The objective of this study was to validate an ear-tag accelerometer sensor (CowManager SensOor, Agis Automatisering BV, Harmelen, the Netherlands) using direct visual observations in a grazing dairy herd. Lactating crossbred cows (n = 24) were used for this experiment at the University of Minnesota West Central Research and Outreach Center grazing dairy (Morris, MN) during the summer of 2016. A single trained observer recorded behavior every minute for 6 h for each cow (24 cows × 6 h = 144 h of observation total). Direct visual observation was compared with sensor data during August and September 2016. The sensor detected and identified ear and head movements, and through algorithms the sensor classified each minute as one of the following behaviors: rumination, eating, not active, active, and high active. A 2-sided t-test was conducted with PROC TTEST of SAS (SAS Institute Inc., Cary, NC) to compare the percentage of time each cow's behavior was recorded by direct visual observation and sensor data. For total recorded time, the percentage of time of direct visual observation compared with sensor data was 17.9 and 19.1% for rumination, 52.8 and 51.9% for eating, 17.4 and 11.9% for not active, and 7.9 and 21.1% for active. Pearson correlations (PROC CORR of SAS) were used to evaluate associations between direct visual observations and sensor data. Furthermore, concordance correlation coefficient (CCC), bias correction factors, location shift, and scale shift (epiR package of R version 3.3.1; R Foundation for Statistical Computing, Vienna, Austria) were calculated to provide a measure of accuracy and precision. Correlations between visual observations for all 4 behaviors were highly to weakly correlated (rumination: r = 0.72, CCC = 0.71; eating: r = 0.88, CCC = 0.88; not active: r = 0.65, CCC = 0.52; and active: r = 0.20, CCC = 0.19) compared with sensor data. The results suggest that the sensor accurately monitors rumination and eating behavior of grazing dairy cattle. However, active behaviors may be more difficult for the sensor to record than others.
The objective of the study was to develop a grazing algorithm for an ear tag-based accelerometer system (Smartbow GmbH, Weibern, Austria) and to validate the grazing algorithm with data from a noseband sensor. The ear tag has an acceleration sensor, a radio chip, and temperature sensor for calibration and it can monitor rumination and detect estrus and localization. To validate the ear tag, a noseband sensor (Ru-miWatch, Itin and Hoch GmbH, Liestal, Switzerland) was used. The noseband sensor detects pressure and acceleration patterns, and, with a software program specific to the noseband, pressure and acceleration patterns are used to classify data into eating, ruminating, drinking, and other activities. The study was conducted at the
Holstein and crossbred dairy cows from an organic grazing and low-input conventional herd were evaluated for activity and rumination across 4 yr (January 2014 to December 2017). Data were from two herds, an organic grazing (ORG) and a low-input conventional (CONV) that were managed similarly at the University of Minnesota West Central Research and Outreach Center, Morris, MN. Breed groups and total cows across the 4-yr study in the analysis for both herds were Holstein (HO, n = 114), 1964 HO genetic line (H64, n = 83); crossbreds sired by Montbéliarde, Viking Red, and HO (MVH, n = 248), and Normande, Jersey, and Viking Red (NJV, n = 167). During the summer grazing season (May to October) ORG cows were on pasture and supplemented daily with 2.72 kg of corn per cow, and CONV cows were fed a total mixed ration (TMR) in an outdoor confinement dry-lot. During the winter season (November to April) ORG and CONV cows were fed a TMR consisting of corn silage, alfalfa haylage, corn, soybean meal, and minerals in an outwintering lot and a compost barn. Activity (reported in activity units by daily and bihourly periods) and rumination, (min/d and min/2 h) from SCR DataFlow II software, were monitored electronically using HR-LD Tags (SCR Engineers Ltd, Netanya, Israel) for the 4-yr period. Daily activity was greater for 2016 and 2017 (P < 0.05) than for 2014 and 2015 for the ORG and CONV herds. Daily rumination varied by year, and 2015 and 2016 were lower (P < 0.05) than 2014 and 2017 in both herds. The HO and crossbred cows were not different (P > 0.05) for activity in both the ORG and CONV herds. The H64 cows had lower (P < 0.05) rumination than the other breed groups in the ORG and CONV herds. For ORG primiparous cows, the H64 cows had lower rumination than MVH cows, and the ORG multiparous H64 cows had lower (P < 0.05) rumination than HO and MVH breed groups. For CONV primiparous cows, the HO cows had greater (P < 0.05) rumination the other breed groups, and the CONV multiparous HO, MVH, and NJV cows had greater (P < 0.05) rumination than the H64 cows. Results from this study suggest that activity and rumination are different between breeds in the experimental low-input dairy herds.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.