Journal Articles

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Variations in the 24 h temporal patterns and time budgets of grazing, rumination, and idling behaviors in grazing dairy cows in a New Zealand system.
(Oxford University Press, 2023-01-28) Iqbal MW; Draganova I; Henry Morel PC; Todd Morris S
This study investigated the variations in the temporal distributions and the lengths of times utilized for grazing, ruminating, and idling behaviors by grazing dairy cows over 24 h. Spring-calved lactating dairy cows (N = 54) from three breeds, Holstein-Friesian (HFR), Jersey (JE), and KiwiCross (KC) in different lactations (1st, 2nd, 3rd) and with different breeding worth index values (103 < BW > 151) were selected. The cows were managed through a rotational grazing scheme and milked once a day at 0500 hours. The cows grazed mainly pasture and consumed additional feeds (maize silage and turnips) in the summer and autumn seasons. AfiCollar was used to record grazing and rumination behaviors (min/h) in the individual cows throughout the lactation period (~270 d). The time neither utilized for grazing nor rumination was counted as idling behavior (min/h). A repeat measure design with PROC MIXED was performed in SAS considering the effects of breed, lactation, individual cow, the hour of the day, season, day within the season, and supplementary feed within the season to evaluate the difference in grazing, rumination, and idling behaviors. Hour of the day, season, day within season, and supplementary feed had significant effects on grazing, rumination, and idling behaviors. Regardless of the season and supplementary feed, cows spent most of the daytime grazing and most of the nighttime ruminating. Grazing activity remained consistently high throughout the day with two peaks around dawn and dusk and a short peak around midnight. Rumination activity remained high from the late evening until early morning. Grazing and ruminating patterns were similar between different breeds and lactations, however, JE cows grazed slightly longer than HFR and KC, and first-lactation cows grazed slightly longer than those in higher lactations. The onset and cessation of grazing activity by the cows were adjusted according to varying day lengths by season. Cows finished grazing earlier when they consumed additional supplements or silage along with pasture. Cows from different breed groups and lactations spent most of their 24 h grazing followed by ruminating and idling. Season and supplementary feed potentially affected the variations in behavior time budgets. These findings should support improving measures for grazing management to address pasture allocation and additional feed demands, and animal welfare in varying environmental and/or managemental conditions.
The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Dogs (Canis familiaris): A Validation Study
(MDPI (Basel, Switzerland), 2024-09-13) Redmond C; Smit M; Draganova I; Corner-Thomas R; Thomas D; Andrews C; Fullwood DT; Bowden AE
Assessing the behaviour and physical attributes of domesticated dogs is critical for predicting the suitability of animals for companionship or specific roles such as hunting, military or service. Common methods of behavioural assessment can be time consuming, labour-intensive, and subject to bias, making large-scale and rapid implementation challenging. Objective, practical and time effective behaviour measures may be facilitated by remote and automated devices such as accelerometers. This study, therefore, aimed to validate the ActiGraph® accelerometer as a tool for behavioural classification. This study used a machine learning method that identified nine dog behaviours with an overall accuracy of 74% (range for each behaviour was 54 to 93%). In addition, overall body dynamic acceleration was found to be correlated with the amount of time spent exhibiting active behaviours (barking, locomotion, scratching, sniffing, and standing; R2 = 0.91, p < 0.001). Machine learning was an effective method to build a model to classify behaviours such as barking, defecating, drinking, eating, locomotion, resting-asleep, resting-alert, sniffing, and standing with high overall accuracy whilst maintaining a large behavioural repertoire.
How Lazy Are Pet Cats Really? Using Machine Learning and Accelerometry to Get a Glimpse into the Behaviour of Privately Owned Cats in Different Households
(MDPI (Basel, Switzerland), 2024-04-19) Smit M; Corner-Thomas R; Draganova I; Andrews C; Thomas D; Friedrich CM
Surprisingly little is known about how the home environment influences the behaviour of pet cats. This study aimed to determine how factors in the home environment (e.g., with or without outdoor access, urban vs. rural, presence of a child) and the season influences the daily behaviour of cats. Using accelerometer data and a validated machine learning model, behaviours including being active, eating, grooming, littering, lying, scratching, sitting, and standing were quantified for 28 pet cats. Generalized estimating equation models were used to determine the effects of different environmental conditions. Increasing cat age was negatively correlated with time spent active (p < 0.05). Cats with outdoor access (n = 18) were less active in winter than in summer (p < 0.05), but no differences were observed between seasons for indoor-only (n = 10) cats. Cats living in rural areas (n = 7) spent more time eating than cats in urban areas (n = 21; p < 0.05). Cats living in single-cat households (n = 12) spent more time lying but less time sitting than cats living in multi-cat households (n = 16; p < 0.05). Cats in households with at least one child (n = 20) spent more time standing in winter (p < 0.05), and more time lying but less time sitting in summer compared to cats in households with no children (n = 8; p < 0.05). This study clearly shows that the home environment has a major impact on cat behaviour.

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