Application of mixed models for the assessment genotype and environment interactions in cotton (Gossypium hirsutum) cultivars in Mozambique

Abstract

In the process of introducing cotton cultivars, it is essential to assess their productive behavior for different environments for which they will be recommended. Knowledge of the magnitude of the genotype interaction with environment allows the evaluation of the stability and adaptability of genotypes where one intends to introduce them, in addition to enabling the evaluation of the production potential and possible limitations of each environment. The study was conducted to determine the productivity, genotypic adaptability and genotypic stability of nine cotton cultivars (Gossypium hirsutum) in Mozambique, from 2004 to 2010 growing seasons. The genotypic stability and genotypic adaptability were assessed by Residual Maximum Likelihood (REML) and predict breeding values using Best Linear Unbiased Prediction (BLUP) methodology. The cultivars ISA 205, STAM 42 and REMU 40 showed superior productivity when they were selected by the Harmonic Mean of Genotypic Values (HMGV) criterion in relation to others. In turn, the cultivars CA 222, STAM 42 and ISA-205 were superior when selected by the Relative Performance of Genotypic Values (RPGV) and Harmonic Mean of the Relative Performance of Genotypic Values (HMRPGV). The cultivars CA 324 had the lower values for all criterions above. The cultivars CA 222 and STAM 42 will be the most recommended for farmers in cotton-growing regions and for the Cotton Breeding Program of Mozambique.

Assessment of Stability for Seed Cotton Yield of Cotton Genotypes Across Different Environmental Conditions of Sindh Province

“…This study showed that some varieties had superior performance over a single environment and little performance in others, which shows change in their average performance over multi-environments and revealed significant G×E interaction. Similar trends were observed by (Riaz et al, 2013;Maleia et al, 2010;Moiana et al, 2014;de Carvalho et al, 2015;Pretorius et al, 2015) who evaluated cotton cultivars in various environmental experiments in Pakistan, Mozambique, South Africa and Brazil. Performance of genotypes at various sites is presented in (Table 3).…”

Usefulness of the HMRPGV method for simultaneous selection of upland cotton genotypes with greater fiber length and high yield stability

“…These values are lower than those observed in other studies on cotton crop (Souza et al, 2006;Suinaga et al, 2006;Hoogerheide et al, 2007;Silva Filho et al, 2008;Moiana et al, 2014;Carvalho et al, 2015a). According to Cruz et al (2014), for continuously distributed phenotypic traits, CV e values lower than 20% reflect excellent experimental accuracy.…”

“…However, its application in cotton is still scarce and restricted to a single study by Moiana et al (2014). In the present study, we aimed to use mixed models to select cotton genotypes that simultaneously have longer fiber lengths, high fiber yield, and phenotypic stability in these traits.…”

Research Article Adaptability and stability of cotton cultivars (Gossypium hirsutum L. race latifolium H.) using factor analytic model

“…Although the AMMI method is recognized for allowing the simultaneous study of stability and adaptability, in a single approach, separating pattern of noise, it has the inherent limitations of fixed effects models, such as difficulty in dealing with heteroscedastic data set. Moiana et al (2014), on the other hand, using REML/BLUP methodologies (mixed models) to evaluate adaptability and harmonic means to evaluate stability founds genotypes presenting stability and adaptability for Mozambique. Interestingly, the methodology used by these authors is for ranking of the predictions of genotypes and do not evaluate separately the stability and adaptability since the harmonic means of genotypic values is correlated with marginal BLUPs.…”

“…The last one is often considered more important to MET in plant breeding, since it has impact on the genotypic classification and the selection. The method used in this study allows detailed analysis in this direction, as well as several advantages when compared to traditional methods of analysis as the AMMI fixed effects model and even conventional mixed models such as those used in Maleia et al (2010), Moiana et al (2014) and Naveed et al (2006).…”