Measurement and classification of genetic diversity in okra (Abelmoschus esculentus)

Eighteen accessions of okra of diverse ecological background were evaluated for genetic variability through the techniques of coefficient of racial likeness (CRL) and principal coordinate analysis (PCO). The variation patterns among the accessions were classified by using the techniques of metroglyph analysis and single-linkage cluster analysis. CRL and PCO produced similar results on the diversity of the accessions but the grouping of the accessions by metroglyph analysis and SLCA produced different results. The usefulness of metroglyph analysis depends on the amount of variation accounted for by the two principal characters on which the two co-ordinate axes are constructed. The techniques are compared.     

Numerical Analysis of Variation Among Accessions of Okra [Abelmoschus esculentus (L.) Moench], Malvaceae

Thirty quantitative and qualitative characters were observedon 20 accessions of okra which consisted of 19 accessions fromdifferent geographical areas of Nigeria and one accession fromIndia. Principal Component Analysis (PCA) and Single LinkageCluster Analysis (SLCA) were employed to analyse the variationpatterns in these accessions. The first three principal componentsaccounted for 49.94% of the total variation while the firstfive axes accounted for 65.7% SLCA summarized the relationshipsamong the accessions at various levels of similarity into adendrogram, while the accessions were sorted into six distinctgroups by the FASTCLUS technique and the mean percentage similaritybetween some groups suggested some degree of phenetic resemblance. Abelmoschus esculentus, okra, variation, principal component analysis, cluster analysis     

Genotype X environment interaction and simultaneous selection for high yield and stability in soybeans (Glycine max (L.) Merr.)

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, S³ 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.