Abstract. This research aimed to present prediction models for daily total milk yield and percentage of total fat from the partial productions in the morning (a.m.) and the afternoon (p.m.). This research was carried out with three dairy herds in Antioquia. 182 Holstein dairy cows milked twice a day were sampled monthly over a year. Recorded data included: information on entering time, milk yield, fat percentage, parity, days in milk, calving season and milking intervals. Predictive models were created from multiple regression analysis. The effects of duration of days in milk, milking interval, birth number, fat percentage and the quadratic terms of the previously mentioned effects were significant for the percentage of fat per day and the total milk production per day. Fat percentage and total milk production are affected by different environmental factors. In order to have more accurate estimates of production, it is necessary to consider adjustment factors for the known effects. / Resumen. En esta investigacion se pretenden plantear modelos de prediccion para la produccion de leche total y el porcentaje de grasa total dia, a partir de las producciones parciales de la manana (a.m.) y de la tarde (p.m.) La investigacion se llevo a cabo en tres hatos lecheros del departamento de Antioquia. Se realizo muestreo de leche mensual a 182 vacas Holstein en dos ordenos durante un ano. Se capturo informacion de hora de entrada al ordeno, produccion de leche, porcentaje de grasa, numero de partos, dias en lactancia, epoca de parto e intervalo entre ordenos. Los modelos de prediccion fueron realizados a partir de un analisis de regresion multiple. Los efectos de dias en lactancia, intervalo entre ordenos, numero de partos, porcentaje de grasa y la distribucion cuadratica de los efectos mencionados anteriormente fueron significativos sobre el porcentaje de grasa dia y la produccion total dia. El porcentaje de grasa y la produccion total de leche estan afectados por diferentes factores ambientales y para tener estimativas mas precisas de produccion es necesario considerar factores de ajuste sobre los efectos conocidos.