Models for optimizing selection based on adaptability and stability of cotton genotypes

Marco Antônio Peixoto,Luiz Paulo Carvalho,Paulo Eduardo Teodoro,Leonardo Lopes Bhering,Rodrigo Silva Alves,Larissa Pereira Ribeiro Teodoro,Jeniffer Santana Pinto Coelho Evangelista,Francisco José Correa Farias

doi:10.1590/0103-8478cr20200530

Abstract

ABSTRACT: In multi-environment trials (MET), large networks are assessed for results improvement. However, genotype by environment interaction plays an important role in the selection of the most adaptable and stable genotypes in MET framework. In this study, we tested different residual variances and measure the selection gain of cotton genotypes accounting for adaptability and stability, simultaneously. Twelve genotypes of cotton were bred in 10 environments, and fiber length (FL), fiber strength (FS), micronaire (MIC), and fiber yield (FY) were determined. Model selection for different residual variance structures (homogeneous and heterogeneous) was tested using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance components were estimated through restricted maximum likelihood and genotypic values were predicted through best linear unbiased prediction. The harmonic mean of relative performance of genetic values (HMRPGV) were applied for simultaneous selection for adaptability, stability, and yield. According to BIC heterogeneous residual variance was the best model fit for FY, whereas homogeneous residual variance was the best model fit for FL, FS, and MIC traits. The selective accuracy was high, indicating reliability of the prediction. The HMRPGV was capable to select for stability, adaptability and yield simultaneously, with remarkable selection gain for each trait.

Highlights

Upland cotton (Gossypium hirsutum L.) is an herbaceous crop and is the most cultivated species worldwide for fiber production
Bayesian Information Criterion (BIC) is more conservative compared to Akaike Information Criterion (AIC), and its use is preferential to that of AIC when there is a strong preference for models of lower dimensionality (KASS et al, 2014)
Its consistence is related with the probability next to the unit (1) to select a true model among all models available (YANG, 2005), which reinforces it superiority in this case

Summary

Introduction

Upland cotton (Gossypium hirsutum L.) is an herbaceous crop and is the most cultivated species worldwide for fiber production. It provides over 90% of the world’s cotton. Its cultivation as an annual crop is widespread from south to north, from subtropical regions to temperate latitudes well over 30° (D’EECKENBRUGGE AND LACAPE, 2014). In this sense, the genotype × environment interaction (G × E) plays an essential role in genotypic expression and must be considered in the evaluation and selection of superior genotypes for cotton cultivation (MALOSETTI et al, 2013; VAN EEUWIJK et al, 2016; LI et al, 2017). To Approved 10.23.20 Returned CR-2020-0530.R2 by the author 12.06.2C0 iência

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