GxE interaction of seventeen dual purpose barley genotypes evaluated at ten major barley locations of the country by non parametric methods. Non parametric measures had been well established and expressed ad-vantages over their counter parts i.e. parametric measures. Simple descriptive measures based on the ranks of gen-otypes i.e. Mean of ranks (MR) pointed towards RD2925 and BH1008 and standard deviation of ranks (SD) for KB1401 and UPB1054 whereas Coefficient of variation (CV) for JB322 and RD2925 as stable genotypes. Nonpara-metric measures based on original values (Si1, Si2, Si3, Si4, Si5, Si6, Si7) indicated the stable performance of NDB1650, JB322 and UPB1054 while UPB1053, RD2715, RD2927 and RD2035 were observed of unstable nature. CSi1, CSi2, CSi3, CSi4, CSi5, CSi6 and CSi7 measures based on the ranks of corrected grain yield identified JB322, RD2552, RD2925 and NDB1650 as stable genotypes. Spearman’s rank correlation established highly significant positive correlation of yield with SD (0.67), Si1(0.65), Si2(0.59), Si5(0.68), Si7(0.67) whereas negative association observed for CMR (Mean of corrected ranks) (-0.62), CMed (Median of corrected ranks) (-0.60). NPi(2) expressed negative correlation with CV(-0.32), Si6 (-0.30), CMR(-0.34) and CMed(-0.48). More over NPi(3) maintained negative correlation with most of the measures though the magnitude was of low magnitude.
GxE interaction, Non parametric methods, Rank correlation, Ward’s clustering
Ebadi, S. A., Sabaghpour, S.H., Dehghani, H. and Kamrani, M. (2008). Non-parametric measures of phenotypic stability in chickpea genotypes (Cicer arietinum L.). Euphytica, 2:221-229
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
Huehn, M. (1979). Beitrage zur erfassung der phanotypischen stabilitat. EDV Med Biol 10:112-117
Huehn, M. (1990). Non-parametric measures of phenotypic stability: Part 2. Application. Euphytica, 47:195-201
Hussein, M.A., Bjornstad, A. and Aastveit, A.H. (2000). SASG × ES¬TAB: A SAS program for computing genotype and environ¬ment stability statistics. Agron J, 92:454-459.
Karimizadeh, R. , Mohammadi, M. , Sabaghnia, N. and Shefazadeh, M. K.(2012). Using Huehn’s nonparametric stability statistics to investigate Genotype × Environment interaction. Not Bot Horti Agrobo.,40(1):293-301.
Khalili, M. and Pour-Aboughadareh, A. (2016). Parametric and non-parametric measures for evaluating yield stability and adaptability in barley doubled haploid lines. J. Agr. Sci. Tech., 18: 789-803
Kharub, A.S., Verma, R. P. S. , Kumar, D., Kumar, V., Selvakumar, R. and Sharma, I. (2013). Dual purpose barley (Hordeum vulgare L.) in India: Performance and potential. J. Wheat Res., 5 (1) : 55-58
Lima, L.K., Ramalho, M.N.P., Ferreira, R.A.D.C. and Abreu, A.F.B. (2013). Repeatability of adaptability and stability parameters of common bean in unpredictable environments. Pesqui Agropecuá Bras., 48:1254–1259
Liu, Y.J., Duan C., Tian M.L., Hu, E.L. and Huang, Y.B. (2010). Yield stability of maize hybrids evaluated in maize regional trials in southwestern china using nonparametric methods. Agric Sci China, 9:1413-1422
Lu, H.Y. (1995). PC-SAS program for Estimation Huehn’s non¬parametric stability statistics. Agron. J. 87:888-891
Mohammadi, R., Haghparast, R., Sadeghzadeh, B., Ahmadi, H., Solimani, K. and Amri, A. (2014). Adaptation patterns and yield stability of durum wheat landraces to highland cold rainfed areas of Iran. Crop Sci., 54:944–954
Mohammadi, R., Farshadfarar, E. and Amri, A. (2016). Comparison of rank-based stability statistics for grain yield in rainfed durum wheat. New Zealand J. Crop and Hort Sci., 44(1): 25–40
Mortazavian, S. M. M. and Azizinia, S. (2014). Nonparametric stability analysis in multi-environment trial of canola. Tur J Field Crops, 19(1): 108-117
Parmar, D. J., Patel, J. S., Mehta, A. M., Makwana, M. G. and Patel, S. R. (2012). Non- Parametric methods for interpreting Genotype×Environment interaction of Rice Genotypes (Oryza sativa L.). J. Ric. Res., 5: 33-39
Rasoli, V. , Farshadfar E. and Ahmadi, J. (2015). Evaluation of Genotype × Environment Interaction of grapevine genotypes (Vitis vinifera L.) by non parametric method. J. Agr. Sci. Tech, 17: 1279-1289
Sabaghnia, N., Karimizadeh, R. and Mohammadi, M. (2012). The use of corrected and uncorrected nonparametric stability measurements in Durum wheat multi-environmental Trials. Span. J. Agric. Res,
Scapim, C.A., Pacheco, C.A.P., do Amaral ATJúnior, Vieira R.A., Pinto R.J.B. and Conrado, T.V. (2010). Correlations between the stability and adaptability statistics of popcorn cultivars. Euphytica, 174:209–218
Thennarasu, K. (1995). On certain non-parametric procedures for studying genotype × environment interactions and yield stability. PhD. Thesis. P.G. School, IARI, New Delhi, India.
Ward, J.H. (1963). Hierarchical grouping to optimize an objective function. J. Am Stat Assoc., 58:236–224
This work is licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) © Author (s)