By Antler, A. and Bahr, C. and Berckmans, D. and Halachmi, I. and Lokhorst, C. and Maltz, E. and Romanini, C. E. B. and Schlageter-Tello, A. and Van Hertem, T. and Viazzi, S., Journal of Dairy Science, 2013
Research Paper Web Link / URL:
http://www.sciencedirect.com/science/article/pii/S0022030213003561
http://www.sciencedirect.com/science/article/pii/S0022030213003561
Description
The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm’s daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow’s performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY = 0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4 d before diagnosis; the slope coefficient of the daily milk yield 4 d before diagnosis; the nighttime to daytime neck activity ratio 6 d before diagnosis; the milk yield week difference ratio 4 d before diagnosis; the milk yield week difference 4 d before diagnosis; the neck activity level during the daytime 7 d before diagnosis; the ruminating time during nighttime 6 d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well.
The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm’s daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow’s performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY = 0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4 d before diagnosis; the slope coefficient of the daily milk yield 4 d before diagnosis; the nighttime to daytime neck activity ratio 6 d before diagnosis; the milk yield week difference ratio 4 d before diagnosis; the milk yield week difference 4 d before diagnosis; the neck activity level during the daytime 7 d before diagnosis; the ruminating time during nighttime 6 d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well.
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