Estimating genetic variability and correlation of agronomic traits in Burley tobacco for selection purposes
The breeding values, genotypic and phenotypic variances, heritability and the correlation coefficients of the agronomic traits are some of the key parameters, which determine the efficiency of a breeding program. The phenotypic correlation is important because it shows how selection for one trait influences the expression of other traits. Therefore, the objective of this study was to obtain the genetic parameters estimated among the traits including the genotypic variances, phenotypic variances, genotype by environment variances, heritability and correlation coefficients, aiming to improve selection for important agronomic traits. The performance of 24 Burley tobacco hybrids, developed by Alliance One International, was studied. Yield, nicotine and quality index grade were evaluated across seven environments in the southern region of Brazil. The trials were conducted in a randomized complete blocks design, with three replications with five commercial controls. Statistical analyses were performed in the JMP software program (SAS Institute Inc.) using the restricted maximum likelihood method (REML) and Pearson’s correlation coefficient. Genetic parameters were estimated via REML, with genotypic means adjusted and estimated using the best linear unbiased predictor (BLUP) procedure. The likelihood ratio test (LRT) was performed and the significance was verified by the Chi-Square test. Based on the predicted mean values, Pearson's correlation between traits were estimated. There were statistically significant differences for the effect of genotype, indicating there is genetic variability for this effect. The behaviour of the hybrids within the environments was verified, allowing the selection of hybrids according to the studied environments. The heritability estimates decreased due to the complexity of the character evaluated. The characters leaf quality index and nicotine have a positive association. The statistical method used showed to be efficient for this type of data set, allowing greater efficiency with regard to the selection of superior genotypes.