Principal component analysis for yield and its contributing traits in bread wheat<i>(Triticum aestivum)</i>genotypes under late sown condition

Kumar, Jaydev; Kumar, Anuj; Singh, SK; Singh, Lokendra; Kumar, Arun; Chaudhary, Mohit; Kumar, S. Senthil; Singh, Sanjay Kumar

doi:10.5958/2394-4471.2016.00011.3

Cited by 6 publications

(11 citation statements)

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“…These results showed that number of genotype in different cluster as expected varied. The grouping of genotypes based on multivariate analysis has also been reported earlier by Singh et al (2014), Verma et al (2014), Tewari et al (2015), Kumar et al (2016b) and Vora et al (2017). The inter cluster distance was higher than the intra cluster distance indicating wide genetic diversity among the genotypes (Table 4).…”

Section: Genetic Diversity Analysismentioning

confidence: 75%

“…The maximum intra cluster distance was mainly due to wide genetic diversity among the genotypes of these clusters. The maximum intra cluster distance was (13.96) by Verma et al (2014), (391) by Kumar et al (2016b), and (26.40) by Vora et al (2017) reported in wheat crop. On the other hand, cluster IV (263.49) had minimum intra cluster distance indicating that compactness of clusters changed in different environments.…”

Section: Genetic Diversity Analysismentioning

confidence: 85%

“…This information would be very useful in planning wheat breeding programme particularly for improving grain yield in wheat crop. Verma et al, (2014), Tewari et al (2015), Kumar et al (2016b) and Vora et al Girnam Singh et al / J. Appl. & Nat.…”

Section: Genetic Diversity Analysismentioning

confidence: 99%

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Genetic diversity analysis for various morphological and quality traits in bread wheat (Triticum aestivum L.)

Singh

Kumar

et al. 2018

JANS

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Abstract:The present investigation was carried out during 2011-12 in a randomized block design (RBD) with 35 diverse wheat genotypes to assess the genetic diversity for various morphological and quality traits. The analysis of variance for grain yield and its contributing components namely days to 50% flowering, days to maturity, productive tillers, plant height, spike length, spikelets pet spike, grains per spikelet, biological yield, harvest index, 1000 grain weight, grain yield and gluten content showed highly significant differences (at <1% level of significance) among the genotypes under present study. High heritability along with high genetic advance and high phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) for grain yield (g), biological yield (g), harvest index (%), spike length (cm) and 1000 grain weight (g) indicated substantial contribution of additive gene action in the expression and thus selection would be effective for genetic improvement of these traits for improving grain yield in wheat. On the basis of multivariate analysis, 35 genotypes were grouped into '6' clusters based on genetic divergence (D 2 ) value. The compositions of clusters revealed that the Cluster IV contained the highest number of genotypes (9) followed by Cluster II (8), Cluster VI (8) and Cluster III (7). The highest inter cluster values were recorded between cluster III and V (8357.19) followed by cluster IV and V (7513.88), cluster IV and VI (6009.44) and cluster III and VI (5530.40) exhibiting wide genetic diversity. Among different traits, biological yield (32.12%), productive tillers (28.74%), harvest index (26.71%), plant height (24.20%), grain yield (19.23%) and grains per spikelets (14.89%) had maximum contribution to total genetic divergence, therefore may be used as selection parameters in transgressive segregants. Selection of genotypes from the clusters may be used as potential donors for further hybridization programme to develop genotypes with high yield potential in wheat crop.

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Section: Genetic Diversity Analysismentioning

confidence: 75%

Section: Genetic Diversity Analysismentioning

confidence: 85%

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Genetic diversity analysis for various morphological and quality traits in bread wheat (Triticum aestivum L.)

Singh

Kumar

et al. 2018

JANS

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“…The second principal component includes Days to Spike Emergence (X 2 ) having a positive loading sign whereas Awn Length (X 5 ) having a negative loading sign that explains that these traits have a higher effect on grain yield. A similar kind of investigation has also been carried out in wheat by Kumar et al, (2016) and reported that six principal components including yield parameters accounted for 81.75% of the total variation for grain yield. Beheshtizadeh et al, (2013) also concluded that four principal components out of eleven agronomic parameters were responsible for about 76% of the total variation among traits in bread wheat cultivars.…”

Section: Principal Component Analysismentioning

confidence: 77%

Statistical modeling for analyzing grain yield of durum wheat under rainfed conditions in Azad Jammu Kashmir, Pakistan

Abbas

Hussain

et al. 2022

Braz. J. Biol.

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One of the most important traits that plant breeders aim to improve is grain yield which is a highly quantitative trait controlled by various agro-morphological traits. Twelve morphological traits such as Germination Percentage, Days to Spike Emergence, Plant Height, Spike Length, Awn Length, Tillers/Plant, Leaf Angle, Seeds/Spike, Plant Thickness, 1000-Grain Weight, Harvest Index and Days to Maturity have been considered as independent factors. Correlation, regression, and principal component analysis (PCA) are used to identify the different durum wheat traits, which significantly contribute to the yield. The necessary assumptions required for applying regression modeling have been tested and all the assumptions are satisfied by the observed data. The outliers are detected in the observations of fixed traits and Grain Yield. Some observations are detected as outliers but the outlying observations did not show any influence on the regression fit. For selecting a parsimonious regression model for durum wheat, best subset regression, and stepwise regression techniques have been applied. The best subset regression analysis revealed that Germination Percentage, Tillers/Plant, and Seeds/Spike have a marked increasing effect whereas Plant thickness has a negative effect on durum wheat yield. While stepwise regression analysis identified that the traits, Germination Percentage, Tillers/Plant, and Seeds/Spike significantly contribute to increasing the durum wheat yield. The simple correlation coefficient specified the significant positive correlation of Grain Yield with Germination Percentage, Number of Tillers/Plant, Seeds/Spike, and Harvest Index. These results of correlation analysis directed the importance of morphological characters and their significant positive impact on Grain Yield. The results of PCA showed that most variation (70%) among data set can be explained by the first five components. It also identified that Seeds/Spike; 1000-Grain Weight and Harvest Index have a higher influence in contributing to the durum wheat yield. Based on the results it is recommended that these important parameters might be considered and focused in future durum wheat breeding programs to develop high yield varieties.

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“…Similar results have also been reported in GT biplot analysis in common bean by Beheshtizadeh et al (2013) who reported the first four PCs contributed about 76 per cent of total variation. Bhanupiya et al (2014) also reported that first two PC,s accounted for about 46 per cent of variation with weight of grains, the number of spikelets spike , spike length, Kumar et al (2016) reported that first six principal components showed Eigen values more than one and cumulatively they explained 81.75% variability. Pooja et al (2018) reported that in a RIL population of…”

Section: Sadiah Shafi Et Almentioning

confidence: 98%

Assessment of variability in phenological, morphological and yield traits in a biparental RIL population in wheat (Triticum aestivum L)

Shafi

Sofi²,

Singh³

et al. 2020

EJPB

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A HUW-234 x HUW-468 RIL population in wheat comprising 160 RIL's along with two parents and two released varieties were evaluated for phenological, morphological and seed traits in an augmented block design and variability was assessed through principal component analysis. PCA concentrated variability in first five principal components. The total variance explained with the first six PC's was 76.65 per cent. Latent roots (Eigen values) for significant PCs ranged from 3.39 (PC1) to 1.05 (PC5). The first two PC's that were used for constructing biplot graphs explained 42.77 %. The first PC contributed 25.48 per cent of total variation mainly contributed by the grain yield per plant (30.59 per cent), seed growth rate (30.44 per cent), spikelets per spike (16.15 per cent) and 1000-grain weight (16.03 per cent). The second component explained 17.29 per cent of variation contributed largely by days to seed fill (38.31 per cent), days to flowering (35.68 per cent) and seed length (11.41 per cent), while as the third, fourth and fifth component explained 15.29, 9.95 and 8.63 per cent of variations respectively. Six variables including seed yield per plant, seed growth rate, days to seed fill, spikelets per spike, 1000-grain weight and days to flowering contributed above the expected average to the variability in PC1 and PC2. Grain yield was positively correlated with seed growth rate followed by spikelets per spike, 1000-grain weight and seed breadth. A number of RILs were identified based on their superior performance for various phonological and yield traits. RILs 3, 11, 89 and 150 had superior performance for multiple traits indicating desirable segregation for various traits.

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Principal component analysis for yield and its contributing traits in bread wheat(Triticum aestivum)genotypes under late sown condition

Cited by 6 publications

References 0 publications

Genetic diversity analysis for various morphological and quality traits in bread wheat (Triticum aestivum L.)

Genetic diversity analysis for various morphological and quality traits in bread wheat (Triticum aestivum L.)

Statistical modeling for analyzing grain yield of durum wheat under rainfed conditions in Azad Jammu Kashmir, Pakistan

Assessment of variability in phenological, morphological and yield traits in a biparental RIL population in wheat (Triticum aestivum L)

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