Author(s)

Alan Junior de Pelegrin¹, Ivan Ricardo Carvalho¹, Andrei Caíque Pires Nunes², Gustavo Henrique Demari¹, Vinicíus Jardel Szareski¹, Mauricio Horbach Barbosa³, Tiago Corazza da Rosa¹, Mauricio Ferrari¹, Maicon Nardino¹, Osmarino Pires dos Santos¹, Marcos Deon Vilela de Resende², Velci Queiróz de Souza⁴, Antonio Costa de Oliveira¹, Luciano Carlos da Maia¹

¹Federal University of Pelotas, Center of Genomics and Plant Breeding, Avenida Eliseu Maciel, Capao do Leao, Brazil.
²Federal University of Vicosa, Center of Exact and Technological Sciences, Vicosa, Brazil.
³Federal University of Santa Maria, Frederico Westphalen Campus, Frederico Westphalen, Brazil.
⁴Federal University of Pampa, Campus Dom Pedrito, Bagé, Brazil.

The aim of this work was to estimate the adaptability and stability of grain yield per hectare and percentage of crude protein of maize grains combined in an index, and to establish a multicharacter selection through mixed models based on an objective character and 15 auxiliary traits. The trials were conducted in the 2013/2014 agricultural year in four growing environments of the Rio Grande do Sul, BR state. The experimental design was randomized blocks arranged in a factorial scheme, being four growing sites × 15 single cross maize hybrids, arranged in three repetitions. The genotypic index, composed by the grain yield and the crude protein percentage in the grains, is the best selection strategy to achieve maize superior genotypes. The multivariate genotypes selection, considering grain yield and crude protein, is efficient. The genotypes FORMULA TL^®, AS1656PRO^®, P30F53Hx^®, LG6304YG^® and 30F53 are more adapted and stable for grain yield and percentage of crude protein, in the conditions of this study. The mixed models were efficient to employ the multicharacter selection and to contribute for maize genetic breeding.

Plant Breeding, Zea mays L., Phenotypic Index, Genetic Parameters, Multicharacter

de Pelegrin, A. , Carvalho, I. , Nunes, A. , Demari, G. , Szareski, V. , Barbosa, M. , da Rosa, T. , Ferrari, M. , Nardino, M. , dos Santos, O. , de Resende, M. , de Souza, V. , de Oliveira, A. and da Maia, L. (2017) Adaptability, Stability and Multivariate Selection by Mixed Models. American Journal of Plant Sciences, 8, 3324-3337. doi: 10.4236/ajps.2017.813224.