Genotypic superiority of Psidium Guajava S1 families using mixed modeling for truncated and simultaneous selection

Autor: Moisés Ambrósio, Alexandre Pio Viana, Rodrigo Moreira Ribeiro, Sandra Costa Preisigke, Natan Ramos Cavalcante, Flavia Alves da Silva, Géssica Xavier Torres, Carlos Misael Bezerra de Sousa
Jazyk: English<br />Spanish; Castilian<br />Portuguese
Rok vydání: 2020
Předmět:
Zdroj: Scientia Agricola, Vol 78, Iss 2 (2020)
Druh dokumentu: article
ISSN: 1678-992X
1678-992x
DOI: 10.1590/1678-992x-2019-0179
Popis: ABSTRACT: The purpose of this study was to conduct selection, genetic parameter estimation, and prediction of genetic values for 18 S1 families of guava trees using mixed model methodology and simultaneous selection of traits by means of the additive selection index, multiplicative selection index, and mean rank adapted from Mulamba. All families analyzed were obtained by means of self-fertilization of superior genotypes (full siblings) from the genetic breeding program of guava trees at the Universidade Estadual do Norte Fluminense. An experimental randomized block design with 18 S1 families, three replicates, and ten plants per plot was used. A total of 540 genotypes (individual plants) of guava tree were evaluated. Genetic parameter estimation and selection of the best genotypes based on the genetic value were performed using the statistical procedure, from the Selegen-REML/BLUP program. The analyses of the additive selection index, multiplicative selection index, and the sum of rank adapted from Mulamba were also performed under the Selegen program. During the evaluation by the individual BLUPs, families 1, 12, 4, 6, and 8 contributed to most of the genotypes selected for the traits under evaluation, suggesting their significant potential to generate high quality and high yield genotypes. In the selection indexes via mixed models, the multiplicative index showed higher values for genetic gains (74 %), followed by the mean rank index adapted from Mulamba (19 %), and the additive index (2 %).
Databáze: Directory of Open Access Journals
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