Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

Browse

Filters

2022 - 20245

Settings

Author: search.filters.author.Draganova ISubject: search.filters.subject.Animals

Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Cats (Felis catus): A Validation Study
    (MDPI (Basel, Switzerland), 2023-08-14) Smit M; Ikurior SJ; Corner-Thomas RA; Andrews CJ; Draganova I; Thomas DG; Vanwanseele B
    Animal behaviour can be an indicator of health and welfare. Monitoring behaviour through visual observation is labour-intensive and there is a risk of missing infrequent behaviours. Twelve healthy domestic shorthair cats were fitted with triaxial accelerometers mounted on a collar and harness. Over seven days, accelerometer and video footage were collected simultaneously. Identifier variables (n = 32) were calculated from the accelerometer data and summarized into 1 s epochs. Twenty-four behaviours were annotated from the video recordings and aligned with the summarised accelerometer data. Models were created using random forest (RF) and supervised self-organizing map (SOM) machine learning techniques for each mounting location. Multiple modelling rounds were run to select and merge behaviours based on performance values. All models were then tested on a validation accelerometer dataset from the same twelve cats to identify behaviours. The frequency of behaviours was calculated and compared using Dirichlet regression. Despite the SOM models having higher Kappa (>95%) and overall accuracy (>95%) compared with the RF models (64-76% and 70-86%, respectively), the RF models predicted behaviours more consistently between mounting locations. These results indicate that triaxial accelerometers can identify cat specific behaviours.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Review and update of a Nutrient Transfer model used for estimating nitrous oxide emissions from complex grazed landscapes, and implications for nationwide accounting
    (John Wiley and Sons Inc on behalf of American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, 2022-09-30) Vibart R; Giltrap D; Saggar S; Mackay A; Betteridge K; Costall D; Rollo M; Draganova I; Zhu-Barker. X
    In New Zealand, nitrous oxide emissions from grazed hill pastures are estimated using different emission factors for urine and dung deposited on different slope classes. Allocation of urine and dung to each slope class needs to consider the distribution of slope classes within a landscape and animal behavior. The Nutrient Transfer (NT) model has recently been incorporated into the New Zealand Agricultural GHG Inventory Model to account for the allocation of excretal nitrogen (N) to each slope class. In this study, the predictive ability of the transfer function within the NT model was explored using urine deposition datasets collected with urine sensor and GPS tracker technology. Data were collected from three paddocks that had areas in low (<12°), medium (12-24°), and high slopes (>24°). The NT model showed a good overall predictive ability for two of the three datasets. However, if the urine emission factors (% of urine N emitted as N2 O-N) were to be further disaggregated to assess emissions from all three slope classes or slope gradients, more precise data would be required to accurately represent the range of landscapes found on farms. We have identified the need for more geospatial data on urine deposition and animal location for farms that are topographically out of the range used to develop the model. These new datasets would provide livestock urine deposition on a more continuous basis across slopes (as opposed to broad ranges), a unique opportunity to improve the performance of the NT model.