Modeling cow somatic cell count using sensor data as input to generalized additive models.
Additive model
automatic milking rotary
somatic cell count
udder health
Journal
The Journal of dairy research
ISSN: 1469-7629
Titre abrégé: J Dairy Res
Pays: England
ID NLM: 2985125R
Informations de publication
Date de publication:
Aug 2020
Aug 2020
Historique:
pubmed:
5
9
2020
medline:
15
7
2021
entrez:
5
9
2020
Statut:
ppublish
Résumé
This research paper presents a study investigating if sensor data from an automatic milking rotary could be used to model cow somatic cell count (composite milk SCC: CMSCC). CMSCC is valuable for udder health monitoring and individual cow udder health surveillance could be improved by predicting CMSCC between routine samplings. Data regularly recorded in the automatic milking rotary, in one German dairy herd, were collected for analysis. The cows (Holstein-Friesian, n = 372) were milked twice daily and sampled once weekly in afternoon milkings for 8 weeks for CMSCC. From the potential independent variables, including quarter conductivity, milk flow, blood in milk, kick-offs, not milked quarters and incomplete milkings, new variables that combined quarter data were created. Past period records, i.e. lags, of up to seven days before the actual CMSCC sampling event were added in the dataset to investigate if they were of use in modeling the cell count. Univariable generalized additive models (GAM) were used to screen the data to select potential independent variables. Furthermore, several multivariable GAM were fitted in order to compare the importance of the potential independent variables and to explore how the model performance would be affected by using data from various number of days before the CMSCC sampling event. The result of the model selection showed that the best explanation of CMSCC was provided by the model incorporating all significant variables from the variable screening for the seven preceding days, including the day of the CMSCC sampling event. However, using data from only three days before the CMSCC sampling event is suggested to be sufficient to model CMSCC. Variables combining conductivity quarter data, together with quarter conductivity, are suggested to be important in describing CMSCC. We conclude that CMSCC can be modeled with a high degree of explanation using the information routinely recorded by the milking robot.
Identifiants
pubmed: 32883374
doi: 10.1017/S0022029920000692
pii: S0022029920000692
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM