PERFORMANCE EVALUATION OF UPLAND COTTON GENOTYPES IN TERMS OF SEED COTTON YIELD UNDER INCONSISTENT ENVIRONMENTAL CONDITIONS
Keywords:Agro-environments, Cotton, Genotype by environment interaction, Genotype Selection Index, Yield
Yield constancy is a crucial characteristic for a variety to become popular among growers. To study this aspect, the present trial was carried out at seven locations in the cotton belt of Punjab during 2020-21. Twenty-five upland cotton strains from different breeding stations were tested along with the standard variety CIM-602. The main objective was to choose super-yielding plus stable strains. Maximum variability due to environments (65%) followed by GEI (22.8%) was observed. The first two interaction principal components (IPC) were squeezed with 72.3% of cumulative variability due to GEI. The analysis of additive main effects and multiplicative interaction (AMMI) diagnosed AMMI5 as an appropriate model. Strain CKC-5 gave maximum mean yield (1812kg ha-1) and winner in all AMMI models. Test sites were split into two mega environments (ME). ENT7 (CRS Faisalabad) site was bearing the highest mean seed cotton yield of (2532kg ha-1) with the biggest (52.18) IPC1 score. The correlation between sites and IPC1 scores was (0.68) as recorded by AMMI analysis. AMMI1 ranks depicted that (PCI2) CIM-875 bears yield advantage of (29.09%) at ENT6 (Vehari) site over (trial winner strain CKC-5) due to micro adaptations. Genotype Selection Index (GSI) discriminated strain BS-J5 as yielder cum stable one with the least GSI value. Approval of this strain for general cultivation from the respective forum may boost cotton production in the province.
Abdi, H., Williams, I.J. (2010). Principal Component Analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2(4):433-459.
Agahi, K., Ahmadi, J., Oghan, H.A., Fotokian, M.H., and Orang, S.F. (2020). Analysis of genotype x environment interaction for seed yield in spring oilseed rape using the AMMI model. Crop Breeding and Applied Biotechnology. 20(1):e26502012, 2020.
Akande, S.R. (2009). Biplot analysis of genotype by environment interaction of cowpea grain yield in the forest and southern Guinea savanna agro-ecologies of Nigeria Journal of Food and Agricultural Environment. 5:464-467.
Alghamdi, S.S. (2004). Yield stability of some soybean genotypes across a diverse environment. Pakistan Journal of Biological Sciences. 7(12), 2109-2114.
Ashraf, J., Zuo, D., Wang, Q., Malik, W., Zhang, Y., Abid, M.A., and Song, G. (2018). Recent insights into cotton functional genomics: progress and future perspectives. Plant Biotechnology. 16(3), 699-713.
Askari, H., Kazemitabar, K.S., Zarrini, N.H., and Saberi, H.M. (2017). Analysis of the genotype by salt interaction of barley (Hordeum vulgar L) the genotypes at early growth stage by graphical models. International Journal of Agriculture & Environmental Research. 3:190-196.
Bose, L.K., Jambhulkar, N.N., Pande, K., and Singh, O.N. (2014). Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions. Chilean Journal of Agricultural Research. 7(1): 1-9.
Cornelius, P.L., Seyed, M.S., and Crossa, J. (1992). Using the shifted multiplicative model to search for “reparability” in crop cultivar trials. Theoretical & Applied Genetics. 4:161-172.
Ebdon, J.S., and Gauch, H.J. (2002). Additive main effect and multiplicative interaction analysis of national turf grass performance trails and cultivars recommendations. Crop Sciences. 42, 497-506.
El-Hashash, E. F., Tarek, S.M., Rehab, A.A., and Tharwat, M.A. (2019). Comparison of non-parametric stability statistics for selecting stable and adapted soybean genotypes under different environments. Asian Journal of Research in Crop Sciences. 4(4):1-16.
Farshadfar, E. (2008). Incorporation of AMMI stability value and grain yield in a single non-parametric index (GSI) in bread wheat. Pakistan Journal of Biological Sciences. 11:1791–1796.
Farshadfar, E., Mohammadi, R., Aghaee, M., and Visi, Z. (2012). GGE biplot analysis of genotype × environment interaction in wheat-barley disomic addition lines. Australian Journal of Crop Sciences. 6(6): 1074-1079.
Gauch, H.G. (2013). A Simple Protocol for AMMI analysis of yield trials. Crop Sciences. 53, 1860-1869.
Giridhar, K., Kumari, S.S., Sarada, C., and Naram, L. (2016). Stability for seed yield in ajwain based on a genotype selection index. Indian Journal of Agricultural Research. 50 (3): 244-248.
Gurmu, F., Mohammed, H., and Alemaw, G. (2009). Genotype and environment interaction and stability of soybean for grain yield and nutritional quality. African Crop Sciences Journal. 17: 87-99.
Kandus, M., Almorza, D., Ronceros, B., and Salerno, J.C. (2010). Statistical models for evaluating the genotype-environment interaction in maize (Zea mays L.). Phyton -Revista Internacional de Botanica Experimental 79(26):39-46.
Kaya, Y. C., Palta, S., and Taner. (2002). Additive main effects and multiplicative interactions analysis of yield performance in bread wheat genotypes across environments. Turk Journal of Agriculture. 26:275-279.
Khalid, M., Hassan, U., Hanzala, M., Amjad, I., & Hassan, A. (2022). Current situation and prospects of cotton production in pakistan. Bulletin of Biological and Allied Sciences Research, 2022(1), 27. https://doi.org/10.54112/bbasr.v2022i1.27
Khalid, M., & Amjad, I. (2019). Combining ability and heterosis studies in upland cotton (Gossypium hirsutum L.). Bulletin of Biological and Allied Sciences Research, 2019(1), 20. https://doi.org/10.54112/bbasr.v2019i1.20
Khalid, M., & Amjad, I. (2018). Repercussions of waterlogging stress at morpho-physiological level on cotton and ways to lessen the damage to crop yields. Bulletin of Biological and Allied Sciences Research, 2018(1), 16. https://doi.org/10.54112/bbasr.v2018i1.16
Krishnamurthy, S.L., Sharma, P.C., and Sharma, D.K. (2021). Additive main effects and multiplicative interaction analyses of yield performance in rice genotypes for general and specific adaptation to salt stress in locations in India. Euphytica 217(20):1-15.
Kumar, P., and Singh, N.K. (2015). Determining behaviour of maize genotypes and growing environments using AMMI statistics. SAARC Journal of Agriculture. 13(1):162-173.
Malik, A., & Rasheed, M. (2022). An overview of breeding for drought stress tolerance in cotton. Bulletin of Biological and Allied Sciences Research, 2022(1), 22. https://doi.org/10.54112/bbasr.v2022i1.22
Mohammadi, R., and Amri, A. (2007). Comparison of parametric and nonparametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica 159: 419-432.
Naveed, M., Nadeem, M., and Khan, N.I. (2007). AMMI Analysis of some upland cotton genotypes for yield stability in different milieus. World Journal of Agricultural Sciences. 3(1): 39-44.
Ntawuruhunga, P.H., Rubaihayo, P.R., and Osiru, D.S.O. (2001). Additive main effects and multiplicative interaction analysis for storage root yield of cassava genotypes evaluation in Uganda. African Crop Sciences Journal. 9: 591-598.
Purchase, J.L. (1997). Parametric analysis to describe G x E interaction and yield stability in winter wheat. Ph. D Thesis. Depart. Of Agron. Fac. of Agric., Uni. of the Orange Free State, Bloemfontein, South Africa.
Riaz, M., Naveed, M., Farooq, J., Farooq, A., and Sadiq, A. (2013). AMMI analysis for stability, adaptability and GE interaction studies in cotton (Gossypium hirsutum L.) Journal of Animal and Plant Sciences. 23(3):865-871.
Sial, K.B., Kalhoro, A.D., Ahsan, M.Z., Mojidano, M.S., and Kerio, A. (2014). Performance of different upland cotton varieties under the climatic condition of the central zone of Sindh. American-Eurasian Journal of Agriculture and Environmental Sciences. 14: 1447–1449.
Sharifi, P., Hashem, A., Rahman, E., Ali, M., and Abouzar, A. (2017). Evaluation of genotype × environment interaction in rice based on the AMMI model in Iran. Rice Sciences. 24: 173-180.
Steel, R.G.D., Torrie, J.H., and Dickey, D.A. (1997. Principles and procedure of statistics: A biometrical approach 3rd ed. McGraw-Hill Book Co., New York.
Sumathi, P., Govindaraj, M., and Govintharaj, P. (2017). Identifying promising pearl millet hybrids using AMMI and clustering models. International Journal of Microbiology & Applied Sciences. 6(2):1348- 1359.
Workie, A., Zeleke, H., and Dessalegn, Y. (2013). Genotype X environment interaction of maize (Zea mays L.) across northwestern Ethiopian Journal of Plant Breeding & Crop Sciences. 5 (9):171-181.
Yan, W., and Rajcan, I. (2002). Biplots analysis of the test sites and trait relations of soybean in Ontario. Crop Sciences. 42, 11-20.
Yayis, R., Bekele, A., and Goa, Y. (2014). GGE and AMMI biplot analysis for field pea yield stability in SNNPR state, Ethiopia. International Journal of Sustainable Agriculture Research. 1(1):28-38.
Zare, M. (2012). Evaluation of drought tolerance indices for the selection of Iranian barley (Hordeum vulgare) Cultivars. African Journal of Biotechnology. 11(4):975-981.
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