Combining Ability Analysis of Blast Disease Resistance and Agronomic Traits in Finger Millet [Eleusine coracana (L.) Gaertn]

Lawrence Owere, Pangirayi Tongoona, John Derera, Nelson Wanyera

Abstract


Blast disease is the most important biotic constraint to finger millet production. Therefore disease resistant varieties are required. However, there is limited information on combining ability for resistance and indeed other agronomic traits of the germplasm in Uganda. This study was carried out to estimate the combining ability and gene effects controlling blast disease resistance and selected agronomic traits in finger millet. Thirty six crosses were generated from a 9 × 9 half diallel mating design. The seed from the 36 F1 crosses were advanced by selfing and the F2 families and their parents were evaluated in three replications. General combining ability (GCA) for head blast resistance and the other agronomic traits were all highly significant (p ≤ 0.01), whereas specific combining ability (SCA) was highly significant for all traits except grain yield and grain mass head-1. On partitioning the mean sum of squares, the GCA values ranged from 31.65% to 53.05% for head blast incidence and severity respectively, and 36.18% to 77.22% for the other agronomic traits measured. Additive gene effects were found to be predominant for head blast severity, days to 50% flowering, grain yield, number of productive tillers plant-1, grain mass head-1, plant height and panicle length. Non-additive gene action was predominant for number of fingers head-1, finger width and panicle width. The parents which contributed towards high yield were Seremi 2, Achaki, Otunduru, Bulo and Amumwari. Generally, highly significant additive gene action implied that progress would be made through selection whereas non-additive gene action could slow selection progress and indicated selection in the later generations.


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DOI: http://dx.doi.org/10.5539/jas.v8n11p138

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Journal of Agricultural Science   ISSN 1916-9752 (Print)   ISSN 1916-9760 (Online)  E-mail: jas@ccsenet.org

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