Evidence map›Paper›PMID 38003174›Full record

ArticleAnimals : an open access journal from MDPI2023

Behavioral Adaptations of Nursing Brangus Cows to Virtual Fencing: Insights from a Training Deployment Phase.

Shelemia Nyamuryekung'e, Andrew Cox, Andres Perea, Richard Estell, Andres F Cibils, John P Holland, Tony Waterhouse, Glenn Duff, Micah Funk, Matthew M McIntosh and 3 more

Open access · goldAbstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.4field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 3 countries.

Shelemia Nyamuryekung'eDivision of Food Production and Society, Norwegian Institute of Bioeconomy Research (NIBIO), PB 115, N-1431 Ås, Norway.ORCID 0000-0001-9323-0208
Andrew CoxDepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA.
Andres PereaDepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA.
Richard EstellUnited States Department of Agriculture-Agriculture Research Service, Jornada Experimental Range, Las Cruces, NM 88003, USA.
Andres F CibilsUnited States Department of Agriculture Southern Plains Climate Hub, United States Department of Agriculture-Agriculture Research Service, Oklahoma and Central Plains Agricultural Research Center, El Reno, OK 73036, USA.ORCID 0000-0002-4733-6463
John P HollandSRUC Hill and Mountain Research Centre, Scotland's Rural College, Kirkton Farm, Crianlarich, Perthshire FK20 8RU, UK.ORCID 0000-0003-0183-8465
Tony WaterhouseSRUC Hill and Mountain Research Centre, Scotland's Rural College, Kirkton Farm, Crianlarich, Perthshire FK20 8RU, UK.ORCID 0000-0003-3666-6077
Glenn DuffDepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA.
Micah FunkDepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA.
Matthew M McIntoshUnited States Department of Agriculture-Agriculture Research Service, Jornada Experimental Range, Las Cruces, NM 88003, USA.ORCID 0000-0002-8957-8735
Sheri SpiegalUnited States Department of Agriculture-Agriculture Research Service, Jornada Experimental Range, Las Cruces, NM 88003, USA.
Brandon BestelmeyerUnited States Department of Agriculture-Agriculture Research Service, Jornada Experimental Range, Las Cruces, NM 88003, USA.
Santiago UtsumiDepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA.
New Mexico State University · USUnited States Department of Agriculture · USScotland's Rural College · GBNorwegian Institute of Bioeconomy Research · NO

Funding

National Institute of Food and Agriculture 12726269
6 · The paper itself

Abstract

Virtual fencing systems have emerged as a promising technology for managing the distribution of livestock in extensive grazing environments. This study provides comprehensive documentation of the learning process involving two conditional behavioral mechanisms and the documentation of efficient, effective, and safe animal training for virtual fence applications on nursing Brangus cows. Two hypotheses were examined: (1) animals would learn to avoid restricted zones by increasing their use of containment zones within a virtual fence polygon, and (2) animals would progressively receive fewer audio-electric cues over time and increasingly rely on auditory cues for behavioral modification. Data from GPS coordinates, behavioral metrics derived from the collar data, and cueing events were analyzed to evaluate these hypotheses. The results supported hypothesis 1, revealing that virtual fence activation significantly increased the time spent in containment zones and reduced time in restricted zones compared to when the virtual fence was deactivated. Concurrently, behavioral metrics mirrored these findings, with cows adjusting their daily travel distances, exploration area, and cumulative activity counts in response to the allocation of areas with different virtual fence configurations. Hypothesis 2 was also supported by the results, with a decrease in cueing events over time and increased reliance with animals on audio cueing to avert receiving the mild electric pulse. These outcomes underscore the rapid learning capabilities of groups of nursing cows in responding to virtual fence boundaries.

Indexed as

accelerometer and activityanimal trackingconditional behaviorGPS locationprecision livestock farmingvirtual fence

Identifiers

PMID38003174
PMCPMC10668737
OpenAlexW4388761105

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.