Suitability of feeding and chewing time for estimation of feed intake in dairy cows

Abstract

Monitoring of feeding and rumination behaviour can provide useful information for dairy herd management. The feeding behaviour of dairy cows can be recorded by different techniques, such as video cameras, weighing troughs or chewing sensors. Among feeding characteristics, individual feed intake of cows is of utmost interest, but as weighing troughs have high space and cost requirements they are used primarily in research studies. The objective of the present study was to evaluate whether records on feeding time or chewing activity or a combination of both contain enough information to estimate feed intake with sufficient accuracy. Feed intake and feeding time per cow were recorded by means of weighing troughs. Concurrently, chewing activity of seven cows was recorded by MSR-ART pressure sensors during five to eight measuring days per cow. Feeding and chewing behaviour were evaluated in time slots (1 min) and additionally assigned to feeding bouts for further analysis. The 1 min time slots were classified into feeding/no feeding or chewing/no chewing by the two systems, and agreement was found in 92.2% of the records. On average, cows spent 270 ± 39 min/day at the feeding troughs and chewed 262 ± 48 min/day. The average fresh matter intake (FMI) was 49.6 ± 5.1 kg/day. Feed intake was divided into 9.7 bouts/day during which cows fed in average 27.8 ± 21.7 min/ bout and chewed 27.0 ± 23.1 min/bout. The correlation between FMI and feeding time was r = 0.891 and between FMI and chewing time r = 0.780 overall cows. Hence, both systems delivered suitable information for estimating feed intake.

A Review of Welfare Indicators of Indoor-Housed Dairy Cow as a Basis for Integrated Automatic Welfare Assessment Systems

“…A good alternative might be to measure feeding time, since this can be measured with less costly systems, e.g., chewing activity monitors [86]. Feeding time has been reported to correlate highly with feed intake [87] and was found here to show a similar consistent pattern across welfare issues. Milk yield also shows an almost consistent pattern across welfare issues, with decreased milk production usually indicating reduced welfare (see Table 2).…”

Predicting feed intake using modelling based on feeding behaviour in finishing beef steers

“…Similarly, positive correlations ( r = 0.13; P < 0.05) between time at the feeder and DMI have been reported in beef steers fed a high corn ( Montanholi et al, 2010 ) and high concentrate ( r = 0.42; P < 0.05; Parsons et al, 2020 ) diets, although a negative correlation ( r = −0.16; P < 0.05) with number of visits to the feeder was also noted ( Montanholi et al, 2010 ). Higher correlations between feeding time and FWI ( r = 0.53–0.95) have been noted in dairy cows fed a total mixed ration ( De Mol et al, 2016 , Pahl et al, 2016 ). Feeding is regulated by negative feedback signals from gut-fill to avoid over stretching of the gut wall and metabolic signals based on availability of nutrients ( Forbes and Gregorini, 2015 ).…”

Real Time Analysis of Bioanalytes in Healthcare, Food, Zoology and Botany

“…Wearable biosensors also contribute to in situ monitoring of animals and plants, with applications that include disease detection of livestock [ 174 ], estimating water status of leaves [ 175 ] and many others [ 176 , 177 , 178 ]. As dogs perform many roles for people (e.g., hunting, herding, assisting police and military, companionship and aiding handicapped individuals), wearable sensors have been used on canines to monitor their health status for improving human-canine interactions.…”