Evaluation of genetic diversity in short duration cotton (Gossypium hirsutum L.)

Akter, Taslima; Islam, A. K. M. Aminul; Rasul, M. G.; Kundu, S.; Khalequzzaman, -; Ahmed, J. U.

doi:10.1186/s42397-018-0018-6

Cited by 16 publications

(20 citation statements)

References 10 publications

(8 reference statements)

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“…The maximum similarity occurred between seed and lint cotton yield/plant traits as well as between P 1 and BC 1 generations, while the minimum similarity occurred between seed index and lint percentage traits as well as between F 1 and F 2 generations. Based on 100 cotton genotypes of Gossypium hirsutum L., the mean performances of yield and yield components traits were grouped into six different clusters (Akter et al 2019). The tree diagram had exhibited the highest correlation between the traits or generations inside each cluster.…”

Section: Correlation Analysismentioning

confidence: 99%

“…Cotton crop is one of the most founder crops and most important natural bers for textile manufacture in the old and new world (Akter et al 2019). It is a owering plant belonging to the family Malvaceae and genus Gossypium (G).…”

Section: Introductionmentioning

confidence: 99%

“…A successful breeding program relies on comprehensive knowledge and understanding of relationships among yield traits and genetic diversity within and between the elite genetic materials of existing germplasm (Akter et al 2019). The Pearson's correlation coe cient allows determining the magnitude of the relationships between essential and important traits (Abasianyanga et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Correlation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yehia¹,

El-Hashash²

2021

Preprint

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“…Early maturity is an important feature which is essential if growing more than one crop per year or to escape from late season environmental stresses. An early maturing genotype also requires less irrigation as well as less fertilizer and chemical inputs (Bednarz and Nichols 2005;Cober et al 2010;Akter et al 2019). One study was conducted to detect SNPs related to early maturity in upland cotton using 137 RILs.…”

Section: Earlinessmentioning

confidence: 99%

Role of SNPs in determining QTLs for major traits in cotton

et al. 2019

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A single nucleotide polymorphism is the simplest form of genetic variation among individuals and can induce minor changes in phenotypic, physiological and biochemical characteristics. This polymorphism induces various mutations that alter the sequence of a gene which can lead to observed changes in amino acids. Several assays have been developed for identification and validation of these markers. Each method has its own advantages and disadvantages but genotyping by sequencing is the most common and most widely used assay. These markers are also associated with several desirable traits like yield, fibre quality, boll size and genes respond to biotic and abiotic stresses in cotton. Changes in yield related traits are of interest to plant breeders. Numerous quantitative trait loci with novel functions have been identified in cotton by using these markers. This information can be used for crop improvement through molecular breeding approaches. In this review, we discuss the identification of these markers and their effects on gene function of economically important traits in cotton.

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“…Cotton is one of the major cash crops worldwide and the natural fiber crop in textile industries [1,2]. Cotton is also fine chemical raw material and an oil crop [3] planted in more than 80 countries around the world [4].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Impact of Climate Change on Cotton Yield Using Spatial Analysis and Statistical Modeling in the Indus River Basin, Pakistan

M¹,

Hs²,

Qm³

et al. 2021

annagriccropsci

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Cotton is an excessive growing fiber crop in the world. In Pakistan, climate warming is hurting the cotton crop. To identify countermeasures of climate change impacts on crop production, it is needed to explore the changes in crop yield and their relationship to climate change. This study, we conducted spatial analysis of cotton yield and climate data in the Indus River basin in Pakistan from 1989- 2018. We analyzed the trend in cotton production indices and climate variables using the Mann-Kendal nonparametric test, and the impact of climate change on cotton yield through Pearson’s correlation and best-subset multivariate linear regression model. Sunshine hours positively impacted on cotton yield, but an increasing trend in average temperature, minimum temperature and air relative humidity in main crop areas had a negative impact on the cotton crop. Our results illustrate the relationship between the cotton yield and climate change variables on regional basis in the Indus River basin, Pakistan. This research could provide a reference for understanding the intolerance of cotton in the Indus River basin, Pakistan to climate change.

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Evaluation of genetic diversity in short duration cotton (Gossypium hirsutum L.)

Cited by 16 publications

References 10 publications

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

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

Role of SNPs in determining QTLs for major traits in cotton

Analyzing the Impact of Climate Change on Cotton Yield Using Spatial Analysis and Statistical Modeling in the Indus River Basin, Pakistan

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