Evaluation of automated monitoring calving prediction in dairy buffaloes a new tool for calving management

Detalhes bibliográficos
Autor(a) principal: Quddus,R. A.
Data de Publicação: 2022
Outros Autores: Ahmad,N., Khalique,A., Bhatti,J. A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Journal of Biology
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1519-69842022000100289
Resumo: Abstract Buffalo is one of the leading milk-producing dairy animals. Its production and reproduction are affected due to some factors including inadequate monitoring around parturition, which cause economic losses like delayed birth process, increased risk of stillbirth, etc. The appropriate calving monitoring is essential for dairy herd management. Therefore, we designed a study its aim was, to predict the calving based on automated machine measured prepartum behaviors in buffaloes. The data were collected from n=40 pregnant buffaloes of 2nd to 5th parity, which was synchronized. The NEDAP neck and leg logger tag was attached to each buffalo at 30 days before calving and automatically collected feeding, rumination, lying, standing, no. of steps, no. of switches from standing to lying (lying bouts) and total motion activity. All behavioral data were reduced to -10 days before the calving date for statistical analysis to use mixed model procedure and ANOVA. Results showed that feeding and rumination time significantly (P<0.05) decreased from -10 to -1 days before calving indicating calving prediction. Moreover, Rumination time was at lowest (P<0.001) value at 2h before the calving such behavioral changes may be useful to predict calving in buffaloes. Similarly, lying bouts and standing time abruptly decreased (P<0.05) from -3 to -1 days before calving, while lying time abruptly increased (P<0.01) from -3 to -1 days before calving (531.57±23.65 to 665.62±18.14, respectively). No. of steps taken and total motion significantly (P<0.05) increased from -10 to -1 days before calving. Feeding time was significantly (P<0.02) lowered in 3rd parity buffaloes compared with 2nd, 4th and 5th parity buffaloes, while standing time of 5th parity buffaloes were lowered (P<0.05) as compared to 2nd to 4th parity buffalos at -1 day of prepartum. However, rumination, lying, no. of steps taken and total motion activity at -1 day of prepartum was independent (P>0.05) of parity in buffaloes. Neural network analysis for combined variables from NEDAP technology at the daily level yielded 100.0% sensitivity and 98% specificity. In conclusion NEDAP technology can be used to measured behavioral changes -10 day before calving as it can serve as a useful guide in the prediction calving date in the buffaloes.
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spelling Evaluation of automated monitoring calving prediction in dairy buffaloes a new tool for calving managementcalving predicationbuffaloesNEDAP logger technologyautomated monitored prepartum behaviorsAbstract Buffalo is one of the leading milk-producing dairy animals. Its production and reproduction are affected due to some factors including inadequate monitoring around parturition, which cause economic losses like delayed birth process, increased risk of stillbirth, etc. The appropriate calving monitoring is essential for dairy herd management. Therefore, we designed a study its aim was, to predict the calving based on automated machine measured prepartum behaviors in buffaloes. The data were collected from n=40 pregnant buffaloes of 2nd to 5th parity, which was synchronized. The NEDAP neck and leg logger tag was attached to each buffalo at 30 days before calving and automatically collected feeding, rumination, lying, standing, no. of steps, no. of switches from standing to lying (lying bouts) and total motion activity. All behavioral data were reduced to -10 days before the calving date for statistical analysis to use mixed model procedure and ANOVA. Results showed that feeding and rumination time significantly (P<0.05) decreased from -10 to -1 days before calving indicating calving prediction. Moreover, Rumination time was at lowest (P<0.001) value at 2h before the calving such behavioral changes may be useful to predict calving in buffaloes. Similarly, lying bouts and standing time abruptly decreased (P<0.05) from -3 to -1 days before calving, while lying time abruptly increased (P<0.01) from -3 to -1 days before calving (531.57±23.65 to 665.62±18.14, respectively). No. of steps taken and total motion significantly (P<0.05) increased from -10 to -1 days before calving. Feeding time was significantly (P<0.02) lowered in 3rd parity buffaloes compared with 2nd, 4th and 5th parity buffaloes, while standing time of 5th parity buffaloes were lowered (P<0.05) as compared to 2nd to 4th parity buffalos at -1 day of prepartum. However, rumination, lying, no. of steps taken and total motion activity at -1 day of prepartum was independent (P>0.05) of parity in buffaloes. Neural network analysis for combined variables from NEDAP technology at the daily level yielded 100.0% sensitivity and 98% specificity. In conclusion NEDAP technology can be used to measured behavioral changes -10 day before calving as it can serve as a useful guide in the prediction calving date in the buffaloes.Instituto Internacional de Ecologia2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1519-69842022000100289Brazilian Journal of Biology v.82 2022reponame:Brazilian Journal of Biologyinstname:Instituto Internacional de Ecologia (IIE)instacron:IIE10.1590/1519-6984.257884info:eu-repo/semantics/openAccessQuddus,R. A.Ahmad,N.Khalique,A.Bhatti,J. A.eng2022-06-07T00:00:00Zoai:scielo:S1519-69842022000100289Revistahttps://www.scielo.br/j/bjb/https://old.scielo.br/oai/scielo-oai.phpbjb@bjb.com.br||bjb@bjb.com.br1678-43751519-6984opendoar:2022-06-07T00:00Brazilian Journal of Biology - Instituto Internacional de Ecologia (IIE)false
dc.title.none.fl_str_mv Evaluation of automated monitoring calving prediction in dairy buffaloes a new tool for calving management
title Evaluation of automated monitoring calving prediction in dairy buffaloes a new tool for calving management
spellingShingle Evaluation of automated monitoring calving prediction in dairy buffaloes a new tool for calving management
Quddus,R. A.
calving predication
buffaloes
NEDAP logger technology
automated monitored prepartum behaviors
title_short Evaluation of automated monitoring calving prediction in dairy buffaloes a new tool for calving management
title_full Evaluation of automated monitoring calving prediction in dairy buffaloes a new tool for calving management
title_fullStr Evaluation of automated monitoring calving prediction in dairy buffaloes a new tool for calving management
title_full_unstemmed Evaluation of automated monitoring calving prediction in dairy buffaloes a new tool for calving management
title_sort Evaluation of automated monitoring calving prediction in dairy buffaloes a new tool for calving management
author Quddus,R. A.
author_facet Quddus,R. A.
Ahmad,N.
Khalique,A.
Bhatti,J. A.
author_role author
author2 Ahmad,N.
Khalique,A.
Bhatti,J. A.
author2_role author
author
author
dc.contributor.author.fl_str_mv Quddus,R. A.
Ahmad,N.
Khalique,A.
Bhatti,J. A.
dc.subject.por.fl_str_mv calving predication
buffaloes
NEDAP logger technology
automated monitored prepartum behaviors
topic calving predication
buffaloes
NEDAP logger technology
automated monitored prepartum behaviors
description Abstract Buffalo is one of the leading milk-producing dairy animals. Its production and reproduction are affected due to some factors including inadequate monitoring around parturition, which cause economic losses like delayed birth process, increased risk of stillbirth, etc. The appropriate calving monitoring is essential for dairy herd management. Therefore, we designed a study its aim was, to predict the calving based on automated machine measured prepartum behaviors in buffaloes. The data were collected from n=40 pregnant buffaloes of 2nd to 5th parity, which was synchronized. The NEDAP neck and leg logger tag was attached to each buffalo at 30 days before calving and automatically collected feeding, rumination, lying, standing, no. of steps, no. of switches from standing to lying (lying bouts) and total motion activity. All behavioral data were reduced to -10 days before the calving date for statistical analysis to use mixed model procedure and ANOVA. Results showed that feeding and rumination time significantly (P<0.05) decreased from -10 to -1 days before calving indicating calving prediction. Moreover, Rumination time was at lowest (P<0.001) value at 2h before the calving such behavioral changes may be useful to predict calving in buffaloes. Similarly, lying bouts and standing time abruptly decreased (P<0.05) from -3 to -1 days before calving, while lying time abruptly increased (P<0.01) from -3 to -1 days before calving (531.57±23.65 to 665.62±18.14, respectively). No. of steps taken and total motion significantly (P<0.05) increased from -10 to -1 days before calving. Feeding time was significantly (P<0.02) lowered in 3rd parity buffaloes compared with 2nd, 4th and 5th parity buffaloes, while standing time of 5th parity buffaloes were lowered (P<0.05) as compared to 2nd to 4th parity buffalos at -1 day of prepartum. However, rumination, lying, no. of steps taken and total motion activity at -1 day of prepartum was independent (P>0.05) of parity in buffaloes. Neural network analysis for combined variables from NEDAP technology at the daily level yielded 100.0% sensitivity and 98% specificity. In conclusion NEDAP technology can be used to measured behavioral changes -10 day before calving as it can serve as a useful guide in the prediction calving date in the buffaloes.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1519-69842022000100289
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1519-69842022000100289
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1519-6984.257884
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Instituto Internacional de Ecologia
publisher.none.fl_str_mv Instituto Internacional de Ecologia
dc.source.none.fl_str_mv Brazilian Journal of Biology v.82 2022
reponame:Brazilian Journal of Biology
instname:Instituto Internacional de Ecologia (IIE)
instacron:IIE
instname_str Instituto Internacional de Ecologia (IIE)
instacron_str IIE
institution IIE
reponame_str Brazilian Journal of Biology
collection Brazilian Journal of Biology
repository.name.fl_str_mv Brazilian Journal of Biology - Instituto Internacional de Ecologia (IIE)
repository.mail.fl_str_mv bjb@bjb.com.br||bjb@bjb.com.br
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