Modeling cow somatic cell count using sensor data as input to generalized additive models. Issue 3 (4th August 2020)

• Record Type: Journal Article
• Authors: Anglart, Dorota; Hallén-Sandgren, Charlotte; Waldmann, Patrik; Wiedemann, Martin; Emanuelson, Ulf
• Is Part Of: Journal of dairy research. Volume 87:Issue 3(2020)
• Page Start: 282
• Page End: 289
• Publication Date: 2020-08-04
• Subjects: Additive model, -- automatic milking rotary, -- somatic cell count, -- udder health; Dairying -- Periodicals; Dairying -- Research -- Periodicals; 636.214205
• DOI: 10.1017/S0022029920000692
• Languages: English
• ISSNs: 0022-0299

Abstract

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.