Open AccessAssessment of yield stability in sorghum using univariate and multivariate statistical approaches
Author(s) -
Adugna Asfaw
Publication year - 2008
Publication title -
hereditas
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.819
H-Index - 50
eISSN - 1601-5223
pISSN - 0018-0661
DOI - 10.1111/j.0018-0661.2008.2023.x
Subject(s) - univariate , ammi , multivariate statistics , rank correlation , statistics , biplot , multivariate analysis , mathematics , stability (learning theory) , rank (graph theory) , biology , correlation , sorghum , ranking (information retrieval) , genotype , gene–environment interaction , agronomy , combinatorics , genetics , computer science , artificial intelligence , geometry , machine learning , gene
The experiment was carried out to estimate GEI in sorghum for grain yield using univariate and multivariate statistical approaches based on two sets of performance trials (T1 and T2). While T1 consisted of 15 genotypes and tested in 8 environments, T2 that consisted of 13 genotypes was carried out in 13 environments. Because the combined ANOVA of each trial revealed significant differences among the genotypes, among the environments and GEI, the five univariate stability estimates: CV i ,,,, b i and were evaluated for ranking the genotypes. There was positive rank‐correlation between CVi and and among,, and b i . had significant positive rank‐correlation with and bi in T1 but weak rank‐correlation with the remaining parameters in both trials. The three types of univariate stability estimates and the only multivariate stability estimate, the AMMI analysis declared genotypes 2 and 5 to be the most stable in T1, but they gave quite unrelated ranking in T2. Because of the lack of correspondence among the tested stability estimates in the two trials, it was difficult to reach a conclusion on producing genotype recommendation based on the univariate statistical approach. However, as GEI has multivariate nature, the multivariate approach is believed to give more robust inference. Hence, some stable genotypes were suggested using the AMMI model for sorghum growing dry lowlands of the country.