Simultaneous selection of yield and yield stability in chickpea genotypes using the GGE biplot technique

Farshadfar, Ezatollah

doi:10.1556/aagr.61.2013.3.2

Cited by 21 publications

(29 citation statements)

References 28 publications

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“…The significant interaction differences of genotypes × environment implied that application of stability analysis for identifying widely and/or specifically adapted upland rice genotype (s) is essential. These results are in agreement with those of Balestre et al, [10], Farshadfar et al, [17] and Biswas et al, [18]. The significant GE interaction in this study suggests the possible existence of different mega-environments.…”

Section: Analysis Of Variance and Agronomic Performancesupporting

confidence: 94%

Evaluation of Upland Rice Genotypes and Mega Environment Investigation Based on GGE-Biplot Analysis

Tariku¹

2017

J Rice Res

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Though a recent introduction, rice is becoming the most important food and cash crop in Ethiopia. However, its productivity is being constrained mainly by lack of improved varieties. Identification of high yielding and stable genotype(s) and desirable environment(s) has been the most important objectives of multi-environment trials. The objectives of the present study were to explore the effect of genotype and genotype × environment interaction on grain yield of upland rice genotypes and to examine the possible existence of different mega environments. Grain yield performances of 13 upland rice genotypes were evaluated under rainfed conditions at three locations (Woreta, Pawe, Metema) in North Western Ethiopia from 2011 to 2013. The study was laid out in a randomized complete block design with three replications. The first two principal components (IPC1 and IPC2) were used to create a twodimensional GGE biplot that accounted for 76.80% and 20.05%, respectively. A combined analysis of variance showed that both main effects (environment and genotype) and GE interactions were highly significant. The polygon tool of the biplot suggested the existence of two upland rice mega-environments with wining genotypes G3, G9, G8 and G12. Visualizing the mean and stability parameters of the genotypes in the biplot indicated that genotypes NERICA-13 (G3), FOFIFA-3730 (G11) and NERICA-12 (G2) are adapted to rainfed upland rice growing areas of North Western Ethiopia. According to the ideal-genotype biplot, genotypes G3 followed by G2 and G11 were better genotypes demonstrating their high mean yield and stability performance.Based on discrimination and representation, Woreta was identified as the ideal location for generally adaptable upland rice genotypes and Metema for selecting specifically adaptable upland rice genotypes. Finally, it was concluded that genotypes; G2, G3 and G11 showed high grain yield and stability performance across environments and were promoted to variety verification. Of which, G2 has been officially released for large scale production with the common name "NERICA-12''.

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Section: Analysis Of Variance and Agronomic Performancesupporting

confidence: 94%

Evaluation of Upland Rice Genotypes and Mega Environment Investigation Based on GGE-Biplot Analysis

Tariku¹

2017

J Rice Res

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“…The perpendicular lines are equality lines between adjacent genotypes on the polygon, which facilitate visual comparison of them. The equality lines divide the biplot into sectors, and the winning genotype for each sector is the one located on the respective vertex (Yan & Tinker, 2006;Farshadfar et al, 2011). In the biplot using the first two principal components, the variables disease severity, yield, and oil content were located on three different sectors (Figure 2), within the third and fourth concentric circles (Figure 3).…”

Section: Resultsmentioning

confidence: 99%

Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

Leite

Oliveira

2015

Pesq. agropec. bras.

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-The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.Index terms: Alternariaster helianthi, Helianthus annuus, disease resistance, principal component analysis. Agrupamento de genótipos de girassol quanto à produtividade, ao teor de óleo e à reação à mancha de alternária por GGE biplotResumo -O objetivo deste trabalho foi avaliar a adequação do uso do método multivariado de análise de componentes principais (ACP), com uso do programa GGE biplot, para o agrupamento de genótipos de girassol quanto à sua reação à mancha de alternária (Alternariaster helianthi), e quanto à produtividade e ao teor de óleo. Sessenta e nove genótipos foram avaliados quanto à severidade da doença em campo, na fase de desenvolvimento R3, em sete safras, em Londrina, PR, com uso de uma escala diagramática própria, desenvolvida para esta doença. A produtividade e o teor de óleo também foram avaliados. Os dados foram padronizados com o programa Statistica, e o GGE biplot foi utilizado para ACP e exibição gráfica dos dados. Os dois primeiros componentes principais explicaram 77,9% da variação total. De acordo com o biplot poligonal, obtido com os dois primeiros componentes principais e as três variáveis resposta, os genótipos foram divididos em sete setores. Os genótipos alocados nos setores 1 e 2 apresentaram alta produtividade e alto teor de óleo, respectivamente, e os agrupados no setor 7 apresentaram tolerância à doença e alta produtividade, apesar de elevada severidade da doença. O método de análise de componentes principais com uso do GGE biplot é eficiente para agrupar genótipos de girassol com base nas variáveis estudadas.Termos para indexação: Alternariaster helianthi, Helianthus annuus, resistência a doenças, análise de componentes principais.

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“…4) was observed for SPS, except for the high response of genotype HR to all environments. Farshadfar et al (2012) and Farshadfar (2013) showed different values of variability and G × E interactions for 16 genotypes of bread wheat (Triticum aestivum L.) in six environments and for 20 genotypes of chickpea in eight environments for yield and yield components. …”

Section: G×e Interaction In Wheat−barley Addition Linesmentioning

confidence: 99%

Path analysis of genotype × environment interaction in wheat-barley disomic addition lines

Farshadfar

2013

Acta Agronomica Hungarica

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In order to locate QTLs controlling the phenotypic stability and drought tolerance of yield and yield components in barley, seven disomic addition lines were sown together with their parents (donor and recipient) in a randomized complete block design with three replications under four rainfed and irrigated conditions. The descriptive diagram of yield and yield components exhibited a genotype (G) × environment (E) interaction and moderate variability over different environments, indicating the possibility of selection for stable and drought-tolerant entries. The AMMI stability value (ASV) and yield stability index (YSI) discriminated addition lines 2H and 4H as the most stable and droughttolerant.Path analysis revealed that the relative contribution of the number of seeds per plant (NSPP) (0.71) to grain yield (GY) was higher than that of the number of seeds per spike (SPS) (−0.44) and of thousand-seed weight (TSW) (-0.14). Therefore, the contribution of NSPP to the stability of GY over different environments was higher than that of other yield components. In other words, the instability of GY was caused by TSW and SPS in different environments. Path analysis on the drought susceptibility index revealed that most of the QTLs controlling drought tolerance and drought susceptibility in barley are located on chromosomes 3H and 6H, respectively.

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Simultaneous selection of yield and yield stability in chickpea genotypes using the GGE biplot technique

Cited by 21 publications

References 28 publications

Evaluation of Upland Rice Genotypes and Mega Environment Investigation Based on GGE-Biplot Analysis

Evaluation of Upland Rice Genotypes and Mega Environment Investigation Based on GGE-Biplot Analysis

Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

Path analysis of genotype × environment interaction in wheat-barley disomic addition lines

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