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

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.

Number of harvest years and selection for productivity, witches’ broom resistance, stability, and adaptability in cacao

“…(2020) and Peixoto et al. (2021). The selection of these biparental crosses will incorporate into the breeding program resilient genotypes, capable of adapting to micro‐ and macro‐environmental variations, which, ultimately, will promote greater security for producers.…”

“…This significance justifies the use of a methodology that capitalizes the GHI effects, since the objective was to elevate the selective accuracy and select the most stable and adaptable biparental crosses . The HMRPGV, which ranks the biparental crosses based on stability, adaptability, and productivity and/or resistance simultaneously, proved to be adequate to identify the genotypes that met these requirements, showing that those with greater genotypic values are also those with greater stability and adaptability, a fact also observed by Evangelista et al (2020) and Peixoto et al (2021). The selection of these biparental crosses will incorporate into the breeding program resilient genotypes, capable of adapting to micro-and macro-environmental variations, which, ultimately, will promote greater security for producers.…”

Genotype selection for fiber quality traits in cotton in the Brazilian Northeast

“…The variance estimates of the genotype × year interaction can be considered low and indicate that the different climatic conditions that occurred from one season to another in the same site were not sufficient to alter the ranking of the genotypes. Several studies, when estimating variances related to genotypic and environmental effects and their interaction using mixed models, have found a predominance of genotypic variance in the expression of total phenotypic variance (Carvalho et al., 2016; Farias et al., 2016; Peixoto et al., 2020; Peixoto et al., 2021).…”

“…Upland cotton ( Gossypium hirsutum L.) is an herbaceous crop and the most cultivated species worldwide for the production of fiber, providing more than 90% of the world's cotton (Peixoto et al., 2021). In addition to increasing yield, the major target of breeding programs for the agricultural crops, cotton breeding programs seek to improve fiber quality traits to meet the textile industry's requirements (Scarpin et al., 2022; Shahzad et al, 2019; Teodoro et al., 2019).…”

“…Several methodologies have been proposed in the last decades to recommend genotypes when G × E interaction is present (van Eeuwijk et al., 2016). Recently, the estimation of variance components by linear mixed models using the restricted maximum likelihood method (REML) and the prediction of genotypic values by best linear unbiased prediction (BLUP) of random effects are one of the main procedures used to investigate the G × E interaction in cotton (Carvalho et al., 2016; Farias et al., 2016; Liu et al., 2022; Peixoto et al., 2020; Peixoto et al., 2021).…”