Theoretical and Applied Genetics

, Volume 89, Issue 7–8, pp 911–921 | Cite as

Heritabilities and genetic correlations for estimated growth curve parameters in maritime pine

  • F. Danjon


Height growth curves and several other characters were measured in five maritime pine (Pinus pinaster Ait) progeny tests aged from 18 to 27 years (about half the rotation age), with sample sizes of 272–1555 trees. These curves were fitted with a reparametrized Lundqvist-Matèrn sigmoidal growth function with global estimation of two of the four parameters. Each curve was characterized by two parameters:

  • the maximal growth rate (r), approximately proportional to the stem height at age 16 years, and essentially determined by the height increments around age 6 years.

  • the asymptote (A), which is an extrapolation of growth after the measurement age. A is essentially determined by the latter growth period (around age 20 years), and is also related to the shape of the observed curve.

The modelling framework appeared to be well suited to the characteristics of the data studied, and the estimation standard errors of the parameters were reasonably low. The heritabilities yielded for the growth curve parameters were high, similar to the heritabilities of cumulative heights. The genetic correlation between r and A was low, pointing to a poor juvenile-mature correlation. Discrepancies from one trial to another in heritabilities and in the correlation pattern were observed, they probably originated from environmental stresses. Maritime pine is actually selected using height and butt angle of lean at age 10 years as criteria. Improvements in the breeding program are suggested.

Key words

Height growth curves Genetic parameters Nonlinear regression Pinus pinaster 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Balocchi CE, Bridgwater FE, Zobel BJ, Jahromi S (1993) Age trends in genetic parameters for tree height in a nonselected population of loblolly pine. For Sci 39:231–251Google Scholar
  2. Baradat P (1976) Use of juvenile mature relationships and information from relatives in combined multitrait selection. In Proc IUFRO Conf Joint meet on advanced generation breeding, Bordeaux, pp 121–128Google Scholar
  3. Bastien J-C, Roman-Amat B (1990) Predicting douglas fir (Pseudotsuga menziesii [Mirb.] Franco) volume at age 15 with early traits. Silv Genet 39:29–35Google Scholar
  4. Bergoin A (1993) Efficacité de la sélection réalisée en foret et en test de descendances chez le pin maritime (Pinus pinaster Ait). Prédiction du gain génétique sur le volume à partir des composantes de la hauteur. Diplome d'Etudes Approfondies, University of Nancy I/INRA PierrotonGoogle Scholar
  5. Cannel MGR (1982) ‘Crop’ and ‘Isolation’ ideotypes: evidence for progeny differences in nursery-grown Picea sitchensis. Silv Genet 31:60–66Google Scholar
  6. Cotterill PP (1987) Short note: on estimating heritability according to practical applications. Silv Genet 36(1):46–48Google Scholar
  7. Cotterill PP, Dean CA (1988) Changes in the genetic control of growth of radiata pine to 16 years and efficiencies of early selection. Silv Genet 37:138–146Google Scholar
  8. Cotterill PP, James JW (1984) Number of offspring and plot sizes required for progeny testing. Silv Genet 33:203–209Google Scholar
  9. Danjon F (1992) Variabilité génétique des courbes de croissance en hauteur du pin maritime (Pinus pinaster Ait.). PhD thesis, University of Lyon I, FranceGoogle Scholar
  10. Danjon F (1994) Stand features and height growth in a 36-year-old maritime pine provenance test. Silv Genet 43:52–62Google Scholar
  11. Danjon F, Hervé JC (1994) Choice of a model for height growth curves in maritime pine. Ann Sci For 51(6) (in press)Google Scholar
  12. Day NE (1966) Fitting curves to longitudinal data. Biometrics 22:276–291Google Scholar
  13. Durel C-E (1990) Paramètres génétiques et sélection en deuxième génération d'amélioration du Pin maritime Pinus pinaster Ait. PhD thesis, IN A Paris-Grignon — FranceGoogle Scholar
  14. Franklin EC (1979) Model relating levels of genetic variance to stand development of four North American conifers. Silv Genet 28:207–212Google Scholar
  15. Gill JGS (1987) Juvenile-mature correlations and trends in genetic variances in Sitka spruce in Britain. Silv Genet 36:189–194Google Scholar
  16. Guignard P (1983) Controle génétique du développement de semis de pin maritime (Pinus pinaster Ait.). Mise en évidence des effets maternels sur la croissance juvénile. Diplome d'Etudes Approfondies, University of Bordeaux II/INRA PierrotonGoogle Scholar
  17. Hodge GR, White TL (1992) Genetic parameter estimates for growth traits at different ages in slash pine and some implications for breeding. Silv Genet 41:252–261Google Scholar
  18. Illy G (1966) Recherches sur l'amélioration génétique du Pin maritime. Ann Sci For 23:769–948Google Scholar
  19. INRA (1991) Sélect — manuel d'utilisation.Google Scholar
  20. Kachman SD, Baker RL, Gianola D (1988) Phenotypic and genetic variability of estimated growth curve parameters in mice. Theor Appl Genet 76:148–156Google Scholar
  21. Kang H (1991) Components of juvenile-mature correlations in forest trees. Theor Appl Genet 81:173–184Google Scholar
  22. Kremer A (1981a) Déterminisme de la croissance en hauteur du Pin maritime (Pinus pinaster Ait.). I. Role du polycyclisme. Ann Sci For 38:199–222Google Scholar
  23. Kremer A (1981b) Déterminisme de la croissance en hauteur du Pin maritime (Pinus pinaster Ait.). IL Comportement interannuel, interaction génotype x année. Ann Sci For 38:331–355Google Scholar
  24. Kremer A (1981c) Déterminisme de la croissance en hauteur du Pin maritime (Pinus pinaster Ait.). III. Evolution des composantes de la variance phénotypique et génotypique. Ann Sci For 38:355–375Google Scholar
  25. Kremer A (1992) Prediction of age-age correlations of total height based on serial correlations between height increments in maritime pine (Pinus pinaster Ait.) Theor Appl Genet 85:152–158Google Scholar
  26. Lambeth CC (1980) Juvenile-mature correlations in Pinacea and implications for early selection. For Sci 26:571–580Google Scholar
  27. Lambeth CC, van Buijtenen JP, Duke SD, McCullough RB (1983) Early selection is effective in 20-year-old genetic tests of loblolly pine. Silv Genet 32:210–215Google Scholar
  28. Lemoine B (1979) Pin maritime et sécheresses dans les Landes de Gascogne. Croissances en circonférence. C R Acad Agric Fr 65:694–702Google Scholar
  29. Lemoine B (1991) Growth and yield of maritime Pine (Pinus pinaster Ait) the average dominant tree of the stand. Ann Sci For 48:593–611Google Scholar
  30. McCutchan BG, Namkoong G, Giesbrecht FG (1989) Design efficiencies with planned and unplanned unbalance for estimating heritability in forestry. For Sci 35:801–815Google Scholar
  31. McKeand SE (1988) Optimum age for family selection for growth in genetic tests of loblolly pine. For Sci 34:400–411Google Scholar
  32. Magnussen S (1993) Growth differentiation in white spruce crop tree progenies. Silv Genet 42:258–266Google Scholar
  33. Magnussen S, Kremer A (1994) Selection for optimum growth curve. Silv Genet 42:322–335Google Scholar
  34. Mangin B, Vincourt P (1992) Schémas de sélection: de la représentation généalogique au modèle statistique. Elaboration du modèle. Genet Sel Evol 24:71–84Google Scholar
  35. Matèrn B (1959) Some remarks on the extrapolation of height growth. For Res Inst Sweden Stat Rep no 2Google Scholar
  36. Moé JJ (1977) The Levenberg-Marquardt algorithm: implementation and theory. In Watson GA (ed) Numerical analysis. Springer, (Lecture notes in mathematics, vol 630). Berlin Heidelberg New York, pp 105–116Google Scholar
  37. Namkoong G, Conkle M (1976) Time trends in genetic control of height growth in ponderosa pine. For Sci 22:2–12Google Scholar
  38. Namkoong G, Matzinger DF (1975) Selection for annual growth curves in Nicotiana tabacum L.. Genetics 81:377–386Google Scholar
  39. Namkoong G, Usanis RA, Silen RR (1972) Age-related variation in genetic control of height growth in douglas-fir. Theor Appl Genet 42:151–159Google Scholar
  40. Rehfeldt GE, Wykoff WR, Hoff RJ, Steinhoff RJ (1991) Genetic gains in growth and simulated yield of Pinus monticola. For Sci 37:326–342Google Scholar
  41. Richards FJ (1959) A flexible growth function for empirical use. J Exp Bot 10:290–300Google Scholar
  42. Riemenschneider (1988) Heritability, age-age correlations, and inferences regarding juvenile selection in Jack Pine. For Sci 34:1076–1082Google Scholar
  43. Ross GJS (1970) The efficient use of function minimization in non-linear maximum-likelihood estimation. Appl Stat 19:205–221Google Scholar
  44. Seber GAF, Wild CJ (1989) Nonlinear regression. J. Wiley & Sons, New YorkGoogle Scholar
  45. Shelbourne CJA, Carson MJ, Wilcox MD (1989) New techniques in the improvement of radiata pine. Commonw For Rev 68:191–201Google Scholar
  46. Veiling P, Tigerstedt PMA (1984) Harvest index in a progeny test of Scots pine with reference to the model of selection. Suva Fenn 18:21–32Google Scholar
  47. Ying CC, Morgenstern EK (1979) Correlations of height growth and heritabilities at different ages in white spruce. Silv Genet 28:181–185Google Scholar

Copyright information

© Springer-Verlag 1994

Authors and Affiliations

  • F. Danjon
    • 1
  1. 1.INRA, Laboratoire Croissance et ProductionPierrotonCestasFrance

Personalised recommendations