Repeatability estimates and minimum number of evaluations for selection of elephant-grass genotypes for herbage production

Erina Vitório Rodrigues, Rogério Figueiredo Daher, Geraldo de Amaral Gravina, Alexandre Pio Viana, Maria do Socorro Bezerra de Araújo, Maria Lorraine Fonseca Oliveira, Marcelo Vivas, Bruna Rafaela da Silva Menezes, Antônio Vander Pereira
2019 Bioscience Journal  
In forage-plants breeding, the selection of superior genotypes has been undertaken through successive harvests in previously established intervals. However, this process involves many steps, the evaluation of many traits, and a great spending with costs and labor. Thus the estimate of the repeatability is essential in improvement of perennials, it allows predicting genotypic value of the individual, the minimum number of evaluations in the selection of genotypes and minimizes resources and time
more » ... resources and time in the selection of promising individuals. The objective of this study was to estimate the repeatability coefficient for morphological traits in elephant grass and determine the number of evaluations needed for phenotypic selection more efficient. The experimental randomized block design with 53 genotypes and two replications. The repeatability coefficients were estimated for variables plant height, number of tillers, stem diameter and dry matter yield, using the methods of Anova, Principal Components and Structural Analysis. We observed significant differences between genotypes (P <0.01) for all variables. The main components provide larger estimates of repeatability when compared to other methods. Estimates of the repeatability coefficients are of high magnitude average for the variables plant height (0.44) number of tillers (0.44) and stem diameter (0.63) and low magnitude for dry matter production (0.27). The Principal Components method requires five, five, two and eleven measurements for plant height, number of tillers, stem diameter and dry matter yield, respectively, with 80% reliability.
doi:10.14393/bj-v36n1a2020-42075 fatcat:ucndndkmkna2vc2uojfxxvg3wm