Ajuste de peso para ovinos deslanados em crescimento

• Ano de defesa: 2020
• Autor(a) principal: Herbster, Caio Julio Lima
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Peso de corpo vazio; Modelos matemáticos; Ganho de peso; Validação cruzada; Regressão
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/56305

The objective of the study was to establish the relationships between body weight (BW), fasting BW (FBW), empty BW (EBW), and between average daily gain (ADG), and empty BW gain (EBWG) for hair sheep in growing and finishing phases in tropical climates. Databases were obtained from 32 studies, for a total of 1145 observations; there were 3 sex classes (non-castrated male, castrated male and female), and two feeding systems (pasture and feedlot). The FBW (kg), EBW and EBWG (kg/day) were estimated according to linear regression. A random coefficient model was adopted, considering the study as a random effect and including the possibility of covariance between the slope and the intercept. It was not possible to test the effect of the production system due to the smaller number of studies and because the fact that each study contained only one type of feeding system. The coefficients obtained from the linear regression of the FBW against the BW, EBW against the FBW and EBWG against the ADG did not differ between sex class (P > 0.05) and genotype (P > 0.05). The equations generated to estimate FBW; EBW; and EBWG are as follows: FBW= -0.5470 (±0.2025) + 0.9313(± 0.019) × BW; EBW= - 1.4944 (±0.3639) + 0, 8816 (±0.018) × FBW; and EBWG= 0.906 (±0.019) × ADG, respectively. The small biases found in the bootstrap analysis for the intercepts were -0.00639 and -0.0000003 for Equations 1 and 2, respectively. The small biases in the slopes of 0.000279, 0.00014 and -0.000309 for Equations 1, 2 and 3, respectively, suggested that the variables were consistent and sufficient for predicting the FBW, EBW and EBWG. The low root mean squared error ( values found in the cross-validation confirmed the reliability of these equations. The average correlation r and R2 between the predicted and observed values of each model were higher (r=0.97 and R2=0.94) for the FBW, EBW and EBWG predictor models. Considering a sheep with a BW of 30 kg and a 100 g ADG, the estimated FBW, EBW and EBWG calculated using the generated equations are 27, 22.65 and 0.090 kg, respectively.