Genotype x Environment and Stability Analysis of Oil Content in Sesame (Sesamum indicum L.) Evaluated Across Diverse Agro-ecologies of the Awash Valleys in Ethiopia

Abate, Mohammed; Mekbib, Firew; Ayana, Amsalu; Nigussie, M.

doi:10.9734/ajea/2015/18482

Cited by 11 publications

(8 citation statements)

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“…On the other hand, the genotypes G9, G10, G15, G13 and G10 were far from AEC (long vector) indicating their least stability (figure 2). These results are in agreement with that obtained on rice [2], on coffee [16], on bread wheat [21] and on wheat [23]. E1=Chora2, E2=Mettu2, E3=Gore2, E4=Chora1, E5=Mettu1, E6=Gore1, G1=P-28, G2=233/71, G3=744, G4=P-23, G5=236/71, G6=P-30, G7=P-16, G8=8/72/74, G9=243/71, G10=363/73, G11=74140, G12=227/71, G13=678, G14=, G15=P-17, G16=229/71, G17=74110.…”

Section: Ranking Of Genotypes Based On Mean Yield and Stability Perfo...supporting

confidence: 93%

Stability Analysis of Coffee (<i>Coffea arabica</i> L.) Bean Yield Using GGE Biplot in South Western Ethiopia

Legesse¹,

Alamrew²,

Tesfaye³

2022

AJBES

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Coffee is the largest export crop, and the backbone of the Ethiopian economy. The experiment were conducted at Metu, Gore and Chora for two consecutive years with the objective of determining the nature and magnitude of genotype x environment interaction, to classify environment based on genotype performance and identifying stable genotypes on coffee yield by using GGE biplot analysis. A total 17 advanced coffee genotypes were laid out using randomized complete block design with three replications. The analysis of variance for coffee bean yield revealed the presence of highly significant difference (P<0.01) among genotypes, environments and genotype by environment interaction. Results of GGE biplot showed that the first two principal components (PC1 and PC2) justified 65.66% of the sum of squares with PC1=49.12% and PC2=16.54% of the GGE sum of squares. The six test environments were divided into four different coffee growing mega-environments. Among the test location Metu2 and Gore2 were the most representative and most discriminating environment while Chora2 was less powerful to discriminate genotypes or less desirable as a testing location for coffee bean yield. Genotype G5 (236/71), followed by G8 (872/74) and G12 (227/71) were stable and high yielder across coffee growing environments and it recommended for mega environment production.

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Section: Ranking Of Genotypes Based On Mean Yield and Stability Perfo...supporting

confidence: 93%

Stability Analysis of Coffee (<i>Coffea arabica</i> L.) Bean Yield Using GGE Biplot in South Western Ethiopia

Legesse¹,

Alamrew²,

Tesfaye³

2022

AJBES

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“…The environments had different impacts on the seed yield and oil yield potential of the genotypes. Mohammed et al (2015) and Fiseha et al (2015) reported that significance variation among genotypes, environments and GEI components showed highly significant variation for all traits. Thirteen sesame genotypes were evaluated in three locations in North western and Western Tigray from 2011 to 2013 E-mail: yirgabelay66@gmail.com.…”

Section: Kindeya 971mentioning

confidence: 93%

“…While, genotypes the main sources of variation for seed yield. Sufficient GEI studies have been undertaken elsewhere in the world to meet either or all of the objectives, among which Yebio et al (1993), Kumaresani and Nadarajan (2010), Adebisi (2009), Zenebe and Hussien (2010), Hagos and Fetien (2011), Mirza et al (2013), Fiseha et al (2015), Mekonnen et al (2015) and Mohammed et al (2015). Even all the above studies had undertaken GEI studies on sesame in Ethiopia and the rest of the world, there was no specifically work did on globally preferred white seeded sesame genotypes in Ethiopia and on clustering and correlation of white seeded sesame oil yield.…”

Section: Kindeya 971mentioning

confidence: 99%

Correlation and cluster analysis of white seeded sesame (Sesamum indicum L.) genotypes oil yield in northern Ethiopia

Kindeya¹

2017

Afr. J. Agric. Res.

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The experiment was carried out to access the inter relationship of genotypes in their oil yield with seed yield, oil content and other yield components and to determine cluster of white seeded sesame genotypes and environments based on their oil yield. Seventeen sesame genotypes were tested at six environments in northern Ethiopia during 2015 main season. The experiment was laid out in Randomized Complete Block Designs (RCBD) with three replications across all the environments. Seed yield had a significant and positive correlation (P≤0.01) with number of branches (r = 0.414) and number of capsules (r =0.468). Positive correlation seed yield with number of capsules and number of capsules indicated that better yield can obtain from highly branched plants and high number of capsules because the number of capsules increases with the degree of branching. Seed yield was negative significant correlated with days to maturity (r =-0.500) and flowering time (r = -0.626), significant at (p≤0.05) and (p≤0.01), respectively. The negative association between seed yield and days to flowering and maturity indicated that moisture stress after flowering might have caused relatively a yield reduction in the late maturing genotypes. Whereas, the early flowering and early maturing genotypes can escape the moister stress conditions. Based on the cluster analysis of 17 white seeded sesame genotypes classified in to different four distinct clusters. Cluster II, III and IV had high mean yield, oil content and oil yield than the other clusters. Genotypes grouped in those clusters had also greater genetic divergence important for farther yield, oil yield and oil content improvement program breeding in northern Ethiopia. Sesame growing environments also clustered in to four groups. Environments grouped in Cluster I, II and IV had high seed yield, oil content and oil yield. Hence, environments grouped in those clusters are important for seed yield, oil content and oil yield production improvement program in the study areas.

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“…used cluster analysis and PCA in sesame to group sesame genotypes based on their agronomic traits and the authors suggested to scholars to manipulate PCA to efficiently downsize multiple traits from multiple environments in a fewer and understandable dimension. Furthermore ( Daba et al., 2014 ; Abate et al., 2015 ; He et al., 2009 ; Misganaw et al., 2015 ; Tadesse and Abay, 2011 ), reported that the use of bi-plots from AMMI model, which is attractive and easy to understand, is crucial to identify stable and high yielding sesame genotypes.…”

Section: Introductionmentioning

confidence: 99%

Multivariate analysis for yield and yield-related traits of sesame (Sesamum indicum L.) genotypes

Baraki

Gebregergis

Belay

et al. 2020

Heliyon

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Sesame production under irrigation is limited in Ethiopia because of in availability of high yielding varieties, inadequate and inefficient irrigation schemes, and insignificant awareness of producers. This study, comprising 13 sesame genotypes, was conducted around Humera and Werer during 2018 and 2019 under irrigation. The design was randomized completely block design with three replications and the objectives were to develop high yielding genotypes and identify important agronomic traits. Multivariate statistical methods like Additive Main Effect and Multiplicative Interaction (AMMI) model, Principal Component Analysis, Cluster and factor analyses were used. The genotypes (6.22%), environments (42.62) and Genotype × Environment Interactions (25.09%) were statistically (p < 0.001) significant for the agronomic traits. The grain yield in each observation varied from 383 kg/ha to 2044 kg/ha and the grand mean yield was 820.19 kg/ha. The highest mean yield was recorded from G12 (948.6 kg/ha) followed by G4 (938.9 kg/ha) while the lowest was recorded from G8 (703.1 kg/ha). G1, G4, G12, G5, G8, G11 and G13 are identified as unstable genotypes while G2, G3, G6, and G9 are stable genotypes. The genotypes were grouped in to four clusters and cluster-II was characterized as the high yielding cluster and it was also associated with grain yield, pods per plant, branches per plant and thousand seed weight. Branches per plant, pods per plant and thousand seed weight may be most determinant and crucial in developing high yielding sesame varieties. This finding recommends that G4 and G6 are desirable genotypes and can be used for irrigation production.

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Genotype x Environment and Stability Analysis of Oil Content in Sesame (Sesamum indicum L.) Evaluated Across Diverse Agro-ecologies of the Awash Valleys in Ethiopia

Cited by 11 publications

References 0 publications

Stability Analysis of Coffee (<i>Coffea arabica</i> L.) Bean Yield Using GGE Biplot in South Western Ethiopia

Stability Analysis of Coffee (<i>Coffea arabica</i> L.) Bean Yield Using GGE Biplot in South Western Ethiopia

Correlation and cluster analysis of white seeded sesame (Sesamum indicum L.) genotypes oil yield in northern Ethiopia

Multivariate analysis for yield and yield-related traits of sesame (Sesamum indicum L.) genotypes

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Genotype x Environment and Stability Analysis of Oil Content in Sesame (Sesamum indicum L.) Evaluated Across Diverse Agro-ecologies of the Awash Valleys in Ethiopia

Cited by 11 publications

References 0 publications

Stability Analysis of Coffee (&lt;i&gt;Coffea arabica&lt;/i&gt; L.) Bean Yield Using GGE Biplot in South Western Ethiopia

Stability Analysis of Coffee (&lt;i&gt;Coffea arabica&lt;/i&gt; L.) Bean Yield Using GGE Biplot in South Western Ethiopia

Correlation and cluster analysis of white seeded sesame (Sesamum indicum L.) genotypes oil yield in northern Ethiopia

Multivariate analysis for yield and yield-related traits of sesame (Sesamum indicum L.) genotypes

Contact Info

Product

Resources

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Stability Analysis of Coffee (<i>Coffea arabica</i> L.) Bean Yield Using GGE Biplot in South Western Ethiopia

Stability Analysis of Coffee (<i>Coffea arabica</i> L.) Bean Yield Using GGE Biplot in South Western Ethiopia