Univariate parametric and nonparametric techniques for analyzing fiber quality and yield stability in Sea Island cotton (Gossypium barbadense L.)
Main Article Content
Abstract
The techniques used to evaluate the stability and adaptability of genotypes across environments and to study genotype-by-environment interactions (GEIs) are ever-evolving. In this sense, employing multiple approaches to measure the nature of the GEI from multiple aspects is frequently preferable rather than relying solely on a single analysis. A panel of 50 Gossypium barbadense genotypes was assessed over three years at the research sites using a randomized full-block design. The results of the additive main effects and multiplicative interaction (AMMI) model indicated that the number of bolls (NB), single plant yield (SPY), fiber length (UHML), and fiber strength (FS) were significantly impacted by genotype, environment, and GEI. Based on the multiplicative effects analysis of AMMI into interaction principal components (IPCs), the studied traits had two significant components. The AMMI model predicted that the stable genotypes for NB were G30 (ICB13), G10 (ICB35), and G31 (ICB176), while those for SPY were G38 (ICB16), G34 (ICB244), G19 (ICB73), G29 (ICB207), and G41 (CCB11A). Genotypes, G23 (ICB262), G29 (ICB207), G7 (ICB220), and G21 (ICB143) for UHML and G19 (ICB73) and G39 (ICB39) for FS were considered to be stable. In this study, for yield traits, the E1 environment better differentiated the genotypes, whereas for quality traits, all three environments showed their discriminativeness. In terms of identifying highly stable and high-yielding genotypes, all of the SSI models were performed, which revealed that genotype G41 (CCB 41), an advanced breeding line, had good stability across environments with relatively high yields coupled with good fiber quality.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Aboughadareh, A P., Barati, A., Koohkan, S.A. et al. 2022. Dissection of genotype-by-environment interaction and yield stability analysis in barley using AMMI model and stability statistics. Bull Natl Res Cent 46, 19. https://doi.org/10.1186/s42269-022-00703-5
Abro, S., Rizwan, M., Rajput, M., Sial, M., Deho Z.A. 2022. Evaluation of upland cotton genotypes for stability over different locations using AMMI and GGE Biplot analysis. Pak. J. Bot. 1: 54(5):1733-9. http://dx.doi.org/10.30848/PJB2022-5(1)
Ajay, B. C., Bera, S. K., Singh, A. L., Kumar, N., Gangadhar, K., and Kona, P. 2020. Evaluation of genotype x environment interaction and yield stability analysis in peanut under phosphorus stress condition using stability parameters of AMMI model. Agric. Res. 9, 477–486. https://doi.org/10.1007/s40003-020-00458-3
Alake, C. O., and Ariyo, O. J. 2012. Comparative analysis of genotype x environment interaction techniques in West African okra, (Abel moschuscaillei, a. chevstevels). J. Agric. Sci. 4 (4), 135. https://doi.org/10.5539/jas.v4n4p135
Annicchiarico, P., Russi, L., Piano, E., Veronesi, F. (2006). Cultivar adaptation across Italian locations in four turfgrass species. Crop science. 46(1):264-72.
Anuradha, N., Patro, T.S.S.K, Singamsetti, A., Sandhya Rani, Y., Triveni, U., Nirmala Kumari, A., Govanakoppa, N., Lakshmi Pathy, T., and Tonapi, V.A. 2022. Comparative Study of AMMI- and BLUP-Based Simultaneous Selection for Grain Yieldand Stability of Finger Millet [Eleusine coracana (L.) Gaertn.] Genotypes.Front. Plant Sci. 12:786839. https://doi.org/10.3389/fpls.2021.786839
Baghyalakshmi K., Manickam S., Amutha M., Sampathkumar A., Yamuna M. G. and Prakash A. H. 2023. Site regression and multivariate analysis for genetic diversity in Gossypium barbadense accessions. EJPB, 14(3): 775 – 786. https://doi.org/10.37992/2023.1403.088
Bhartiya, A., Aditya, J. P., Kumari, V., Kishore, N., Purwar, J. P., Agrawal, A.,et al. 2017. GGE biplot & ammi analysis of yield stability in multi-environmenttrial of soybean [Glycine max (L.) Merrill] genotypes under rainfed condition of north western Himalayan hills. J. Anim. Plant Sci. 27 (1), 227–238.
Bocianowski, J., Prażak, R. 2022. Genotype by year interaction forselected quantitative traits in hybrid lines of Triticum aestivum L. with Aegilop skotschyi Boiss. and Ae. Variabilis Eig. Using the additive main effects and multiplicative interaction model. Euphytica 218(2):11. https://doi.org/10. 1007/ s10681- 022- 02967-4
Cheloei, G., Ranjbar, G. A., Babaeian Jelodar, N., Bagheri, N., and Noori, M. Z. 2020. Using AMMI model and its parameters for yield stability analysis of rice (Oryza sativa L.) advanced mutant genotypes of Tarrom-Mahalli. Iran. J. Genet.Plant Breed. 9, 70–83. https://doi.org/10.30479/IJGPB.2020.13219.1271
da Silva, A.R. and da Silva, M.A.R. 2017. Package ‘biotools’. Avaliable online at: https://CRAN. R-project. org/package=~ biotools. Accessed on 11 June 2023
Ebem, E.C., Afuape, S.O., Chukwu, S.C., Ubi B.E. 2021. Genotype× environment interaction and stability analysis for root yield in sweet potato [Ipomoea batatas (L.) lam] Front. Agron., 3, pp. 1-14. https://doi.org/10.3389/fagro.2021.665564
Gauch, H.G. 1988. Model selection and validation for yield trials with interaction. Biometrics 44:705–715
Gauch, H.G., Piepho, H.P., Annicchiarico, P. 2008. Statistical analysis of yield trials by AMMI and GGE: further considerations. Crop Sci 48:866–889. https://doi.org/10.2135/crops ci2007.09.0513
Hailemariam H, M. 2022. Adaptability and stability for soybean yield by AMMI and GGE models in Ethiopia. Front. Plant Sci. 13:950992. https://doi.org/10.3389/fpls.2022.950992
Hasan, M.J., Kulsum, M.U., Sarker, U., Matin, M.Q., Shahin, N.H., Kabir, M.S., Ercisli, S., Marc, R.A. 2022. Assessment of GGE, AMMI, regression, and its deviation model to identify stable rice hybrids in Bangladesh. Plants. 7; 11 (18):2336. https://doi.org/10.3390/plants11182336
Hashim, N., Rafii, M. Y., Oladosu, Y., Ismail, M. R., Ramli, A., Arolu, F., et al. 2021. Integrating multivariate and univariate statistical models to investigate genotype–environment interaction of advanced fragrant rice genotypes under rainfed condition. Sustainability 13 (8), 4555. https://doi.org/10.3390/su13084555
Jamil, M., Saeed, M., Abdullah, M., Faheem, U., Haya,t K., Ahmad, S., Ahmad, G., Hussain, A., Hussain, F., Akhtar, I., Javed, K. 2023. Performance evaluation of Upland cotton genotypes in terms of seed cotton yield under inconsistent environmental conditions. Biological and Clinical Sciences Research Journal. 2023(1):226. https://doi.org/10.54112/bcsrj.v2023i1.226
Khan, M.M.H., Rafii, M.Y., Ramlee, S.I. et al. 2021. AMMI and GGE biplot analysis for yield performance and stability assessment of selected Bambara groundnut (Vigna subterranea L. Verdc.) genotypes under the multi-environmental trials (METs). Sci Rep 11, 22791 https://doi.org/10.1038/s41598-021-01411-2
Lee, J.J. et al. 2007. Gene expression changes and early events in cotton fibre development. Ann. Bot. 100, 1391–1401. https://doi.org/10.1093/aob/mcm232
Li, F. et al. 2014. Genome sequence of the cultivated cotton Gossypium arboreum. Nat. Genet. 46, 567–572. https://doi.org/10.1038/ng.2987
Mogale, T. E. 2018. Multi-Location Field Evaluation of Bambara Groundnut (Vigna subterranean (L) Verdc) for Agronomic Performance and Seed Protein, Doctoral dissertation.
Mulugeta, A., Sisay, K., Seltene, A., and Zelalem, F. 2013. GGE biplots to analyze soybean multi-environment yield trial data in north Western Ethiopia. J.Plant Breed. Crop Sci. 5 (12), 245–254. https://doi.org/10.5897/JPBCS13.0403
Murphy, S., Lee. E et al. 2009. Genotype × Environment interaction and stability for isoflavone content in soybean. Crop Sci. 49, 1313–1321. https://doi.org/10.2135/cropsci2008.09.0533
Mushoriwa, H., Mathew, I., Gwata, E. T., Tongoona, P., and Derera, J. 2022. Grain yield potential and stability of soybean genotypes of different ages across diverse environments in southern Africa. Agronomy 12 (5), 1147. https://doi.org/10.3390/agronomy12051147
Mwiinga, B., Sibiya, J., Kondwakwenda, A., Musvosvi, C., and Chigeza, G. 2020. Genotype x environment interaction analysis of soybean (Glycine max (L.) Merrill) grain yield across production environments in southern Africa. Field Crops Res. 256, 107922. https://doi.org/10.1016/j.fcr.2020.107922
Oladosu, Y. et al. 2017. Genotype × environment interaction and stability analyses of yield and yield components of established and mutant rice genotypes tested in multiple locations in Malaysia. Acta Agric. Scand. B Soil Plant Sci. 67(7), 590–606. https://doi.org/10.1080/09064710.2017.1321138
Olivoto, T., Lúcio, A.D. 2020. metan: An R package for multi‐environment trial analysis. Methods in Ecology and Evolution. 11 (6):783-9. https://doi.org/10.1111/2041-210X.13384
Olivoto, T., Lúcio, A. D., da Silva, J. A., Sari, B. G. & Diel, M. I. 2019. Mean performance and stability in multi-environment trials II: Selection based on multiple traits. Agron. J. 111, 2961–2969. https://doi.org/10.2134/agronj2019.03.0221
Orawu, M., Amoding, G., Serunjogi, L., Ogwang, G., Ogwang, C. 2017. Yield stability of cotton genotypes at three diverse agro-ecologies of Uganda. Journal of plant breeding and genetics. 22;5(3):101-14.
Rakshit, S., Ganapathy, K. N., Gomashe, S. S., Rathore, A., Ghorade, R. B., Kumar, M. V., et al. 2012. GGE biplot analysis to evaluate genotype, environment and their interactions in sorghum multi-location data. Euphytica 185 (3), 465–479. https://doi.org/10.1007/s10681-012-0648-6
Rehman, H.U., Farooq, U., Bhutta, M.A., Ahmad, S., Akram, M., Shahid, M.R., Hussnain, H., Shahid, M., Iqba, M.M., Raza, A., Iqbal, M. 2022. Genetic variability and performance of cotton (Gossypium hirsutum L.) genotypes for yield related agro-morphologic and fiber quality traits under water deficit natural environment. Sarhad Journal of Agriculture. 38(2):657-68.
Sabaghnia, N., Mohammadi, M., and Karimizadeh, R. 2013. Parameters of AMMI model for yield stability analysis in durum wheat. Agric Con spec. Sci. 78, 119–124. https://doi.org/10.2478/v10129-011-0063-5
Shahzad, K., Qi, T., Guo, L., Tang, H., Zhang, X., Wang, H., Qiao, X., Zhang, M., Zhang, B., Feng, J., Shahid, Iqba. M. 2019. Adaptability and stability comparisons of inbred and hybrid cotton in yield and fiber quality traits. Agronomy. 6; 9 (9):516. https://doi.org/10.3390/agronomy9090516
Simasiku, M., Lungu, D., Tembo, L. 2020. Genotype by environment interaction of cotton genotypes for seed cotton yield in Zambia. Asian Journal of Research in Crop Science. 5(2):20-8. https://doi.org/10.9734/AJRCS/2020/v5i230092
Simion T. 2018. Adaptability performances of cowpea [Vigna unguiculata (L.) Walp] genotypes in Ethiopia. Food Science and Quality Management.72:43-7.
Taleghani, D., Rajabi, A., Saremirad, A. et al. 2023. Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI. Sci Rep 13, 10019. https://doi.org/10.1038/s41598-023-37217-7
Taleghani, D., Saremirad, A., Hosseinpour, M., Ahmadi, M., Hamidi, H., Nemati, R. 2022. Genotype × Environment Interaction Effect on White Sugar Yield of Winter-Sown Short-Season Sugar Beet (Beta vulgaris L.) Cultivars. Seed and Plant Journal, 38(1): 53-69. https://doi.org/10.22092/spj.2022.360021.1275
Vaezi, B., Pour-Aboughadareh, A., Mohammadi, R., Mehraban, A., Hossein-Pour, T., Koohkan, E., Ghasemi, S., Moradkhani, H., Siddique, K.H. 2019. Integrating different stability models to investigate genotype × environment interactions and identify stable and high-yielding barley genotypes. Euphytica 215:63. https://doi.org/10.1007/s10681-019-2386-5
Vaezi, B., Pour-Aboughadareh, A., Mehraban, A., Hossein-Pour, T.,Mohammadi, R., Armion, M., et al. 2018. The use of parametric and non-parametric measures for selecting stable and adapted barley lines. Arch. Agron. SoilSci. 64 (5), 597–611. https://doi.org/10.1080/03650340.2017.1369529
Wei, T., Simko, V., Levy, M., Xie, Y., Jin, Y. and Zemla, J. 2017. Package ‘corrplot’. Statistician, 56 (316):24. Accessed on October 12, 2023.
Wendel, J.F. 1989. New World tetraploid cotton contains Old World cytoplasm. Proc. Natl. Acad. Sci. U. S. A. 86, 4132–4136. https://doi.org/10.1073/pnas.86.11.4132
Yan, W., Hunt, L.A. 2001. Interpretation of genotype x environment interaction for winter wheat in Ontario. Crop Sci 41:19–25. https://doi.org/10.2135/cropsci2001.41119x
Yan, W., and Kang, M. S. 2002. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists (CRC press: Boca Raton). https://doi.org/10.1201/9781420040371
Yan, W., and Tinker, N. A. 2006. Biplot analysis of multi-environment trial data:Principles and applications. Can. J. Plant Sci. 86 (3), 623–645. https://doi.org/10.4141/P05-169
Yan, W., Kang, M. S., Ma, B., Woods, S. & Cornelius, P. L. 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47(2), 643–653. https://doi.org/10.2135/cropsci2006.06.0374