Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows

The management of body condition score (BCS) during the dry period is associated with the postpartum health outcomes of dairy cows. However, the difference between the actual BCS and the fixed ideal value is not able to accurately predict the occurrence of postpartum diseases. This study aimed to us...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shangru Li, Xiaoli Wei, Jiamei Song, Chengrui Zhang, Yonggen Zhang, Yukun Sun
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	dry period body condition score postpartum diseases subclinical ketosis statistical process control Veterinary medicine SF600-1100 Zoology QL1-991
Acceso en línea:	https://doaj.org/article/64129ac353e840c09942bdce7963f6f3
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:64129ac353e840c09942bdce7963f6f3
record_format	dspace
spelling	oai:doaj.org-article:64129ac353e840c09942bdce7963f6f32021-11-25T16:19:28ZEvaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows10.3390/ani111132242076-2615https://doaj.org/article/64129ac353e840c09942bdce7963f6f32021-11-01T00:00:00Zhttps://www.mdpi.com/2076-2615/11/11/3224https://doaj.org/toc/2076-2615The management of body condition score (BCS) during the dry period is associated with the postpartum health outcomes of dairy cows. However, the difference between the actual BCS and the fixed ideal value is not able to accurately predict the occurrence of postpartum diseases. This study aimed to use statistical process control (SPC) technology to monitor the BCS of dry cows, to evaluate the effect of control charts on nutritional strategies, and to explore the utility of SPC in predicting the incidence of postpartum subclinical ketosis (SCK). The BCS and SCK data of 286 cows from the dry off period to 60 days postpartum were collected to set up the early warning function. Three control charts, including a control chart for the average BCS of the herds, for the BCS of each dry cow, and for individual BCS, were established. The early warning signs for postpartum SCK development were: (1) an individual BCS more than 3.5 that remained unchanged for six weeks; (2) a capability index (CPK), an SPC tool, greater than −0.52. Using these parameters, the early warning signs of SCK development were verified in 429 dry cows. The results showed that the accuracy of early warning signal was 0.64 and the precision was 0.26. The control chart showed that the average BCS of dry cows was consistently higher than the expected upper limit of BCS during the experimental period, and that the addition of new cows to the herds increased the average BCS. In summary, the application of SPC technology to monitor the BCS of dry cows was not a good tool for the prediction of postpartum SCK occurrence but was an appropriate tool for guiding positive nutrition strategies.Shangru LiXiaoli WeiJiamei SongChengrui ZhangYonggen ZhangYukun SunMDPI AGarticledry periodbody condition scorepostpartum diseasessubclinical ketosisstatistical process controlVeterinary medicineSF600-1100ZoologyQL1-991ENAnimals, Vol 11, Iss 3224, p 3224 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dry period body condition score postpartum diseases subclinical ketosis statistical process control Veterinary medicine SF600-1100 Zoology QL1-991
spellingShingle	dry period body condition score postpartum diseases subclinical ketosis statistical process control Veterinary medicine SF600-1100 Zoology QL1-991 Shangru Li Xiaoli Wei Jiamei Song Chengrui Zhang Yonggen Zhang Yukun Sun Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows
description	The management of body condition score (BCS) during the dry period is associated with the postpartum health outcomes of dairy cows. However, the difference between the actual BCS and the fixed ideal value is not able to accurately predict the occurrence of postpartum diseases. This study aimed to use statistical process control (SPC) technology to monitor the BCS of dry cows, to evaluate the effect of control charts on nutritional strategies, and to explore the utility of SPC in predicting the incidence of postpartum subclinical ketosis (SCK). The BCS and SCK data of 286 cows from the dry off period to 60 days postpartum were collected to set up the early warning function. Three control charts, including a control chart for the average BCS of the herds, for the BCS of each dry cow, and for individual BCS, were established. The early warning signs for postpartum SCK development were: (1) an individual BCS more than 3.5 that remained unchanged for six weeks; (2) a capability index (CPK), an SPC tool, greater than −0.52. Using these parameters, the early warning signs of SCK development were verified in 429 dry cows. The results showed that the accuracy of early warning signal was 0.64 and the precision was 0.26. The control chart showed that the average BCS of dry cows was consistently higher than the expected upper limit of BCS during the experimental period, and that the addition of new cows to the herds increased the average BCS. In summary, the application of SPC technology to monitor the BCS of dry cows was not a good tool for the prediction of postpartum SCK occurrence but was an appropriate tool for guiding positive nutrition strategies.
format	article
author	Shangru Li Xiaoli Wei Jiamei Song Chengrui Zhang Yonggen Zhang Yukun Sun
author_facet	Shangru Li Xiaoli Wei Jiamei Song Chengrui Zhang Yonggen Zhang Yukun Sun
author_sort	Shangru Li
title	Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows
title_short	Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows
title_full	Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows
title_fullStr	Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows
title_full_unstemmed	Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows
title_sort	evaluation of statistical process control techniques in monitoring weekly body condition scores as an early warning system for predicting subclinical ketosis in dry cows
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/64129ac353e840c09942bdce7963f6f3
work_keys_str_mv	AT shangruli evaluationofstatisticalprocesscontroltechniquesinmonitoringweeklybodyconditionscoresasanearlywarningsystemforpredictingsubclinicalketosisindrycows AT xiaoliwei evaluationofstatisticalprocesscontroltechniquesinmonitoringweeklybodyconditionscoresasanearlywarningsystemforpredictingsubclinicalketosisindrycows AT jiameisong evaluationofstatisticalprocesscontroltechniquesinmonitoringweeklybodyconditionscoresasanearlywarningsystemforpredictingsubclinicalketosisindrycows AT chengruizhang evaluationofstatisticalprocesscontroltechniquesinmonitoringweeklybodyconditionscoresasanearlywarningsystemforpredictingsubclinicalketosisindrycows AT yonggenzhang evaluationofstatisticalprocesscontroltechniquesinmonitoringweeklybodyconditionscoresasanearlywarningsystemforpredictingsubclinicalketosisindrycows AT yukunsun evaluationofstatisticalprocesscontroltechniquesinmonitoringweeklybodyconditionscoresasanearlywarningsystemforpredictingsubclinicalketosisindrycows
_version_	1718413259616288768

Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows

Ejemplares similares