Random and Fixed Effects of Wheat Genotypes Compared by Rank Based Measures: Northern Hills Zone
Author(s)
AJAY VERMA , GYANENDRA PRATAP SINGH ,
Download Full PDF Pages: 99-118 | Views: 446 | Downloads: 122 | DOI: 10.5281/zenodo.4286944
Abstract
Rank based measures of stability based on random effects of wheat genotypes for the first year of study, Sis measures identified G11, G12, G1, G22 would be stable yield. Corrected yield measures CSis selected G12, G17, G19,G21, G22genotypes as possessing for stable yield. NPi(s) identified G4, G12, G19, G22 as desirable genotypes for this zone. Kendall ’ s coefficient of concordanceexpressed dependence among measures for ranking of wheat genotypes. Association analysis observed positive correlations of Sis, CSis & NPi(s) with other measures. Biplot analysis observed largest cluster comprised of CSD, CCV, Si1, SD, Si5, Si7 CSi1, CSi2, CSi3, CSi4, CSi5, CSi6, CSi7 Z1, Z2measures. Fixed effects of genotypes, measures Sis found G2, G11, G21, G22 as suitable genotypes. Values of CSis identified G3, G9, G21, G22as compared to G3, G11, G19, G22 by NPi(s) measures. Positive correlations exhibited by Sis, CSis,NPi(s) with values of other measures. Values of Kendall coefficient observed dependence among ranking of genotypes . Biplot graphical analysis seen affinity of CV with NPi(2), NPi(3),NPi(4) Si3, Si6 & CSi3i n graphical analysis.Measures settled for G1, G2, G5, G7 genotypes as per random effects for second year of study (2017-18).G1, G5, G7 by CSis values whereas NPi(s) settledG1, G2,G5, G7 genotypes of stable performance. Measures Sis, CSis, NPi(s) exhibited direct relationships with other rank based measures. Larger consisted of Si1, Si2, Si3, Si5, Si7 ,CCV, CSD, NPi(1), Si7 ,CSi1, CSi2, CSi3, CSi4, CSi5, CSi6 measures in biplot graphical analysis.Wheat genotypesG1, G4, G5, G7 identified bySis measures considering BLUE estimates whereas G1, G5 ,G7 favoured byCSis. Least values ofNPi(s)settled for G1, G5,G4. Direct relations expressed by Sis, CSis & NPi(s) measures. Larger cluster among four groups comprised of large cluster of Si1, Si2 , Si4, Si5, Si7 ,CCV, CSD, NPi(1),CSi1, CSi2, CSi3, CSi4, CSi5, CSi6 CSi7 measures as last group.
Keywords
BLUP, BLUE, Si(s), CSi(s), NPi(s), Coefficient of concordance, Biplot analysis
References
i. Ahmadi, J., Vaezi, B., Shaabani, A., Khademi, K., Ourang, S., & Pour-Aboughadareh, A. 2015. Non-parametric Measures for Yield Stability in Grass Pea (Lathyrus sativus L.) Advanced Lines in Semi Warm Regions. Journal of Agricultural Science and Technology 17:1825-1838.
ii. Edicleide, M. da S. , Elaine, W. L. P. N. , José, M. da C. , Anânkia de O. R. , Glauber, H de S. N. and Fernando A. S. de A. 2019. Genotype x environment interaction, adaptability and stability of ‘Piel de Sapo’ melon hybrids through mixed models. Crop Breeding and Applied Biotechnology 19(4): 402-411.
iii. Farshadfar, E., Mahmudi, N. and Sheibanirad, A.2014. Nonparametric methods for interpreting genotype×environment interaction in bread wheat genotypes. Journal of Biodiversity & Environmental Sciences 4: 55-62.
iv. Gabriel de M. C. G., Regina, L.F.G. , Ângela, C. de A. L. and Paulo, F. de M.J. V. 2020. Adaptability and yield stability of soybean genotypes by REML/BLUP and GGE Biplot. Crop Breeding and Applied Biotechnology 20(2): e282920217.
v. Huehn, M. 1990a. Non-parametric measures of phenotypic stability. Part 1: Theory. Euphytica 47:189-194.
vi. Huehn, M. 1990b. Non-parametric measures of phenotypic stability: Part 2. Application. Euphytica 47:195-201
vii. Igrejas, G., Branlard, G. 2020. The Importance of Wheat. In: Wheat Quality For Improving Processing And Human Health, Igrejas G, Ikeda T, Guzmán C (eds), pp1-17 Springer, Cham.
viii. Karimizadeh, R. , Mohammadi M. , Sabaghnia, N. and Shefazadeh, M.K.2012. Using Huehn’s nonparametric stability statistics to Investigate Genotype × Environment interaction. Notulae Botanicae Horti Agrobotanici Cluj-Napoca 40:293-301
ix. Khalili, M., and Pour-Aboughadareh, A. 2016. Parametric and non- parametric measures for evaluating yield stability and adaptability in barley doubled haploid lines. Journal of Agricultural Science and Technology 18: 789–803.
x. Kilic, H., M. Akcura and Aktaş, H. 2010. Assessment of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in multi-environments. Not. Bot. Horti. Agrobo., 38, 271-279.
xi. Mohammadi, R., Farshadfarar E. and Amri, A .2016. Comparison of rank-based stability statistics for grain yield in rainfed durum wheat. New Zealand Journal of Crop & Horticulture Science 44: 25–40.
xii. Mohammadi, R. and Amri, A. 2008. Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica 159: 419-432.
xiii. Mortazavian, S. M. M. and Azizinia, S.2014. Nonparametric stability analysis in multi-environment trial of canola. Turkish Journal Field Crops 19(1): 108-117.
xiv. Nassar, R . and Huehn, M .1987. Studies on estimation of phenotypic stability: tests of significance for non-parametric measures of phenotypic stability. Biometric 43: 45- 53.
xv. Piepho, H.P. and Lotito S. 1992. Rank correlation among parametric and nonparametric measures of phenotypic stability. Euphytica 64: 221–225.
xvi. Pour-Aboughadareh, A., Yousefian, M., Moradkhani, H., Poczai P., Siddique, K. H. M. 2019. STABILITYSOFT: A new online program to calculate parametric and non- parametric stability statistics for crop traits. Applications in Plant Sciences 7(1): e1211
xvii. Sabaghnia, N., Karimizadeh, R. and Mohammadi, M .2012. The use of corrected and uncorrected nonparametric stability measurements in Durum wheat multi-environmental Trials. Spanish Journal of Agricultural Research 10: 722-730
xviii. Smith, A.B., Borg, L.M., Gogel, B.J. Cullis, B. R.2019. Estimation of Factor Analytic Mixed Models for the Analysis of Multi-treatment Multi-environment Trial Data. Journal of Agricultural, Biological, and Environmental Statistics 24: 573–588 .
xix. Sousa, A.M.C.B., Silva, V.B., Lopes, A.C.A., Ferreira, G. R.L. and Carvalho L.C.B. 2020. Prediction of grain yield, adaptability, and stability in landrace varieties of lima bean (Phaseolus lunatus L.) Crop Breeding and Applied Biotechnology 20: e295120115
xx. Souza, T.J.F., Rocha M.M., Damasceno, S. K.J., Bertini C.H.C.M., Silveira, L.M., Sousa R.R. and Sousa J.L.M. 2019. Simultaneous selection for yield, adaptability, and genotypic stability in immature cowpea using REML/ BLUP. Pesquisa Agropecuária Brasileira 54: 1-9.
xxi. Thennarasu, 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
xxii. Vaezi, B., A. Pour-Aboughadareh, A. Mehraban, T. Hossein-Pour, R. Mohammadi, M. A, and Dorri, M. 2018. The use of parametric and non- parametric measures for selecting stable and adapted barley lines. Archives of Agronomy and Soil Science 64: 597–611
xxiii. Van, E. F.A., Bustos K.D.V. and Malosetti M. 2016. What should students in plant breeding know about the statistical aspects of genotype x environment interactions? Crop Science 56: 2119-2140.
Cite this Article: