Study on genetic divergence, association between morphological traits and path analysis among different okra (Abelmoschus esculentus L. Moench) genotypes
Description
To identify genotypes with greater potentials for appreciable yield and yield related components from diverse genotypes of okra and donors for improved productivity, an experiment was conducted with 25 indigenous and 25 exotic collections of okra genotypes following randomized complete block design with three replications. The data of morphological characters were analysed using R studio software factoextra package and GENRES. The results revealed that, out of nine principal components PC1, PC2 and PC3 eigen values of 4.07, 1.64, 1.00 respectively for yield and yield attributing traits. Several exotic accessions viz., G36, G50, G37, G47, G35, along with few indigenous collections viz., G21 and G24 were closely associated with the yield and attributing traits. Identified genotypes viz., G36, G37, G35, G24 and G50 with higher number of nodes per plant, fruit yield per plant and fruit width showed higher potentials for improved yield and are thus recommended for further genetic resource enhancement approaches. According to the present study findings, the eigen values of the principal component within PCA signify the extent of variability observed in characteristics such as fruit weight, fruit length, number of fruits per plant, and number of nodes per plant, which exhibited significant contributions to dissimilarity, besides fruit yield per plant (Table 3). The traits, namely fruit weight, fruit length, and number of fruits per plant, are genuinely valuable for future breeding initiatives, given their strong correlation with fruit yield. Therefore, they can be deliberately selected for breeding in okra to achieve the desired objective of developing high-yielding genotypes from segregants. Among the various genotypes under scrutiny, it was determined that G21 (IC33248) displayed the most substantial variation in Dimension 1 and 2. It was concluded that genotypes such as G36(EC133336), G50(EC169344), G37(EC133412), G47(EC169341), G35(EC116383), and G24(IC33302) made significant contributions to both yield and traits associated with yield. Traits viz., fruit length, fruit weight and number of fruits per plant exhibited positive direct effect on fruit yield per plant and thus these traits could be helpful during the selection process.
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Multivariate analysis used to classify unique parents with yield and yield attributes from a diversified geographical location. It reduces the number of variables that may be associated into a smaller number of uncorrelated variables. The main advantage of principal component analysis (PCA) is to, measure the significance of each dimension in explaining the variability of a dataset. It relies upon eigen vectors and eigen values to signify data. Thus, the current study highlighting the significance of understanding the genetic and morphological characteristics of okra accessions to improve yield and overall productivity in okra cultivation. Direct selection based on yield is ineffective because yield is depending on several component traits. Yield is a dependent character which is determined by the interactions of different component traits with themselves and with the growing environment of the crop. Knowledge of the extent of association yield with its attributes is important in breeding programme. The phenotype of a character is affected by action of genes existing in the genotypes as well as the environment making the evaluation this character difficult. Thus, correlation and path analysis relies upon yield with its associating triats used to determine yield contributors among the genotypes taken for the study. Materials and Methods The field experiment was conducted with 50 indigenous and exotic collections of okra genotypes received from National Bureau of Plant Genetic Resources, New Delhi (Table1) at Plant Breeding Farm, Department of Genetics and Plant Breeding, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu, India during Kharif 2023-24. Three seeds per hill, with a row length of 3m, were sowed in each replication, with a 60 cm gap between rows and 30 cm between plants following randomized block design with three replications. Thinning was done on the tenth day, leaving one strong and vigorous seedling per mound. Standard cultivation, agro-technical practices and plant protection measures were trailed to maintain healthy vegetation. Data was collected for such traits as days to 50% flowering, plant height (cm), number of fruits per plant, number of nodes per plant, internode distance (cm), fruit length (cm), fruit width (cm), fruit weight (g) and fruit yield per plant (g). Statistical package Principal Component Analysis and Biplot technique were done using “factoextra package” in R studio version R-4.0.2. Simple correlation co-efficient between yield and attributing traits as well as interrelationship among yield components were calculated as suggested by Panse and Sukhatme (1967). The GENRES statistical software was used to obtain standard path coefficient, which are partial regression coefficient. The path coefficient analysis taking yield as a dependent variable was carried out following Dewey and Lu (1959) and Wright (1921) to study the impact of direct and indirect effects of yield and its associated components.
Institutions
- Tamil Nadu Agricultural University
- Annamalai University