Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 441636
Title Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity
Author(s) Hertem, T. van; Maltz, E.; Antler, A.; Romanini, C.E.B.; Viazzi, S.; Bahr, C.; Schlageter-Tello, A.; Lokhorst, C.; Berckmans, D.; Halachmi, I.
Source Journal of Dairy Science 96 (2013). - ISSN 0022-0302 - p. 4286 - 4298.
DOI http://dx.doi.org/10.3168/jds.2012-6188
Department(s) LR - Innovation Processes
Publication type Refereed Article in a scientific journal
Publication year 2013
Keyword(s) limb movement variables - dairy-cattle - risk-factors - monitoring rumination - clinical lameness - locomotion score - gait assessment - foot disorders - lying behavior - cows
Abstract 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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