Caracterização fenotípica de população segregante de arroz (Oryza sativa L.) para características de importância agronômica

Abstract

Rice (Oryza sativa L.) is a fundamental staple food in diet for almost four billion people and ranks as the second most cultivated cereal globally. Consequently, its production holds great significance for humanity. In the first chapter, the segregation patterns and heritability of traits of agronomic importance were investigated in F3 segregating families, derived from the cross between the rice cultivars BR-IRGA 409 and BR-IRGA 410. Five characters were evaluated: number of tillers per plant, plant height, number of panicles per plant, length of the largest panicle and weight of the largest panicle. Data distribution analysis revealed variable segregation patterns for each characteristic. Heritability estimates indicate substantial genetic control for the evaluated traits. Furthermore, the use of Generalized Linear Mixed Models (GLMM) allowed the analysis of data with different distribution patterns. In the second chapter, the same families in the F4 generation were used to evaluate the phenotypic correlation and linear regression between characters of agronomic importance (plant height, days to flowering, number of panicles, length of the largest panicle, number of full grains, number of empty grains and percentage of number of full grains). The results indicated that the character number of panicles maintains a positive correlation of intermediate magnitude with the characters length of the largest panicle, weight of the largest panicle and number of gains filled with the largest panicle, showing that the use of this variable allows the selection of the most productive families. The data also showed that the plant height trait maintains a positive correlation with the same variables. The heritability and segregation results suggest that, even between similar genotypes, it is possible to obtain offspring with phenotypic variation, useful for rice genetic improvement programs, and that, based on the identified correlations, some indirect selection strategies can be used.