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- Title
Adaptability of cotton (Gossypium hirsutum) genotypes analysed using a Bayesian AMMI model.
- Authors
Teodoro, Paulo Eduardo; Azevedo, Camila Ferreira; Farias, Francisco José Correia; Alves, Rodrigo Silva; de Azevedo Peixoto, Leonardo; Ribeiro, Larissa Pereira; Paulo de Carvalho, Luiz; Bhering, Leonardo Lopes
- 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. Cotton is a crop of great industrial importance. However, cotton cultivars have different productivities and fibre qualities when submitted to different environments. In this work, we use modern statistical techniques to identify cotton cultivars that have stable behaviour for these characteristics throughout Brazilian environments
- Subjects
BRAZIL; MATO Grosso (Brazil : State); COTTON quality; GENOTYPE-environment interaction; MARKOV chain Monte Carlo; COTTON; GENOTYPES; MONTE Carlo method
- Publication
Crop & Pasture Science, 2019, Vol 70, Issue 7, p615
- ISSN
1836-0947
- Publication type
Article
- DOI
10.1071/CP18318