The objective of this work was to analyze the genetic diversity using conventional methods and artificial neural networks among 12 colored fiber cotton genotypes, using technological characteristics of the fiber and productivity in terms of cottonseed and cotton fiber yield. The experiment was conducted in an experimental area located at Fazenda Capim Branco, belonging to the Federal University of Uberlândia, in the city of Uberlândia, Minas Gerais. Twelve genotypes of colored fiber cotton were evaluated, 10 from the Cotton Genetic Improvement Program (PROMALG): UFUJP - 01, UFUJP - 02, UFUJP - 05, UFUJP - 08, UFUJP - 09, UFUJP - 10, UFUJP - 11, UFUJP - 13, UFUJP - 16, UFUJP - 17 and two commercial cultivars: BRS Rubi (RC) and BRS Topázio (TC). The experimental design used was complete randomized block (CRB) with three replications. The following evaluations were carried out at full maturation: yield of cottonseed (kg ha-1) and the technological characteristics, which include, fiber length, micronaire, maturation, length uniformity, short fiber index, elongation and strength, using the HVI (High volume instrument) device. Genetic dissimilarity was measured using the generalized Mahalanobis distance and after obtaining the dissimilarity matrix, the genotypes were grouped using a hierarchical clustering method (UPGMA). A discriminant analysis and the Kohonen Self-Organizing Map (SOM) by Artificial Neural Networks (ANN’s) were performed through computational intelligence. SOM was able to detect differences and organize the similarities between accesses in a more coherent way, forming a larger number of groups, when compared to the method that uses the Mahalanobis matrix. It was also more accurate than the discriminant analysis, since it made it possible to differentiate groups more coherently when comparing their phenotypic behavior. The methods that use computational intelligence proved to be more efficient in detecting similarity, with Kohonen's Self-Organizing Map being the most adequate to classify and group cotton genotypes.

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.

Cotton is one of the main agricultural products produced in Brazil. With such a high demand in the market, it is necessary that the cotton cultivars present high productivity and fiber quality. In order to favor the expression of the potential of the genotypes, the cultivation must occur in climatic conditions that provide good development of the plants, being the sowing time a primordial factor for the good performance of the cotton plant. In order to establish an ideal sowing season for different cotton genotypes, the present study aimed to evaluate the best sowing season of cotton genotypes for the environment of Uberlândia (Minas Gerais State), aiming at productivity and fiber quality. The experiment was carried out in field conditions, in the 2016/2017 harvest in the experimental area located at Fazenda Capim Branco, in the city of Uberlândia, Minas Gerais State. A randomized complete block design (DBC) with four replications in a 4x7 factorial scheme was used: 4/12 sowing dates: 05/12, 19/12, 30/12, 13/01 and 7 genotypes. 5 strains of the breeding program of the Federal University of Uberlândia (UFU) and 2 commercial cultivars. The evaluated characteristics were: seed cotton yield, feather yield, micronaire index, maturity index, fiber length, uniformity of length, short fibers, resistance and elongation. It was concluded that the best sowing season for a high productivity was the one performed on 12/05/16, with emphasis on the UFUJP-Z genotype. For fiber quality, UFUJP-C showed the best results at the 12/19/16 sowing season.

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The genetic breeding of soybean aims to obtain productive genotypes, so it is necessary that the genetic components, environment and the interaction between them be understood. The G x E interaction is the differential behavior of the genotypes against environmental. The objective was to study the G x E interaction and analyze the adaptability and stability of soybean genotypes under natural rust infection without fungicide. The experiment was conducted in the Genetic Breeding Program of the Federal University of Uberlândia. Fourteen soybean genotypes were evaluated, with 10 lines developed by the UFU Program