A Pilot Study Using Accelerometers to Characterise the Licking Behaviour of Penned Cattle at a Mineral Block Supplement

Abstract

Identifying the licking behaviour in beef cattle may provide a means to measure time spent licking for estimating individual block supplement intake. This study aimed to determine the effectiveness of tri-axial accelerometers deployed in a neck-collar and an ear-tag, to characterise the licking behaviour of beef cattle in individual pens. Four, 2-year-old Angus steers weighing 368 ± 9.3 kg (mean ± SD) were used in a 14-day study. Four machine learning (ML) algorithms (decision trees [DT], random forest [RF], support vector machine [SVM] and k-nearest neighbour [kNN]) were employed to develop behaviour classification models using three different ethograms: (1) licking vs. eating vs. standing vs. lying; (2) licking vs. eating vs. inactive; and (3) licking vs. non-licking. Activities were video-recorded from 1000 to 1600 h daily when access to supplement was provided. The RF algorithm exhibited a superior performance in all ethograms across the two deployment modes with an overall accuracy ranging from 88% to 98%. The neck-collar accelerometers had a better performance than the ear-tag accelerometers across all ethograms with sensitivity and positive predictive value (PPV) ranging from 95% to 99% and 91% to 96%, respectively. Overall, the tri-axial accelerometer was capable of identifying licking behaviour of beef cattle in a controlled environment. Further research is required to test the model under actual grazing conditions.

Predicción del consumo de materia seca de forraje en vacas lecheras mediante el uso de acelerómetros

“…Esta afirmación es corroborada por Carpinelli et al, (2019), que evaluando el uso de acelerómetros para estimar el CMS en vacas Holstein, manifiestan que las vacas tienen movimientos específicos altamente correlacionados con el consumo de alimento, lo que hace posible la construcción de modelos de predicción. Simanungkalit et al, (2021) empleando acelerómetros en el cuello y la oreja de vacas tipo carne, reportan que los acelerómetros ubicados en el cuello generan señales más consistentes de aceleración y orientación de la cabeza. Estos mismos autores modelando los datos generados por los acelerómetros con algoritmos de aprendizaje automático, lograron determinar el comportamiento alimentario con una exactitud que varió entre el 88 y 98%.…”

Application of Precision Technologies to Characterize Animal Behavior: A Review

“…This behavior was detected with 98.4% accuracy [41] (Table 1a). Another type of behavior was monitored with a neck-collar and ear-tag accelerometer in an intensive system, which considered licking, where the overall performance of both types was acceptable (88 and 98% in accuracy) but with a small advantage in favor of the neck collar [42]. Calves' behaviors were also monitored, such as suckling behaviors.…”

“…The combination of accelerometers and GPS results in a synergistic relationship that exploits the strengths of both sensors to provide a good understanding of ruminants. Australia Accuracy of 88% to 98% in monitoring licking behavior [42] Australia 4-month-old calves suckled fewer times, but for longer [73] United Kingdom Classification of rumination, eating, and other behaviors with precision of 0.83 [74] Pasture-based France The accuracy of prediction of the main behaviors was 98% [40] Semi-enclosed barn United States Accuracy of rumination detection was 86.2% [41] Three dairy farms Italy Accuracy of behavior detection was 85.12% [75] Dairy farm Italy Accuracy of classifying behavior was 96% [76] GPS Extensive United States Cattle followed water more than salt [3] Hungary Weather fronts affected the herd's route [64] Pasture-based Malaysia Observation of the grazing patterns was accurate [63] England Cattle tended to favor shorter material during the day and material of higher crude fiber in the evening [66] Commercial farm Spain Sensor was able to detect hotspots of dung deposition [77] GPS-GPRS Extensive Spain Distance traveled daily was 3147 m [65] Accelerometer, GPS Pasture-based Australia Description of the animals' movement and some behaviors was successful [78] Spain Accuracy of classification of behavior was 93% [70] Accelerometer, RFID Pasture-based Australia Accelerometer correlated highly with the observed duration of drinking events [79] Accelerometer, magnetometer Intensive Tasmania Grazing, ruminating, and resting were identified accurately [80] Accelerometer, cameras Intensive China Accuracy of 94.9% in recognizing behavior [81] Table 1. Cont.…”

Ruminant Lick Blocks, Particularly in China: A Review

“…Therefore, NBs are also called multi-nutrient blocks, among which urea molasses multi-nutrient block is common [25]. The second type, which is called a mineral block (MB) [26], salt block or mineral salt block [27], supplies mainly minerals. MB is made with mainly salt as a carrier, and the salt content usually exceeds 65% (Table 1, MB 1 and MB 2 ), while most NBs are made with molasses as a carrier (Table 1, NB 5 and NB 17 ).…”

“…The measurement of LB intake is required to determine whether the LB meets the needs of animals. It is important to feed LB accurately, which can be measured by an electronic feeder [82] or accelerometer [26]. Ruminants licking an LB is due to their "nutrition wisdom", which stimulates them to seek and consume salt at a level that meets or exceeds their requirement for sodium [83].…”