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Automatic assessment of feeding, ruminating and locomotion behaviours in dairy cows naturally affected by diseases during peripartum period
Mahmoud, F., D'Andrea, L., Alsaaod...
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Objectives
Changes in cow behaviour are one of the most important criteria to assess animal welfare and health. Several parameters can be used to build up an early disease warning system aiming to gain higher animal health standards, above all during the transition period when a negative energy balance may favour the onset of diseases. Considering the premises, the goal of the study was to assess and compare the feeding, ruminating and locomotion behaviour between cows naturally affected by diseases and healthy cows in the first week after calving.
Materials and methods
Forty-two, free-stall-housed, pluriparous Holstein x Friesian cows were enrolled from 14 days (d) before up to 1 week (wk) after calving. Based on the output of a 3-dimensional accelerometer placed either in a halter equipped with a nose-band sensor or a pedometer [RumiWatch®, ITIN+HOCH GmbH, Fütterungstechnik, Liestal, Switzerland], different feeding, rumination and locomotion behaviors were continuously recorded during this period. Eating time, ruminating time, ruminating boluses, eating chews, ruminating chews, other activity time (time spent not in eating, ruminating or drinking), lying time, standing time, walking time, lie down, stand up, lying bouts, standing bouts, walking bouts and number of strides were the parameters considered. Cows’ health status was continuously monitored by means of general clinical examination (every other day) and weekly complete blood analysis [d -14 and d -7 pre-calving, day of calving (d 0) as well as + d 7 post-calving]. Animals affected by ≥1 disease were considered sick.
RumiWatch® data were converted into 24-hours summaries, and days around calving (d -1, d 0 and d +1) were excluded from the analysis. The mean values of wk -2 (from d -14 to d -8), wk -1 (from d -7 to d -2) and wk +1 (from d +2 to d +7) relative to calving were calculated. Moreover, activities registered on the day, the disease was first clinically diagnosed (dd0), one and two days before disease diagnosis were also described (dd -1 and dd -2, respectively). Lastly, differences between dd0 vs. dd-1 (ΔD1), dd0 vs. wk -1 (ΔD2), and wk +1 vs. wk -1 (Δweeks) were assessed.
Results
At the end of the clinical monitoring phase, cows were divided in group S (n=24 sick cow; all of them diagnosed in wk +1) and group H (n=18 healthy cows). No intra-group difference was observed between wk -2 and -1, for both of them. In group S, eating and ruminating parameters were significantly decreased in wk +1 compared to wk -1, while no difference was detected in group H, for the same time period. In groups S and H, standing and walking time as well as number of strides were significantly increased in wk +1 compared to wk -1. Lying time was instead significantly decreased in wk +1 compared to wk -1, in both groups.
Regarding the inter-group difference in feeding and rumination behaviours, at wk +1 and dd0, eating and ruminating time, eating chews and ruminating chews, as well as ruminating boluses, were significantly lower in group S compared to group H, while other activity time was significantly higher. For ΔD2 and Δweeks, the difference between eating and ruminating time, eating and ruminating chews was significantly lower in group S compared to group H. Regarding the locomotion behaviours, at wk +1 and dd-2, the lying time in group S was significantly higher compared to group H, while the standing time was significantly lower. In addition, the number of strides was significantly lower in group S compared to group H, at wk +1.
Conclusions
The present study investigates for the first time behavioural changes over time of cows associated with spontaneous diseases. The combined use of Rumiwatch® noseband sensor and pedometer allows to detect the disease state mainly at the day of clinical diagnosis. The model considering the change over time of the variables eat chews, ruminate chews and other activity time achieved the highest accuracy in detecting cows with a health disorder in week +1. The results of the study show that novel precision dairy farming technologies may provide essential support for early disease detection, allowing to improve animals’ health and well-being as well as the overall farm efficiency.
Keywords: Behaviour, accelerometer, cow, precision farming, peripartum.
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