productivity in Eucalyptus globulus. For. Genet. 8: 213–224.Google Scholar
  42. Schaeffer L.R., 2006. Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genet. 123: 218–223.PubMedCrossRefGoogle Scholar
  43. Schimleck L.R., Evans R., Ilic J., and Matheson A.C., 2002. Estimation of wood stiffness of increment cores by near-infrared spectroscopy. Can. J. For. Res. 32: 129–135.CrossRefGoogle Scholar
  44. Schimleck L.R., Evans R., Jones P.D., Peter G., Daniels R.F., and Clark A., 2005. Estimation of microfibril angle and stiffness by near infrared spectroscopy using sample sets having limited wood density variation. IAWA J. 26: 175–187.Google Scholar
  45. Schneeberger M., Barwick S.A., Crow G.H., and Hammond K., 1992. Economic indices using breeding values predicted by blup. J. Anim. Breed. Genet. 107: 180–187.CrossRefGoogle Scholar
  46. Searle S.R., 1965. The value of indirect selection: I. mass selection. Biometrics 21: 682–707.PubMedCrossRefGoogle Scholar
  47. Shelbourne C.J.A., 1997. Genetics of adding value to the end-products of radiata pine. In: Burdon R.D. and Moore J.M. (Eds.), IUFRO ’97 Genetics of Radiata Pine, Rotorua, New Zealand, 129–141.Google Scholar
  48. Smith D.M., 1954. Maximum moisture content method for determining specific gravity of small wood samples. Report 2014, Forest Products Laboratory, Forest Service, US Department of Agriculture.Google Scholar
  49. Sorensson C.T. and Shelbourne C.J.A., 2005. NZIF Forestry handbook, New Zealand Institute of Forestry, chapter Clonal forestry, pp. 92–96.Google Scholar
  50. Tsehaye A., Buchanan A., and Walker J.C.F., 2000. Sorting of logs using acoustics. Wood Sci. Technol. 34.Google Scholar
  51. Van Vleck L.D., 1993. Selection Index and introduction to mixed model methods. CRC Press, Boca Raton.Google Scholar
  52. Walker J.C.F. and Butterfield B.G., 1995. The importance of microfibril angle for the processing industries. New Zeal. For. 40: 34–40.Google Scholar
  53. White T.L., Adams W.T., and Neale D., 2007. Forest genetics, CAB international.Google Scholar
  54. Wielinga B., Raymond C.A., James R., and Matheson A.C., 2009. Genetic parameters and genotype by environment interactions for green and basic density and stiffness of Pinus radiata d. don estimated using acoustics. Silvae Genet. (in press).Google Scholar
  55. Wilcox P.L., Carson S.D., Richardson T.E., Ball R.D., Horgan G.P., and Carter P., 2001. Benefit-cost analysis of dna marker based-selection in progenies of Pinus radiata seed orchard parents. Can. J. For. Res. 31: 2213–2224.Google Scholar
  56. Woolaston R.R. and Jarvis S.F., 1995. The importance of breeding objectives in forest tree improvement. In: Eucalypt plantations: improving fibre yield and quality, Hobart, Tasmania, Australia, 184–188.Google Scholar
  57. Wu H.X., Powell M.B., Yang J.L., Ivkovich M., and McRae T.A., 2007. Efficiency of early selection for rotation-aged wood quality traits in radiata pine. Ann. For. Sci. 64: 1–9.CrossRefGoogle Scholar
  58. Yamashita K., Hirakawa Y., Nakatani H., and Ikeda M., 2009. Longitudinal shrinkage variations within trees of sugi (Cryptomeria japonica) cultivars. J. Wood Sci. 55: 1–7.CrossRefGoogle Scholar
  59. Zamudio F., Baettyg R., Vergara A., Guerra F., and Rozenberg P., 2002. Genetic trends in wood density and radial growth with cambial age in a radiata pine progeny test. Ann. For. Sci. 59: 541–549.CrossRefGoogle Scholar

Copyright information

© Springer S+B Media B.V. 2009

Authors and Affiliations

  1. 1.School of ForestryUniversity of CanterburyChristchurchNew Zealand

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