Multivariate best linear unbiased predictor as a tool to improve multi-trait selection in sugarcane

Authors

  • Ivan Ricardo Carvalho Universidade Regional do Noroeste do Rio Grande do Sul, Departamento de Estudos Agrários, Avenida do Comércio, no 3.000, Bairro Universitário, CEP 98700-000 Ijuí, RS.
  • Vinícius Jardel Szareski Universidade Federal de Pelotas, Avenida Eliseu Maciel, s/no, CEP 96160-000 Capão do Leão, RS.
  • José Antônio Gonzalez da Silva Universidade Regional do Noroeste do Rio Grande do Sul, Departamento de Estudos Agrários, Avenida do Comércio, no 3.000, Bairro Universitário, CEP 98700-000 Ijuí, RS.
  • Andrei Caíque Pires Nunes Universidade Federal do Sul da Bahia, Centro de Formação em Ciências Agroflorestais, CEP 45613-204 Itabuna, BA.
  • Tiago Corazza da Rosa Universidade Federal de Pelotas, Avenida Eliseu Maciel, s/no, CEP 96160-000 Capão do Leão, RS.
  • Maurício Horbach Barbosa Universidade Federal de Pelotas, Avenida Eliseu Maciel, s/no, CEP 96160-000 Capão do Leão, RS.
  • Deivid Araújo Magano Universidade Regional do Noroeste do Rio Grande do Sul, Departamento de Estudos Agrários, Avenida do Comércio, no 3.000, Bairro Universitário, CEP 98700-000 Ijuí, RS.
  • Giordano Gelain Conte Universidade Regional do Noroeste do Rio Grande do Sul, Departamento de Estudos Agrários, Avenida do Comércio, no 3.000, Bairro Universitário, CEP 98700-000 Ijuí, RS.
  • Braulio Otomar Caron Universidade Federal de Santa Maria, Linha 7 de Setembro, BR-386, Km 40, CEP 98400-000 Frederico Westphalen, RS.
  • Velci Queiróz de Souza Universidade Federal do Pampa, Rua 21 de abril, no 80, CEP 96450-000 São Gabriel, RS.

DOI:

https://doi.org/10.1590/S1678-3921.pab2020.v55.26739

Keywords:

Saccharum officinarum, multi-trait selection, multivariate models, repeatibility

Abstract

The objective of this work was to evaluate the use of the multivariate best linear unbiased predictor (BLUP) method for multi-trait selection, to estimate the genetic parameters in sugarcane (Saccharum officinarum) genotypes. The experiment was carried out in a randomized complete block design with 21 sugarcane genotypes, in seven crop years, in a factorial arrangement with three replicates. The measured traits were: total yield of stems per hectare, total volume of juice per hectare, production of total soluble sugars, and stem length. The source variation in the crop years strongly contributed for the obtention of the expected values of the sum of squares, without causing distortions in the variance components and genetic variables. The measured traits showed genetic variability and allowed of efficient univariate and multivariate selections. The highest selection efficiency was obtained by using more than eight measurements, since they favored the estimates of heritability, accuracy, and repeatability. The 'IAC873396', 'Nova Iraí', 'IACSP 93-6006', and 'RB 835089' genotypes were superior as to the traits tested, regardless of the crop year. The BLUP multivariate technique for multi-trait selection is robust and allows of the increasing of the selection gains, accuracy, and reliability of predictions for sugarcane breeding.

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Published

2020-08-13

How to Cite

Carvalho, I. R., Szareski, V. J., Silva, J. A. G. da, Nunes, A. C. P., Rosa, T. C. da, Barbosa, M. H., … Souza, V. Q. de. (2020). Multivariate best linear unbiased predictor as a tool to improve multi-trait selection in sugarcane. Pesquisa Agropecuaria Brasileira, 55(X), e00518. https://doi.org/10.1590/S1678-3921.pab2020.v55.26739

Issue

V.55, Jan./Dec., 2020 :Continuos publication

Section

GENETICS