Dairy farmers already strap accelerometers onto their cows’ ankles to detect oestrus and lameness. A new study suggests those same devices could do something far more ambitious: automatically tell whether the animals are feeling good.
Conventional welfare audits typically focus on the absence of negative indicators such as lameness or dirty animals, but the absence of bad welfare is not the same as the presence of good welfare. Qualitative Behaviour Assessment, or QBA, captures positive states by asking a trained observer to score an animal’s body language against descriptive terms such as happy, relaxed, fearful or bored. The problem is that QBA requires trained personnel and considerable time, a serious constraint for a sector facing chronic labour shortages.
The team enrolled sixteen farms that already used activity monitoring technology, but only one sensor company, Peacock Technologies, could provide sufficiently detailed raw data for analysis. That left four farms, one in northern England and three in southern and western Scotland, contributing sensor records from 107 adult lactating cows of Holstein, Friesian or Holstein-Friesian breeding. Fifty of the animals were housed in free-stall barns with bedded mattresses and fifty-seven were at pasture. The IceTag sensors recorded mean standing and lying durations, bout frequencies and durations, maximum bout durations and step counts, all derived from the same kind of data farmers already collect for oestrus detection.
On each farm, a single trained researcher carried out QBA once during the housing period in February 2022 and once at pasture in June 2022, always immediately after morning milking. Twenty cows were randomly selected per visit, watched for five minutes and scored against twenty descriptive terms from the Welfare Quality protocol on a 12.5-centimetre visual analogue scale. Animals being treated for poor health were excluded, though mild lameness was not a disqualifier. The researchers also verified that their samples were representative: repeated random subsamples of cows and days produced mean lying times and step counts that were statistically indistinguishable from the full herd distributions.
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