Adaptability of cotton (Gossypium hirsutum) genotypes analysed using a Bayesian AMMI model
Paulo Eduardo Teodoro A , Camila Ferreira Azevedo B , Francisco José Correia Farias C , Rodrigo Silva Alves B , Leonardo de Azevedo Peixoto B , Larissa Pereira Ribeiro B D , Luiz Paulo de Carvalho C and Leonardo Lopes Bhering BA Federal University of Mato Grosso do Sul (UFMS/CPCS), 79560-000 Chapadão do Sul, MS, Brazil.
B Federal University of Viçosa (UFV), 36570-900 Viçosa, MG, Brazil.
C National Cotton Research Center, Embrapa Cotton (Embrapa - CNPA), 58428-095 Campina Grande, PB, Brazil.
D Corresponding author. Email: larissa.uems@gmail.com
Crop and Pasture Science 70(7) 615-621 https://doi.org/10.1071/CP18318
Submitted: 1 July 2018 Accepted: 21 June 2019 Published: 23 July 2019
Abstract
Cotton (Gossypium spp.) provides ~90% of the world’s textile fibre. The aim of this study was to use the principal additive effects and multiplicative interaction (AMMI) model under the Bayesian approach to recommend cotton genotypes for the Central-West region of Brazil. Eight trials with upland cotton genotypes were conducted during the 2008–09 harvest in the State of Mato Grosso, Brazil. The experiment included a randomised block design with 16 genotypes. The genotypes were evaluated for fibre yield, length and strength. Chains were simulated via the Markov chain Monte Carlo method with 300 000 iterations for the parameters of the Bayesian AMMI model. From the chains generated, the first 20 000 burn-in observations were discarded and samples were taken by jumping every 20 observations (thin). Bayesian analysis provided additional results to those obtained by the frequentist approach, highlighting the credibility regions in the biplot for the genotypic and environmental scores. Bayesian AMMI model allowed identification of a genotype that can be widely recommended; this genotype has genotypic values above the overall mean for the three evaluated traits and did not contribute to the genotype × environment interactions observed in these traits. In addition, adaptability of genotypes to specific environments was observed, which makes it possible to capitalise the positive effect of the genotype × environment interaction.
Additional keywords: genetic selection, MCMC.
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