Soybean productivity, stability, and adaptability through mixed model methodology

Jeniffer Santana Pinto Coelho Evangelista,Leonardo Lopes Bhering,Felipe Lopes Da Silva,Marcos Deon Vilela De Resende,Marco Antônio Peixoto,Rodrigo Silva Alves,Paulo Eduardo Teodoro

doi:10.1590/0103-8478cr20200406

Jeniffer Santana Pinto Coelho Evangelista, Leonardo Lopes Bhering + Show 5 more

Open Access

https://doi.org/10.1590/0103-8478cr20200406

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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.

Highlights

Soybean [Glycine max (L.) Merrill] is the fourth most widely grown crop in the world and it is a source of oil, protein, and raw material for biodiesel production (SILVA et al, 2017)
The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genetic selection
The environment influences on the phenotypic expression, in order to provide the G×E interaction, is one of the biggest challenges that breeders deal with in plant breeding

Summary

INTRODUCTION

Soybean [Glycine max (L.) Merrill] is the fourth most widely grown crop in the world and it is a source of oil, protein, and raw material for biodiesel production (SILVA et al, 2017). The present work aimed to evaluate the applicability and efficiency of multiple-trait best linear unbiased prediction (BLUP; SOUZA et al, 2020) This procedure allows the simultaneous selection via the three parameters mentioned and presents the following advantages: (i) it considers the genotypic effects as random and provides genotypic and non-phenotypic adaptability and stability; (ii) it allows analyzes with unbalance data; (iii) it enables to use a non-orthogonal designs; (iv) it allows to deal with variance heterogeneity; (v) it allows to consider correlated errors within environments; (vi) it provides already discounted (penalized) genotypic values of the instability; (vii) it does not depend on the estimation and interpretation of other parameters; (viii) it eliminates the noise of the G×E interaction as it considers the heritability of these effects; (ix) it generates results on the magnitude or scale of the evaluated trait itself; and, (x) it allows to compute the genetic gain with the selection by the three attributes simultaneously (RESENDE, 2007). The present study aimed to estimate genetic parameters and to compare different selection strategies in the context of mixed models for soybean breeding

MATERIALS AND METHODS

RESULTS AND DISCUSSION

CONCLUSION