Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differen...

Autores
Bonamy, Martin; de Iraola, Julieta Josefina; Prando, Alberto José; Baldo, Andres; Giovambattista, Guillermo; Rogberg Muñoz, Andres
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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 analysis were 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. Results. Longitudinal mixed models show differences in growing curves parameters between grazing conditions, that were not detected by cross sectional analysis. Differences (P < 0.05) in first derivative of growth curves (daily gain) until 289 days were observed between treatments, being AG 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 less animals in the trial, improving animal welfare and reducing investigation costs.
Fil: Bonamy, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentina
Fil: de Iraola, Julieta Josefina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentina
Fil: Prando, Alberto José. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias; Argentina
Fil: Baldo, Andres. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias; Argentina
Fil: Giovambattista, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentina
Fil: Rogberg Muñoz, Andres. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentina
Materia
PUBERTY
HEIFER
GRAZING
REARING
GROWTH
LONGITUDINAL DATA
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/106043
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/106043
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differential puberty onsetBonamy, Martinde Iraola, Julieta JosefinaPrando, Alberto JoséBaldo, AndresGiovambattista, GuillermoRogberg Muñoz, AndresPUBERTYHEIFERGRAZINGREARINGGROWTHLONGITUDINAL DATAhttps://purl.org/becyt/ford/4.3https://purl.org/becyt/ford/4Background. 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 analysis were 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. Results. Longitudinal mixed models show differences in growing curves parameters between grazing conditions, that were not detected by cross sectional analysis. Differences (P < 0.05) in first derivative of growth curves (daily gain) until 289 days were observed between treatments, being AG 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 less animals in the trial, improving animal welfare and reducing investigation costs.Fil: Bonamy, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; ArgentinaFil: de Iraola, Julieta Josefina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; ArgentinaFil: Prando, Alberto José. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias; ArgentinaFil: Baldo, Andres. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias; ArgentinaFil: Giovambattista, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; ArgentinaFil: Rogberg Muñoz, Andres. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; ArgentinaJohn Wiley & Sons Ltd2019-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/106043Bonamy, Martin; de Iraola, Julieta Josefina; Prando, Alberto José; Baldo, Andres; Giovambattista, Guillermo; et al.; Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differential puberty onset; John Wiley & Sons Ltd; Journal of the Science of Food and Agriculture; 100; 2; 10-2019; 714-7200022-5142CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1002/jsfa.10072info:eu-repo/semantics/altIdentifier/doi/10.1002/jsfa.10072info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:58:09Zoai:ri.conicet.gov.ar:11336/106043instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:58:09.465CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differential puberty onset
title Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differential puberty onset
spellingShingle Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differential puberty onset
Bonamy, Martin
PUBERTY
HEIFER
GRAZING
REARING
GROWTH
LONGITUDINAL DATA
title_short Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differential puberty onset
title_full Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differential puberty onset
title_fullStr Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differential puberty onset
title_full_unstemmed Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differential puberty onset
title_sort Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differential puberty onset
dc.creator.none.fl_str_mv Bonamy, Martin
de Iraola, Julieta Josefina
Prando, Alberto José
Baldo, Andres
Giovambattista, Guillermo
Rogberg Muñoz, Andres
author Bonamy, Martin
author_facet Bonamy, Martin
de Iraola, Julieta Josefina
Prando, Alberto José
Baldo, Andres
Giovambattista, Guillermo
Rogberg Muñoz, Andres
author_role author
author2 de Iraola, Julieta Josefina
Prando, Alberto José
Baldo, Andres
Giovambattista, Guillermo
Rogberg Muñoz, Andres
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv PUBERTY
HEIFER
GRAZING
REARING
GROWTH
LONGITUDINAL DATA
topic PUBERTY
HEIFER
GRAZING
REARING
GROWTH
LONGITUDINAL DATA
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.3
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv 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 analysis were 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. Results. Longitudinal mixed models show differences in growing curves parameters between grazing conditions, that were not detected by cross sectional analysis. Differences (P < 0.05) in first derivative of growth curves (daily gain) until 289 days were observed between treatments, being AG 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 less animals in the trial, improving animal welfare and reducing investigation costs.
Fil: Bonamy, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentina
Fil: de Iraola, Julieta Josefina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentina
Fil: Prando, Alberto José. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias; Argentina
Fil: Baldo, Andres. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias; Argentina
Fil: Giovambattista, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentina
Fil: Rogberg Muñoz, Andres. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentina
description 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 analysis were 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. Results. Longitudinal mixed models show differences in growing curves parameters between grazing conditions, that were not detected by cross sectional analysis. Differences (P < 0.05) in first derivative of growth curves (daily gain) until 289 days were observed between treatments, being AG 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 less animals in the trial, improving animal welfare and reducing investigation costs.
publishDate 2019
dc.date.none.fl_str_mv 2019-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/106043
Bonamy, Martin; de Iraola, Julieta Josefina; Prando, Alberto José; Baldo, Andres; Giovambattista, Guillermo; et al.; Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differential puberty onset; John Wiley & Sons Ltd; Journal of the Science of Food and Agriculture; 100; 2; 10-2019; 714-720
0022-5142
CONICET Digital
CONICET
url http://hdl.handle.net/11336/106043
identifier_str_mv Bonamy, Martin; de Iraola, Julieta Josefina; Prando, Alberto José; Baldo, Andres; Giovambattista, Guillermo; et al.; Application of longitudinal data analysis allows to detect differences in pre‐breeding growing curves of 24‐month calving Angus heifers under two pasture‐based system with differential puberty onset; John Wiley & Sons Ltd; Journal of the Science of Food and Agriculture; 100; 2; 10-2019; 714-720
0022-5142
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1002/jsfa.10072
info:eu-repo/semantics/altIdentifier/doi/10.1002/jsfa.10072
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv John Wiley & Sons Ltd
publisher.none.fl_str_mv John Wiley & Sons Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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