Mega-environment analysis of maize breeding data from Brazil

• Autor(a) principal: Pereira, Francielly de Cássia
• Data de Publicação: 2022
• Outros Autores: Ramalho, Magno Antonio Patto, Resende Junior, Marcio Fernando Ribeiro de, Von Pinho, Renzo Garcia
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/53334
• topic: Genotype × environment interaction; Unbalance data; Hybrid recommendation process; Variance components; Plant breeding; Interação genótipo x ambiente; Dados não balanceados; Processo de recomendação híbrido; Componentes de variação; Melhoramento de plantas

The development and recommendation of single cross maize hybrids (SH) to be used in extensive land areas (mega-environments), and in different crop seasons requires many experiments under numerous environmental conditions. The question we asked is if the data from these multi-environment experiments are sufficient to identify the best hybrid combinations. The aim of this study was to critically analyze the phenotype data of experiments of yield, established by a large seed producing company, under a high level of imbalance. Data from evaluation of 2770 SH were used from experiments conducted over four years, involving the first and second crop seasons, in 50 locations of different years and regions of Brazil. Different types of analysis were carried out and genetic and non-genetic components were estimated, with emphasis on the different interactions of the SH with the environments. Results showed that the coincidence of common hybrids in these experiments is normally small. The estimates of the correlations between of the hybrids coinciding in the environments two by two is of low magnitude. The hybrid × crop season interaction was always expressive; however, the interactions of hybrids and other environmental variables were also important. Under these conditions, alternatives were discussed for making with the information obtained from the experiments, can be more efficient on the process to obtain new hybrids by companies.