Conducting studies on the chewing habits of horses may just have got a whole lot easier.
Swiss researchers have found that it is feasible to use an automatic sensor-based measurement system developed for cattle on horses.
Jessica Werner and her colleagues, writing in the journal Livestock Science, said the great benefit of the the RumiWatchSystem was its use of a non-invasive method to monitor jaw movements.
The system consists of a noseband pressure sensor integrated into a halter, supported by a software package.
To investigate the accuracy of the system in horses, five mares and five stallions were equipped with the device.
Additionally, they were observed visually as a reference method.
To ensure similar conditions, the horses were stabled individually and fed twice daily with roughage and twice or three times with concentrate. Three different feeds were offered – hay, haylage and concentrate.
The researchers then compared the results of the visual observation to the automatic measurement.
“The overall agreement of the observed and automatically measured data within all feedstuffs was 93 percent,” they reported.
The agreement of feeding roughage was even higher, with 95% agreement.
However, for concentrate the visual observations and automatic measurements agreed at 91.4 percent.
“The decreased agreement compared to the roughage is due to the high sensitivity of the automated system,” they said.
“Horses tend to display a high amount of lip movements towards the end of the concentrate intake. This is different compared to cattle behaviour and their feeding regime.”
However, the system had not been specifically adapted to horses so far and could be optimized in order to improve accuracy, they wrote. Automatic identification of lip movements without jaw movement could further improve accuracy, they said.
“The system has a high potential to become a reliable tool for research and practical use,” they concluded.
Validation of a sensor-based automatic measurement system for monitoring chewing activity in horses
J. Werner, C. Umstatter, N. Zehner, J.J. Niederhauser, M. Schick.
The abstract can be read here.