Use of artificial neural network to model reproductive performance and mortality of non-descript rabbits
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta Scientiarum. Animal Sciences (Online) |
Texto Completo: | https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/47715 |
Resumo: | This study was carried out to predict average number of kits per birth and mortality number of non-descript rabbits in Plateau State, Nigeria using artificial neural network (ANN). Data were obtained from a total of 100 rabbit farmers. The predicted mean value for number of kits per birth using ANN (6.60) was similar to the observed value (6.52). As regards mortality, the predicted mean value using ANN (17.75) was also similar to the observed value (17.80). Primary occupation, experience in rabbit keeping, flock size and credit type were the parameters of utmost importance in predicting number of kits per birth. The fairly high coefficient of determination (R2) (55.7%) and low root mean square error (RMSE) value of 1.22 conferred reliability on the ANN model. The R2 value obtained in the prediction of mortality using ANN implies that 61.1% of the variation in the number of mortality can be largely explained by the explanatory variables such as flock size, age of farmers, experience in rabbit keeping and average number of kits per birth. The low RMSE value of 3.82 also gave credence to the regression model. The present information may be exploited in taking appropriate management decisions to boost production. |
id |
UEM-7_1494041613fe1e397d2ea7af12b90661 |
---|---|
oai_identifier_str |
oai:periodicos.uem.br/ojs:article/47715 |
network_acronym_str |
UEM-7 |
network_name_str |
Acta Scientiarum. Animal Sciences (Online) |
repository_id_str |
|
spelling |
Use of artificial neural network to model reproductive performance and mortality of non-descript rabbitsUse of artificial neural network to model reproductive performance and mortality of non-descript rabbitsconnectionist; reproduction; lagomorphs; tropics.connectionist; reproduction; lagomorphs; tropics.This study was carried out to predict average number of kits per birth and mortality number of non-descript rabbits in Plateau State, Nigeria using artificial neural network (ANN). Data were obtained from a total of 100 rabbit farmers. The predicted mean value for number of kits per birth using ANN (6.60) was similar to the observed value (6.52). As regards mortality, the predicted mean value using ANN (17.75) was also similar to the observed value (17.80). Primary occupation, experience in rabbit keeping, flock size and credit type were the parameters of utmost importance in predicting number of kits per birth. The fairly high coefficient of determination (R2) (55.7%) and low root mean square error (RMSE) value of 1.22 conferred reliability on the ANN model. The R2 value obtained in the prediction of mortality using ANN implies that 61.1% of the variation in the number of mortality can be largely explained by the explanatory variables such as flock size, age of farmers, experience in rabbit keeping and average number of kits per birth. The low RMSE value of 3.82 also gave credence to the regression model. The present information may be exploited in taking appropriate management decisions to boost production.This study was carried out to predict average number of kits per birth and mortality number of non-descript rabbits in Plateau State, Nigeria using artificial neural network (ANN). Data were obtained from a total of 100 rabbit farmers. The predicted mean value for number of kits per birth using ANN (6.60) was similar to the observed value (6.52). As regards mortality, the predicted mean value using ANN (17.75) was also similar to the observed value (17.80). Primary occupation, experience in rabbit keeping, flock size and credit type were the parameters of utmost importance in predicting number of kits per birth. The fairly high coefficient of determination (R2) (55.7%) and low root mean square error (RMSE) value of 1.22 conferred reliability on the ANN model. The R2 value obtained in the prediction of mortality using ANN implies that 61.1% of the variation in the number of mortality can be largely explained by the explanatory variables such as flock size, age of farmers, experience in rabbit keeping and average number of kits per birth. The low RMSE value of 3.82 also gave credence to the regression model. The present information may be exploited in taking appropriate management decisions to boost production.Editora da Universidade Estadual de Maringá2020-06-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/4771510.4025/actascianimsci.v42i1.47715Acta Scientiarum. Animal Sciences; Vol 42 (2020): Publicação contínua; e47715Acta Scientiarum. Animal Sciences; v. 42 (2020): Publicação contínua; e477151807-86721806-2636reponame:Acta Scientiarum. Animal Sciences (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttps://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/47715/751375150181Copyright (c) 2020 Acta Scientiarum. Animal Scienceshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessYakubu, AbdulmojeedNimyak, Philip2020-11-16T18:33:11Zoai:periodicos.uem.br/ojs:article/47715Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAnimSciPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAnimSci/oaiactaanim@uem.br||actaanim@uem.br|| rev.acta@gmail.com1807-86721806-2636opendoar:2020-11-16T18:33:11Acta Scientiarum. Animal Sciences (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Use of artificial neural network to model reproductive performance and mortality of non-descript rabbits Use of artificial neural network to model reproductive performance and mortality of non-descript rabbits |
title |
Use of artificial neural network to model reproductive performance and mortality of non-descript rabbits |
spellingShingle |
Use of artificial neural network to model reproductive performance and mortality of non-descript rabbits Yakubu, Abdulmojeed connectionist; reproduction; lagomorphs; tropics. connectionist; reproduction; lagomorphs; tropics. |
title_short |
Use of artificial neural network to model reproductive performance and mortality of non-descript rabbits |
title_full |
Use of artificial neural network to model reproductive performance and mortality of non-descript rabbits |
title_fullStr |
Use of artificial neural network to model reproductive performance and mortality of non-descript rabbits |
title_full_unstemmed |
Use of artificial neural network to model reproductive performance and mortality of non-descript rabbits |
title_sort |
Use of artificial neural network to model reproductive performance and mortality of non-descript rabbits |
author |
Yakubu, Abdulmojeed |
author_facet |
Yakubu, Abdulmojeed Nimyak, Philip |
author_role |
author |
author2 |
Nimyak, Philip |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Yakubu, Abdulmojeed Nimyak, Philip |
dc.subject.por.fl_str_mv |
connectionist; reproduction; lagomorphs; tropics. connectionist; reproduction; lagomorphs; tropics. |
topic |
connectionist; reproduction; lagomorphs; tropics. connectionist; reproduction; lagomorphs; tropics. |
description |
This study was carried out to predict average number of kits per birth and mortality number of non-descript rabbits in Plateau State, Nigeria using artificial neural network (ANN). Data were obtained from a total of 100 rabbit farmers. The predicted mean value for number of kits per birth using ANN (6.60) was similar to the observed value (6.52). As regards mortality, the predicted mean value using ANN (17.75) was also similar to the observed value (17.80). Primary occupation, experience in rabbit keeping, flock size and credit type were the parameters of utmost importance in predicting number of kits per birth. The fairly high coefficient of determination (R2) (55.7%) and low root mean square error (RMSE) value of 1.22 conferred reliability on the ANN model. The R2 value obtained in the prediction of mortality using ANN implies that 61.1% of the variation in the number of mortality can be largely explained by the explanatory variables such as flock size, age of farmers, experience in rabbit keeping and average number of kits per birth. The low RMSE value of 3.82 also gave credence to the regression model. The present information may be exploited in taking appropriate management decisions to boost production. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-06-08 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/47715 10.4025/actascianimsci.v42i1.47715 |
url |
https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/47715 |
identifier_str_mv |
10.4025/actascianimsci.v42i1.47715 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/47715/751375150181 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Acta Scientiarum. Animal Sciences https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Acta Scientiarum. Animal Sciences https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da Universidade Estadual de Maringá |
publisher.none.fl_str_mv |
Editora da Universidade Estadual de Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Animal Sciences; Vol 42 (2020): Publicação contínua; e47715 Acta Scientiarum. Animal Sciences; v. 42 (2020): Publicação contínua; e47715 1807-8672 1806-2636 reponame:Acta Scientiarum. Animal Sciences (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta Scientiarum. Animal Sciences (Online) |
collection |
Acta Scientiarum. Animal Sciences (Online) |
repository.name.fl_str_mv |
Acta Scientiarum. Animal Sciences (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
actaanim@uem.br||actaanim@uem.br|| rev.acta@gmail.com |
_version_ |
1799315362868625408 |