Genetic Analysis of the Traits Related to Mechanical Harvesting in Chickpea (Cicer arietinum)
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Date
2016-08
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University of Agricultural Science, Dharwad
Abstract
The objective of the present study was to assess the genetic variability, inter character association, diversity, gene action and to identify potential cross combinations for the traits related to mechanical harvesting and yield attributes in chickpea. Present investigation revealed considerable variability and heritability for majority of traits. Correlation studies indicated highly significant negative relationship between productivity traits and traits related to mechanical harvesting. D2 analysis revealed that number of seeds per plant, days to maturity and seed yield per plant contributed maximum to the divergence. In total, 58 genotypes were screened and twelve genetically diverse genotypes were selected for developing cross combinations in LxT design. For the traits related to mechanical harvesting, line ICCX 40183 and tester ICCX 40111 were the best general combiners. Genotypes like RVSSG 9, BGD 1068 and Phule G 0215-2 were found to be very good sources of favorable genes for yield related traits. Crosses like AT 2-3 x RVSSG 10 and Phule G 0215-2 x ICCX 40111 were found superior for most of the mechanical harvesting traits. For productivity traits, crosses like GJG 0907 x BGD 1070, GJG 0907 x BG 256 and RVSSG 9 x ICCX 40111 showed significant sca effects. Among 36 crosses, seventeen were evaluated for generation mean analysis. The magnitude of non-additive gene action was predominant in nature with duplicate epistasis for most of the traits studied suggesting development of multiple crosses and raising of large segregating populations followed by intermating of desired segregants could help in enhance the frequency of additive genes. Based on the combining ability studies and performance of F2 crosses, segregants of crosses viz., AT 2-3 x ICCX 40111, RVSSG 9 x RVSSG 10 and RVSSG 9 x BG 256 were found to be potential for combining mechanical harvesting and productivity traits.