Modern livestock farming under tropical conditions using sensors in grazing systems
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1038/s41598-022-06650-5 http://hdl.handle.net/11449/234151 |
Resumo: | The aim of this study was to evaluate a commercial sensor—a three-axis accelerometer—to predict animal behavior with a variety of conditions in tropical grazing systems. The sensor was positioned on the underjaw of young bulls to detect the animals’ movements. A total of 22 animals were monitored in a grazing system, during both seasons (wet and dry), with different quality and quantity forage allowance. The machine learning (ML) methods used were random forest (RF), convolutional neural net and linear discriminant analysis; the metrics used to determine the best method were accuracy, Kappa coefficient, and a confusion matrix. After predicting animal behavior using the best ML method, a forecast for animal performance was developed using a mechanistic model: multiple linear regression to correlate intermediate average daily gain (iADG) observed versus iADG predicted. The best ML method yielded accuracy of 0.821 and Kappa coefficient of 0.704, was RF. From the forecast for animal performance, the Pearson correlation was 0.795 and the mean square error was 0.062. Hence, the commercial Ovi-bovi sensor, which is a three-axis accelerometer, can act as a powerful tool for predicting animal behavior in beef cattle production developed under a variety tropical grazing condition. |
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Repositório Institucional da UNESP |
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spelling |
Modern livestock farming under tropical conditions using sensors in grazing systemsThe aim of this study was to evaluate a commercial sensor—a three-axis accelerometer—to predict animal behavior with a variety of conditions in tropical grazing systems. The sensor was positioned on the underjaw of young bulls to detect the animals’ movements. A total of 22 animals were monitored in a grazing system, during both seasons (wet and dry), with different quality and quantity forage allowance. The machine learning (ML) methods used were random forest (RF), convolutional neural net and linear discriminant analysis; the metrics used to determine the best method were accuracy, Kappa coefficient, and a confusion matrix. After predicting animal behavior using the best ML method, a forecast for animal performance was developed using a mechanistic model: multiple linear regression to correlate intermediate average daily gain (iADG) observed versus iADG predicted. The best ML method yielded accuracy of 0.821 and Kappa coefficient of 0.704, was RF. From the forecast for animal performance, the Pearson correlation was 0.795 and the mean square error was 0.062. Hence, the commercial Ovi-bovi sensor, which is a three-axis accelerometer, can act as a powerful tool for predicting animal behavior in beef cattle production developed under a variety tropical grazing condition.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Animal Science São Paulo State University (Unesp), Via de Acesso Prof. Paulo Donato Castellane s/n, SPDepartment of Engineering and Exact Sciences São Paulo State University (Unesp), SPDepartment of Animal Science and Rural Development Federal University of Santa Catarina (UFSC), SCDepartment of Animal Science São Paulo State University (Unesp), Via de Acesso Prof. Paulo Donato Castellane s/n, SPDepartment of Engineering and Exact Sciences São Paulo State University (Unesp), SPCNPq: 150985/2019-3FAPESP: 2015/16631-5FAPESP: 2018/20753-7Universidade Estadual Paulista (UNESP)Universidade Federal de Santa Catarina (UFSC)Romanzini, Eliéder Prates [UNESP]Watanabe, Rafael Nakamura [UNESP]Fonseca, Natália Vilas Boas [UNESP]Berça, Andressa Scholz [UNESP]Brito, Thaís Ribeiro [UNESP]Bernardes, Priscila ArrigucciMunari, Danísio Prado [UNESP]Reis, Ricardo Andrade [UNESP]2022-05-01T13:41:38Z2022-05-01T13:41:38Z2022-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1038/s41598-022-06650-5Scientific Reports, v. 12, n. 1, 2022.2045-2322http://hdl.handle.net/11449/23415110.1038/s41598-022-06650-52-s2.0-85124775943Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengScientific Reportsinfo:eu-repo/semantics/openAccess2024-06-07T18:40:25Zoai:repositorio.unesp.br:11449/234151Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:09:05.858530Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Modern livestock farming under tropical conditions using sensors in grazing systems |
title |
Modern livestock farming under tropical conditions using sensors in grazing systems |
spellingShingle |
Modern livestock farming under tropical conditions using sensors in grazing systems Romanzini, Eliéder Prates [UNESP] |
title_short |
Modern livestock farming under tropical conditions using sensors in grazing systems |
title_full |
Modern livestock farming under tropical conditions using sensors in grazing systems |
title_fullStr |
Modern livestock farming under tropical conditions using sensors in grazing systems |
title_full_unstemmed |
Modern livestock farming under tropical conditions using sensors in grazing systems |
title_sort |
Modern livestock farming under tropical conditions using sensors in grazing systems |
author |
Romanzini, Eliéder Prates [UNESP] |
author_facet |
Romanzini, Eliéder Prates [UNESP] Watanabe, Rafael Nakamura [UNESP] Fonseca, Natália Vilas Boas [UNESP] Berça, Andressa Scholz [UNESP] Brito, Thaís Ribeiro [UNESP] Bernardes, Priscila Arrigucci Munari, Danísio Prado [UNESP] Reis, Ricardo Andrade [UNESP] |
author_role |
author |
author2 |
Watanabe, Rafael Nakamura [UNESP] Fonseca, Natália Vilas Boas [UNESP] Berça, Andressa Scholz [UNESP] Brito, Thaís Ribeiro [UNESP] Bernardes, Priscila Arrigucci Munari, Danísio Prado [UNESP] Reis, Ricardo Andrade [UNESP] |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Universidade Federal de Santa Catarina (UFSC) |
dc.contributor.author.fl_str_mv |
Romanzini, Eliéder Prates [UNESP] Watanabe, Rafael Nakamura [UNESP] Fonseca, Natália Vilas Boas [UNESP] Berça, Andressa Scholz [UNESP] Brito, Thaís Ribeiro [UNESP] Bernardes, Priscila Arrigucci Munari, Danísio Prado [UNESP] Reis, Ricardo Andrade [UNESP] |
description |
The aim of this study was to evaluate a commercial sensor—a three-axis accelerometer—to predict animal behavior with a variety of conditions in tropical grazing systems. The sensor was positioned on the underjaw of young bulls to detect the animals’ movements. A total of 22 animals were monitored in a grazing system, during both seasons (wet and dry), with different quality and quantity forage allowance. The machine learning (ML) methods used were random forest (RF), convolutional neural net and linear discriminant analysis; the metrics used to determine the best method were accuracy, Kappa coefficient, and a confusion matrix. After predicting animal behavior using the best ML method, a forecast for animal performance was developed using a mechanistic model: multiple linear regression to correlate intermediate average daily gain (iADG) observed versus iADG predicted. The best ML method yielded accuracy of 0.821 and Kappa coefficient of 0.704, was RF. From the forecast for animal performance, the Pearson correlation was 0.795 and the mean square error was 0.062. Hence, the commercial Ovi-bovi sensor, which is a three-axis accelerometer, can act as a powerful tool for predicting animal behavior in beef cattle production developed under a variety tropical grazing condition. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-05-01T13:41:38Z 2022-05-01T13:41:38Z 2022-12-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1038/s41598-022-06650-5 Scientific Reports, v. 12, n. 1, 2022. 2045-2322 http://hdl.handle.net/11449/234151 10.1038/s41598-022-06650-5 2-s2.0-85124775943 |
url |
http://dx.doi.org/10.1038/s41598-022-06650-5 http://hdl.handle.net/11449/234151 |
identifier_str_mv |
Scientific Reports, v. 12, n. 1, 2022. 2045-2322 10.1038/s41598-022-06650-5 2-s2.0-85124775943 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Scientific Reports |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1808128468172406784 |