Using the distributed-delay model to predict egg production in laying hens
ABSTRACT: using mathematical models to characterize and estimate egg production curves is of great importance for assessing the productive efficiency of hens. These models can be used in identifying and modeling real-time factors affecting animal production and implementing corrective measures to mi...
- Autores:
-
Galeano Vasco, Luis Fernando
Cerón Muñoz, Mario Fernando
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2013
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/8341
- Acceso en línea:
- http://hdl.handle.net/10495/8341
- Palabra clave:
- Rights
- openAccess
- License
- Atribución-NoComercial-CompartirIgual 2.5 Colombia (CC BY-NC-SA 2.5 CO)
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|
dc.title.spa.fl_str_mv |
Using the distributed-delay model to predict egg production in laying hens |
dc.title.alternative.spa.fl_str_mv |
Uso del modelo de distribución con retardo para predecir la producción de huevos en gallinas ponedoras |
title |
Using the distributed-delay model to predict egg production in laying hens |
spellingShingle |
Using the distributed-delay model to predict egg production in laying hens |
title_short |
Using the distributed-delay model to predict egg production in laying hens |
title_full |
Using the distributed-delay model to predict egg production in laying hens |
title_fullStr |
Using the distributed-delay model to predict egg production in laying hens |
title_full_unstemmed |
Using the distributed-delay model to predict egg production in laying hens |
title_sort |
Using the distributed-delay model to predict egg production in laying hens |
dc.creator.fl_str_mv |
Galeano Vasco, Luis Fernando Cerón Muñoz, Mario Fernando |
dc.contributor.author.none.fl_str_mv |
Galeano Vasco, Luis Fernando Cerón Muñoz, Mario Fernando |
description |
ABSTRACT: using mathematical models to characterize and estimate egg production curves is of great importance for assessing the productive efficiency of hens. These models can be used in identifying and modeling real-time factors affecting animal production and implementing corrective measures to minimize its effect. Objective: we compared the ability to model and adjust the egg production curve in hens using the distributed-Delay model versus the Adams-Bell and Lokhorst models. Methods: 225 records of weekly production of Hy Line Brown (62 data), Lohmann LSL (54 data), Isa Brown (54 data), and Lohmann Brown (55 data) were used. All analyzed flocks were raised at Hacienda La Montaña Farm, owned and managed by the University of Antioquia (Colombia). Models used were Adams-Bell, Lokhorst and Delay; all were validated and contrasted by Durbin-Watson statistic, MAD, determination (R2) and correlation (r) coefficients. Results: the Delay and Lokhorst models resulted in R2 values greater than 0.8 and r-values greater than 0.9 (p < 0.01). For the Lohmann Brown curve, the Adams-Bell model had the lowest R2 value (0.81), while the Lokhorst and Delay models resulted in the highest R2 value for the Isa Brown curve (1.0). The Delay model fit the curve (28 and 40 for the k parameter; 63 and 64 for the DEL parameter). The Hy Line Brown curve presented a high number of irregularities, generating great difficulty for adjustment with the evaluated models. Conclusion: Delay and Lokhorst models are efficient for predicting egg production curve of the bird strains tested. Unlike the Adams-Bell and Lokhorst models, goodness of fit of the Delay model could be increased by including physiological relationships and supply/demand of resources as input variables, which would allow the model to fit the fluctuations observed in the production curves. |
publishDate |
2013 |
dc.date.issued.none.fl_str_mv |
2013 |
dc.date.accessioned.none.fl_str_mv |
2017-09-26T20:12:58Z |
dc.date.available.none.fl_str_mv |
2017-09-26T20:12:58Z |
dc.type.spa.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.redcol.spa.fl_str_mv |
https://purl.org/redcol/resource_type/ART |
dc.type.local.spa.fl_str_mv |
Artículo de investigación |
format |
http://purl.org/coar/resource_type/c_2df8fbb1 |
status_str |
publishedVersion |
dc.identifier.citation.spa.fl_str_mv |
Galeano-Vasco, L. Cerón-Muñoz M, Rodríguez D, Cotes JM. Using the distributed-delay model to predict egg production in laying hens. Rev. Colomb. Cienc. Pecu. 2013;26(4):270-279. |
dc.identifier.issn.none.fl_str_mv |
0120-0690 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10495/8341 |
dc.identifier.eissn.none.fl_str_mv |
2256-2958 |
identifier_str_mv |
Galeano-Vasco, L. Cerón-Muñoz M, Rodríguez D, Cotes JM. Using the distributed-delay model to predict egg production in laying hens. Rev. Colomb. Cienc. Pecu. 2013;26(4):270-279. 0120-0690 2256-2958 |
url |
http://hdl.handle.net/10495/8341 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofjournalabbrev.spa.fl_str_mv |
Rev. Colomb. Cienc. Pecu. |
dc.rights.*.fl_str_mv |
Atribución-NoComercial-CompartirIgual 2.5 Colombia (CC BY-NC-SA 2.5 CO) |
dc.rights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.uri.*.fl_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/co/ |
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http://purl.org/coar/access_right/c_abf2 |
dc.rights.creativecommons.spa.fl_str_mv |
https://creativecommons.org/licenses/by-nc-sa/4.0/ |
rights_invalid_str_mv |
Atribución-NoComercial-CompartirIgual 2.5 Colombia (CC BY-NC-SA 2.5 CO) https://creativecommons.org/licenses/by-nc-sa/2.5/co/ http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by-nc-sa/4.0/ |
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openAccess |
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application/pdf |
dc.publisher.spa.fl_str_mv |
Universidad de Antioquia, Facultad de Ciencias Agrarias |
dc.publisher.place.spa.fl_str_mv |
Medellín, Colombia |
institution |
Universidad de Antioquia |
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Galeano Vasco, Luis FernandoCerón Muñoz, Mario Fernando2017-09-26T20:12:58Z2017-09-26T20:12:58Z2013Galeano-Vasco, L. Cerón-Muñoz M, Rodríguez D, Cotes JM. Using the distributed-delay model to predict egg production in laying hens. Rev. Colomb. Cienc. Pecu. 2013;26(4):270-279.0120-0690http://hdl.handle.net/10495/83412256-2958ABSTRACT: using mathematical models to characterize and estimate egg production curves is of great importance for assessing the productive efficiency of hens. These models can be used in identifying and modeling real-time factors affecting animal production and implementing corrective measures to minimize its effect. Objective: we compared the ability to model and adjust the egg production curve in hens using the distributed-Delay model versus the Adams-Bell and Lokhorst models. Methods: 225 records of weekly production of Hy Line Brown (62 data), Lohmann LSL (54 data), Isa Brown (54 data), and Lohmann Brown (55 data) were used. All analyzed flocks were raised at Hacienda La Montaña Farm, owned and managed by the University of Antioquia (Colombia). Models used were Adams-Bell, Lokhorst and Delay; all were validated and contrasted by Durbin-Watson statistic, MAD, determination (R2) and correlation (r) coefficients. Results: the Delay and Lokhorst models resulted in R2 values greater than 0.8 and r-values greater than 0.9 (p < 0.01). For the Lohmann Brown curve, the Adams-Bell model had the lowest R2 value (0.81), while the Lokhorst and Delay models resulted in the highest R2 value for the Isa Brown curve (1.0). The Delay model fit the curve (28 and 40 for the k parameter; 63 and 64 for the DEL parameter). The Hy Line Brown curve presented a high number of irregularities, generating great difficulty for adjustment with the evaluated models. Conclusion: Delay and Lokhorst models are efficient for predicting egg production curve of the bird strains tested. Unlike the Adams-Bell and Lokhorst models, goodness of fit of the Delay model could be increased by including physiological relationships and supply/demand of resources as input variables, which would allow the model to fit the fluctuations observed in the production curves.RESUMEN: los modelos matemáticos permiten caracterizar y estimar las curvas de producción de huevos, siendo de gran importancia para la evaluación de la eficiencia productiva de las gallinas, posibilitando identificar factores que afecten la producción animal y aplicar correctivos para minimizar su efecto. Objetivo: se comparó la capacidad para ajustar la curva de producción de huevos utilizando el modelo de distribución con retardo (Delay) y los modelos Adams-Bell y Lokhorst. Métodos: se utilizaron 225 datos de registros semanales de producción de cuatrolíneas: Hy Line Brown (62 datos), Lohmann LSL (54 datos), Isa Brown (54 datos), y Lohmann Brown (55 datos). Los lotes analizados pertenecieron a la Hacienda La Montaña, de la Universidad de Antioquia (Colombia). Los modelos fueron validados y contrastados con MAD, el coeficiente de determinación (R2) y de correlación (r), y el estadístico Durbin-Watson. Resultados: los modelos Delay y Lokhorst presentaron valores de R2 superiores a 0,8 y valores de r superiores a 0,9 (p < 0,01). El modelo Adams-Bell para la curva Lohmann Brown obtuvo el menor valor de r (0,81), mientras que los modelos Delay y Lokhorst presentaron el valor más alto de R2 (1,0) para la curva de Isa Brown. El modelo Delay se ajustó a la curva, con valores de 28 y 40 para el parámetro k, y de 63 y 64 para el parámetro DEL. La curva de la línea Hy line Brown presentó gran cantidad de irregularidades (altibajos), generando mayor dificultad para ser ajustada con los modelos evaluados. Conclusión: los modelos Delay y Lokhorst son eficientes para predecir la curva de producción de huevos de aves de las estirpes probadas. La bondad de ajuste del modelo Delay podría aumentarse mediante la inclusión de otras variables de entrada tales como las relaciones fisiológicas, relaciones de oferta y demanda de recursos, y variables ambientales, posibilitando que el modelo Delay se ajuste a las fluctuaciones de las curvas.application/pdfengUniversidad de Antioquia, Facultad de Ciencias AgrariasMedellín, Colombiainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_2df8fbb1https://purl.org/redcol/resource_type/ARTArtículo de investigaciónhttp://purl.org/coar/version/c_970fb48d4fbd8a85Atribución-NoComercial-CompartirIgual 2.5 Colombia (CC BY-NC-SA 2.5 CO)info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/co/http://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by-nc-sa/4.0/Using the distributed-delay model to predict egg production in laying hensUso del modelo de distribución con retardo para predecir la producción de huevos en gallinas ponedorasRev. Colomb. Cienc. Pecu.Revista Colombiana de Ciencias Pecuarias270279234CC-LICENSElicense_urllicense_urltext/plain; charset=utf-849http://bibliotecadigital.udea.edu.co/bitstream/10495/8341/2/license_url4afdbb8c545fd630ea7db775da747b2fMD52license_textlicense_texttext/html; charset=utf-80http://bibliotecadigital.udea.edu.co/bitstream/10495/8341/3/license_textd41d8cd98f00b204e9800998ecf8427eMD53license_rdflicense_rdfapplication/rdf+xml; charset=utf-80http://bibliotecadigital.udea.edu.co/bitstream/10495/8341/4/license_rdfd41d8cd98f00b204e9800998ecf8427eMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://bibliotecadigital.udea.edu.co/bitstream/10495/8341/5/license.txt8a4605be74aa9ea9d79846c1fba20a33MD55ORIGINALGaleanoLuis_2013_UsingDistributedLayingHens.pdfGaleanoLuis_2013_UsingDistributedLayingHens.pdfArtículo de investigaciónapplication/pdf593330http://bibliotecadigital.udea.edu.co/bitstream/10495/8341/1/GaleanoLuis_2013_UsingDistributedLayingHens.pdfc1eb585a99d573787026e2eca58d66baMD5110495/8341oai:bibliotecadigital.udea.edu.co:10495/83412021-05-21 20:29:43.94Repositorio Institucional Universidad de Antioquiaandres.perez@udea.edu.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 |