Growth and survival of Eucalyptus viminalis in a frost-prone site in southern Brazil, and implications for genetic management - Tambarussi, Evandro V. [UNESP]; Silva, Eder D. B.; Cost, Rodolfo M. L. da; Santos, Jessica F. F.; Jatzek, Vitor A. [UNESP]; Recende, Rafael T. (Scion, 2023)
Background: As the climate in southern Brazil is cold with frequent frosts, Eucalyptus species that can resist these climatic conditions are needed for commercial plantations. This study aims to evaluate provenances and families of Eucalyptus viminalis Labill. and compare them to 11 other Eucalyptus spp. to select superior genotypes with high production potential and resistance to frost. Methods: A total of 58 open-pollinated families from 16 provenances in Australia were planted in a frost-prone site in Irati, Parana state, Brazil, using a randomised block design, with three replicates, linear plots of three plants, and a 2 x 2 m spacing, for a total experimental area of one hectare. Another eleven Eucalyptus species were planted in linear plots ranging from four to 59 plants alongside the experiment.Results: Survival for E. viminalis ranged from 0 to 68%, indicating genetic variability for frost resistance in this species. The variation among provenances was high (56%) for total genetic variation, indicating relatively high additive genetic differentiation among them. The other half of the total additive genetic variation was within (24%) and among families (20%), showing good variability among genotypes of each provenance.Conclusions: In relation to the other 11 species, E. viminalis shows promise for frost-prone sites as the results are comparable to other species used in this region and clones from breeding programmes developed for these conditions. Thus, E. viminalis offers the potential for selecting superior genotypes to be cloned for immediate genetic gains, as well as for the next generation of breeding.
Keywords: Bayesian analysis; multi-species conservation; provenance and progeny trial; tree breeding; selection gain; spatial modeling