Estrategia informático-analítica para generar indicadores de eficiencia reproductiva en tambos

La obtención de indicadores reproductivos y productivos de establecimientos lecheros a escala regional puede realizarse a través de la combinación de la información que se releva automáticamente durante el monitoreo regular tanto de variables de la producción como del manejo reproductivo. El volumen de datos que se generan a partir de controles lecheros y de sistemas para la gestión de las preñeces y partos en un tambo es cuantiosa. Para combinar los dos tipos de información es necesario explicitar el objetivo de la concatenación de los distintos tipos de datos. En este trabajo ilustramos una estrategia de análisis de datos de controles lecheros (1.465.011) e historia reproductiva (1.112.319) provenientes del seguimiento (Año 2007-2008) de 291 tambos fundamentalmente de la Provincia de Córdoba y de la zona centro y sur de Santa Fe. Las herramientas estadísticas aplicadas luego de unir y depurar ambas bases fueron: curvas de sobrevida de Kaplan-Meier, regresión de riesgo proporcionales de Cox y regresión logística múltiple. Los resultados sugieren que, en relación al objetivo de preñar las vacas lo antes posibles, los indicadores días abiertos y la tasa de preñez acumulada a los 100 días son de relevante información para evaluar la eficiencia reproductiva de un tambo.To obtain regional productive and reproductive indicators from dairy herds we can use and combine the automatic generate information of the milk tests and the regular reproductive monitoring, and management information. Normally, the amount of data in a herd is very large and hard to handle because of milk test and the regular reproduction monitoring. So, before match the two kind of information we need think very well the objectives. In this paper we illustrate an analysis strategy of milk tests (1,465,011) and reproductive management data (1,112,319) from 291 dairy herds. These herds were followed for two years (2997-2008) and are mainly from Cordoba and from the central and south area of Santa Fe. After cleaning and joining the two data sets we performed three statistical analyses and these were: Kaplan-Meier survival curves, Cox proportional hazard ratio, and multiple logistic regression. Following the objective of gets pregnant as soon as possible the cows, the results suggest that days open and 100 days cumulative pregnancy rate are good indicators of reproductive efficiency of dairy herds.Sociedad Argentina de Informática e Investigación Operativ

Fitting milk production curves through nonlinear mixed models

The aim of this work was to fit and compare three non-linear models (Wood, Milkbot and diphasic) to model lactation curves from two approaches: with and without cow random effect. Knowing the behaviour of lactation curves is critical for decision-making in a dairy farm. Knowledge of the model of milk production progress along each lactation is necessary not only at the mean population level (dairy farm), but also at individual level (cow-lactation). The fits were made in a group of high production and reproduction dairy farms; in first and third lactations in cool seasons. A total of 2167 complete lactations were involved, of which 984 were first-lactations and the remaining ones, third lactations (19 382 milk yield tests). PROC NLMIXED in SAS was used to make the fits and estimate the model parameters. The diphasic model resulted to be computationally complex and barely practical. Regarding the classical Wood and MilkBot models, although the information criteria suggest the selection of MilkBot, the differences in the estimation of production indicators did not show a significant improvement. The Wood model was found to be a good option for fitting the expected value of lactation curves. Furthermore, the three models fitted better when the subject (cow) random effect was considered, which is related to magnitude of production. The random effect improved the predictive potential of the models, but it did not have a significant effect on the production indicators derived from the lactation curves, such as milk yield and days in milk to peak.Fil: Piccardi, Mónica Belén. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Area de Estadística y Biometría; ArgentinaFil: Macchiavelli, Raúl. Universidad de Puerto Rico; Puerto RicoFil: Funes, Ariel Capitaine. DAIRYTECH; ArgentinaFil: Bó, Gabriel A.. Universidad Nacional de Villa María; Argentina. Instituto de Reproducción Animal Córdoba; ArgentinaFil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Area de Estadística y Biometría; Argentin