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Modeling cow somatic cell count using sensor data as input to generalized additive models

Anglart, Dorota and Hallen-Sandgren, Charlotte and Waldmann, Patrik and Wiedemann, Martin and Emanuelson, Ulf (2020). Modeling cow somatic cell count using sensor data as input to generalized additive models. Journal of Dairy Research. 87 , PII S0022029920000692 , 282-289
[Research article]

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Abstract

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.

Authors/Creators:Anglart, Dorota and Hallen-Sandgren, Charlotte and Waldmann, Patrik and Wiedemann, Martin and Emanuelson, Ulf
Title:Modeling cow somatic cell count using sensor data as input to generalized additive models
Series Name/Journal:Journal of Dairy Research
Year of publishing :2020
Volume:87
Article number:PII S0022029920000692
Number of Pages:8
Publisher:CAMBRIDGE UNIV PRESS
ISSN:0022-0299
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 402 Animal and Dairy Science > Animal and Dairy Science.
Keywords:Additive model, automatic milking rotary, somatic cell count, udder health
URN:NBN:urn:nbn:se:slu:epsilon-p-108275
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-108275
Additional ID:
Type of IDID
DOI10.1017/S0022029920000692
Web of Science (WoS)000572945900004
ID Code:23399
Faculty:VH - Faculty of Veterinary Medicine and Animal Science
Department:(VH) > Dept. of Clinical Sciences
(VH) > Dept. of Animal Breeding and Genetics
Deposited By: SLUpub Connector
Deposited On:03 May 2021 19:23
Metadata Last Modified:04 May 2021 05:01

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