Assessing Inter-Relationship of Sesame Genotypes and their Traits Using Cluster Analysis and Principal Component Analysis Methods

Baraki, Fiseha; Tsehaye, Yemane; Abay, Fetien

doi:10.3923/ijpbg.2015.228.237

Cited by 8 publications

(9 citation statements)

References 7 publications

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“…In contrast to this, PCA1 was negatively associated with days to 50% flowering and 50% maturity (Figure 3). [11,14] also found similar results to this finding from their study on sesame genotypes.…”

Section: Ordination Of the Sesame Genotypes And Eir Agronomicsupporting

confidence: 86%

“…[1] also found a significant difference of different agronomic traits of wheat evaluated across years. ere is a possibility for occurrence of such variation of genotypes in their agronomic traits across years and even across locations, and this may be both a challenge and an opportunity for plant breeding and breeders [11]. e highest grain yield of the sesame genotypes was recorded in the second growing season (2014).…”

Section: Performance Of the Sesame Genotypes Across The Reementioning

confidence: 99%

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Evaluating Performance of Sesame (Sesamum indicumL.) Genotypes in Different Growing Seasons in Northern Ethiopia

Baraki

Berhe

2019

International Journal of Agronomy

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Ethiopia is one of the famous and major producers of sesame in sub-Saharan Africa, and Ethiopian sesame is among the highest quality in the world. The experiment was conducted in Northern Ethiopia for three growing seasons (2013–2015) under a rain fed condition with the objective of identifying high-yielding genotypes and their agronomic traits. The experiment consisted of twelve genotypes laid down in randomized complete block design with three replications. The genotype, year, and genotype × year interaction components showed statistically highly significant variation (p<0.001) for most of the agronomic traits which clearly confirms the presence of genotype × year interaction in this study. The highest combined mean grain yield (906.3 kg/ha) was obtained from Hirhir followed by Serkamo white (756.5 kg/ha), and from the three growing seasons, the highest grain yield (1161.5 kg/ha) was recorded from Hirhir grown in the second growing season (2014). The growing seasons were different from one another in allowing the genotypes to have a different performance, and all of the agronomic traits, except thousand seed weight, were statistically different across the three growing seasons. In the ordination of the genotypes and agronomic traits, PCA1, which accounted for 38.3% of the variation, was positively associated with grain yield, branches per plant, length of the pod-bearing zone, plant height, number of pods per plant, number of seeds per pod, and thousand seed weight. On the contrary, PCA2, which accounted for 19.7% of the variation, was positively associated with days to 50% flowering and days to 50% maturity.

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Section: Ordination Of the Sesame Genotypes And Eir Agronomicsupporting

confidence: 86%

Section: Performance Of the Sesame Genotypes Across The Reementioning

confidence: 99%

Evaluating Performance of Sesame (Sesamum indicumL.) Genotypes in Different Growing Seasons in Northern Ethiopia

Baraki

Berhe

2019

International Journal of Agronomy

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“…This indicates sesame grain yield and yield related traits were highly affected by the growing environments. And this is true for sesame both in rain fed ( Baraki et al., 2015a ; Tantawy et al., 2007 ; Nath et al., 2001 ) and irrigation production ( El Mahdi et al., 2007 ; Nadeem et al., 2015 ; Ebrahimian et al., 2019 ). ( El Mahdi et al., 2007 ) also reported that a sowing date difference in sesame significantly affects sesame seed yield and yield related traits under irrigation condition and this may be mainly sesame is highly sensitive to temperature.…”

Section: Resultsmentioning

confidence: 99%

“…Principal component analysis (PCA) can effectively downsize the structure of a two-way Multi Environment Trial (MET) data matrix of genotypes (G) across different environments (E) dimension in a fewer dimensions and this method is considered to be efficient ( Cruz-Castillo et al., 1994 ). ( Crossa, 1990 ) stated that cluster analysis is a numerical arrangement technique that explains groups of individuals and hierarchical classification, which important to categorizes individuals into groups having the objective of studying associations in the data ( Zhang et al., 2008 ; Baraki et al., 2015a , 2015b ; Jinxiong et al., 1995 ). 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.…”

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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“…The highest intra-cluster distance observed in cluster I which indicates that the germplasm of cluster I was more diverged than any other cluster. There was comparative divergence of one cluster from another and higher divergence seen between clusters III and IV (2717.76) followed by clusters I and IV(1760.59), selection of lines to use them as parents for hybridization programme from these clusters will result in unique genotypes and generation of promising crossed material as proposed by [1,4,[11][12][13][14][15][16][17][18].…”

Section: Resultsmentioning

confidence: 99%

Assessment of Genetic Diversity in Sesame (Sesamum indicum L.) Based on Agro-Morphological Traits

Mohanty

Singh

et al. 2020

CJAST

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Genetic divergence among parents is of paramount importance in selecting them for hybridization programme for crop improvement. Genetic divergence was assessed among 30 sesame genotypes which were evaluated in RBD with an objective to classify and understand the nature and magnitude of genetic diversity with regard to grain yield, yield components and quality traits using Mahalanobis D2 statistics. The genotypes differed significantly regarding the characters studied and displayed marked divergence and were grouped into four clusters following Tocher’s method. Cluster I had twenty-seven genotypes while the Cluster II, cluster III and cluster IV were monogenotypic comprised only one genotype each. The oil content exhibited maximum contribution towards divergence. The maximum inter cluster distance was recorded between cluster III and IV (2717.76) followed by cluster I and IV (1760.59), cluster II and III (991.96), cluster I and II (695.67), cluster I and III (637.32) and cluster II and IV (584.59) indicating the chances of getting high yielding recombinants would be better if the crosses are made among the genotypes of these groups under timely sown condition. The genotypes in cluster III and cluster IV, due to maximum inter cluster distance between them, exhibited high degree of genetic diversity and thus may be utilized under inter varietal hybridization programme (transgressive breeding) for getting high yielding recombinants. The analysis of divergence indicated significant differences among parental lines for all the agro-morphological characters. Based on results obtained in the present investigation, it is concluded that the allelic diversity can be used for future breeding program. The traits under study are largely associated with each other and should be taken into consideration either simultaneously or alone for selecting a high yielding sesame genotype.

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Assessing Inter-Relationship of Sesame Genotypes and their Traits Using Cluster Analysis and Principal Component Analysis Methods

Cited by 8 publications

References 7 publications

Evaluating Performance of Sesame (Sesamum indicumL.) Genotypes in Different Growing Seasons in Northern Ethiopia

Evaluating Performance of Sesame (Sesamum indicumL.) Genotypes in Different Growing Seasons in Northern Ethiopia

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

Assessment of Genetic Diversity in Sesame (Sesamum indicum L.) Based on Agro-Morphological Traits

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