The effects of global navigation satellite system (GNSS) collars on cattle ( Bos taurus) behaviour

Manning, Jaime; Cronin, G. M.; González, L. A.; Hall, Evelyn; Merchant, Andrew; Ingram, Lachlan J.

doi:10.1016/j.applanim.2016.11.013

Cited by 19 publications

(18 citation statements)

References 33 publications

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“…Refer to Table 1 in Manning et al [16] for a list of observed behaviours and observation schedule. Peak grazing times were determined for the GNSS collar period (Days 6-10), based on the behavioural observation data when grazing was accounted for ≥50% of recorded behaviours per hour for the herd.…”

Section: Behaviour Observations and Analysesmentioning

confidence: 99%

“…On the morning of Day 11, cattle were again brought back into the yards and the collars removed before the cows were returned to the same paddock for a further five days. No significant behavioural effects of cattle wearing a GNSS collar were found, nor was a habituation period required [16]. The GNSS collars received a positional fix every 10 s using the Navstar Global Positioning System, enabling the investigation of paddock utilisation by cattle over the five days (Days 6-10) collars were worn (GNSS collar period).…”

Section: Gnss Collar Deployment and Analysesmentioning

confidence: 99%

“…A model was devised for each of the 12 individual behaviours (refer to Manning et al [16] for studied behaviours). Fixed effects considered for inclusion were Day, Age, and Pregnancy.…”

Section: Statistical Analysesmentioning

confidence: 99%

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The Behavioural Responses of Beef Cattle (Bos taurus) to Declining Pasture Availability and the Use of GNSS Technology to Determine Grazing Preference

et al. 2017

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Abstract:Combining technologies for monitoring spatial behaviour of livestock with technologies that monitor pasture availability, offers the opportunity to improve the management and welfare of extensively produced beef cattle. The aims of the study were to investigate changes to beef cattle behaviour as pasture availability changed, and to determine whether Global Navigation Satellite System (GNSS) technology could determine livestock grazing preference and hence improve pasture management and paddock utilisation. Data derived from GNSS collars included distance travelled and location in the paddock. The latter enabled investigation of individual animal interactions with the underlying Normalised Difference Vegetation Index (NDVI) and pasture biomass of the paddock. As expected, there was a significant temporal decrease in NDVI during the study and an increase in distance travelled by cattle (P < 0.001; r 2 = 0.88). The proportion of time budget occupied in grazing behaviour also increased (P < 0.001; r 2 = 0.71). Cattle showed a partial preference for areas of higher pasture biomass/NDVI, although there was a large amount of variation over the course of the study. In conclusion, cattle behaviour changed in response to declining NDVI, highlighting how technologies that monitor these two variables may be used in the future as management tools to assist producers better manage cattle, to manipulate grazing intensity and paddock utilisation.

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Section: Behaviour Observations and Analysesmentioning

confidence: 99%

Section: Gnss Collar Deployment and Analysesmentioning

confidence: 99%

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The Behavioural Responses of Beef Cattle (Bos taurus) to Declining Pasture Availability and the Use of GNSS Technology to Determine Grazing Preference

et al. 2017

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“…Collars were mounted on the 26 chosen animals at 5:00 p.m. on 30 April 2018 and removed 5 days later at the same time, which corresponds to both the duration of the complete grazing rotation and the autonomy of the sensor battery. It should be noted that a period of habituation to the device by the cows was not necessary as the complete device represented approximately 0.1% of the animal’s weight [ 16 ]. The data collected from 21:00 h to 05:00 h were not used in this study to focus on the daytime behavior exclusively.…”

Section: Methodsmentioning

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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“…In the state of the art work, some of the most relevant studies apply AmI to the following fields: Monitoring the position of the animal and analysis of the behavior of the herd : By monitoring the position of livestock, it is possible to study the behavior of both individual animals and the herd as a whole. Since it may be argued that wearable technology annoys animals and alters their habitual behavior, studies that deny this theory have been carried out [ 14 ]. Wearable technology designed for animals usually takes the form of necklaces that make use of GPS, although there are also devices that make use of other technologies such as ZigBee [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Combination of Multi-Agent Systems and Wireless Sensor Networks for the Monitoring of Cattle

Barriuso

González

Paz

et al. 2018

Sensors

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Precision breeding techniques have been widely used to optimize expenses and increase livestock yields. Notwithstanding, the joint use of heterogeneous sensors and artificial intelligence techniques for the simultaneous analysis or detection of different problems that cattle may present has not been addressed. This study arises from the necessity to obtain a technological tool that faces this state of the art limitation. As novelty, this work presents a multi-agent architecture based on virtual organizations which allows to deploy a new embedded agent model in computationally limited autonomous sensors, making use of the Platform for Automatic coNstruction of orGanizations of intElligent Agents (PANGEA). To validate the proposed platform, different studies have been performed, where parameters specific to each animal are studied, such as physical activity, temperature, estrus cycle state and the moment in which the animal goes into labor. In addition, a set of applications that allow farmers to remotely monitor the livestock have been developed.

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The effects of global navigation satellite system (GNSS) collars on cattle ( Bos taurus) behaviour

Cited by 19 publications

References 33 publications

The Behavioural Responses of Beef Cattle (Bos taurus) to Declining Pasture Availability and the Use of GNSS Technology to Determine Grazing Preference

The Behavioural Responses of Beef Cattle (Bos taurus) to Declining Pasture Availability and the Use of GNSS Technology to Determine Grazing Preference

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

Combination of Multi-Agent Systems and Wireless Sensor Networks for the Monitoring of Cattle

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