Diallel analysis for technological traits in upland cotton.

Final cotton quality is of great importance, and it depends on intrinsic and extrinsic fiber characteristics. The objective of this study was to estimate general (GCA) and specific (SCA) combining abilities for technological fiber traits among six upland cotton genotypes and their fifteen hybrid combinations, as well as to determine the effective genetic effects in controlling the traits evaluated. In 2015, six cotton genotypes: FM 993, CNPA 04-2080, PSC 355, TAM B 139-17, IAC 26, and TAMCOT-CAMD-E and fifteen hybrid combinations were evaluated at the Experimental Station of Embrapa Algodão, located in Patos, PB, Brazil. The experimental design was a randomized block with three replications. Technological fiber traits evaluated were: length (mm); strength (gf/tex); fineness (Micronaire index); uniformity (%); short fiber index (%), and spinning index. The diallel analysis was carried out according to the methodology proposed by Griffing, using method II and model I. Significant differences were detected between the treatments and combining abilities (GCA and SCA), indicating the variability of the study material. There was a predominance of additive effects for the genetic control of all traits. TAM B 139-17 presented the best GCA estimates for all traits. The best combinations were: FM 993 x TAM B 139-17, CNPA 04-2080 x PSC 355, FM 993 x TAMCOT-CAMD-E, PSC 355 x TAM B 139-17, and TAM B 139-17 x TAMCOT-CAMD-E, by obtaining the best estimates of SCA, with one of the parents having favorable estimates for GCA.

Cotton genotypes selection through artificial neural networks.

Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.

Heterosis and correlation in interspecific and intraspecific hybrids of cotton.

Interspecific and intraspecific hybrids show varying degrees of heterosis for yield and yield components. Yield-component traits have complex genetic relationships with each other. To determine the relationship of yield-component traits and fiber traits with seed cotton yield, six lines (Bt. CIM-599, CIM-573, MNH-786, CIM-554, BH-167, and GIZA-7) and three test lines (MNH-886, V4, and CIM-557) were crossed in a line x tester mating design. Heterosis was observed for seed cotton yield, fiber traits, and for other yield-component traits. Heterosis in interspecific hybrids for seed cotton yield was more prominent than in intraspecific hybrids. The interspecific hybrid Giza-7 x MNH-886 had the highest heterosis (114.77), while among intraspecific hybrids, CIM-554 x CIM-557 had the highest heterosis (61.29) for seed cotton yield. A major trait contributing to seed cotton yield was bolls/plant followed by boll weight. Correlation studies revealed that bolls/plant, boll weight, lint weight/boll, lint index, seed index, lint/seed, staple length, and staple strength were significantly and positively associated with seed cotton yield. Selection based on boll weight, boll number, lint weight/boll, and lint index will be helpful for improving cotton seed yield.