Automatic assessment of dairy cows' rumen function over time and links to feed changes and milk production

A 3-dimensional (3D) vision-based system was previously designed to automatically estimate the rumen motility of individual cows. This longitudinal study aimed to explore the associations between 3D vision-based rumen function assessment and dairy cow feed changes and milk production on a commercial farm. The 3D vision system was attached to an automatic milking robot to estimate the ruminal contraction frequency and rumen fill in 42 lactating cows during each milking event for 66 d. Additionally, we collected data on milk production, milk composition, general health, and changes in feeding practices. The 3D vision system showed that half the cows displayed a drastic decrease in the estimated rumen fill when all cows began grazing. The grazing and decreased rumen fill were also associated with herd-level milk fat depression. Over the 66 d, one cow was detected with reduced milk production and suspected rumen dysfunction by the farmer. The 3D vision system, however, identified this cow as having sudden decreases in estimated ruminal contraction frequency and rumen fill 4 d before detection by the farmer. In this longitudinal study, the 3D vision-based rumen function assessment system showed potential as a useful management-supporting tool for dairy farmers. The system, however, requires further validation with more cows of various breeds and ages. We suggest validating the 3D vision system with rumen boluses, quantified adjustments in feeding practices, more cases with ruminal dysfunction, and systematic health assessments for future studies.

Saved in:
Bibliographic Details
Main Authors: Song, X., van Mourik, S., Bokkers, E.A.M., Groot Koerkamp, P.W.G., van der Tol, P.P.J.
Format: Article/Letter to editor biblioteca
Language:English
Subjects:Life Science,
Online Access:https://research.wur.nl/en/publications/automatic-assessment-of-dairy-cows-rumen-function-over-time-and-l
Tags: Add Tag
No Tags, Be the first to tag this record!