Optimum selection age for height in Pinus taeda L. in Zimbabwe

D.P. Gwaze, John Woolliams, P.J. Kanowski

Research output: Contribution to journalArticlepeer-review

Abstract

Four progeny tests of P. tacda L. planted in the Eastern highlands of Zimbabwe were assessed for height at 1.5, 9.5, 13.5, and 22.5 years. Age-age genetic correlations were all positive and high 10.76 to 0.97), with low tu moderate standard errors (0.01 to 0.10). Genetic correlations were always higher than corresponding phenotypic correlations. Two linear models were fitted, by regressing genetic correlation on ii) the natural logarithm of tho ratio of the younger age to the older age (LAR), and on (2) age difference. The age difference model fitted the data better than LAR model, indicating that the commonly used logarithm models are not necessarily the most appropriate. Predictions of genetic correlations by models based on genetic correlation were more accurate than those estimated using the common model based on phenotypic correlation. Where flowering age was assumed to be 10 years, either genetic or phenotypic models predicted annual genetic gain to be greatest at 10 years. However, the phenotypic model underestimated genetic gain at all ages-particularly at very young ages, for which the potential gain was less than a quarter of that predicted by the other models. When flowering age was reducing to 3 years, optimum selection age under the phenotypic model was 6 years, but was reduced to 3 Sears using the genetic models. Reducing flowering age to 3 years increased annual genetic gain by 100%, indicating the potential of artificially inducing flowering for enhancing genetic progress with P. taeda in Zimbabwe.
Original languageUndefined/Unknown
Pages (from-to)358-365
Number of pages8
JournalSilvae Genetica
Volume46
Issue number6
Publication statusPublished - 1998

Keywords

  • genetic correlation phenotypic correlation genetic gain optimum selection age Pinus taeda loblolly-pine genetic-parameters stand development patula schiede wood density trends growth heritability inheritance variance

Cite this