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.
Colaborador(es): Bonamy, Martin. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Departamento de Producción Animal. Cátedra de Producción de Bovinos. La Plata, Buenos Aires, Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria (UNLP. IGEVET). La Plata, Buenos Aires, Argentina. CONICET - Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria (UNLP. IGEVET). La Plata, Buenos Aires, Argentina
| Iraola, Julieta Josefina de. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Departamento de Producción Animal. Cátedra de Producción de Bovinos. La Plata, Buenos Aires, Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria (UNLP. IGEVET). La Plata, Buenos Aires, Argentina. CONICET - Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria (UNLP. IGEVET). La Plata, Buenos Aires, Argentina | Prando, Alberto Juan. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Departamento de Producción Animal. Cátedra de Producción de Bovinos. La Plata, Buenos Aires, Argentina | Baldo, Andrés. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Departamento de Producción Animal. Cátedra de Producción de Bovinos. La Plata, Buenos Aires, Argentina | Giovambattista, Guillermo. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria (UNLP. IGEVET). La Plata, Buenos Aires, Argentina. CONICET - Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria (UNLP. IGEVET). La Plata, Buenos Aires, Argentina | Rogberg Muñoz, Andrés. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria (UNLP. IGEVET). La Plata, Buenos Aires, Argentina. CONICET - Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria (UNLP. IGEVET). La Plata, Buenos Aires, Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal. Buenos Aires, Argentina.
ISSN: 0022-5142.Tipo de material: Artículos y capítulos. Recurso electrónico.Tema(s): PUBERTY | HEIFER | GRAZING | REARING | GROWTH | LONGITUDINAL DATA | Recursos en línea: Haga clic para acceso en línea | LINK AL EDITOR
En: Journal of the science of food and agriculture vol.100, no.2 (2020), p.714–720, tbls., grafs.Resumen: 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.
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.
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