Selection index for enhanced yield in rice genotypes
2024
M, Pallavi | Shanthi, P | Reddy, VLN | Nirmal Kumar, AR | Maruthi Prasad, BP
Plant breeding heavily relies on artificial selection to enhance grain yield through various contributing traits. The challenge in selecting complex quantitative traits, such as rice grain yield, lies in low heritability and a significant influence of environmental factors. This study involved 42 rice genotypes with the aim of identifying yield-contributing traits and establishing simultaneous selection models based on discriminant functions. The anticipated genetic gain for grain yield, when all the studied traits were considered in simultaneous selection, was notably higher (445.69%) compared to selecting grain yield alone (13.35%). The discriminant function comprised five traits: plant height (X1), number of filled grains per panicle (X4), number of grains per panicle (X5), seedling vigour index (X6), and grain yield per plant (X7). This combination exhibited a substantial genetic advance of 435.87% and a remarkable relative efficiency of 3264.05%. The relative efficiency of selection, considering grain yield alone, was set at 100%. However, when six ((X1, X3, X4, X5, X6, and X7) and seven traits were simultaneously considered, the efficiency increased to 3044.338 and 3337.606, respectively. Based on the ideal discriminant function, MTU3626 emerged as the best genotype with the highest selection score of 1706.046, followed by SM227 (1669.16) and NDP3 (1555.33). Conversely, MTU1010 ranked last with a selection score of 765.076.
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Эту запись предоставил Indian Council of Agricultural Research