Diversidade genética entre genótipos de algodoeiro visando ampliação da variabilidade

Miranda, Melissa Cristina de Carvalho

doi:10.14393/ufu.di.2019.1284

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…In general, commercial genotypes had the best averages, with the exception of elongation, which shows that the variability needs to be explored between these genotypes (Table 1), due to the responsiveness of PROMALG genotypes to the environment. For the dendrogram, the cut was made considering the abrupt change in level (Miranda 2019). With this it is possible to observe the formation of four distinct groups (Figure 3).…”

Section: Resultsmentioning

confidence: 99%

Use of computational intelligence in the genetic divergence of colored cotton plants

Cardoso¹,

Medeiros²,

Carvalho³

et al. 2021

Biosci. J.

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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.

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Section: Resultsmentioning

confidence: 99%

Use of computational intelligence in the genetic divergence of colored cotton plants

Cardoso¹,

Medeiros²,

Carvalho³

et al. 2021

Biosci. J.

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show abstract

“…According to Miranda (2019), the quality characteristics of the fiber, although quantitative, are less influenced by the environment in relation to the agronomic variables, as proven by its limits for the variation coefficients lower and with consequent higher precisions. Penna (2005) also proposes that, in general, the influence of the environment is high for productivity, average for the production components and low for the physical characteristics of the fiber.…”

Section: Contentsmentioning

confidence: 99%

Productivity and quality of cotton fiber in different planting seasons

et al. 2020

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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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Diversidade genética entre genótipos de algodoeiro visando ampliação da variabilidade

Cited by 2 publications

References 20 publications

Use of computational intelligence in the genetic divergence of colored cotton plants

Use of computational intelligence in the genetic divergence of colored cotton plants

Productivity and quality of cotton fiber in different planting seasons

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