Predição de ganhos genéticos em progênies de Eucalyptus benthamii Maiden & Cambage por diferentes métodos de seleção - Da Costa, Rodolfo Manoel Lemes [UNESP]; Estopa, Regiane Abjad [UNESP]; Biernaski, Fabrício Antônio [UNESP]; Mori, Edson Seizo [UNESP] (Instituto de Pesquisa e Estudos Florestais (IPEF), 2016)
The high productivity of eucalyptus forests in Brazil nowadays is a result of several factors, one of which is breeding, which contributed significantly to this advance. Recurrent selection can increase productivity over generations of breeding. This study compared gains with selection by three methods: mass selection, selection among and within progenies and individual selection for the trait wood volume (VOL m3) in sevenyear- old Eucalyptus benthamii progenies, tested in Telêmaco Borba - PR. The experiment was established in randomized blocks, with 34 open-pollinated progenies, nine replications and row plots of six plants. Variances and genetic parameters were estimated by the REML/BLUP method. The progeny development was satisfactory in comparison with other studies; the individual additive genetic coefficient of variation (CVa (%) = 24.9%) and the average heritability among progenies (h2mp = 0.61) were high, indicating the possibility of gains with selection. A selection intensity of 10% of superior plants was used, for all methods. The estimated selection gain was highest for the individual selection method (SG = 20.5%), followed by selection among and within progenies (SG = 17.3%) and lowest for mass selection (SG = 15.6%). However, in terms of effective population size and genetic diversity estimate, the genetic basis was extremely narrowed by individual selection. Taking these factors into account, selection among and within progenies, with high genetic gain without relevant loss of genetic variability, seems more promising.
Prediction of genetics gains in Eucalyptus benthamii Maiden & Cambage progenies by different selection methods
Keywords: Eucalyptus benthamii; Forest improvement; Gains estimates; Selection methods