The production loss and health issues due to the presence of high bulk milk tank somatic cell count in dairy herds makes it essential to implement a consistent effort to maintain this indicator at levels below those required by law. For veterinary practitioners, providing evidence-based advice to clients in order to reduce risk factors of increasing somatic cell count is a difficult task. Statistical Process Control tools allow to verify with statistical certainty when process performance is improving, staying the same, or getting worse and they can be used in dairy farms. The main purpose of the project was to improve understanding in bulk milk somatic cell count variation related to daily temperature and relative humidity, and to build a model which could be predictive of future performance of somatic cell count. Daily bulk milk samples of thirteen commercial dairy farms included in the study were collected and data on daily mean temperature and relative humidity were used. Statistical analysis was performed using Generalized Additive Mixed Models to assess the impact of climatic variables on somatic cell count. We could describe a regression model which shows that the effect of temperature on response appears approximately linear while the one of humidity varies in a more complex way. The model fits well for all herds except one, and explanations are provided. The model constitutes a solid basis for further study of the relationship between daily temperature and humidity, and daily bulk milk somatic cell count. It will allow to set up a quality control on dairy farm using atmospheric temperature and humidity data. Hence it will be possible to provide evidence-based advice to dairy farmers with the use of control charts created on the basis of our statistical model.

INFLUENCE OF ATMOSPHERIC TEMPERATURE AND RELATIVE HUMIDITY ON BULK MILK SOMATIC CELL COUNT IN DAIRY HERDS

TESTA, FRANCESCO
2012

Abstract

The production loss and health issues due to the presence of high bulk milk tank somatic cell count in dairy herds makes it essential to implement a consistent effort to maintain this indicator at levels below those required by law. For veterinary practitioners, providing evidence-based advice to clients in order to reduce risk factors of increasing somatic cell count is a difficult task. Statistical Process Control tools allow to verify with statistical certainty when process performance is improving, staying the same, or getting worse and they can be used in dairy farms. The main purpose of the project was to improve understanding in bulk milk somatic cell count variation related to daily temperature and relative humidity, and to build a model which could be predictive of future performance of somatic cell count. Daily bulk milk samples of thirteen commercial dairy farms included in the study were collected and data on daily mean temperature and relative humidity were used. Statistical analysis was performed using Generalized Additive Mixed Models to assess the impact of climatic variables on somatic cell count. We could describe a regression model which shows that the effect of temperature on response appears approximately linear while the one of humidity varies in a more complex way. The model fits well for all herds except one, and explanations are provided. The model constitutes a solid basis for further study of the relationship between daily temperature and humidity, and daily bulk milk somatic cell count. It will allow to set up a quality control on dairy farm using atmospheric temperature and humidity data. Hence it will be possible to provide evidence-based advice to dairy farmers with the use of control charts created on the basis of our statistical model.
19-gen-2012
Inglese
dairy cows ; bulk milk ; somatic cell count ; risk factors ; atmospheric temperature ; atmospheric relative humidity ; regression analysis
MORONI, PAOLO
GENCHI, CLAUDIO
Università degli Studi di Milano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/103209
Il codice NBN di questa tesi è URN:NBN:IT:UNIMI-103209