Genetic evaluation and selection of F₄ brinjal (Solanum melongena L.) segregants derived from Odisha landraces for yield, quality and tolerance to bacterial wilt

Abstract

A field experiment was conducted at the All India Coordinated Research Project (AICRP) on Vegetable Crops, Odisha University of Agriculture and Technology (OUAT), Bhubaneswar, Odisha, India, during the rabi season, 2023-2024, with broad objectives of developing superior, green round-fruited brinjal genotypes for higher fruit yield, fruit quality and tolerance to bacterial wilt from local landraces. Additional objectives included genetic analysis, including studies of genetic variability, character association and path analysis, along with genetic divergence analysis for future population improvement of brinjal using local landraces. A total of 41 genotypes of brinjal, including 9 local landraces of Odisha as parents, 31 F4 segregants and one check (BB-67), were evaluated by using randomised block design (RBD) with 2 replications. The analysis of variance revealed significant differences among all traits, suggesting enormous scope for effective phenotypic selection in brinjal. The genetic variability study revealed a high broad-sense heritability (> 60 %) coupled with high genetic advance as % of the mean (> 20 %) for most of the traits except plant height (cm), days to 1st flowering and days to 50 % flowering respectively, hence suitable for phenotypic selection. Similarly, study on character association revealed that total fruit yield per plant in brinjal was significantly and positively associated with average fruit weight and number of fruits per plant at both genotypic and phenotypic level whereas the path analysis showed direct positive effects on total fruit yield per plant with traits viz., number of fruits per plant, average fruit weight, plant height at final harvest, days required to 50 % flowering, fruit length, fruit breadth, plant spread (NS) and fruit length breadth ratio, indicating that the direct selection for these traits would be effective in brinjal improvement. Principal Component Analysis (PCA) revealed 3 principal components with eigenvalues greater than unity (1.0), accounting for 78.647 % of the total variance.  Principal Component 1 (PC1) contributed the highest proportion of total variance (42.282 %) with an eigenvalue of 4.228. PC2 and PC3 explained 24.045 % and 12.32 % of the variance with the eigenvalues of 2.404 and 1.232 respectively.