Dissimilaridade de ambientes no melhoramento do algodoeiro

• Ano de defesa: 2015
• Autor(a) principal: Libério Filho, Adeone
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Lavras; Programa de Pós-Graduação em Genética e Melhoramento de Plantas; UFLA; brasil; Departamento de Biologia
• Palavras-chave em Português: GGE biplot; Genótipos; Algodão; Dissimilaridade; Ambientes; Genotypes; Cotton; Dissimilarity; Environments; Melhoramento Vegetal
• Link de acesso: http://repositorio.ufla.br/jspui/handle/1/10843

This work was conducted with the objective of evaluating environmental dissimilarity, and phenotypic stability and adaptability of cotton genotypes (Gossypium hirsutum L.) by means of the GGE biplot method, using data from Value of Cultivation and Use (VCU) trials. The cotton production in kg/ha was used as response variable. The trials were conducted in the states of Goiás (GO), Bahia (BA) and Mato Grosso (MT), Brazil. The data were obtained by means of the evaluation of thirty cotton genotypes in two harvests, 2012/2013 and 2013/2014, and six locations/environments: Trindade-GO/E1, Luís Eduardo Magalhães-BA/E2, Poxoréo-MT/E3, Barreiras-BA/E4, Correntina-BA/E%, Pedra Preta-MT/E6. The data were first submitted to individual and joint analyses of variance, and, subsequently, to graphical analyses with GGE, using the “Discrimination vs Representability”; Who won Where”; “Mean vs Stability”; “Ideal Environment”; “Ideal Genotype” graphs to capture the proposed objective. The formation of two mega-environments, I and II, occurred, in which E3 and E6 were similar, E2 is the ideal environment for selecting superior genotypes. Regarding the genotypes, G17 was to most stable, ideal for recommendation for all six environments. G19 presented high performance, however, it is nor stable, presenting specific adaptation, and can be recommended for mega-environment II. However, the decision of attitudes regarding environment elimination and the formation of mega-environments requires more experiment repeatability.