Estimados lectores, el sitio no está funcionando correctamente debido a que se encuentra en proceso la migración de la revista completa a la nueva plataforma
Sepan disculpar las molestias que esto pudiera ocasionarles.
Genotype-environment interactions, megaenvironments and winner genotypes and environments for soybean grain yield in Argentina. | Lúquez | Revista de la Facultad de Agronomía, La Plata

Genotype-environment interactions, megaenvironments and winner genotypes and environments for soybean grain yield in Argentina.

Julia Lúquez, M. Capurro, Luis Erazzú

Resumen


In Argentina, soybean [(Glycine max L.) Mer.] can be planted in a wide area. Currently, multienvironment trials (MET’s) for yield performance for cultivars of different Maturity Groups are conducted in three different agroecological regions: North, North Pampean and South Pampean. Analysis and interpretation of MET’s data related with genotype x environment interactions (GE) and selection of best genotypes have been made with analysis of variance and mean comparisons where GE interactions were not exploited. The objectives of this study were to identify megaenvironments and winner genotypes and environments using GGE biplot based on the site and genotype regression (SREG and GREG) models to exploit MET’s data sets from soybean regional trials. The GGE biplots display graphically the relationship among test environments, genotypes and GE interactions. Grain yield data of 19 soybean cultivars of Maturity Group long IV from three seasons (2005, 2007 and 2008) across 27 environments in the three agroecological regions in Argentina were analyzed. The GGE biplots based on the SREG model showed that yield grain performance of soybean cultivars was determined by environments and GE interactions. Practically, three megaenvironments were determined (27 environments were grouped here) suggesting useful cultivar specific adaptations (Principal Components 1 and 2 explained 70.1% of variation). The GGE biplots based on the GREG model showed that two megaenvironments concentrated all cultivars on 13 locations with the best grain yields (Principal Components 1 and 2 explained 87.8% of variation) in contrast with the test locations typically used. The utilization of this information, could lead to improve soybean cultivars evaluation in Argentina.

Palabras clave


Glycine max, GGE biplot, megaenvironments, GE interactions, grain yield

Texto completo:

PDF

Enlaces refback

  • No hay ningún enlace refback.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.