Heifers don't care: no evidence of negative impact on animal welfare of growing heifers when using virtual fences compared to physical fences for grazing

Hamidi, Dina; Grinnell, Natascha Alexandria; Komainda, Martin; Riesch, F.; Horn, Juliane; Ammer, S.; Traulsen, Imke; Palme, Rüpert; Hamidi, M.; Isselstein, J.

doi:10.1016/j.animal.2022.100614

Cited by 29 publications

(26 citation statements)

References 34 publications

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“…As cows weighted between 500 and 900 kg depending on the breed and age, collars made up about 0.3-0.5% of the body weight. These devices fell under the recommended threshold of 3-5% of an animal's body mass (Arnemo et al, 2011;Soulsbury et al, 2020;Hamidi et al, 2022;Sonne et al, 2022).…”

Section: Study Animalsmentioning

confidence: 99%

Classification of behaviors of free-ranging cattle using accelerometry signatures collected by virtual fence collars

et al. 2023

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Precision farming technology, including GPS collars with biologging, has revolutionized remote livestock monitoring in extensive grazing systems. High resolution accelerometry can be used to infer the behavior of an animal. Previous behavioral classification studies using accelerometer data have focused on a few key behaviors and were mostly conducted in controlled situations. Here, we conducted behavioral observations of 38 beef cows (Hereford, Limousine, Charolais, Simmental/NRF/Hereford mix) free-ranging in rugged, forested areas, and fitted with a commercially available virtual fence collar (Nofence) containing a 10Hz tri-axial accelerometer. We used random forest models to calibrate data from the accelerometers on both commonly documented (e.g., feeding, resting, walking) and rarer (e.g., scratching, head butting, self-grooming) behaviors. Our goal was to assess pre-processing decisions including different running mean intervals (smoothing window of 1, 5, or 20 seconds), collar orientation and feature selection (orientation-dependent versus orientation-independent features). We identified the 10 most common behaviors exhibited by the cows. Models based only on orientation-independent features did not perform better than models based on orientation-dependent features, despite variation in how collars were attached (direction and tightness). Using a 20 seconds running mean and orientation-dependent features resulted in the highest model performance (model accuracy: 0.998, precision: 0.991, and recall: 0.989). We also used this model to add 11 rarer behaviors (each< 0.1% of the data; e.g. head butting, throwing head, self-grooming). These rarer behaviors were predicted with less accuracy because they were not observed at all for some individuals, but overall model performance remained high (accuracy, precision, recall >98%). Our study suggests that the accelerometers in the Nofence collars are suitable to identify the most common behaviors of free-ranging cattle. The results of this study could be used in future research for understanding cattle habitat selection in rugged forest ranges, herd dynamics, or responses to stressors such as carnivores, as well as to improve cattle management and welfare.

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Section: Study Animalsmentioning

confidence: 99%

Classification of behaviors of free-ranging cattle using accelerometry signatures collected by virtual fence collars

et al. 2023

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“…I vår studie kunde vi se att förhållandet mellan antal ljudsignaler och antal elstötar förbättrades över tid, dvs. djuren fick färre elstötar per ljudsignal (tabell 3) vilket även det överensstämmer med tidigare studier (Aaser et al, 2022;Campbell et al, 2019;Hamidi et al, 2022;Lomax et al 2019). Vi kunde även se att hela flocken efter inlärningsperioden till stor del uppehöll sig och betade vid den virtuella gränsen (beskrivet mer ingående nedan) vilket även var fallet i en tysk studie med nötkreatur (Hamidi, et al, 2022) och opublicerade resultat från RISE studie med får 2022.…”

Section: Teknikens Inverkan På Djuren Och Lantbrukarens Arbetsinsatsunclassified

Virtuella stängsel: ett flexibelt verktyg för skötsel av naturbetesmarker

Wahlund,

Hiron

2023

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Variation i livsmiljöer knutna till åker- och betesmark har identifierats som avgörande för en varierad flora och fauna i jordbrukslandskapet och särskilt viktiga för den biologiska mångfalden är gräsmarker som betas. Idag sköts många gräsmarker homogent med antingen för högt eller lågt betestryck, vilket kan leda till att marker betas för intensivt eller att de växer igen. Att skapa variation inom en betesmark genom optimerad och kontrollerad styrning av betestrycket utifrån naturvårdsbehov kräver stor arbetsinsats. För att främja den biologiska mångfalden i jordbrukslandskapet finns ett stort behov av flexibla lösningar för skötsel av värdefulla betesmarker. Virtuella stängsel, en teknik där betesdjuren hägnas in digitalt med hjälp av GPS-halsband som avger ljudsignaler och elstötar om djuren går utanför en definierad GPS-gräns (virtuell stängselgräns) skulle kunna underlätta för en mer flexibel och skräddarsydd skötsel av gräsmarker med höga naturvärden. Syftet med studien var att undersöka möjligheten att med hjälp av virtuell stängselteknik hägna in nötkreatur och styra betestrycket i en naturbetesmark. Tekniken testades på sju kvigor under sju veckor från maj-juli 2022. Flera olika virtuella stängselgränser introducerades och flyttades inom en befintlig hage med konventionellt elstängsel runt. Djuren lärde sig snabbt att associera ljudsignalen med de virtuella gränserna och anpassade sin vistelseyta utifrån dessa. Detta avspeglades även i resultatet från vegetationsmätningar som visade på en variation i gräshöjd i olika delar av hagen. Virtuell stängselteknik kan därför vara ett snabbt, flexibelt och effektivt verktyg för skötsel och styrning av betestryck. Vi ser många möjliga naturvårdstillämpningar för tekniken i Sverige men fler studier kring specifika skötselåtgärder bör prioriteras för att få mer kunskap om teknikens fulla potential.

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“…de In this respect, virtual fencing has been proposed as one major advantage of facilitated grazing. 7 Specifically, detailed geoinformation about the condition of grasslands can facilitate informed management decisions, contribute to developing spatially and temporally flexible virtual fences, and verify compliance with agri-environmental measures. 8,9 Subsequently, software-based decision support tools collect large amounts of available information, break them down, and link them (e.g., through models or optimization methods) for the users.…”

Section: Introductionmentioning

confidence: 99%

“…Within PLF, sensor technologies are becoming indispensable for optimizing grazing management and improving pasture utilization 5 , 6 . In this respect, virtual fencing has been proposed as one major advantage of facilitated grazing 7 . Specifically, detailed geoinformation about the condition of grasslands can facilitate informed management decisions, contribute to developing spatially and temporally flexible virtual fences, and verify compliance with agri-environmental measures 8 , 9 .…”

Section: Introductionmentioning

confidence: 99%

UAV LiDAR-based grassland biomass estimation for precision livestock management

Hütt,

Isselstein,

Komainda

et al. 2024

J. Appl. Rem. Sens.

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We present an approach for grassland management using uncrewed aerial vehicles (UAV) LIDAR data and statistical modeling techniques integrated within a softwarebased multi-level information system (SMI). The primary objective is to utilize UAV LiDAR data and statistical modeling techniques within an SMI to accurately estimate compressed sward height (CSH) and above-ground biomass for precision farming applications. As a case study, four UAV LiDAR flights were conducted over rotational grazing farmland, and the collected data were processed to a point cloud. A statistical model was developed to estimate CSH values (R 2 ¼ 0.59, RMSE = 5.9 cm) using LiDAR metrics of the point cloud data. In addition, destructive sampling of grassland facilitated the calibration process, enabling the modeling of biomass based on the CSH values, specifically expressed as above-ground herbage dry biomass (R 2 ¼ 0.89, RMSE ¼ 0.2669 Mg ha −1 ). The collected data further enabled the approximation of biomass across the entire area of interest, which covered ∼200 ha, utilizing a 2.5 × 2.5 m polygon grid. The data were subsequently transferred to an SMI, which operates on the same grid and complements the information, thus offering a comprehensive foundation for decision-making, optimizing grazing systems, and efficient resource allocation. We contribute to advancing precision farming and sustainable grassland management.

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Heifers don't care: no evidence of negative impact on animal welfare of growing heifers when using virtual fences compared to physical fences for grazing

Cited by 29 publications

References 34 publications

Classification of behaviors of free-ranging cattle using accelerometry signatures collected by virtual fence collars

Classification of behaviors of free-ranging cattle using accelerometry signatures collected by virtual fence collars

Virtuella stängsel: ett flexibelt verktyg för skötsel av naturbetesmarker

UAV LiDAR-based grassland biomass estimation for precision livestock management

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