S0049-3848(14)00284-9

Original Article

Adaptability Measures for Wheat Genotypes Evaluated under Northern Hills Zone of Country for Irrigated Timely Sown Conditions

Year: 2020 | Month: March | Volume 13 | Issue 1

References (23)

1.Baretta, D., Nardino, M., Carvalho, I.R., Oliveira, A.C., Souza, V.Q. and Maia, L.C. 2016. Performance of Maize Genotypes of Rio Grande do Sul using Mixed Models. Cientifica, 44: 403-411.

View at Google Scholar

2.Crespo-Herrera, L.A., Crossa J., Huerta-Espino J., Autrique E., Mondal, S. and Velu, G. 2017. Genetic yield gains in CIMMYT’s International Elite Spring Wheat Yield Trials by modeling the genotype × environment interaction.Crop Sci., 57: 789–801.

View at Google Scholar

3.Cullis, B.R., Jefferson P., Thompson R. and Smith A.B. 2014. Factor analytic and reduced animal models for the investigation of additive genotype by environment interaction in outcrossing plant species with application to a pinus radiata breeding program. Theor. Appl. Genet., 127: 2193–2210.

View at Google Scholar

4.de Pelegrin, A.J., Carvalho, I.R., Nunes, A.C.P., Demari, G.H., Szareski, V.J., Barbosa, M.H., da Rosa, T.C., Ferrari, M., Nardino, M., dos Santos, O.P., de Resende, M.D.V., de Souza, V.Q., de Oliveira, A.C. and da Maia, L.C. 2017. Adaptability, Stability and Multivariate Selection by Mixed Models. American Journal of Plant Sciences, 8: 3324- 3337.

View at Google Scholar

5.Diego, B., Maicon N., Ivan R.C., Antonio C. de O., Velci Q. de S. and Luciano C. da M. .2016. Performance of maize genotypes of Rio Grande do Sul using mixed models. Científica, Jaboticabal, 44(3): 403-411.

View at Google Scholar

6.Elesandro, B., Giovani B., Lindolfo S., Leomar G. W., Thiago D., Matheus G.S. and Sergio, V.M. 2017. Statistical methods to study adaptability and stability of wheat genotypes. Bragantia, Campinas, 76(1): 1-10.

View at Google Scholar

7.Friesen, L.F., Brule-Babel A.L., Crow, G.H. and Rothenburger, P.A. 2016. Mixed model and stability analysis of spring wheat genotype yield evaluation data from Manitoba, Canada. Can. J. Plant Sci., 96(2): 305–320

View at Google Scholar

8.Gogel, B.J., Smith A.B. and Cullis, B.R . 2018. Comparison of a one and two-stage mixed model analysis of Australia’s National Variety Trial Southern Region wheat data. Euphytica., 214(2): 44-64.

View at Google Scholar

9.Kleinknecht, K., Laidig, F., Piepho, H.P. and Möhring, J. 2011. Best linear unbiased prediction (BLUP): Is it beneficial in official variety performance trials? Biuletyn Oceny Odmian, 33: 21–33.

View at Google Scholar

10.Mendes, F.F., Guimarães L.J.M., Souza J.C., Guimarães, P.E.O., Pacheco, C.A.P., Machado, J.R. de A, Meirelles, W.F., Silva, A.R. da and Parentoni, S. 2012. Adaptability and stability of maize varieties using mixed model methodology. Crop Breeding and Applied Biotechnology, 12(2): 111-117.

View at Google Scholar

11.Moiana, L.D., Vidigal Filho, P.D., Gonçalves-Vidigal, M.C. and Maleia, M.P. 2014. Application of mixed models for the assessment genotype and environment interactions in cotton (Gossypium hirsutum) cultivars in Mozambique. Afr. J. Biotechnology, 13: 1985-1991.

View at Google Scholar

12.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.

View at Google Scholar

13.Nuvunga, J.J., Oliveira, L.A., Silva, C.P., Pamplona, A.K.A., Silva, A.Q., Moura, E.G., Maleia, M.P. and Balestre, M. 2018. Adaptability and stability of cotton cultivars (Gossypium hirsutum L. race latifolium H.) using factor analytic model. Genet. Mol. Res., 17(1): 1-10.

View at Google Scholar

14.Oliveira, I.J. de, Atroch, A.L., Dias, M.C., Guimarães, L.J. and Paulo, E. de O.G. 2017. Selection of corn cultivars for yield, stability and adaptability in the state of Amazonas, BrazilPesq. Agropec. Bras., Brasília, 52(6): 455-463.

View at Google Scholar

15.Piepho, H.P., Möhring, J., Melchinger, A.E. and Büchse, A. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica, 161: 209-228.

View at Google Scholar

16.Resende, M.D.V. and Duarte, J.B. 2007. Precision and quality control in variety trials. Pesquisa Agropecuária Tropical, 37: 182-194.

View at Google Scholar

17.Santos, E.A., Viana, A.P., Freitas, J.C.O., Rodrigues, D.L., Tavares, R.F., Paiva, C.L. and Souza, M.M. 2015. Genotype selection by REML/BLUP methodology in a segregating population from na interspecific Passiflora spp. crossing. Euphytica, 204(1): 1-11.

View at Google Scholar

18.Silva, P. da., Bisognin D.A., Locatelli A.B. and Storck, L. 2014. Adaptability and stability of corn hybrids grown for high grain yield. Acta Scientiarum. Agronomy, 36: 175-181.

View at Google Scholar

19.Smith, A.B. and Cullis B.R. 2018. Plant breeding selection tools built on factor analytic mixed models for multienvironment trial data. Euphytica, 214(8): 143-161.

View at Google Scholar

20.Tadege, M.B., Utta, H.Z. and Aga, A.A. 2014. Association of statistical methods used to explore genotype x environment interaction (GEI) and cultivar stability. African Journal of Agricultural Research, 9: 2231-2237

View at Google Scholar

21.Torres, F.E., Teodoro, P.E., Sagrillo, E., Ceccon, G. and Correa, A.M. 2015. Genotype × Environment Interaction in Semiprostrade Cowpea Genotypes via Mixed Models. Bragantia, 74: 255-260.

View at Google Scholar

22.Verardi, C.K., Resende M.D.Z.V., Costa, R.B. and Gonçalves P.S. 2009. Adaptabilidade e estabilidade da produção de borracha e seleção em progênies de seringueira. Pesquisa Agropecuária Brasileira, 44: 1277-1282.

View at Google Scholar

23.Yan, W. and Kang, M.S. 2003. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists. CRC Press. Boca Raton, FL.

View at Google Scholar

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