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- Anglart, Dorota
(författare)
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Indicators of mastitis and milk quality in dairy cows : data, modeling, and prediction in automatic milking systems
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Methods for generating predictions of important and generally accepted indicators of udder inflammation and poor milk quality, such as somatic cell count (SCC) or changes in milk homogeneity, are few. The aim of this thesis was to investigate methods to identify indicators of mastitis and poor milk quality in dairy cows using data generated by automatic milking systems (AMS).The first part of the project investigated the relationship between SCC and data regularly recorded by the AMS using models that could capture nonlinear associations between the explanatory variables and the outcome. This information could be used in modeling the SCC. Furthermore, three statistical methods, generalized additive model, random forest and multilayer perceptron, were compared for their ability to predict SCC using data generated by the AMS. The results showed that equally low prediction error was obtained using generalized additive model or multilayer perceptron for prediction of SCC based on AMS data.The second part explored the dynamics of changes in milk homogeneity in cows milked in AMS using descriptive statistics for clots collected by inline filters, scored for density. Clots were found among certain cows and cow periods and appeared in new quarters over time. Models were fitted for detecting and predicting clots in single cow milkings as well as for detecting clots in milkings over a longer period. The models successfully distinguished periods of milking free of changes in milk homogeneity, although the detection and prediction performance was poor. The prediction target and severity grade of each density category is discussed.
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| 2. |
- Anglart, Dorota, et al.
(författare)
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Modeling cow somatic cell count using sensor data as input to generalized additive models
- 2020
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Ingår i: Journal of Dairy Research. - 0022-0299 .- 1469-7629. ; 87, s. 282-289
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Tidskriftsartikel (refereegranskat)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.
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