Application of longitudinal data analysis allows to detect differences in pre - breeding growing curves of 24 - month calving Angus heifers under two pasture - based systems with differential puberty onset.

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BACKGROUND: Longitudinal data analysis contributes to detect differences in the growing curve by exploiting all the information involved in repeated measurements, allowing to distinguish changes over time within individuals, from differences in the baseline levels among groups. In this research, lon...

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Detalles Bibliográficos
Otros Autores: Bonamy, Martin, Iraola, Julieta Josefina de, Prando, Alberto Juan, Baldo, Andrés, Giovambattista, Guillermo, Rogberg Muñoz, Andrés
Formato: Artículo
Lenguaje:Inglés
Materias:
PUBERTY
HEIFER
GRAZING
REARING
GROWTH
LONGITUDINAL DATA
Acceso en línea:http://ri.agro.uba.ar/files/intranet/articulo/2020bonamy.pdf
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Aporte de:Registro referencial: Solicitar el recurso aquí
Biblioteca Central (FAUBA) de Universidad de Buenos Aires
Descripción
Sumario:BACKGROUND: Longitudinal data analysis contributes to detect differences in the growing curve by exploiting all the information involved in repeated measurements, allowing to distinguish changes over time within individuals, from differences in the baseline levels among groups. In this research, longitudinal and cross-sectional analysiswere compared to evaluate differences in growth in Angus heifers under two different grazing conditions, ad libitum (AG) and controlled (CG) to gain 0.5 kg day−1. RESULTS: Longitudinalmixed models show differences in growing curve parameters between grazing conditions, that were not detected by cross-sectional analysis. Differences (P smaller than 0.05) in first derivative of growth curves (daily gain) until 289 days were observed between treatments, AG being higher than CG. Correspondingly, pubertal heifer proportion was also higher in AG at the end of rearing (AG, 0.94; CG, 0.67). CONCLUSION: In longitudinal studies, the power to detect differences between groups increases by exploiting the whole information of repeated measures, modelling the relation between measurements performed on the same individual. Under a proper analysis, valid conclusion can be drawn with fewer animals in the trial, improving animal welfare and reducing investigation costs.
ISSN:0022-5142

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