Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken

Mathematical models provide valuable information for livestock improvement programmes. In this study, we evaluated the ability of five mathematical models (3P and 4P Gompertz, 3P and 4P logistic and neural network) to predict the growth of six tropically adapted dual purpose (TADP) chicken breeds (F...
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Autor*in:	Oludayo Michael Akinsola [verfasserIn] Emmanuel Babafunso Sonaiya [verfasserIn] Oladeji Bamidele [verfasserIn] Waheed Akinola Hassan [verfasserIn] Abdulmojeed Yakubu [verfasserIn] Folasade Olubukola Ajayi [verfasserIn] Uduak Ogundu [verfasserIn] Olayinka Olubunmi Alabi [verfasserIn] Oluwafunmilayo Ayoka Adebambo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	body weight mathematical models dual-purpose chickens neural network performance

Übergeordnetes Werk:	In: Journal of Applied Animal Research - Taylor & Francis Group, 2018, 49(2021), 1, Seite 158-166
Übergeordnetes Werk:	volume:49 ; year:2021 ; number:1 ; pages:158-166

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1080/09712119.2021.1915792

Katalog-ID:	DOAJ014528495

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allfields	10.1080/09712119.2021.1915792 doi (DE-627)DOAJ014528495 (DE-599)DOAJee2d99b779ab4bf78a001c8d09c664b4 DE-627 ger DE-627 rakwb eng SF600-1100 Oludayo Michael Akinsola verfasserin aut Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mathematical models provide valuable information for livestock improvement programmes. In this study, we evaluated the ability of five mathematical models (3P and 4P Gompertz, 3P and 4P logistic and neural network) to predict the growth of six tropically adapted dual purpose (TADP) chicken breeds (Fulani, FUNAAB Alpha, Kuroiler, Noiler, Sasso and Shika-Brown) under on-station and on-farm in Nigeria. Data for body weight were collected every 14 days from 1939 birds reared on-station, and every 28 days from 58,639 birds reared on-farm. Parameters used to evaluate the growth models were the adjusted coefficient of determination (AdjR2), Akaike’s information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The AdjR2 for Gompertz 3P was higher than or equal to the AdjR2 for logistics 3P, Gompertz 4P and logistics 4P but was equal to or lower than the AdjR2 for the neural network (NN) for all TADP chickens raised on-station. Based on the goodness-of-fit criteria, Gompertz 3P had the best predictive values (AdjR2 = 0.989–0.998) for TADP chickens raised on-station, while logistic 3P was the best-fit model for TADP chickens raised on-farm. In conclusion, non-linear models and NN models yielded a good fit with the age-weight data of TADP chickens on-station and on-farm. body weight mathematical models dual-purpose chickens neural network performance Veterinary medicine Emmanuel Babafunso Sonaiya verfasserin aut Oladeji Bamidele verfasserin aut Waheed Akinola Hassan verfasserin aut Abdulmojeed Yakubu verfasserin aut Folasade Olubukola Ajayi verfasserin aut Uduak Ogundu verfasserin aut Olayinka Olubunmi Alabi verfasserin aut Oluwafunmilayo Ayoka Adebambo verfasserin aut In Journal of Applied Animal Research Taylor & Francis Group, 2018 49(2021), 1, Seite 158-166 (DE-627)620142308 (DE-600)2541051-9 09741844 nnns volume:49 year:2021 number:1 pages:158-166 https://doi.org/10.1080/09712119.2021.1915792 kostenfrei https://doaj.org/article/ee2d99b779ab4bf78a001c8d09c664b4 kostenfrei http://dx.doi.org/10.1080/09712119.2021.1915792 kostenfrei https://doaj.org/toc/0971-2119 Journal toc kostenfrei https://doaj.org/toc/0974-1844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 49 2021 1 158-166
spelling	10.1080/09712119.2021.1915792 doi (DE-627)DOAJ014528495 (DE-599)DOAJee2d99b779ab4bf78a001c8d09c664b4 DE-627 ger DE-627 rakwb eng SF600-1100 Oludayo Michael Akinsola verfasserin aut Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mathematical models provide valuable information for livestock improvement programmes. In this study, we evaluated the ability of five mathematical models (3P and 4P Gompertz, 3P and 4P logistic and neural network) to predict the growth of six tropically adapted dual purpose (TADP) chicken breeds (Fulani, FUNAAB Alpha, Kuroiler, Noiler, Sasso and Shika-Brown) under on-station and on-farm in Nigeria. Data for body weight were collected every 14 days from 1939 birds reared on-station, and every 28 days from 58,639 birds reared on-farm. Parameters used to evaluate the growth models were the adjusted coefficient of determination (AdjR2), Akaike’s information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The AdjR2 for Gompertz 3P was higher than or equal to the AdjR2 for logistics 3P, Gompertz 4P and logistics 4P but was equal to or lower than the AdjR2 for the neural network (NN) for all TADP chickens raised on-station. Based on the goodness-of-fit criteria, Gompertz 3P had the best predictive values (AdjR2 = 0.989–0.998) for TADP chickens raised on-station, while logistic 3P was the best-fit model for TADP chickens raised on-farm. In conclusion, non-linear models and NN models yielded a good fit with the age-weight data of TADP chickens on-station and on-farm. body weight mathematical models dual-purpose chickens neural network performance Veterinary medicine Emmanuel Babafunso Sonaiya verfasserin aut Oladeji Bamidele verfasserin aut Waheed Akinola Hassan verfasserin aut Abdulmojeed Yakubu verfasserin aut Folasade Olubukola Ajayi verfasserin aut Uduak Ogundu verfasserin aut Olayinka Olubunmi Alabi verfasserin aut Oluwafunmilayo Ayoka Adebambo verfasserin aut In Journal of Applied Animal Research Taylor & Francis Group, 2018 49(2021), 1, Seite 158-166 (DE-627)620142308 (DE-600)2541051-9 09741844 nnns volume:49 year:2021 number:1 pages:158-166 https://doi.org/10.1080/09712119.2021.1915792 kostenfrei https://doaj.org/article/ee2d99b779ab4bf78a001c8d09c664b4 kostenfrei http://dx.doi.org/10.1080/09712119.2021.1915792 kostenfrei https://doaj.org/toc/0971-2119 Journal toc kostenfrei https://doaj.org/toc/0974-1844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 49 2021 1 158-166
allfields_unstemmed	10.1080/09712119.2021.1915792 doi (DE-627)DOAJ014528495 (DE-599)DOAJee2d99b779ab4bf78a001c8d09c664b4 DE-627 ger DE-627 rakwb eng SF600-1100 Oludayo Michael Akinsola verfasserin aut Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mathematical models provide valuable information for livestock improvement programmes. In this study, we evaluated the ability of five mathematical models (3P and 4P Gompertz, 3P and 4P logistic and neural network) to predict the growth of six tropically adapted dual purpose (TADP) chicken breeds (Fulani, FUNAAB Alpha, Kuroiler, Noiler, Sasso and Shika-Brown) under on-station and on-farm in Nigeria. Data for body weight were collected every 14 days from 1939 birds reared on-station, and every 28 days from 58,639 birds reared on-farm. Parameters used to evaluate the growth models were the adjusted coefficient of determination (AdjR2), Akaike’s information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The AdjR2 for Gompertz 3P was higher than or equal to the AdjR2 for logistics 3P, Gompertz 4P and logistics 4P but was equal to or lower than the AdjR2 for the neural network (NN) for all TADP chickens raised on-station. Based on the goodness-of-fit criteria, Gompertz 3P had the best predictive values (AdjR2 = 0.989–0.998) for TADP chickens raised on-station, while logistic 3P was the best-fit model for TADP chickens raised on-farm. In conclusion, non-linear models and NN models yielded a good fit with the age-weight data of TADP chickens on-station and on-farm. body weight mathematical models dual-purpose chickens neural network performance Veterinary medicine Emmanuel Babafunso Sonaiya verfasserin aut Oladeji Bamidele verfasserin aut Waheed Akinola Hassan verfasserin aut Abdulmojeed Yakubu verfasserin aut Folasade Olubukola Ajayi verfasserin aut Uduak Ogundu verfasserin aut Olayinka Olubunmi Alabi verfasserin aut Oluwafunmilayo Ayoka Adebambo verfasserin aut In Journal of Applied Animal Research Taylor & Francis Group, 2018 49(2021), 1, Seite 158-166 (DE-627)620142308 (DE-600)2541051-9 09741844 nnns volume:49 year:2021 number:1 pages:158-166 https://doi.org/10.1080/09712119.2021.1915792 kostenfrei https://doaj.org/article/ee2d99b779ab4bf78a001c8d09c664b4 kostenfrei http://dx.doi.org/10.1080/09712119.2021.1915792 kostenfrei https://doaj.org/toc/0971-2119 Journal toc kostenfrei https://doaj.org/toc/0974-1844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 49 2021 1 158-166
allfieldsGer	10.1080/09712119.2021.1915792 doi (DE-627)DOAJ014528495 (DE-599)DOAJee2d99b779ab4bf78a001c8d09c664b4 DE-627 ger DE-627 rakwb eng SF600-1100 Oludayo Michael Akinsola verfasserin aut Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mathematical models provide valuable information for livestock improvement programmes. In this study, we evaluated the ability of five mathematical models (3P and 4P Gompertz, 3P and 4P logistic and neural network) to predict the growth of six tropically adapted dual purpose (TADP) chicken breeds (Fulani, FUNAAB Alpha, Kuroiler, Noiler, Sasso and Shika-Brown) under on-station and on-farm in Nigeria. Data for body weight were collected every 14 days from 1939 birds reared on-station, and every 28 days from 58,639 birds reared on-farm. Parameters used to evaluate the growth models were the adjusted coefficient of determination (AdjR2), Akaike’s information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The AdjR2 for Gompertz 3P was higher than or equal to the AdjR2 for logistics 3P, Gompertz 4P and logistics 4P but was equal to or lower than the AdjR2 for the neural network (NN) for all TADP chickens raised on-station. Based on the goodness-of-fit criteria, Gompertz 3P had the best predictive values (AdjR2 = 0.989–0.998) for TADP chickens raised on-station, while logistic 3P was the best-fit model for TADP chickens raised on-farm. In conclusion, non-linear models and NN models yielded a good fit with the age-weight data of TADP chickens on-station and on-farm. body weight mathematical models dual-purpose chickens neural network performance Veterinary medicine Emmanuel Babafunso Sonaiya verfasserin aut Oladeji Bamidele verfasserin aut Waheed Akinola Hassan verfasserin aut Abdulmojeed Yakubu verfasserin aut Folasade Olubukola Ajayi verfasserin aut Uduak Ogundu verfasserin aut Olayinka Olubunmi Alabi verfasserin aut Oluwafunmilayo Ayoka Adebambo verfasserin aut In Journal of Applied Animal Research Taylor & Francis Group, 2018 49(2021), 1, Seite 158-166 (DE-627)620142308 (DE-600)2541051-9 09741844 nnns volume:49 year:2021 number:1 pages:158-166 https://doi.org/10.1080/09712119.2021.1915792 kostenfrei https://doaj.org/article/ee2d99b779ab4bf78a001c8d09c664b4 kostenfrei http://dx.doi.org/10.1080/09712119.2021.1915792 kostenfrei https://doaj.org/toc/0971-2119 Journal toc kostenfrei https://doaj.org/toc/0974-1844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 49 2021 1 158-166
allfieldsSound	10.1080/09712119.2021.1915792 doi (DE-627)DOAJ014528495 (DE-599)DOAJee2d99b779ab4bf78a001c8d09c664b4 DE-627 ger DE-627 rakwb eng SF600-1100 Oludayo Michael Akinsola verfasserin aut Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mathematical models provide valuable information for livestock improvement programmes. In this study, we evaluated the ability of five mathematical models (3P and 4P Gompertz, 3P and 4P logistic and neural network) to predict the growth of six tropically adapted dual purpose (TADP) chicken breeds (Fulani, FUNAAB Alpha, Kuroiler, Noiler, Sasso and Shika-Brown) under on-station and on-farm in Nigeria. Data for body weight were collected every 14 days from 1939 birds reared on-station, and every 28 days from 58,639 birds reared on-farm. Parameters used to evaluate the growth models were the adjusted coefficient of determination (AdjR2), Akaike’s information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The AdjR2 for Gompertz 3P was higher than or equal to the AdjR2 for logistics 3P, Gompertz 4P and logistics 4P but was equal to or lower than the AdjR2 for the neural network (NN) for all TADP chickens raised on-station. Based on the goodness-of-fit criteria, Gompertz 3P had the best predictive values (AdjR2 = 0.989–0.998) for TADP chickens raised on-station, while logistic 3P was the best-fit model for TADP chickens raised on-farm. In conclusion, non-linear models and NN models yielded a good fit with the age-weight data of TADP chickens on-station and on-farm. body weight mathematical models dual-purpose chickens neural network performance Veterinary medicine Emmanuel Babafunso Sonaiya verfasserin aut Oladeji Bamidele verfasserin aut Waheed Akinola Hassan verfasserin aut Abdulmojeed Yakubu verfasserin aut Folasade Olubukola Ajayi verfasserin aut Uduak Ogundu verfasserin aut Olayinka Olubunmi Alabi verfasserin aut Oluwafunmilayo Ayoka Adebambo verfasserin aut In Journal of Applied Animal Research Taylor & Francis Group, 2018 49(2021), 1, Seite 158-166 (DE-627)620142308 (DE-600)2541051-9 09741844 nnns volume:49 year:2021 number:1 pages:158-166 https://doi.org/10.1080/09712119.2021.1915792 kostenfrei https://doaj.org/article/ee2d99b779ab4bf78a001c8d09c664b4 kostenfrei http://dx.doi.org/10.1080/09712119.2021.1915792 kostenfrei https://doaj.org/toc/0971-2119 Journal toc kostenfrei https://doaj.org/toc/0974-1844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 49 2021 1 158-166
language	English
source	In Journal of Applied Animal Research 49(2021), 1, Seite 158-166 volume:49 year:2021 number:1 pages:158-166
sourceStr	In Journal of Applied Animal Research 49(2021), 1, Seite 158-166 volume:49 year:2021 number:1 pages:158-166
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topic_facet	body weight mathematical models dual-purpose chickens neural network performance Veterinary medicine
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authorswithroles_txt_mv	Oludayo Michael Akinsola @@aut@@ Emmanuel Babafunso Sonaiya @@aut@@ Oladeji Bamidele @@aut@@ Waheed Akinola Hassan @@aut@@ Abdulmojeed Yakubu @@aut@@ Folasade Olubukola Ajayi @@aut@@ Uduak Ogundu @@aut@@ Olayinka Olubunmi Alabi @@aut@@ Oluwafunmilayo Ayoka Adebambo @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
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callnumber-first	S - Agriculture
author	Oludayo Michael Akinsola
spellingShingle	Oludayo Michael Akinsola misc SF600-1100 misc body weight misc mathematical models misc dual-purpose chickens misc neural network misc performance misc Veterinary medicine Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken
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topic_title	SF600-1100 Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken body weight mathematical models dual-purpose chickens neural network performance
topic	misc SF600-1100 misc body weight misc mathematical models misc dual-purpose chickens misc neural network misc performance misc Veterinary medicine
topic_unstemmed	misc SF600-1100 misc body weight misc mathematical models misc dual-purpose chickens misc neural network misc performance misc Veterinary medicine
topic_browse	misc SF600-1100 misc body weight misc mathematical models misc dual-purpose chickens misc neural network misc performance misc Veterinary medicine
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title	Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken
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title_full	Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken
author_sort	Oludayo Michael Akinsola
journal	Journal of Applied Animal Research
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author_browse	Oludayo Michael Akinsola Emmanuel Babafunso Sonaiya Oladeji Bamidele Waheed Akinola Hassan Abdulmojeed Yakubu Folasade Olubukola Ajayi Uduak Ogundu Olayinka Olubunmi Alabi Oluwafunmilayo Ayoka Adebambo
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author-letter	Oludayo Michael Akinsola
doi_str_mv	10.1080/09712119.2021.1915792
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title_sort	comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken
callnumber	SF600-1100
title_auth	Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken
abstract	Mathematical models provide valuable information for livestock improvement programmes. In this study, we evaluated the ability of five mathematical models (3P and 4P Gompertz, 3P and 4P logistic and neural network) to predict the growth of six tropically adapted dual purpose (TADP) chicken breeds (Fulani, FUNAAB Alpha, Kuroiler, Noiler, Sasso and Shika-Brown) under on-station and on-farm in Nigeria. Data for body weight were collected every 14 days from 1939 birds reared on-station, and every 28 days from 58,639 birds reared on-farm. Parameters used to evaluate the growth models were the adjusted coefficient of determination (AdjR2), Akaike’s information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The AdjR2 for Gompertz 3P was higher than or equal to the AdjR2 for logistics 3P, Gompertz 4P and logistics 4P but was equal to or lower than the AdjR2 for the neural network (NN) for all TADP chickens raised on-station. Based on the goodness-of-fit criteria, Gompertz 3P had the best predictive values (AdjR2 = 0.989–0.998) for TADP chickens raised on-station, while logistic 3P was the best-fit model for TADP chickens raised on-farm. In conclusion, non-linear models and NN models yielded a good fit with the age-weight data of TADP chickens on-station and on-farm.
abstractGer	Mathematical models provide valuable information for livestock improvement programmes. In this study, we evaluated the ability of five mathematical models (3P and 4P Gompertz, 3P and 4P logistic and neural network) to predict the growth of six tropically adapted dual purpose (TADP) chicken breeds (Fulani, FUNAAB Alpha, Kuroiler, Noiler, Sasso and Shika-Brown) under on-station and on-farm in Nigeria. Data for body weight were collected every 14 days from 1939 birds reared on-station, and every 28 days from 58,639 birds reared on-farm. Parameters used to evaluate the growth models were the adjusted coefficient of determination (AdjR2), Akaike’s information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The AdjR2 for Gompertz 3P was higher than or equal to the AdjR2 for logistics 3P, Gompertz 4P and logistics 4P but was equal to or lower than the AdjR2 for the neural network (NN) for all TADP chickens raised on-station. Based on the goodness-of-fit criteria, Gompertz 3P had the best predictive values (AdjR2 = 0.989–0.998) for TADP chickens raised on-station, while logistic 3P was the best-fit model for TADP chickens raised on-farm. In conclusion, non-linear models and NN models yielded a good fit with the age-weight data of TADP chickens on-station and on-farm.
abstract_unstemmed	Mathematical models provide valuable information for livestock improvement programmes. In this study, we evaluated the ability of five mathematical models (3P and 4P Gompertz, 3P and 4P logistic and neural network) to predict the growth of six tropically adapted dual purpose (TADP) chicken breeds (Fulani, FUNAAB Alpha, Kuroiler, Noiler, Sasso and Shika-Brown) under on-station and on-farm in Nigeria. Data for body weight were collected every 14 days from 1939 birds reared on-station, and every 28 days from 58,639 birds reared on-farm. Parameters used to evaluate the growth models were the adjusted coefficient of determination (AdjR2), Akaike’s information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The AdjR2 for Gompertz 3P was higher than or equal to the AdjR2 for logistics 3P, Gompertz 4P and logistics 4P but was equal to or lower than the AdjR2 for the neural network (NN) for all TADP chickens raised on-station. Based on the goodness-of-fit criteria, Gompertz 3P had the best predictive values (AdjR2 = 0.989–0.998) for TADP chickens raised on-station, while logistic 3P was the best-fit model for TADP chickens raised on-farm. In conclusion, non-linear models and NN models yielded a good fit with the age-weight data of TADP chickens on-station and on-farm.
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container_issue	1
title_short	Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken
url	https://doi.org/10.1080/09712119.2021.1915792 https://doaj.org/article/ee2d99b779ab4bf78a001c8d09c664b4 http://dx.doi.org/10.1080/09712119.2021.1915792 https://doaj.org/toc/0971-2119 https://doaj.org/toc/0974-1844
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author2	Emmanuel Babafunso Sonaiya Oladeji Bamidele Waheed Akinola Hassan Abdulmojeed Yakubu Folasade Olubukola Ajayi Uduak Ogundu Olayinka Olubunmi Alabi Oluwafunmilayo Ayoka Adebambo
author2Str	Emmanuel Babafunso Sonaiya Oladeji Bamidele Waheed Akinola Hassan Abdulmojeed Yakubu Folasade Olubukola Ajayi Uduak Ogundu Olayinka Olubunmi Alabi Oluwafunmilayo Ayoka Adebambo
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doi_str	10.1080/09712119.2021.1915792
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up_date	2024-07-03T23:27:00.694Z
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Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken

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