Capacidade combinatória de linhagens e seleção de híbridos de sorgo sacarino

Sweet sorghum figure as an alternative feedstock for ethanol production. The establishment of this culture in Brazilian production chain depends on the development of more productive and adapted cultivars. The aim of this study was to evaluate the general combining ability (GCA) of sweet sorghum lines and specific combining ability (SCA) of hybrid combinations as the agronomic and technological traits, and additionally to identify promising hybrid combinations for evaluation in advanced trials. Five restorer lines (R) and four male-sterile lines (A) were used in a partial cross diallel yielding 20 hybrids. The parental lines, hybrids and one check were evaluated in experiments carried out in a rectangular lattice design 5x6 with three replicates in two locations. The following traits were measured: flowering time, plant height, green mass yield, dry matter percentage, dry matter yield, juice extraction, total soluble solids content, sucrose content, purity, reducing sugars content, fiber content, sugars reducing total content, total recoverable sugars, hydrous ethanol, tons of per hectare, and ethanol production. There were differences between locations and genotypes for the traits. There was a significant effect of the genotype by environment interaction for most characters, except juice extraction, purity and reducing sugars content. There were a significant effect of GCA and SCA for most traits, indicating that additive and non-additive effects affect the phenotypic expression. Considering the effects of the GCA, the A line 201402B022-A, and R lines BRS 511, CMSXS643, and CMSXS646 were considered promising for exploration as parents in breeding programs of sweet sorghum in order to increase the ethanol production and the quality of the feedstock.The hybrids 201402B010-A x BRS 511, 201402B010-A x BRS 508, 201402B010-A x CMSXS646, 201402B022-A x BRS 511, 201402B022-A x CMSXS643, 201402B022-A x CMSXS646, 201402B022-A x CMSXS647 were the most promising for ethanol yield.

Componentes principais na elaboração de índices de provas de desempenho em bovinos

The principal component analysis assists the producers in making decision of which evaluated features must be maintained in performance tests indexes, according to the variation present in these animals evaluated. The objective in this study was to evaluate a set of characteristics measured in a performance test in semifeedlot cattle of the Simmental and Angus breeds, by means principal component analysis (PC), aim to identify the features that represent most of the phenotypic variation for preparation of indexes. It was used data from 39 Angus and 38 Simmental bulls from the Santa Éster farm, located in Silvianópolis - MG. The performance test period was from october 2014 to february 2015. The features evaluated in the test were: final weight (FW), average daily gain weight (GW), respiratory rate (RR), haircoat temperature (HT) and rectal (RT), hair number (HN), hair length (HL), hair thickness (HT), muscularity (MUSC), racial characteristics, angulation, reproductive and balance (BAL), height of the front and back, width and length of croup, body length, depth and heart girth, subcutaneous fat thickness and rump (FTR), loin eye area and marbling (MAR). It was used PRINCOMP from SAS program for procedure the PC analysis. It was found that of the 27 features evaluated, the first four PC for Simmental breed explained 74% total variation data. The four PC selected with the corresponding weighting coefficients formed the following index: (0.27 * FW) + (0.47 * MUSC) + (0.50 * HL) + (0.39 * HT). Since the characteristics related to the adaptability of great importance for the studied breed, it was decided to keep the index of evidence for the Angus breed, the feature hair number, because there is a feature that presented a great variability and occupied one of the first principal component. Thus, the Angus index was composed by five features, with 79% total variation data, resulting in the following formula: (0.26 * FW) + (0.33 * BAL) + (0.58 * MAR) - (0.43 * FTR) – (0.38 * HN). By the principal component analysis it was possible to minimize the features number to be evaluated on performance tests from that farm, making the animal selection rapidly and accurate.