8.5. Discussion: rumen sensing, feed intake &amp; precise feeding

Halachmi, I.; Tello, A. Schlageter; Fernández, A. Peña; Hertem, T. van; Sibony, V.; Weyl-Feinstein, S.; Verbrugge, A.; Bonneau, M.; Neilson, R.

doi:10.3920/978-90-8686-815-5_38

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“…Other sensors (that detect lying (Steensels et al, 2012), BW changes (Maltz, 1997) or feeding behaviour (Halachmi et al, 2004a and2004b;Miron et al, 2004;Halachmi et al, 2015a and2015b) could potentially create an even more accurate model. A previous study (Steensels et al, 2012) indicated that a model will perform better when it is calibrated on the same farm where it is applied, because between-farm differences in, for example, management routines and housing facilities are excluded.…”

Section: Indicating Research Gapmentioning

confidence: 99%

A decision-tree model to detect post-calving diseases based on rumination, activity, milk yield, BW and voluntary visits to the milking robot

Steensels

Antler

Bahr

et al. 2016

animal

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Early detection of post-calving health problems is critical for dairy operations. Separating sick cows from the herd is important, especially in robotic-milking dairy farms, where searching for a sick cow can disturb the other cows' routine. The objectives of this study were to develop and apply a behaviour-and performance-based health-detection model to post-calving cows in a roboticmilking dairy farm, with the aim of detecting sick cows based on available commercial sensors. The study was conducted in an Israeli robotic-milking dairy farm with 250 Israeli-Holstein cows. All cows were equipped with rumination-and neck-activity sensors. Milk yield, visits to the milking robot and BW were recorded in the milking robot. A decision-tree model was developed on a calibration data set (historical data of the 10 months before the study) and was validated on the new data set. The decision model generated a probability of being sick for each cow. The model was applied once a week just before the veterinarian performed the weekly routine post-calving health check. The veterinarian's diagnosis served as a binary reference for the model (healthy-sick). The overall accuracy of the model was 78%, with a specificity of 87% and a sensitivity of 69%, suggesting its practical value.

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