Estimação de parâmetros genéticos, fenotípicos e seleção simultânea para a identificação de progênies superiores de arroz de terras altas

• Autor(a) principal: Smiderle, Eder Cristian
• Data de Publicação: 2015
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFMT
• Texto Completo: http://ri.ufmt.br/handle/1/2108
• topic: Genética quantitativa; Melhoramento genético de plantas; Oryza sativa L.; Ìndices de seleção; Ganho com a seleção; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA; Quantitative genetics; Plant breeding; Oryza sativa L.; Selection indexes; Gain with selection

This study aimed to estimate the genetic and phenotypic parameters of the selected families of upland rice and to compare some selection indexes in order to obtain earlier lines with lower plant height, good resistance to neck blast and high grain yield. We evaluated 191 F2 progeny: 4 in the Family Income test (ERF) the breeding program of Embrapa Rice and Beans, plus five commercial cultivars used as checks, totaling 196 genotypes. The experiments were conducted in four environments during the 2012/13 crop in Sinop-MT Vilhena-RO and two planting dates in Santo Antônio de Goiás-GO. The design was a simple lattice 14 x 14. We evaluated the following characters: number of days to flowering, plant height, neck blast resistance and grain yield. It was found that there is variability among the genotypes for all traits. Estimates of the expected gain to the selection of the 20% best progenies showed significant values, especially for grain yield and resistance to neck blast for most environments. Gains estimated by the selection indices were also significant, but the indices (Z) and posts Sum, had the highest total earnings and good agreement of results, and achieve greater balance in earnings for the four characters simultaneously. The index (Z) was efficient simultaneous selection for the four characters, ease of obtaining, by the possibility of making the selection graphically and the feasibility of carrying the index variance analysis.