African Journal of Plant Science. Full Length Research Paper. Genotype by environment interaction in sesame. (Sesamum indicum L.) cultivars in Uganda.
Vol. 10(10), pp. 189-202, October 2016 DOI: 10.5897/AJPS2016.1426 Article Number: FD3BBA161075 ISSN 1996-0824 Copyright © 2016 Author(s) retain the copyright of this article http://www.academicjournals.org/AJPS
African Journal of Plant Science
Full Length Research Paper
Genotype by environment interaction in sesame (Sesamum indicum L.) cultivars in Uganda Walter Okello-Anyanga1,2*, Patrick Rubaihayo1, Paul Gibson1 and Patrick Okori1 1
Department of Agricultural Production, School of Agricultural Sciences, Makerere University, Kampala, P.O. Box. 7062, Kampala, Uganda. 2 National Semi-Arid Resources Research Institute (NaSARRI), Serere, P.O. Soroti, Uganda. Received 10 May, 2016; Accepted 4 August, 2016
Sesame (Sesamum indicum L.) is an important and ancient oilseed crop cultivated in hot, dry climates for its oil and protein rich seeds. On the African continent, Uganda ranks seventh in sesame production. The improvement of new genotypes with the desired yield stability and performance in different environments is an important issue in breeding programs. In order to identify high yielding and stable sesame genotypes across environments, field experiments were conducted with 16 genotypes for four seasons (2011-2013) at three locations, viz. Serere, Kaberamaido and Ngetta. The objective of the study was to use additive main effects and multiplicative interaction (AMMI) and genotype by genotype environment interaction (GGE) biplot statistical analysis to identify the stability and yield potential of sixteen sesame genotypes. The results of AMMI analysis of variance for seed yield (kg/ha) showed that all the sources of variations that included treatments, genotypes, environments, blocks, interactions, IPCA1 and IPCA 2 were highly significant (P85 >85 >85 >85 >80 >80 >80 >80 >80 >80 >85 >85
Yield
Resistance to Fusarium wilt disease
Resistance gall midge
High
Resistant
Low
to
L). This showed that there was high interaction between the seasons and locations. Season by location contributed highest in the total sum of squares of variation with 45%. Highly significant difference (P
