REGULAR ARTICLE
Analysis of sunflower data from a multi-attribute genotype × environment trial in Brazil
Marisol García-Peña, Sergio Arciniegas-Alarcón, Kaye Basford, Carlos Tadeu dos Santos Dias1
Commun. Biometry Crop Sci. (2016) 11 (2), 127-139.
ABSTRACT
In multi-environment trials it is common to measure several response variables or
attributes to determine the genotypes with the best characteristics. Thus it is important
to have techniques to analyse multivariate multi-environment trial data. The main objective is
to complement the literature on two multivariate techniques, the mixture maximum likelihood method
of clustering and three-mode principal component analysis, used to analyse genotypes, environments and
attributes simultaneously. In this way, both global and detailed statements about the performance of the
genotypes can be made, highlighting the benefit of using three-way data in a direct way and providing an
alternative analysis for researchers. We illustrate using sunflower data with twenty genotypes, eight
environments and three attributes. The procedures provide an analytical procedure which is relatively
easy to apply and interpret in order to describe the patterns of performance and associations in multivariate multi-environment trials.
Key Words: three-way data; genotype-by-environment interaction; clustering via mixtures; principal components.
