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Morphological characterization of resynthesized Brassica napus L. lines giving priority to earliness

Most. Mahmuda Akter, Afsana Hannan, Priya Ghosh, Gour Gobindo Das, G. H. M. Sagor.


The experiment was conducted to evaluate the performances of fifteen resynthesized Brassica napus L. lines along with one check variety (Binasarisha-4) based on some morphological, yield and yield attributes and also to estimate association among the traits. Randomized Completely Block Design (RCBD) with three replications was used for this experimentation. A wide range of variation was observed among the studied morphological traits which have a great interest for the Brassica breeders. Considering the two most important traits like early flowering and early maturity the RS lines RS-7, RS-2, RS-3, RS-5, RS-11, RS-14, and RS-15 performed best among the germplasms compared with check variety Binasharisha-4. All the studied traits except main leaf area showed high heritability. High heritability coupled with high genetic advance in per cent of mean (>20%) was observed for plant height, flag leaf area, siliqua length, beak length, siliqua angle and yield/plant. This result suggested that this character could be improved more easily through simple selection. Correlation analysis revealed a significant positive correlation of yield/plant with plant height and siliqua length. Days to first flowering and days to maturity were negatively correlated with plant height, siliqua length and beak length whereas a highly significant positive correlation existed between days to first flowering and days to maturity. Principal Component Analysis revealed the utmost suitable combination of the studied attributes and stated that the first four principal components (PC) explained 78.3% of the total data variation. These results provide a scope for selection of RS lines in regard to develop short duration varieties of B. napus with high yield potential.

Key words: Brassica napus L., Resynthesized line, Morphological traits, Genetic variability, Correlation, Principal Component Analysis

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