Soybean productivity, stability, and adaptability through mixed model methodology
Jeniffer Santana Pinto Coelho Evangelista, Rodrigo Silva Alves, Marco Antônio Peixoto, Marcos Deon Vilela de Resende, Paulo Eduardo Teodoro, Felipe Lopes da Silva, Leonardo Lopes Bhering
Abstract
ABSTRACT: The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genetic selection. Thus, the present study aimed to estimate genetic parameters and to compare different selection strategies in the context of mixed models for soybean breeding. For this, data referring to the evaluation of 30 genotypes in 10 environments, regarding the grain yield trait, were used. The variance components were estimated through restricted maximum likelihood (REML) and genotypic values were predicted through best linear unbiased prediction (BLUP). Significant effects of genotypes and G×E interaction were detected by the likelihood ratio test (LRT). Low genotypic correlation was obtained across environments, indicating complex G×E interaction. The selective accuracy was very high, indicating high reliability. Our results showed that the most productive soybean genotypes have high adaptability and stability.