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.

Evaluation of genotype x environment interactions in cotton using the method proposed by Eberhart and Russell and reaction norm models.

Cotton produces one of the most important textile fibers of the world and has great relevance in the world economy. It is an economically important crop in Brazil, which is the world's fifth largest producer. However, studies evaluating the genotype x environment (G x E) interactions in cotton are scarce in this country. Therefore, the goal of this study was to evaluate the G x E interactions in two important traits in cotton (fiber yield and fiber length) using the method proposed by Eberhart and Russell (simple linear regression) and reaction norm models (random regression). Eight trials with sixteen upland cotton genotypes, conducted in a randomized block design, were used. It was possible to identify a genotype with wide adaptability and stability for both traits. Reaction norm models have excellent theoretical and practical properties and led to more informative and accurate results than the method proposed by Eberhart and Russell and should, therefore, be preferred. Curves of genotypic values as a function of the environmental gradient, which predict the behavior of the genotypes along the environmental gradient, were generated. These curves make possible the recommendation to untested environmental levels.

Genetic diversity among cotton cultivars in two environments in the State of Mato Grosso.

Using commercial cultivars to compose crossing blocks in cotton is a promising strategy, because these materials have desirable agronomic and technological characteristics. The objective of this study was to evaluate the genetic diversity among 16 cotton cultivars cultivated in two environments in the State of Mato Grosso, the largest national producer, using agronomical and technological traits. There was significant effect to cultivars for all traits, while genotype x environment interaction was significant only for average boll weight, short fiber index, and maturity of fibers. Therefore, because of the presence of genotype x environment interaction for three traits, we chose to study genetic diversity among cotton cultivars separately in each environment and investigate the interaction impact on the diversity among genotype pairs. Based on agronomical and technological performance and genetic diversity among cultivars in both environments, the most promising cross involves FM 910 and LD CV 02. We also observed that lint percentage and average boll weight presented a higher discrimination capacity in both environments.