Indian Journal of Agricultural Research

  • Chief EditorV. Geethalakshmi

  • Print ISSN 0367-8245

  • Online ISSN 0976-058X

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Indian Journal of Agricultural Research, volume 53 issue 6 (december 2019) : 728-732

Analysis of Phenotypic Stability in 25 Cowpea Genotypes Across Six Environments

Tony Ngalamu, Silvestro Kaka Meseka, Beatrice Elohor Ifie, Kwadwo Ofori, John Saviour Yaw Eleblu
1Department of Crop Science, School of Agricultural Sciences, College of Natural Resources and Environmental Studies, University of Juba, P.O. Box 82, Juba, South Sudan. 
Cite article:- Ngalamu Tony, Meseka Kaka Silvestro, Ifie Elohor Beatrice, Ofori Kwadwo, Eleblu Yaw Saviour John (2019). Analysis of Phenotypic Stability in 25 Cowpea Genotypes Across Six Environments. Indian Journal of Agricultural Research. 53(6): 728-732. doi: 10.18805/IJARe.A-429.
Twenty-five cowpea (Vigna unguiculata L) genotypes were evaluated across six contrasting environments for phenotypic yield stability. Combined analysis of variance revealed significant differences among the genotypes and the main effects. A1B×D, BC×M, L1B×M, A1B×M, and BA×I were the best performing and stable genotypes. The non-parametric analysis showed that genotype IT93K-503-1 had the highest yield and BC×D had the lowest yield. Shukla stability analysis revealed Beledi A and Dan lla as the most stable across test environments and genotypes A1B×D, BC×M and BA×I were good performers. The coefficient of variability graphical approach showed that genotypes BC×I, A1B×M, A1B×D, Dan lla, TA×M, Mouride, L1B×I, BC×M and L1B×D were high yielding. This implies they would do well across the testing sites. However, genotype IT93K-503-1 should be promoted for cultivation in drought-prone environments.
  1. Crossa, J. (1990). Statistical analyses of multilocation trials. Advances in Agronomy. 44: 55–85. (08)60818-
  2. dos Santos, A, Ceccon, G, Chamma, L.M..D, Martinho A. C and Batista V.A. (2014). Correlations and path analysis of yield components in cowpea. Crop Breeding and Applied Biotechnology. 14: 82-87.
  3. Eberhart, S. A., and Russell, W. A. (1966). Stability parameters for comparing varieties. Crop Science. 6(1): 36-40.
  4. Finlay, K.W. and Wilkinson, G. N. (1963). The analysis of adaptation In A Plant-Breeding Programme The ability of some crop varieties to perform well over a wide range of environ­mental conditions has long been appreciated by the agronomist and plant breeder. In the cereal belts of southern Australia. Australian Journal of .Agricultural Research. 14: 742–754.
  5. Francis, T. R. and Kannenberg, L.W. (1978). Yield stability studies in short-season maize: A descriptive method for grouping genotypes. Canadian Journal of Plant Science. 58 (4): 1029-1034.
  6. Huehn, M. (1979). Contributions to the collection of phenotypic stability. I. proposal by some on based ranking information stability parameters. Computer Science in Medicine and Biology. 10: 112-117.
  7. IBM Corp. Released (2013). IBM SPSS Statistics for Windows, Version 22.0. Armonk, NYIBM Corp.
  8. Kang M.S. (1988). A rank method for selecting high yielding and stable crop genotypes. Cereal Research Communications.16:113-115.
  9. Kang, M.S, and Gorman, D. P. (1989). Genotype × environment interaction in maize. Agronomy Journal. 8 (4):662-664.
  10. Lin, C.S. and Binns, M. R. (1988). A superiority performance measure of cultivar performance for cultivar × location data.Canadian Journal of Plant Science. 68: 193-198.
  11. Nassar, R, and Huehn, M. (1987). Studies on estimation of phenotypic stability: Test of significance for non-parametric measures of phenotypic stability. Biometrics. 43: 45-53.
  12. Ombakho, G.A, and Tyagi, A.P. (1987). Correlation and path analyses for yield and its components in cowpea (Vigna unguiculata    (L) Walp). East Africa Agriculture and Forest Journal. 53(1): 23-27.
  13. Pacheco, Á, Vargas, M., Alvarado, G., Rodríguez, F., Crossa, J., Burgueño, J. (2015). GEA-R (Genotype x Environment Analysis with R for Windows) Version 4.1-, hdl:11529/10203, CIMMYT Research Data & Software Repository Network, V16.
  14. Plaisted R. L. (1960). A shorter method for evaluating the ability of selection to yield consistently over locations. American Potato Journal. 37:166-172.
  15. Shukla, G. K. (1972). Some statistical aspect of portioning genotype and environmental components of variance. Heredity. 29: 237-245.
  16. Vaezi, B., Pour-Aboughadareh, A, Mehraban, A, Hossein-Pour, T, Rahmatolah Mohammadi, R, Armion M and Dorri. M. (2017). The use of parametric and non- parametric measures for selecting stable and adapted barley lines. Archives of Agronomy and Soil Science. 64(5): 597-611. doi: 10.1080/03650340.2017.1369529
  17. Vargas, M., Combs, E., Alvarado, G., Atlin, G., Mathews, K., Crossa, J. (2013). META: A suite of SAS programs to analyse multi-environment breeding trials. Agronomy Journal. 105: 11-19.
  18. VSN International. (2015). GenStat for Windows 18th Edition. VSN International Hemel Hempstead, UK.

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