African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6693

Full Length Research Paper

The multivariate approach and influence of characters in selecting superior soybean genotypes

Viviane Formice Vianna1*, Sandra Helena Unêda-Trevisoli¹, Janete Apparecida Desidério², Silviane de Santiago¹, Kauê Charnai¹, José Arantes Ferreira Júnior¹, Antonio Sergio Ferraudo³ and Antonio Orlando Di Mauro¹
1Department of Plant Production, FCAV/UNESP-Jaboticabal/SP, Brazil. 2Department of Biological Sciences, FCAV/UNESP-Jaboticabal/SP, Brazil. 3Department  of Exact Sciences, FCAV/UNESP-Jaboticabal/SP, Brazil.
Email: [email protected]

  •  Accepted: 05 August 2013
  •  Published: 08 August 2013

Abstract

Multivariate analysis can help to select superior genotypes through the simultaneous analysis of original information containing the characters of interest. The present study objectives were to select soybean genotypes for good agronomic attributes with focus on yield from amonggenotypes carrying genes resistantto Asian soybean rustusing multivariate analyses and as well as to define the main plant characters which influence the selection decision. Ninety five soybean genotypes of the F6 endogamous generation were evaluated in a random block experiment with two replications for agronomic characters. The data were submitted to principal component and cluster analyses, besides the use of selection index. In the principal components analysis, four eigenvalues explained 71.6% of the variance in the original information, allowing the identification of seventeen superior genotypes, focusing on yield. In the dendrogram obtained from cluster analysis, the genotypes selected in the principal components were grouped in the same cluster, and a second selection was made in which seven genotypes were selected. Concerning the selection by index, most superior genotypes were coincident with the results of the multivariate analysis. In conclusion, multivariate analyses permitted the selection of superior genotypes for important agronomic characteristics in soybeans, principally for components linked to grain production.

 

Key wordsGlycine max, yield, principal components, cluster analysis.