Skip to main content
SpringerLink
Log in

Analysis of the main effect clustering and decision-making coefficients for F2 generation of upland cotton in Southern Xinjiang

  • Published:
Euphytica Aims and scope Submit manuscript

Abstract

Yield and fibre quality traits respectively accounting for 4 and 5 of their 278 varieties (lines) and their 784 F2 crosses of upland cotton were for their additive and dominance effects by a genetic model comprising additive, dominance and their interaction effects with the environment in two years of Alar, South Xinjiang, People’s Republic of China. Based on the additive and dominance effects, all varieties were clustered using cluster analysis of the R software package.Then, the decision-making coefficient of F2 was analyzed. Results indicated that under the high-density planting mode of "low plant growth (plant height: 0.8–1.0 m), high density (225,000–300,000/hm−2), early ripe and film cover" in Southern Xinjiang Province, the additive effects of 278 parents were divided into fourteen groups. The average additive effects of yield and fibre quality traits of 21 varieties (lines) in the fourth group were at a good level. Obtain offspring was easy with both yield (except Lint percentage) and fibre quality traits by crossing between these varieties (lines). The fifth group had a better average additive effect on fibre quality traits, whilst the tenth group had a higher average additive effect on yield traits. The progeny with complementary yield and fibre quality traits could be obtained by crossing these two kinds of varieties. The dominance effects of 278 parents were divided into thirteen groups. The fourth group included 33 varieties (lines) that yield traits, and fibre quality traits (except lint percentage)were all at a better average level in dominance effect, which could be used as parents for hybrid utilisation of both yield traits and fibre quality traits. The average dominance effect of yield traits was higher in the first group, and the average value of dominance effect of fibre quality traits in the twelfth group was high. Results indicated the order of significant relationships of dominance effect, the decision-making traits of lint percentage to lint yield, length and micronaire. The decision-making traits of strength was length. The main decisive traits and restricted traits for improving the yield and fibre quality of upland cotton were also determined.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Clement JD, Constable GA, Stiller WN, Liu SM (2012) Negative associations still exist between yield and fiber quality in cotton breeding programs in Australia and USA. Field Crop Res 128:1–7

    Article  Google Scholar 

  • Dai YQ, Zhang XY, Sun J (2017) Genetic effect analysis of main breeding targets of three lines hybrids cotton in northern Xinjiang(in Chinese). Xinjiang Agric Sci 54:1–9

    Google Scholar 

  • Kassambara A, Mundt F (2020) factoextra: extract and visualize the results of multivariate data analyses. R package version 1.0.7. https://CRAN.R-project.org/package=factoextra

  • Liu JP, Mei YJ, Zhang LL, Hu SL, Guo WF, Xiong RC (2005) Analyses of heredity and correlation between boll traits and fiber quality traits in “0” plant type island cotton (in Chinese). Acta Agron Sin 08:1069–1073

    Google Scholar 

  • Mei YJ, Guo WF, Fan SL, Song MZ, Pang CY, Yu SX (2014) Analysis of decision-making coefficients of the lint yield of upland cotton (Gossypium hirsutum L.). Euphytica 196:95–104

    Article  Google Scholar 

  • Mei YJ, Zhang GS, Ye ZH, Cao XC (2004a) The decision analysis on the aim yield traits of F1 between “0”and “Long fruit branch” plant type in island cotton (in Chinese ). Acta Agron Sin 11:1164–1168

    Google Scholar 

  • Mei YJ, Zhang GS, Ye ZH, Cao XC, Zhang WY (2004b) Genetic analysis of yield traits and population heterosis for F1 and F2 between different Fruit-Branch type cultivars in island cotton (in Chinese). Acta Agron Sin 10:1026–1030

    Google Scholar 

  • Mei YJ, Zhang GS, Ye ZH, Cao XC, Zhang WY (2004c) Genetic Analysis of fiber traits and population heterosis for F1 and F2 between different Fruit-Branch type cultivars in island cotton (in Chinese). Acta Agron Sin 08:796–800

    Google Scholar 

  • Mei YJ, Yu JW, Xue AL, Fan SL, Song MZ, Pang CY, Pei WF, Yu SX, Zhu J (2016) Association mapping of genetic network for plant morphological traits in cotton. J Zhejiang Univer (agric Life Sci) 42:127–136

    Google Scholar 

  • Qin HD, Feng CH, Zhang YC, Bie S, Zhang JH, Xia SB, Wang XG, Wang QS, Lan JY, Chen QQ, Jiao CH (2021) F1 performance prediction of upland cotton based on partial ncll design (in Chinese). Sci Agric Sin 54:1590–1600

    Google Scholar 

  • Roy U, Paloti MC, Tigga A, Patil RS (2019) Genetic variability studies in the F2 populations of interspecific cotton (G. hirsutum L. × G. barbadense L.) hybrids. Int J Genet 11:660–663

    Google Scholar 

  • Sahar A, Zafar MM, Razzaq A, Manan A, Haroon M, Sajid S, Rehman A, Mo HJ, Ashraf M, Ren MZ, Shakeel A, Yuan YL (2021) Genetic variability for yield and fiber related traits in genetically modified cotton. J Cotton Res 4:219–228

    Article  Google Scholar 

  • Wang BQ, Liu SM, Li B, Li JW, Han YH, Jia XH, (2016) Analysis of genetic effects for fiber quality in upland cotton immotalized F2 populations(in Chinese). Hubei Agric Scis 55:5758–5760

    Google Scholar 

  • Wang CT, Tang YY, Z P, W Q, Wang ZW, Song GS, W Q, (2020) Decision analysis of amino acid quality improvement ways in peanut (in Chinese). Shandong Agric Sci 52:8–16

    Google Scholar 

  • Wang JD, Liang YJ, Gong ZX, Ai XT, Guo JP, Maimaiti MM, Li XY, Zhao SQ, Zheng JY (2021) Cotton seed industry report in Xinjiang cotton planting area in 2019 (in Chinese ). Cotton Sci 43:3–10

    Google Scholar 

  • Yu SX, Fan SL, Wang HT, Wei HL, Pang CY (2016) Advances in high yield breeding of Cotton in China (in Chinese). Sci Agric Sin 49:3465–3476

    Google Scholar 

  • Yuan ZF, Zhou JY, Guo MC, Lei XQ, Xie XL (2001) Decision coefficient—the decision index of path analysis(in Chinese). J Northwest Sci Tech Univer Agric for (nat Sci Ed) 29:131–133

    Google Scholar 

  • Zhu J (1992) Mixed model approaches for estimating genetic variances and covariances. J Biomath 1:1–11

    Google Scholar 

  • Zhu J (1994) General genetic models and new analysis methods for quantitative characteristics. J Zhejiang Agric Univer (in Chin) 20:551–559

    Google Scholar 

  • Zhu J (1997) Analysis methods for genetic models (in Chinese). Chinese Agricultural Publish House, Beijin, pp 56–87; 175–191 (in Chinese with English abstract)

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China Genome-wide Mining of Specific Yield Traits (QTs) in Upland Cotton from Southern Xinjiang (31560408) and the State Key Laboratory of Cotton Biology and State Key Laboratory of Cotton Biology Open Fund (CB2021A28). We thank Zhu J of Zhejiang university and Yuan ZF of Northwest F&A University, China for providing the test method used in this research.

Funding

This study was supported by the National Natural Science Foundation of China Genome-wide Mining of Specific Yield Traits (QTs) in Upland Cotton from Southern Xinjiang (31560408) and the State Key Laboratory of Cotton Biology and State Key Laboratory of Cotton Biology Open Fund (CB2021A28).

Author information

Author notes

  1. Hong Guo and Jiwen Yu are contributed equally to this work.

Authors and Affiliations

  1. College of Agriculture, Tarim University, Alar, 843300, Xinjiang, China

    Hong Guo, Yonghu Guan, Hang Li, Changxi Li, Jinwei Liu, Wei Wang, Zhanpeng Dong & Yongjun Mei

  2. State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China

    Jiwen Yu, Wenfeng Pei & Baoquan Wang

Authors
  1. Hong Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiwen Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenfeng Pei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yonghu Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Changxi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jinwei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Zhanpeng Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Baoquan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yongjun Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

HG and JY planned the experiments and wrote the manuscript. YG, HL, CL, JL, WW and ZD participated in the study. WP, and BW provided advice for experiments and manuscript writing. YM conceived and designed the research and manuscript revision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yongjun Mei.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 169 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, H., Yu, J., Pei, W. et al. Analysis of the main effect clustering and decision-making coefficients for F2 generation of upland cotton in Southern Xinjiang. Euphytica 219, 32 (2023). https://doi.org/10.1007/s10681-023-03164-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10681-023-03164-7

Keywords

Search

Navigation