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Genotype X environment interaction and simultaneous selection for high yield and stability in soybeans (Glycine max (L.) Merr.)
Author(s) -
DASHIELL K E,
ARIYO O J,
OJO K
Publication year - 1994
Publication title -
annals of applied biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.677
H-Index - 80
eISSN - 1744-7348
pISSN - 0003-4746
DOI - 10.1111/j.1744-7348.1994.tb04121.x
Subject(s) - biology , heritability , genotype , selection (genetic algorithm) , yield (engineering) , glycine , gene–environment interaction , linear regression , rank (graph theory) , correlation , stability (learning theory) , regression , regression analysis , statistics , genetics , mathematics , gene , combinatorics , amino acid , materials science , geometry , artificial intelligence , machine learning , computer science , metallurgy
Summary Eighteen genotypes of soybean were grown in five locations in Nigeria. The heritability estimates for seed yield were generally low, ranging from 22.6% to 45.3%. Joint regression analysis indicated the presence of genotype x environment, although a large proportion was non‐linear. The genotypes responded differently to environments, highlighting the possibility of breeding for specific environments. The correlation of regression coefficients with mean yield indicated that high yielding genotypes were responsive to changing environments. The simultaneous selection parameters Pi, S 3 and rank‐sums gave somewhat similar results but Pi produced higher yielding genotypes than others. The correlation between Pi and rank‐sum indicated that either of the techniques could be employed during selection.