Variability and Traits Association Analyses in Bacterial Wilt Resistant Genotypes of Tomato (Solanum lycopersicum L.) under Mid-Hill Conditions of Himachal Pradesh

Sehgal, Nidhi ; Chadha, Sanjay ; Kumar, Sangeet ; Ravita


18 bacterial wilt resistant F4 progenies of tomato were evaluated along with two bacterial wilt resistant checks at the Vegetable Research Farm of the Department of Vegetable Science and Floriculture, Chaudhary Sarwan Kumar Himachal Pradesh Krishi Vishvavidyalaya, Palampur during summer-rainy season’2016 to identify the most promising progenies on the basis of nature and extent of genetic variability and heritability coupled with genetic gain. To ascertain the variability source structure, computation of principal component analysis (PCA) was also done. Estimates for phenotypic coefficient of variation (PCV), genotypic coefficient of variation (GCV), heritability and genetic gain were found to be high for average fruit weight, total fruits per plant, marketable fruits per plant, marketable yield per plant, gross yield per plant and lycopene content. High PCV and GCV indicated the presence of sufficient variability ensuring ample scope for improvement through selection. High heritability allied with high genetic gain suggested the presence of additive gene action and thereby these traits could be considered as reliable indices for selection. For PCA studies, eigen values were calculated for 16 morphological traits of tomato. The results of PCA revealed that out of these 16 traits, initial eight traits exhibited more than 0.5 eigen values and showed more than 95 per cent of genetic variability. Among all, PC1 (days to 50 per cent flowering) has highest eigen value (4.927) and accounts for 30.793 per cent of genetic variability. As a result, these traits might be taken into consideration for effective selection to develop elite bacterial wilt resistant lines in tomato.


Phenotypic coefficient of variation (PCV); genotypic coefficient of variation (GCV); heritability; genetic gain; principal component analysis (PCA)

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