Indian Journal of Agricultural Research

  • Chief EditorV. Geethalakshmi

  • Print ISSN 0367-8245

  • Online ISSN 0976-058X

  • NAAS Rating 5.60

  • SJR 0.293

Frequency :
Bi-monthly (February, April, June, August, October and December)
Indexing Services :
BIOSIS Preview, ISI Citation Index, Biological Abstracts, Elsevier (Scopus and Embase), AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Submit

Research Article

Indian Journal of Agricultural Research, volume 52 issue 6 (december 2018) : 643-648

G × E interaction estimation for forage yield of dual purpose barley by Nonparametric measures

Ajay Verma, J. Singh, V. Kumar, A.S. Kharab, G.P. Singh
1Statistics and Computer Center ICAR-Indian Institute of Wheat and Barley Research, Karnal-132 001, India.
Cite article:- Verma Ajay, Singh J., Kumar V., Kharab A.S., Singh G.P. (2018). G × E interaction estimation for forage yield of dual purpose barley by Nonparametric measures. Indian Journal of Agricultural Research. 52(6): 643-648. doi: 10.18805/IJARe.A-4764.

ABSTRACT

Nonparametric measures were utilized to estimate the genotype-environment interaction for seventeen dual purpose barley genotypes evaluated at 10 major barley growing locations of the country. Average forage yield identified higher yielder genotypes as RD2928, RD2927 and JB325 while descriptive statistics pointed out towards KB1420, RD2927 and KB1401& JB322. However RD2927& RD2928 based on MR, UPB1054 & RD2035 based on SD and genotypes JB325& RD2928 based on CV identified as the unstable genotypes. Nonparametric measures of stability based on corrected forage yield showed highly significant positive correlation among these measures. Most prominent relation was no significant positive or negative association of Si6 with corrected and uncorrected nonparametric measures. Ward’s method of clustering based on 21 nonparametric measures along with average forage yield, clustered the with higher to moderate yielding genotypes into group  comprised of RD29276, AZAD, UPB1053,  RD2552, BH1010 and  KB1401 genotypes.

REFERENCES

  1. Adugna, W. and Labuschagne, M. (2003). Parametric and nonparametric measures of phenotypic stability in linseed (Linum usitatissimum L.).Euphytica.,129: 211-218.
  2. Asghar E. S., Sayyed H. S.,Hamid D., and Morteza K.(2008). Non-parametric measures of phenotypic stabilityin chickpea genotypes (Cicer arietinum L.).Euphytica, 162:221–229
  3. Akcura M., and Kaya Y. (2008).Nonparametric stability methods for interpreting genotype by environment interaction of bread wheat genotypes (Triticum aestivum L.),Genetics and Molecular Biology, 31(4): 906-913. 
  4. Cravero V., Martin E., Anido F.L., and Cointry E. (2010). Stability through years in a non-balanced trial of globe artichoke vari­etal types, Sci Hort,126:73-77.
  5. Dadarwal, M., Gupta, P.C. and Kajala, I.S. (2018) Phenotypic stability studies in pearl millet [Pennisetum glaucum (L.) R.Br.], Indian Journal of Agricultural Research , 52(3): 278-283.
  6. Dehghani H. (2008). Estimating yield stability by nonparametric stability analysis in maize (Zea mays L.), Plant Breed Seed Sci. 58:61-77.
  7. EbadiS,A., Sabaghpour S.H., Dehghani H.and Kamrani M. (2008). Nonparametric measures of phenotypic stability in chickpea genotypes (Cicer arietinum L.). Euphytica. 2:221-229.
  8. Huehn M. (1996). Non-parametric analysis of genotype x environment interactions by ranks. In: Kang M.S. and Gauch H.G. (eds) Genotype by Environment Interaction. CRC Press, Boca Raton, pp 213-228.
  9. Huehn, M. and Leon, J.(1995). Non-parametric analysis of cultivar performance trials: Experimental results and comparison of different procedures based on ranks, Agron J 87: 627-632.
  10. Hussein M.A., Bjornstad A, and Aastveit A.H. (2000). SASG × ES­TAB: A SAS program for computing genotype 3 environ­ment stability statistics. Agron J. 92:454-459.
  11. Kharub A.S., Verma R. P. S. , KumarD., KumarV., Selvakumar R. and Sharma I. (2013). Dual purpose barley (Hordeum vulgare L.) in India: Performance and potential. J. Wheat Res. 5 (1) : 55-58.
  12. Kaya Y. and Taner S. (2002). Estimating genotypes ranks by nonpara­metric stability analysis in bread wheat (Triticum aestivum L.), J Cent Europ Agric 4:47-53.
  13. Ketata H., Yan S.K. and Nachit M.(1989).Relative consistency per­formance across environments. Int Symp on Physiology and Breeding of Winter Cereals for Stressed Mediterranean En­vironments. Montpellier, July 3-6.
  14. Lu H.S. (1995). PC-SAS Program for estimating Huhn’s nonparametric stability statistics, Agron J 87:888-891.
  15. Piepho H.P. and Lotito S.(1992). Rank correlation among parametric and nonparametric measures of phenotypic stability. Euphytica. 64: 221-225.
  16. Rahmatollah K., Mohtasham M., Naser S.and Mohammad K.S.(2012).Using Huehn’s Nonparametric Stability Statistics to Investigate Genotype × Environment Interaction. Not Bot Horti Agrobo, 40(1):293-301.
  17. Sabaghnia N., Karimizadeh R. and Mohammad M. (2012) The use of corrected and uncorrected nonparametric stability measurements in durum wheat multi-environmental trials. Spanish Journal of Agricultural Research. 10(3): 722-730.
  18. Sabaghnia N., Dehghani H. and Sabaghpour S.H. (2006). Nonparametric methods for interpreting genotype x environment interaction of lentil genotypes. Crop Sci. 46:1100-1106.
  19. Truberg B. and Huehn M.(2000). Contribution to the analysis of genotype by environment interactions: comparison of different parametric and non-parametric tests for interactions with emphasis on crossover interactions. J Agron Crop Sci. 185: 267-274.
  20. Ward, J.H. (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–224.
  21. Yau, S. K., and Hamblin J.. (1994). Relative yield as a measure of entry performance in variable environments, Crop Sci. 34:813-817. 
Reviewed By

Download Article
In this Article
    Contact us
    Follow us

    Editorial Board

    View all (0)