Combining ability analysis for yield and its component characters in maize (Zea mays L.)
Line × tester analysis was carried out involving fourty two lines and three testers in maize for assessing the combining ability for yield and its component characters. Hybrids recorded significant variance for all characters studied. Variance due to parents were significant for characters viz., day...
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| Format: | Article |
| Language: | English |
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Indian Society of Plant Breeders
2017-06-01
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| Series: | Electronic Journal of Plant Breeding |
| Subjects: | |
| Online Access: | http://ejplantbreeding.org/index.php/EJPB/article/view/2119 |
| _version_ | 1811198837207859200 |
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| author | K. Kuselan N. Manivannan R. Ravikesavan and V. Paranidharan |
| author_facet | K. Kuselan N. Manivannan R. Ravikesavan and V. Paranidharan |
| author_sort | K. Kuselan |
| collection | DOAJ |
| description | Line × tester analysis was carried out involving fourty two lines and three testers in maize for assessing the combining ability for yield and its component characters. Hybrids recorded significant variance for all characters studied. Variance due to parents were significant for characters viz., days to 50% tasseling, days to 50% silking, plant height (cm), ear height (cm), cob length (cm), cob girth (cm), number of grain rows per cob, number of grains per row, number of grains per cob, 100 grain weight (gm) and grain yield per plant (gm). The variance due to sca was higher than gca indicating the predominance of non-additive type of gene action for the above mentioned traits. Among the lines, the line RML 12, was identified as the best general combiner with higher per se performance for most of the yield contributing traits followed by RML1, RML 26, RML 27 and RML 34. Considering the testers, RML 48 was found as the best general combiner with better mean performance for most of the yield contributing traits followed by RML 47. Among the crosses, RML12 x RML48 was found to be the superior with positive significant sca effects and better mean performance for grain yield, cob girth, number of grain rows per cob and no of grains per cob. Similar superior positive significant sca effects with better mean performance were also observed in RML26 x RML48 (no of grains per cob, grain yield per plant and grain yield per plot), RML27 x RML48 (grain yield per plant, grain rows per cob, grains per cob) and RML37 x RML48 (cob girth, hundred grain weight, grain yield per plant and grain yield per plot. |
| first_indexed | 2024-04-12T01:38:07Z |
| format | Article |
| id | doaj.art-13a768804618464f81eb4e44324fa94a |
| institution | Directory Open Access Journal |
| issn | 0975-928X |
| language | English |
| last_indexed | 2024-04-12T01:38:07Z |
| publishDate | 2017-06-01 |
| publisher | Indian Society of Plant Breeders |
| record_format | Article |
| series | Electronic Journal of Plant Breeding |
| spelling | doaj.art-13a768804618464f81eb4e44324fa94a2022-12-22T03:53:15ZengIndian Society of Plant BreedersElectronic Journal of Plant Breeding0975-928X2017-06-018259160010.5958/0975-928X.2017.00090.4Combining ability analysis for yield and its component characters in maize (Zea mays L.)K. KuselanN. ManivannanR. Ravikesavan andV. ParanidharanLine × tester analysis was carried out involving fourty two lines and three testers in maize for assessing the combining ability for yield and its component characters. Hybrids recorded significant variance for all characters studied. Variance due to parents were significant for characters viz., days to 50% tasseling, days to 50% silking, plant height (cm), ear height (cm), cob length (cm), cob girth (cm), number of grain rows per cob, number of grains per row, number of grains per cob, 100 grain weight (gm) and grain yield per plant (gm). The variance due to sca was higher than gca indicating the predominance of non-additive type of gene action for the above mentioned traits. Among the lines, the line RML 12, was identified as the best general combiner with higher per se performance for most of the yield contributing traits followed by RML1, RML 26, RML 27 and RML 34. Considering the testers, RML 48 was found as the best general combiner with better mean performance for most of the yield contributing traits followed by RML 47. Among the crosses, RML12 x RML48 was found to be the superior with positive significant sca effects and better mean performance for grain yield, cob girth, number of grain rows per cob and no of grains per cob. Similar superior positive significant sca effects with better mean performance were also observed in RML26 x RML48 (no of grains per cob, grain yield per plant and grain yield per plot), RML27 x RML48 (grain yield per plant, grain rows per cob, grains per cob) and RML37 x RML48 (cob girth, hundred grain weight, grain yield per plant and grain yield per plot.http://ejplantbreeding.org/index.php/EJPB/article/view/2119maizeline x tester analysisper se performancecombining ability and heterosis |
| spellingShingle | K. Kuselan N. Manivannan R. Ravikesavan and V. Paranidharan Combining ability analysis for yield and its component characters in maize (Zea mays L.) Electronic Journal of Plant Breeding maize line x tester analysis per se performance combining ability and heterosis |
| title | Combining ability analysis for yield and its component characters in maize (Zea mays L.) |
| title_full | Combining ability analysis for yield and its component characters in maize (Zea mays L.) |
| title_fullStr | Combining ability analysis for yield and its component characters in maize (Zea mays L.) |
| title_full_unstemmed | Combining ability analysis for yield and its component characters in maize (Zea mays L.) |
| title_short | Combining ability analysis for yield and its component characters in maize (Zea mays L.) |
| title_sort | combining ability analysis for yield and its component characters in maize zea mays l |
| topic | maize line x tester analysis per se performance combining ability and heterosis |
| url | http://ejplantbreeding.org/index.php/EJPB/article/view/2119 |
| work_keys_str_mv | AT kkuselan combiningabilityanalysisforyieldanditscomponentcharactersinmaizezeamaysl AT nmanivannan combiningabilityanalysisforyieldanditscomponentcharactersinmaizezeamaysl AT rravikesavanand combiningabilityanalysisforyieldanditscomponentcharactersinmaizezeamaysl AT vparanidharan combiningabilityanalysisforyieldanditscomponentcharactersinmaizezeamaysl |