Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods

Detalhes bibliográficos
Autor(a) principal: Rebuli, Karina Brotto
Data de Publicação: 2023
Outros Autores: Ozella, Laura, Vanneschi, Leonardo, Giacobini, Mario
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/154356
Resumo: Rebuli, K. B., Ozella, L., Vanneschi, L., & Giacobini, M. (2023). Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods. Computers And Electronics In Agriculture, 211(August 2023), [108002]. https://doi.org/10.2139/ssrn.4435365, https://doi.org/10.1016/j.compag.2023.108002---This study is supported by Compagnia di San Paolo (ROL 63369 SIME 2020.1713) and by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS
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spelling Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation PeriodsMulti-algorithm clusteringCluster algorithms merging indexAutomatic Milking SystemFuture lactation periodMilk productionForestryAgronomy and Crop ScienceComputer Science ApplicationsHorticultureSDG 15 - Life on LandRebuli, K. B., Ozella, L., Vanneschi, L., & Giacobini, M. (2023). Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods. Computers And Electronics In Agriculture, 211(August 2023), [108002]. https://doi.org/10.2139/ssrn.4435365, https://doi.org/10.1016/j.compag.2023.108002---This study is supported by Compagnia di San Paolo (ROL 63369 SIME 2020.1713) and by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMSThe introduction of Automated Milking Systems (AMSs), or milking robots, represented a significant advancement in dairy farming techniques. AMSs enable real-time monitoring of udder health and milk quality during each milking episode, which provides a wealth of data that can be utilized to optimize herd management practices. ML algorithms are well-suited for handling large and multi-dimensional datasets, making them a valuable tool for analyzing the vast amount of data generated by AMSs. This study introduces a novel approach to characterize the milk productivity of Holstein Friesians cows milked by AMSs during individual lactation periods and evaluate their stability over time. Four unsupervised ML clustering algorithms were employed to cluster the cows within each lactation period, and a merging index was proposed to combine the clustering results. The dairy cows were grouped into clusters based on their productivity, and the stability of these Productivity Groups (PGs) over time was analyzed. The PGs were found to be weakly stable over time, indicating that selecting cows for insemination based solely on their present or past lactation productivity may not be the most effective strategy. In addition, the results revealed that the High Productivity Group exhibited lower levels of protein, fat, and lactose content in the milk. The proposed methodology was demonstrated using data from one farm with dairy cows that exclusively uses the AMS, however, it can be applied to any context and dataset in which a multi-algorithm clustering analysis is suitable, including data from conventional milking parlors. Understanding milk productivity and its factors in future lactation periods is essential for effective herd management. A comprehensive long-term analysis is of significant importance for the zootechnical sector as it could assists farmers in selecting cows for insemination and making decisions on which ones to retain for future lactation periods.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNRebuli, Karina BrottoOzella, LauraVanneschi, LeonardoGiacobini, Mario2023-06-23T22:24:25Z2023-082023-08-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article11application/pdfapplication/pdfhttp://hdl.handle.net/10362/154356eng0168-1699PURE: 64438625https://doi.org/10.2139/ssrn.4435365info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:36:49Zoai:run.unl.pt:10362/154356Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:55:35.786080Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods
title Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods
spellingShingle Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods
Rebuli, Karina Brotto
Multi-algorithm clustering
Cluster algorithms merging index
Automatic Milking System
Future lactation period
Milk production
Forestry
Agronomy and Crop Science
Computer Science Applications
Horticulture
SDG 15 - Life on Land
title_short Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods
title_full Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods
title_fullStr Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods
title_full_unstemmed Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods
title_sort Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods
author Rebuli, Karina Brotto
author_facet Rebuli, Karina Brotto
Ozella, Laura
Vanneschi, Leonardo
Giacobini, Mario
author_role author
author2 Ozella, Laura
Vanneschi, Leonardo
Giacobini, Mario
author2_role author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Rebuli, Karina Brotto
Ozella, Laura
Vanneschi, Leonardo
Giacobini, Mario
dc.subject.por.fl_str_mv Multi-algorithm clustering
Cluster algorithms merging index
Automatic Milking System
Future lactation period
Milk production
Forestry
Agronomy and Crop Science
Computer Science Applications
Horticulture
SDG 15 - Life on Land
topic Multi-algorithm clustering
Cluster algorithms merging index
Automatic Milking System
Future lactation period
Milk production
Forestry
Agronomy and Crop Science
Computer Science Applications
Horticulture
SDG 15 - Life on Land
description Rebuli, K. B., Ozella, L., Vanneschi, L., & Giacobini, M. (2023). Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods. Computers And Electronics In Agriculture, 211(August 2023), [108002]. https://doi.org/10.2139/ssrn.4435365, https://doi.org/10.1016/j.compag.2023.108002---This study is supported by Compagnia di San Paolo (ROL 63369 SIME 2020.1713) and by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS
publishDate 2023
dc.date.none.fl_str_mv 2023-06-23T22:24:25Z
2023-08
2023-08-01T00:00:00Z
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://hdl.handle.net/10362/154356
url http://hdl.handle.net/10362/154356
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0168-1699
PURE: 64438625
https://doi.org/10.2139/ssrn.4435365
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 11
application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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