Assessing learning, behaviour, and stress level in goats while testing a virtual fencing training protocol DOI Creative Commons
Lena Marie Wilms, Dina Hamidi,

C H U Lüntzel

et al.

animal, Journal Year: 2024, Volume and Issue: 19(2), P. 101413 - 101413

Published: Dec. 28, 2024

Virtual fencing (VF) is a modern technology using Global Positioning System-enabled collars which emit acoustic signals and, if the animal does not respond, electric pulses. Studies with cattle indicate successful learning and no distinct negative impact on animals' behaviours stress level. However, number of studies testing VF goats relatively small. In this study, we used to test training protocol recently applied heifers assess development goats' avoid pulse, their behaviour, faecal cortisol metabolites (FCMs) as an indicator for physiological in grazing experiment. Twenty adult 'Blobe' offspring were divided into two groups assigned or physical treatment cross-over design periods 12 days each. The involved virtual fence at one side paddock, gradually introduced over first 2 (additional posts visual support). On day eight, areas enlarged by shifting treatment. experiment lasted 4 h per day. During time, following recorded via instantaneous scan sampling all every min: grazing, lying, standing, standing vigilant, walking, running. Additionally, samples collected once, twice daily FCM concentrations measured. delivered pulses duration signals. each goat was calculate 'success ratio'. A significant increase success ratio general decrease signal association group Behavioural analyses revealed clear influence except vigilant. Virtually fenced stood significantly more vigilant than physically ones. free-moving kids could have had influence. effect concentrations, decreased time. summary, showed signs when avoiding receiving responding appropriately higher occurrence vigilance behaviour may suggest insecurity, but did increased stress. Future research needs confirm these results under practical conditions.

Language: Английский

Smart technologies to improve the management and resilience to climate change of livestock housing: a systematic and critical review DOI Creative Commons
Francesco Bordignon, Giorgio Provolo, Elisabetta Riva

et al.

Italian Journal of Animal Science, Journal Year: 2025, Volume and Issue: 24(1), P. 376 - 392

Published: Jan. 24, 2025

Language: Английский

Citations

3
Training cattle for virtual fencing: Different approaches to determine learning success DOI Creative Commons
Dina Hamidi, Natascha A. Grinnell, Martin Komainda

et al.

Applied Animal Behaviour Science, Journal Year: 2024, Volume and Issue: 273, P. 106220 - 106220

Published: March 16, 2024

Virtual fencing (VF) offers promising future perspectives for grazing, as it simplifies through the use of GPS-coordinated VF-lines. Each animal is equipped with a VF-collar, which emits an acoustic signal when approaches The stops immediately turns around but if continues to move towards VF-line, electric pulse emitted. VF based on animal's ability learn associate pulse, and thus, avoid by reacting appropriately signal. intention this study was identify heifers are able VF-system during 12-day-period how successful learning can be evaluated using three different approaches: i) reaction score; ii) collar-stored data; iii) integrated mode change function. 16 Fleckvieh were enrolled in divided into two groups eight. They not familiar prior study. On first day VF-collars assigned adjacent pastures. behaviour four per group continuously observed observers behaviours scored according ethogram (2 h a.m., 2 p.m.). We analysed changes over phases indication learning, measuring (i) behavioural reactions signals pulses, (ii) signals, success ratio confidence ratio. development calculation our way weighing against proportion signals. Further, (iii) we assessed time took device shift from teach operating mode, internal function VF-collars. modes due animals' 20th correct response without receiving pulse. validated until successive rounds (Collar restart start round eight) found significant difference (p<0.0001) between faster occurring two. All suggested learning. From results, separated a) b) interact Therefore, combining necessary ensure sustained

Language: Английский

Citations

11
Smart technologies for sustainable pasture-based ruminant systems: A review DOI

Sara Marchegiani,

G. Gislon, R. Marino

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: 10, P. 100789 - 100789

Published: Jan. 18, 2025

Language: Английский

Citations

1
Harnessing virtual fencing for more effective and adaptive agri-environment schemes to conserve grassland biodiversity DOI Creative Commons
Frank Wätzold, Frank Jauker, Martin Komainda

et al.

Biological Conservation, Journal Year: 2024, Volume and Issue: 297, P. 110736 - 110736

Published: Aug. 1, 2024

Virtual fencing (VF) is an emerging technology that creates virtual boundaries for livestock. Collars equipped with positioning systems, such as GPS, emit acoustic warning signals if animal approaches the fence and electric impulse it continues to move forward, deterring from crossing fence. Compared physical fences, combined enable precise tracking of individual animals out small areas within pastures at high spatio-temporal resolutions low cost. VF has potential enhance agri-environment schemes (AES) aimed conserving biodiversity in three ways. (1) Many existing grassland AES focus on limiting livestock density and/or regulating timing grazing. Monitoring compliance these contract conditions costly, which puts risk. GPS can help overcome issues by continuously monitoring grazing (2) Grazing even densities leads levels biodiversity. Applying exclude provides structural associated organismic diversity. could incentivise farmers (3) patches endangered plants or nests meadow birds may negatively affect small-scale populations species. Unmanned aerial vehicles automated picture analyses be used detect valuable patches, transmit information remunerate them out. The article will explore ideas a conceptual level discuss their benefits drawbacks.

Language: Английский

Citations

6
Mapping activity of grazing cattle using commercial virtual fencing technology DOI Creative Commons
Kareemah Chopra, Tom C. Cameron,

Roger C. Beecroft

et al.

Frontiers in Veterinary Science, Journal Year: 2025, Volume and Issue: 12

Published: March 12, 2025

Identifying where and how grazing animals are active is crucial for informed decision-making in livestock conservation management. Virtual fencing systems, which use animal-mounted location tracking sensors to automatically monitor manage the movement space-use of livestock, increasingly being used control as part Precision Livestock Farming (PLF) approaches. The virtual systems often able capture additional information beyond animal location, including activity levels environmental such temperature, but this data not always made available end user an interpretable form. In study we demonstrate a commercial system (Nofence®) can be map spatiotemporal distribution context grazing. We first Nofence® index measurements correlate strongly with direct in-situ observations intensity by individual cattle. Using methods adapted from ecology analysis home range, subsequently cumulative average cattle spatially mapped analyzed over time using two different approaches: simple computationally efficient cell-count method novel version more complex Brownian Bridge Movement Model. further highlight same also variations temperature. This highlights generated could provide valuable insights managers, potentially leading improved production efficiencies or outcomes.

Language: Английский

Citations

0
Unlocking potential, facing challenges: A review evaluating virtual fencing for sustainable cattle management DOI

Jana Musinska,

Sylvie Skalíčková, Pavel Nevrkla

et al.

Livestock Science, Journal Year: 2025, Volume and Issue: unknown, P. 105693 - 105693

Published: March 1, 2025

Language: Английский

Citations

0
Evaluating virtual fencing as a tool to manage beef cattle for rotational grazing across multiple years DOI Creative Commons

Alexandra J. Harland,

Francisco Novais, Carolyn Fitzsimmons

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 381, P. 125166 - 125166

Published: April 6, 2025

Language: Английский

Citations

0
May the Extensive Farming System of Small Ruminants Be Smart? DOI Creative Commons
Rosanna Paolino, Adriana Di Trana, Adele Coppola

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(9), P. 929 - 929

Published: April 24, 2025

Precision Livestock Farming (PLF) applies a complex of sensor technology, algorithms, and multiple tools for individual, real-time livestock monitoring. In intensive systems, PLF is now quite widespread, allowing the optimisation management, thanks to early recognition diseases possibility monitoring animals’ feeding reproductive behaviour, with an overall improvement their welfare. Similarly, systems represent opportunity improve profitability sustainability extensive farming including those small ruminants, rationalising use pastures by avoiding overgrazing controlling animals. Despite distribution in several parts world, low profit relatively high cost devices cause delays implementing ruminants compared dairy cows. Applying these animals requires customisation systems. many cases, unit prices sensors are higher than developed large due miniaturisation production costs associated lower numbers. Sheep goat farms often mountainous remote areas poor technological infrastructure ineffective electricity, telephone, internet services. Moreover, ruminant usually advanced age farmers, contributing local initiatives implementation. A targeted literature analysis was carried out identify technologies already applied or at stage development management grazing animals, particularly sheep goats, effects on nutrition, production, animal The current developments include wearable, non-wearable, network technologies. review involved main fields application can help most suitable managing goats contribute selecting more sustainable efficient solutions line environmental welfare concerns.

Language: Английский

Citations

0
Observing grazing patterns with collar-mounted accelerometers and spatial data DOI Creative Commons
Kilian Obermeyer, Manfred Kayser, J. Isselstein

et al.

Frontiers in Animal Science, Journal Year: 2025, Volume and Issue: 6

Published: May 8, 2025

In pasture-based dairy farming, animal behavior data can improve data-driven pasture management. Information on the grazing of cows be retrieved from sensor-based data. However, this approach generally requires sophisticated sensor equipment and involves labor-intensive observations. As an alternative, use simple commonly used collar-mounted accelerometers global navigation system services (GNSS) receivers was investigated. our on-farm study, grazed in a rotational or continuous system, with higher sward lower height, respectively. indicator activity, overall dynamic body acceleration (ODBA) calculated accelerometer After differentiating process (forage uptake) into steps (i.e., moving to next feeding station) without true standing) GNSS data, only negligible effect ODBA found. The short swards (3.47 m s −2 ) than tall (2.88 ). also affected by time day, major activity around dusk. These findings show potential collars research patterns cattle monitoring for any three-dimensional existing commercial technology, which allows wide in-field application.

Language: Английский

Citations

0
UAV LiDAR-based grassland biomass estimation for precision livestock management DOI Creative Commons
Christoph Hütt, J. Isselstein, Martin Komainda

et al.

Journal of Applied Remote Sensing, Journal Year: 2024, Volume and Issue: 18(01)

Published: March 14, 2024

We present an approach for grassland management using uncrewed aerial vehicles (UAV) LIDAR data and statistical modeling techniques integrated within a software-based multi-level information system (SMI). The primary objective is to utilize UAV LiDAR SMI accurately estimate compressed sward height (CSH) above-ground biomass precision farming applications. As case study, four flights were conducted over rotational grazing farmland, the collected processed point cloud. A model was developed CSH values (R2=0.59, RMSE = 5.9 cm) metrics of cloud data. In addition, destructive sampling facilitated calibration process, enabling based on values, specifically expressed as herbage dry (R2=0.89, RMSE=0.2669 Mg ha−1). further enabled approximation across entire area interest, which covered ∼200 ha, utilizing 2.5×2.5 m polygon grid. subsequently transferred SMI, operates same grid complements information, thus offering comprehensive foundation decision-making, optimizing systems, efficient resource allocation. contribute advancing sustainable management.

Language: Английский

Citations

2