By Krieter, Joachim and Miekley, Bettina and Traulsen, Imke, Livestock Science, 2012
Research Paper Web Link / URL:
http://www.sciencedirect.com/science/article/pii/S187114131200220X
http://www.sciencedirect.com/science/article/pii/S187114131200220X
Description
The aim of this study was to explore wavelet filtering for early detection of mastitis and lameness. Data were recorded at the Karkendamm dairy research farm between January 2009 and October 2010. In total, data of 237 cows with 46,427 cow days were analysed. Mastitis was specified according to three definitions: (1) udder treatment, (2) udder treatment and/or somatic cell count with more than 100,000 cells/ml and (3) udder treatment and/or somatic cell count exceeding 400,000 cells/ml. Lameness treatments were used to determine two definitions of lameness. They differed in the length of the corresponding disease block: (1) day of treatment including three days before treatment, (2) day of treatment including five days before treatment. Milk electrical conductivity and cow activity were utilised as indicator parameters for detection of mastitis and lameness, respectively. These values were filtered by wavelets. Filtered values of cow activity and the residuals between the observed and filtered values of milk electrical conductivity were applied to a classic and a self-starting CUSUM chart to identify blocks of disease (days of disease). Regarding performance of mastitis detection, the classic chart showed better results than the self-starting chart. The block sensitivity ranged between 72.6% and 76.3% (self-starting chart between 72.1% and 74.5%) while the obtained error rates were between 69.2% and 94.4% (self-starting chart between 73.4% and 95.7%). For both charts, block sensitivity and error rate improved from definition (1) to (3). In the case of lameness detection, the block sensitivity of the classic chart varied between 40.4% and 48.3%, which was lower than the block sensitivities of the self-starting chart (47.2% and 63.5%). The error rates of lameness detection were also high (90.6% to 93.3%). In conclusion, wavelet analysis seems to be applicable to mastitis and lameness detection in dairy cows. Results could probably be enhanced if more traits for a multivariate consideration are used.
The aim of this study was to explore wavelet filtering for early detection of mastitis and lameness. Data were recorded at the Karkendamm dairy research farm between January 2009 and October 2010. In total, data of 237 cows with 46,427 cow days were analysed. Mastitis was specified according to three definitions: (1) udder treatment, (2) udder treatment and/or somatic cell count with more than 100,000 cells/ml and (3) udder treatment and/or somatic cell count exceeding 400,000 cells/ml. Lameness treatments were used to determine two definitions of lameness. They differed in the length of the corresponding disease block: (1) day of treatment including three days before treatment, (2) day of treatment including five days before treatment. Milk electrical conductivity and cow activity were utilised as indicator parameters for detection of mastitis and lameness, respectively. These values were filtered by wavelets. Filtered values of cow activity and the residuals between the observed and filtered values of milk electrical conductivity were applied to a classic and a self-starting CUSUM chart to identify blocks of disease (days of disease). Regarding performance of mastitis detection, the classic chart showed better results than the self-starting chart. The block sensitivity ranged between 72.6% and 76.3% (self-starting chart between 72.1% and 74.5%) while the obtained error rates were between 69.2% and 94.4% (self-starting chart between 73.4% and 95.7%). For both charts, block sensitivity and error rate improved from definition (1) to (3). In the case of lameness detection, the block sensitivity of the classic chart varied between 40.4% and 48.3%, which was lower than the block sensitivities of the self-starting chart (47.2% and 63.5%). The error rates of lameness detection were also high (90.6% to 93.3%). In conclusion, wavelet analysis seems to be applicable to mastitis and lameness detection in dairy cows. Results could probably be enhanced if more traits for a multivariate consideration are used.
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