NÚMERO DE AMOSTRAGENS DA PRODUÇÃO DE LEITE PARA ESTIMATIVA DA PRODUÇÃO MÉDIA DIÁRIA DE LEITE EM VACAS CHAROLÊS

Abstract

The objective of the experiment was to evaluate different number of collections of milk yield to prediction of models to estimate average daily milk yield of Charolais beef cows until 217 days of lactation. The eight evaluations of milk yield were taken from 27 cows, by direct method through milking, at 14, 42, 70, 98, 126, 154, 182 days and weaning (217 days). For the selection of multiple linear regression models, with the objective to estimate the average daily milk yield according to number of milk collections, Stepwise procedure was used. The data were submitted to residual analysis by tests of heterocedasticity of variance (?² statistic), normality (W statistic of Shapiro-Wilk) and diagnosis of outliers (three observations were excluded from original n=30), beyond multicolinearity diagnosis. For validation of the selected regression models, the prediction error sum of squares statistic (PRESS), was used. The selected days of milk yield collections were: day 98 for 1 collection; days 70 and 98 for 2 collections; days 14, 70, and 98 for 3 collections; days 14, 70, 98 and 154 for 4 collections; days 14, 70, 98, 154 and 182 for 5 collections; days 14, 42, 70, 98, 154 and 182 for 6 collections and days 14, 42, 70, 98, 126, 154 and 182 for 7 collections. The adjusted R² for models with 1; 2; 3; 4; 5; 6 and 7 collections were, respectively, 0.72; 0.87; 0.97; 0.97; 0.99; 0.99 and 0.99. All the predicted models with different number of collections of milk yield were satisfactory to estimate average daily milk yield. In a possibility of to execute various collections of milk yield during lactation period, three milk collections chosen in strategic dates during lactation period were sufficient to estimate with high precision the average daily milk yield. KEY WORDS: Lactation, multiple linear regression, selection of independent variables, residual analysis, techniques of validation of regression models.