Multivariate analysis of 102 Indian cowpea (Vigna unguiculata (L.) Walp.) germplasm

Vijayakumar, E.; Thangaraj, Kumarasamy; Thiyagarajan, K.; Vanniarajan, C.; Senthil, N.; Jeyakumar, P.

doi:10.37992/2020.1101.031

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…The characters viz., the number of seeds per pod, plant height, the number of primary branches plant, the number of pods per cluster and pod length expressed positive loading effects in PC4. Similar findings were already reported by Nkoana et al (2019), Mofokeng et al (2020), Mahalingam et al (2020), Vijayakumar et al (2020) and Ashinie et al (2020).…”

Section: Resultssupporting

confidence: 92%

Identification of selection indices for the yield enhancement in cowpea (Vigna unguiculata (L.) Walp.) under sodic condition

2021

EJPB

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The experiment was conducted at sodic soils to determine the relationship and degree of direct and indirect effects of yield and yield contributing traits in 57 cowpea accessions along with checks CO (CP) 7, VBN 1 and VBN 3. The association analysis showed that clusters number per plant, pod numbers per cluster, length of the pod, seeds per pod and hundred seed weight were positively correlated with seed yield. Path analysis indicated that the number of clusters per plant revealed a highly positive direct effect on single plant yield at genotypic level followed by pod length, pods number per cluster and seeds per pod. Principal Component Analysis (PCA) revealed first four PCs together contributed 67.33 per cent of variability. A total of 12 genotypes occupied the first quadrant showed better performance for five characters namely 100 seed weight, seed yield per plant, the number of clusters per plant, the number of pods per cluster and plant height. These selected genotypes namely

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Section: Resultssupporting

confidence: 92%

Identification of selection indices for the yield enhancement in cowpea (Vigna unguiculata (L.) Walp.) under sodic condition

2021

EJPB

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“…The PC1 possessed the highest per cent variance (34.56%) and was the most prominent one, thus the selection of lines for the characters under PC1 might be desirable. Jaime and Anita (2019) and Vijaykumar et al (2020) reported a similar result with the per cent variance when the eigenvalue is more than one.…”

Section: Resultsmentioning

confidence: 54%

Comparative diversity analysis of cowpea genotypes using multivariate approaches

2021

EJPB

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Cowpea improvement is possible if the information on genetic diversity among existing genotypes is available. Principal Component Analysis and Mahalanobis D 2 analysis were used to analyse 30 cowpea genotypes based on eleven quantifiable traits. The whole variance was divided into eleven major principal components using PCA, with the top three PCs with eigenvalues >1 accounting for 69.47 per cent of the total variation. From the analysis of the first two PCs and Mahalanobis D 2 analysis, it was confirmed that genotypes viz., AVCP-1, PGCP-1, PGCP-13, CGD-1406, and CGD-1439 were scattered apart in all four quadrates of the bi-plot and fall in clusters with high inter-cluster distance representing maximum genetic variation. As a result, 30 cowpea genotypes had genetic and phenotypic differences that could be used to improve the cowpea by simple selection and crossing potential parents.

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Multivariate analysis of 102 Indian cowpea (Vigna unguiculata (L.) Walp.) germplasm

Cited by 2 publications

References 21 publications

Identification of selection indices for the yield enhancement in cowpea (Vigna unguiculata (L.) Walp.) under sodic condition

Identification of selection indices for the yield enhancement in cowpea (Vigna unguiculata (L.) Walp.) under sodic condition

Comparative diversity analysis of cowpea genotypes using multivariate approaches

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