In:
LEGUME RESEARCH - AN INTERNATIONAL JOURNAL, Agricultural Research Communication Center, , No. Of ( 2022-5-23)
Abstract:
Background: Absolute understanding of the relationship between yield and yield attributing traits contributing to variance is predominant in a breeding programme. To study the multivariate analysis and interrelationship among the yield and yield attributing traits in the pigeonpea, 68 genotypes were subjected to principal component analysis and association studies. Methods: The 68 pigeonpea genotypes were raised during two seasons viz., rabi, 2019-2020 and rabi, 2020-2021 in a randomized complete block design with two replications. The two-season data was pooled and utilized for multivariate and association studies.
Result: The total variance was split into 12 principal components. Four principal components were found to have eigen values more than one and explained 78.75 per cent of the total variance. The correlation studies revealed that, the single plant yield was highly correlated with the traits viz., days to 50 per cent flowering (rg=0.213, P less than 0.05), days to maturity (rg=0.347, P less than 0.01), plant height (rg=0.536,P less than 0.01), number of branches per plant (rg=0.331,P less than 0.01), number of clusters per plant (rg=0.705,P less than 0.01), number of pods per plant (rg=0.805,P less than 0.01), pod length (rg=0.481, P less than 0.01), number of seeds per pod (rg=0.231, P less than 0.05) and hundred seed weight (rg=0.505, P less than 0.01). Selection for these traits will improve single plant yield. Path analysis showed that the traits viz., number of pods per plant (0.871), shelling percentage (0.391) and hundred seed weight (0.744) had high positive direct effect on single plant yield, whereas the high indirect effect on single plant yield possessed by number of pods per plant through traits viz., days to maturity (0.316), plant height (0.421), pod bearing length (0.454) and number of clusters per plant (0.706).
Type of Medium:
Online Resource
ISSN:
0976-0571
,
0250-5371
Language:
Unknown
Publisher:
Agricultural Research Communication Center
Publication Date:
2022
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