Classification of sheep urination events using accelerometers to aid improved measurements of livestock contributions to nitrous oxide emissions

Lush, Lucy; Wilson, Rory P.; Holton, Mark D.; Hopkins, Philip F.; Marsden, Karina A.; Chadwick, David R.; King, Andrew J.

doi:10.1016/j.compag.2018.04.018

Cited by 38 publications

(26 citation statements)

References 26 publications

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“…To date, most studies divided the multi-sensor data into segments using fixed-time segments [68][69][70] or a sliding sample window with a fixed length [38,71]. However, several studies testing the size of the window showed that it influences classification accuracy and the identification of short behaviours [53,[72][73][74]. Indeed, an individual can express both short and long behaviours, such as burst swimming in lemon sharks or a prey capture in Adélie penguins compared to normal swimming behaviour [37,75].…”

Section: Discussionmentioning

confidence: 99%

Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology

et al. 2020

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The identification of sea turtle behaviours is a prerequisite to predicting the activities and time-budget of these animals in their natural habitat over the long term. However, this is hampered by a lack of reliable methods that enable the detection and monitoring of certain key behaviours such as feeding. This study proposes a combined approach that automatically identifies the different behaviours of free-ranging sea turtles through the use of animal-borne multi-sensor recorders (accelerometer, gyroscope and time-depth recorder), validated by animal-borne video-recorder data. We show here that the combination of supervised learning algorithms and multi-signal analysis tools can provide accurate inferences of the behaviours expressed, including feeding and scratching behaviours that are of crucial ecological interest for sea turtles. Our procedure uses multi-sensor miniaturized loggers that can be deployed on free-ranging animals with minimal disturbance. It provides an easily adaptable and replicable approach for the long-term automatic identification of the different activities and determination of time-budgets in sea turtles. This approach should also be applicable to a broad range of other species and could significantly contribute to the conservation of endangered species by providing detailed knowledge of key animal activities such as feeding, travelling and resting.

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Section: Discussionmentioning

confidence: 99%

Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology

et al. 2020

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“…Published research using the same adhesive attachment mechanism used here demonstrated successful attachment for ca. 30 days 47 . Provided a VHF transponder is used, a ca.…”

Section: Discussionmentioning

confidence: 99%

“…Females often adopt a 'squat' posture when performing urine scent marks, but whether the signal produced in this posture could be discerned from a similar posture adopted when defecating has yet to be established. Scent marking has not yet been studied by concurrent use of GPS and accelerometers (see 47 for an example on livestock elimination events), but doing so may allow researchers to assign both time and location to scent mark observations, addressing the current methodological limitations identified above.…”

Section: A Novel Approach Using Biologging Techniquesmentioning

confidence: 99%

Monitoring canid scent marking in space and time using a biologging and machine learning approach

Bidder

Virgilio

Hunter

et al. 2020

Sci Rep

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for canid species, scent marking plays a critical role in territoriality, social dynamics, and reproduction. However, due in part to human dependence on vision as our primary sensory modality, research on olfactory communication is hampered by a lack of tractable methods. in this study, we leverage a powerful biologging approach, using accelerometers in concert with GpS loggers to monitor and describe scent-marking events in time and space. We performed a validation experiment with domestic dogs, monitoring them by video concurrently with the novel biologging approach. We attached an accelerometer to the pelvis of 31 dogs (19 males and 12 females), detecting raised-leg and squat posture urinations by monitoring the change in device orientation. We then deployed this technique to describe the scent marking activity of 3 guardian dogs as they defend livestock from coyote depredation in California, providing an example use-case for the technique. During validation, the algorithm correctly classified 92% of accelerometer readings. High performance was partly due to the conspicuous signatures of archetypal raised-leg postures in the accelerometer data. Accuracy did not vary with the weight, age, and sex of the dogs, resulting in a method that is broadly applicable across canid species' morphologies. We also used models trained on each individual to detect scent marking of others to emulate the use of captive surrogates for model training. We observed no relationship between the similarity in body weight between the dog pairs and the overall accuracy of predictions, although models performed best when trained and tested on the same individual. We discuss how existing methods in the field of movement ecology can be extended to use this exciting new data type. This paper represents an important first step in opening new avenues of research by leveraging the power of modern-technologies and machine-learning to this field. Scent marking is a critically important form of olfactory communication, shaping affiliative and agonistic interactions within and across species. The use of scent marks provides animals a means to defend resources 1,2 , advertise availability to mates 3-5 , and communicate with conspecifics 6,7. It also serves to minimise the risk of injuries from agonistic encounters with potential competitors 2. Prey species are also known to 'eavesdrop' on these communications to avoid contact with their predators 8. Thus, scent marking is a critical communication system, the nuances of which dictate where animals occur on the landscape, with implications for individual fitness and, ultimately, population dynamics. Despite its importance, research on this and other forms of olfactory communication in the wild is hampered by a lack of tractable methods. This is due in part to human dependence on vision as our primary sensory modality. If new methods could be developed that address the deficiencies in our olfactory senses using modern technology, this could open up numerous avenues for future research on this significa...

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“…Important amounts of cow excreta on small areas may lead to localized releases of nitrogen into the soil. Identifying such areas could help to adapt management practices accordingly [ 6 ]. Finally, focusing on the relationship between ruminants and their environment is probably an interesting way to explore changes of behavior due to challenging situations [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Use of Predicted Behavior from Accelerometer Data Combined with GPS Data to Explore the Relationship between Dairy Cow Behavior and Pasture Characteristics

Riaboff

Couvreur

Madouasse

et al. 2020

Sensors

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Our aim in this study was to investigate whether the behaviors of dairy cows on pasture, predicted with accelerometer data and combined with GPS data, can be used to better understand the relationship between behaviors and pasture characteristics. During spring 2018, 26 Holstein cows were equipped with a 3D-accelerometer and a GPS sensor fixed on a neck-collar for five days. The cows grazed alternatively in permanent and in temporary grasslands. The structural elements, soil moisture, slope and botanical characteristics were identified. Behaviors were predicted every 10 s from the accelerometer data and combined with the GPS data. The time-budgets expressed in each characterized zone of 8 m × 8 m were calculated. The relation between the time-budgets and pasture characteristics was explored with a linear mixed model. In the permanent grassland, dairy cows spent more time under a tree to ruminate (p < 0.001) and to rest (p < 0.001) and more time to graze in areas with Holcus lanatus (p < 0.001). In the temporary grassland, behavior was influenced by the external environment (presence of other animals on the farm; p < 0.05). Thus, this methodology seems relevant to better understand the relationship between the behaviors of dairy cows and grazing conditions to develop precision grazing.

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Classification of sheep urination events using accelerometers to aid improved measurements of livestock contributions to nitrous oxide emissions

Abstract: A. (2018). Classification of sheep urination events using accelerometers to aid improved measurements of livestock contributions to nitrous oxide emissions. Computers and Electronics in Agriculture, 150,[170][171][172][173][174][175][176][177]

Cited by 38 publications

References 26 publications

Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology

Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology

Monitoring canid scent marking in space and time using a biologging and machine learning approach

Use of Predicted Behavior from Accelerometer Data Combined with GPS Data to Explore the Relationship between Dairy Cow Behavior and Pasture Characteristics

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