African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6578

Full Length Research Paper

Strategies for selecting soybean genotypes using mixed models and multivariate approach

Andréa Carla Bastos Andrade
  • Andréa Carla Bastos Andrade
  • Universidade Federal de Viçosa - UFV, Avenida Peter Henry Rolfs - Campus Universitário, ZIP 36570-900, Viçosa, MG, Brazil.
  • Google Scholar
Alysson Jalles da Silva
  • Alysson Jalles da Silva
  • Universidade Estadual Paulista – UNESP/ FCAV, Via de Acesso Prof. Paulo Donato Castellani, s/n, ZIP 14884-900, Jaboticabal - SP, Brazil.
  • Google Scholar
Antônio Sérgio Ferraudo
  • Antônio Sérgio Ferraudo
  • Universidade Estadual Paulista – UNESP/ FCAV, Via de Acesso Prof. Paulo Donato Castellani, s/n, ZIP 14884-900, Jaboticabal - SP, Brazil.
  • Google Scholar
Sandra Helena Unêda-Trevisoli
  • Sandra Helena Unêda-Trevisoli
  • Universidade Estadual Paulista – UNESP/ FCAV, Via de Acesso Prof. Paulo Donato Castellani, s/n, ZIP 14884-900, Jaboticabal - SP, Brazil.
  • Google Scholar
Antônio Orlando Di Mauro
  • Antônio Orlando Di Mauro
  • Universidade Estadual Paulista – UNESP/ FCAV, Via de Acesso Prof. Paulo Donato Castellani, s/n, ZIP 14884-900, Jaboticabal - SP, Brazil.
  • Google Scholar


  •  Received: 14 March 2015
  •  Accepted: 16 October 2015
  •  Published: 07 January 2016

Abstract

The objective of this study was to select soybean genotypes derived from crosses between conventional and transgenic lines Roundup Ready (RR), using jointly Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP) approaches, factors analysis and principal components analysis, processed with favorable agronomic traits, during the 2013/2014 growing season. Three agronomic selection processes were identified to select genotypes that discriminate genotypes containing more specific properties. Process 1 (insertion height of first pod, HFP; number of branches, NB; number of pods, NP; number of nodes, NN; and grain yield, GY) was efficient to select earlier, smaller genotypes with good yield/production components and lodging resistance. The junction between mixed model via REML/BLUP and the applied multivariate statistic using factor analysis helped to select suitable genotypes with high performance to carry on the soybean plant-breeding program.

 

Key words: Glycine max, Restricted Maximum Likelihood/ Best Linear Unbiased Prediction (REML / BLUP), factor analysis, principal components.