V.J. Szareski, I.R. Carvalho, K. Kehl, A.M. Levien, F. Lautenchleger, M.H. Barbosa, G.G. Conte, M. Peter, A.B.N. Martins, F.A. Villela, V.Q. de Souza, L.C. Gutkoski, T. Pedó, T.Z. Aumonde
Published: July 22, 2019
Genet. Mol. Res. 18(3): GMR18223
DOI: https://doi.org/10.4238/gmr18223
Cite this Article:
V.J. Szareski, I.R. Carvalho, K. Kehl, A.M. Levien, F. Lautenchleger, M.H. Barbosa, G.G. Conte, M. Peter, A.B.N. Martins, F.A. Villela, V.Q. de Souza, L.C. Gutkoski, T. Pedó, T.Z. Aumonde (2019). Genetic and phenotypic multi-character approach applied to multivariate models for wheat industrial quality analysis. Genet. Mol. Res. 18(3): GMR18223. https://doi.org/10.4238/gmr18223
About the Authors
V.J. Szareski, I.R. Carvalho, K. Kehl, A.M. Levien, F. Lautenchleger, M.H. Barbosa, G.G. Conte, M. Peter, A.B.N. Martins, F.A. Villela, V.Q. de Souza, L.C. Gutkoski, T. Pedó, T.Z. Aumonde
Corresponding Author
I.R. Carvalho
Email: carvalho.irc@gmail.com
ABSTRACT
We appled a genetic and phenotypic multi-character predicted approach to the use of the multivariate methods Additive Main effects and Multiplicative Interaction (AMMI) and Genotype Main Effects and Genotype Environment Interaction (GGE). The experiment was carried out in the agricultural crop year of 2016 in the state of Rio Grande do Sul, Brazil. The experimental design was a randomized block design, with 14 growing environments x five wheat genotypes arranged in three replications. The characters were falling number, gluten strengthand protein content, which were used to make multi-character the technological index of the industrial quality of the wheat grains and multi-character the technological index of the industrial quality of the wheat grains. Multi-character selection can be a useful tool for identifying genotypes and growing environments that maximize the industrial quality of wheat grain. The GGE method provides greater explicability of the effects of genotype x environment interaction based on multi-character selection. The multicharacter genetic approach predicted for the selection of the industrial quality of wheat grain results in reliable inferences in the indication of adaptability and stability for the AMMI method and for GGE.
Key words: AMMI, Best linear unbiased prediction, GGE, Restricted maximum likelihood, Selection index.