Ajay Verma V. Kumar A.S. Kharab R Chatrath G.P. Singh


GxE interaction to know adaptability of 19 salt salinity tolerant barley genotypes was studied by parametric and non-parametric measures. Genotypes KB1516, RD2907 and RD2794 showed minimum environmental variance over different environments. Superiority index identified genotypes RD2907 and NDB1445 with lowest value accompanied with higher. Wricke’s measure exhibited lower values of DWRB168DWRB165 and NDB1445. Higher values of GAI showed consistent performance of RD2907, NDB1445 and RD2552. Non-parametric measures Si(1), Si(3) and Si(6) the considered DWRB165 and DWRB168  as desirable genotypes. Thennarasu’s first measure NPi(1) found DWRB168 and NDB1445 as desirable adaptable and KB1546, RD2907 and NDB1173 were unstable genotypes. Wricke’s parameter was positively correlated with NPi(1), NPi(3) and Kang. GAI had significant positive with Pi and Kang while negative with Si(6), NPi(2) & NPi(4). Worth to mention the negative association of  Pi with Si(6), NPi(2), NPi(4). Non parametric measures Si (3) Si (6) NPi (2) & NPi (4) clubbed together while Kang, Wi 2, s2i ,Si (1),Si (2) ,NPi (1) & NPi (3)  joined in another cluster.  Left over parametric measures were grouped in two separate clusters i.e. (bi, S2xi ,CVi),(Yield, GAI Pi) respectively.  Biplot analysis based on first two principal components showed three groups among the measures.


Download data is not yet available.




Adaptability G x E interaction, Parametric and nonparametric measures, Salinity tolerant barley

Baxevanos D, Goulas C, Tzortzios S, Mavromatis A (2008). Interrelationship among and repeatability of seven stability indices estimated from commercial cotton (Gossypium hirsutum L.) variety evaluation trials in three Mediterranean countries. Euphytica 161: 371-382
Becker HC, Leon J (1988). Stability analysis in plant breeding. Plant Breed 101:1–23
Dehghani MR, Majidi MM, Mirlohi A, Saeidi G. (2016). Integrating parametric and non-parametric measures to investigate genotype × environment interactions in tall fescue. Euphytica. 208:583–596.
Elahe Noruzi, Asghar Ebadi .(2015). Comparison of Parametric and Non-parametric Methods for Analysing Genotype× Environment Interactions in Sunflower (Helianthus annuus L.) Inbred Lines Jordan Journal of Agricultural Sciences, 11(4 ):959-978.
Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant breeding programme. Aust J Agric Res 14:742–754
Francis, T.R., Kannenberg L.W .1978. Yield stability studied in short-season maize. I. A descriptive method for grouping genotypes. Can J Plant Sci 58:1029–1034
Flores, F., M. T. Moreno, J. I. Cubero (1998). A comparison of univariate and multivariate methods to analyze G x E interaction. Field Crops Research, 56: 271– 286.
Henryk Bujak, Kamila Nowosad, Roman Warzecha (2014). Evaluation of maize hybrids stability using parametric and non-parametric methods Maydica 59: 170-175.
Hühn, M., Nassar, R. (1989). On tests of significance for nonparametric measures of phenotypic stability. Biometrics, 45: 997-1000.
Hussein, M.A., Bjornstad, A. and Aastveit, A.H. (2000). SASG x ESTAB: A SAS program for computing genotype x environment stability statistics. Agron. J., 92: 454-459.
Kang’s, M.S. (1988). A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Res. Commun., 16:113–115.
Lin, C. S. and Binns M. R. (1988). A method of analyzing cultivar × location × year experiments: A new stability parameter. Theor. Appl. Genet., 76: 425–430.
Mohammadi, R. and Amri A. (2008). A. Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica, 159: 419–432.
Piepho, H.P., Lotito, S. (1992). Rank correlation among parametric and nonparametric measures of phenotypic stability. Euphytica 64: 221–225.
Shukla, G.K. (1972). Some statistical aspects of partitioning genotype environmental components of variability. Heredity, 29:237–245.
Sisay Awoke and M.K. Sharma (2016). Parametric and Non-Parametric Methods to Describe Genotype by Environment Interaction and Grain Yield Stability of Bread Wheat Statistics and Applications.14(1&2): 9-29
Thennarasu’s, K. (1995). On certain non-parametric procedures for studying genotype-environment interactions and yield stability. Unpublished Ph.D. Thesis. P.G. School, IARI, New Delhi
Truberg B. and Hühn, M. (2000). Contributions to the analysis of genotype × environments interactions: Comparison of different parametric and non-parametric tests for interactions with emphasis on crossover interaction. J. Agron. Crop Sci., 185:267-274
Van Eeuwijk F.A., Cooper M., DeLacy I.H. (2001). Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials. Euphytica, 122:477–490
Ward, J.H. (1963). Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 58:236–244.
Wricke’s, G. (1962). On a method of understanding the biological diversity in field research. Z. Pflanzenzucht, 47: 92–96.
Citation Format
How to Cite
Verma, A., Kumar, V., Kharab, A., Chatrath, R., & Singh, G. (2018). Parametric vis-a-vis non parametric measures describing G x E interactions for salt salinity tolerant barley genotypes in multi-environment trials. Journal of Applied and Natural Science, 10(2), 557-563. https://doi.org/10.31018/jans.v10i2.1736
More Citation Formats:
Research Articles