Phenotypic plasticity and genetic variation of cotton yield and its related traits under water-limited conditions

Li, Baoqi; Qin, Tian; Wang, Xuwen; Han, Bei; Liu, Li; Kong, Xianhui; Si, Aijun; Wang, Juan; Lin, Zhenhua; Zhang, Xianlong; Yu, Yu; Yang, Xiyan

doi:10.1016/j.cj.2020.02.003

Cited by 21 publications

(16 citation statements)

References 56 publications

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“…The magnitude of CV% indicated that the non-segregation and segregation generations had exploitable genetic variability during the selection of traits examined in cotton. These results are different from those identified by Raza et al ( 2016) and Li et al, (2020) in cotton, where the CV% values were more than 10%.…”

Section: Discussioncontrasting

confidence: 99%

Correlation and multivariate analysis across non-segregation and segregation generations in two cotton crosses

Yehia

El-Hashash

2021

Egyptian Journal of Agricultural Research

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The present research uses Pearson's correlation coefficient and multivariate analysis to assess the interrelationships, similarities, and dissimilarities among non-segregation (P1, P2 and F1) and segregation (F2, BC1 and BC2) generations for seed cotton yield and yield components in the two crosses, Giza 92 x Pima S6 and Giza 93 x C.B. 58. For all variables evaluated in the two crossings, the analysis of variance revealed significantly substantial genetic variability across six generations. For all of the tested features in the two crossings, the F1 performed better than the other generations. Across all six generations of the two crossings, there were positive and highly significant correlations between seed cotton yield/plant, lint cotton yield/plant, and no. of bolls/plant characteristics. Seed cotton yield and yield components in the two crosses showed some favourable connections over the six generations. The UPGMA hierarchical clustering revealed a greater degree of similarity coefficients between the six generations and the attributes investigated. The similarity coefficients for the six generations and the examined features, respectively, ranged from 0.96 to 0.99 and 0.65 to 0.96. In the principal component analysis (PCA), the PCA1 extracted had an Eigenvalue of greater than 1 across six generations for all studied traits in the two crosses. The PCA displayed a total variation of 91.84% among the six generations contributed by PCA1 (79.47%) and PCA2 (12.38%) and mainly distinguished the generations into different groups. The PCA1 and PCA2 were dominated by F1 and segregation generations in the two crosses, respectively, showing high correlations with the first two PCAs. All studied traits, as well as boll weight and lint percentage traits, contributed to positive significant component loadings for the PCA1 and PCA2, respectively. The biplot analysis of the relationship between the six generations revealed that the most appropriate generations for selecting yield traits were F1 in the two crosses and BC1 and BC2 in the cross Giza 93 x C.B. 58. As a result, we recommend considering backcrossing for 2-5 cycles (BC2-BC5) at the C.B. 58 parent in the future to improve Egyptian cotton yield.

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Section: Discussioncontrasting

confidence: 99%

Correlation and multivariate analysis across non-segregation and segregation generations in two cotton crosses

Yehia

El-Hashash

2021

Egyptian Journal of Agricultural Research

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“…The magnitude of CV% indicated that the generations had exploitable genetic variability during selection of traits examined in cotton. The CV% values were high for yield and its components traits in cotton with similar results reported by Raza et al [24] and Li et al [25].…”

Section: Analysis Of Variancesupporting

confidence: 89%

Estimation of Heritability, Genes Number and Multivariate Analysis Using Non- segregation and Segregation Generations in Two Cotton Crosses

El-Hashash

Yehia²

2021

AJBGMB

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This study was conducted to evaluate the heritability methods, the genes number equations and comparison between them, as well as multivariate analysis of cotton yield and its components traits in the two crosses G.85 x TNB and G.86 x Suvin. Broad sense heritability (BSH) using Mahmud & Kramer (1951), Burton (1951), Weber & Moorthy (1952), Modified Weber & Moorthy (1952), Briggs & Krowles (1967), Mather & Jinks (1971), Lawrence & Jinks (1973) and Kotecha & Zimmerman (1978) methods, as well as narrow sense heritability using Warner (1952) and Modified Warner (1952) methods were calculated. The methods of BSH and NSH showed high values (BSH < 0.60) and significant for yield and yield components traits in the two crosses. For BSH estimations, the highest values by Mahmood & Kramer and Burton methods and the lowest values by Mod.Weber & Moorthy were registered for most studied traits. Estimates of genes number affecting traits were obtained with Chen & Line (1995). The genes number values by equation N3 were much higher than the other equations (close to each other or slightly different) for all studied traits in the two crosses. Based on the ranks method and cluster analysis suggested that there are differences between most the methods of BSH and genes number estimations. The methods of Mather & Jinks and Lawrence & Jinks gave the same values, the two methods of Mahmud & Kramer and Briggs & Krowles as well as the two methods Weber & Moorthy and Kotecha & Zimmerman showed equal or close values for the studied traits in the two crosses. While, the others methods of BSH had difference with these methods and with from each other. These methods are calculated based on the components of variance, so a change in each component of the variance can affect it. Thus, these methods differ with respect to the calculation of an environmental variance. According to principal component analysis (PC), the PC1 and PC2 had mainly distinguished the generations in different groups. The PC1 and PC2 contributed towards 86.29% and 92.36% of cumulative variability in the two crosses G.85 x TNB and G.86 x Suvin, respectively, and the PC1 exhibited Eigen value >1 for all studied traits in the six populations. According to biplot and based on the all populations, the PCs with the highest variability showed positive correlation to yield and its components, but, they differed in their degree of significance/insignificant and consistency in quantity. The PC of the relationship between the six generations revealed that the most appropriate generations for selecting these traits were BC1 generation in the cross G.85 x TNB as well as F2 and BC1 generations in the cross G.86 x Suvin. Backcrossing may be done for 2–5 cycles (BC2 – BC5) at Suvin parent for improving cotton yield in Egypt.

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“…The magnitude of CV% indicated that the non-segregation and segregation generations had exploitable genetic variability during the selection of traits examined in cotton. These results are different with those identi ed byRaza et al (2016) andLi et al (2020) in cotton, where the CV% values were more than 10%.…”

contrasting

confidence: 99%

Correlations and Multivariate Analysis Across Non- Segregation and Segregation Generations in Two Cotton Crosses

Yehia¹,

El-Hashash²

2021

Preprint

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Background: The cotton crop is one of the most important natural fibers crops for textile manufacture in the world. The present research uses Pearson’s correlation coefficient and multivariate analysis to assess the interrelationships, similarities and dissimilarities among non- segregation (P1, P2 and F1) and segregation (F2, BC1 and BC2) generations for seed cotton yield and yield components in the two crosses Giza 92 x Pima S6 and Giza 93 x C.B. 58.Results: The analysis of variance exhibited highly significant genetic variability among six generations for all studied traits in the two crosses. The F1 performance was higher than the other generations for all the studied traits in the two crosses. The results had positive and high significant correlations between seed cotton yield/plant, lint cotton yield/plant and No. of bolls/plant traits across all six generations in the two crosses. A number of positive correlations were observed among the six generations for all studied traits in the two crosses. The UPGMA hierarchical clustering showed higher level of similarity coefficients among the six generations and among the studied traits. Similarity coefficients ranged from 0.96 to 0.99 and from 0.65 to 0.96, respectively. In the principal component analysis (PCA), the PCA1 extracted had Eigenvalue >1 across six generations for all studied traits in the two crosses. The PCA displayed total variation of 91.84% among the six generations contributed by PCA1 (79.47%) and PCA2 (12.38%) and had mainly distinguished the generations in different groups. The PCA1 and PCA2 were dominated by F1 and segregation generations in the two crosses, respectively, showing high correlations with the first two PCAs. All studied traits as well as boll weight and lint percentage traits contributed positive significant component loadings for the PCA1 and PCA2, respectively. The biplot analysis of the relationship between the six generations revealed that the most appropriate generations for selecting yield traits were F1 in the two crosses and BC1 and BC2 in the cross Giza 93 x C.B. 58. Conclusion: From the obtained results, we recommend considering backcrossing may be done for 2–5 cycles (BC2 – BC5) at C.B. 58 parent for improving Egyptian cotton yield in the future.

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Phenotypic plasticity and genetic variation of cotton yield and its related traits under water-limited conditions

Cited by 21 publications

References 56 publications

Correlation and multivariate analysis across non-segregation and segregation generations in two cotton crosses

Correlation and multivariate analysis across non-segregation and segregation generations in two cotton crosses

Estimation of Heritability, Genes Number and Multivariate Analysis Using Non- segregation and Segregation Generations in Two Cotton Crosses

Correlations and Multivariate Analysis Across Non- Segregation and Segregation Generations in Two Cotton Crosses

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