Bayesian AMMI applied to food-type soybean multi-environment trials

• Autor(a) principal: Freiria,Gustavo Henrique
• Data de Publicação: 2020
• Outros Autores: Gonçalves,Leandro Simões Azeredo, Zeffa,Douglas Mariani, Lima,Wilmar Ferreira, Fonseca Júnior,Nelson da Silva, Prete,Cássio Egídio Cavenaghi, Fonseca,Inês Cristina de Batista
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista ciência agronômica (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000400417
• topic: Glycine max; Bayesian inference; Genotype - environment; Functional food; Grain yield

ABSTRACT A complicating factor for the selection of plant strains is the influence of a genotype-environment (GE) interaction. The Bayesian approach is a tool to increase the efficiency of adaptability and stability methodologies. In this context, the objective of this study was to evaluate the linear and bi-linear parameters of the additive main effects and multiplicative interaction (AMMI) analysis using the Bayesian approach for selection of food-type soybean genotypes in multi-environment trials. The grain yields of five lipoxygenase-free lines intended for human consumption of from the soybean breeding program of the Londrina State University and two commercial standards (BRS 257 and BMX Potência RR) were evaluated in four counties of the State of Paraná, Brazil, in the 2014/15, 2015/16 and 2016/17 growing seasons. Of the evaluated lines, only UEL 110 and UEL 122 had positive posterior genotypic effects, exceeding a probability of 95% against the commercial standard BRS 257. Only lines UEL 115 and UEL 123 did not contribute significantly to the GE interaction. Lines UEL 110 and UEL 122 proved adaptable to the largest number of environments with significant GE interaction and are therefore promising for the development of new food-type soybean cultivars. The use of AMMI1 (PC1 vs. effects genotypes) showed results for the stability of genotypes similar to AMMI2 (PC1 vs PC2), allowing a direct selection by the biplot for productivity and stability.