Using fuzzy logic to select coloured-fibre cotton genotypes based on adaptability and yield stability

Cotton (Gossypium hirsutum L.) is the world’s leading natural textile fibre and is grown in over 60 countries, including Brazil, where it is an important agricultural commodity. The cultivation area currently covers approximately one million hectares in Brazil and has expanded into every region of t...

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Bibliographic Details
Published in:	Acta scientiarum. Agronomy Vol. 43; p. e50530
Main Authors:	Cardoso, Daniel Bonifácio Oliveira, Oliveira, Lírian França, Souza, Gabriela Santana de, Garcia, Myllena Fernandes, Medeiros, Luiza Amaral, Faria, Priscila Neves, Cruz, Cosme Damião, Sousa, Larissa Barbosa de
Format:	Journal Article
Language:	English
Published:	Editora da Universidade Estadual de Maringá - EDUEM 01-01-2021 Eduem (Editora da Universidade Estadual de Maringá)
Subjects:	AGRONOMY computational intelligence; gossypium hirsutum; plant breeding computational intelligence Gossypium hirsutum plant breeding
Online Access:	Get full text
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Summary:	Cotton (Gossypium hirsutum L.) is the world’s leading natural textile fibre and is grown in over 60 countries, including Brazil, where it is an important agricultural commodity. The cultivation area currently covers approximately one million hectares in Brazil and has expanded into every region of the country, especially the Cerrado biome. Because of this expansion, it is necessary to analyse the influence of the environment on the genotype behaviour to optimize yields. Thus, the objective of this study was to compare fuzzy logic to traditional methods for selecting coloured-fibre cotton genotypes with high adaptability and yield stability. The experiment was conducted on the 2013/2014, 2014/2015, 2015/2016, and 2016/2017 crops of the Capim Branco farm at the Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil. The following methods were used to select genotypes for adaptability and stability: the Lin and Binns model, additive main effects and multiplicative interaction (AMMI) analysis and the Sugeno fuzzy logic controller. An interaction of the genotype with the environment that affected yield was detected. Environment 4 (the 2016/2017 crop) showed to the lowest genotype to environment interaction. The fuzzy logic approach showed agreement with AMMI and the nonparametric Lin and Binns method. The linguistic fuzzy logic used in the Sugeno fuzzy logic controller demonstrated the potential for selecting cotton genotypes in plant breeding programmes. The UFUJP-16 and UFUPJ-17 genotypes were adaptable, stable and showed promising yields within the tested environments. The fuzzy logic method was effective for estimating adaptability and stability.
ISSN:	1679-9275 1807-8621 1807-8621
DOI:	10.4025/actasciagron.v43i1.50530