Validation of an Accelerometer Sensor-Based Collar for Monitoring Grazing and Rumination Behaviours in Grazing Dairy Cows.

Abstract

Simple SummaryGrazing behaviour measures herbage intake and varies according to herbage type, climate conditions, and social status of the animal within the herd. Rumination behaviour indicates the digestive efficiency and health status of the animal and varies according to herbage quality, type, and maturity. Both intake and digestive efficiency substantially affect the animal’s performance. Knowledge about grazing and rumination behaviours could improve the health and welfare of animals, pasture management, and overall production efficiency in a grazing-based system. Dairy farming in New Zealand is characterised by pasture-based grazing systems. In this system, monitoring the individual animal behaviour, especially grazing and rumination, has been less explored, primarily because visual observation is labour intensive and subject to human error. Advancements in precision livestock farming (PLF) have introduced behaviour monitoring tools that can record grazing and rumination behaviours. However, the adoption of PLF is still a challenge in the farming community due to the lack of research-based knowledge to address the accuracy of PLF tools in a grazing-based system. The objective of this study was to evaluate the accuracy of a grazing and rumination behaviour monitoring device (AfiCollar) for grazing dairy cows.This study evaluated the accuracy of a sensor-based device (AfiCollar) to automatically monitor and record grazing and rumination behaviours of grazing dairy cows on a real-time basis. Multiparous spring-calved dairy cows (n = 48) wearing the AfiCollar were selected for the visual observation of their grazing and rumination behaviours. The total observation period was 36 days, divided into four recording periods performed at different times of the year, using 12 cows in each period. Each recording period consisted of nine daily observation sessions (three days a week for three consecutive weeks). A continuous behaviour monitoring protocol was followed to visually observe four cows at a time for each daily observation session, from 9:00 a.m. to 5:00 p.m. Overall, 144 observations were collected and the data were presented as behaviour activity per daily observation session. The behaviours visually observed were also recorded through an automated AfiCollar device on a real-time basis over the observation period. Automatic recordings and visual observations were compared with each other using Pearson’s correlation coefficient (r), Concordance correlation coefficient (CCC), and linear regression. Compared to visual observation (VO), AfiCollar (AC) showed slightly higher (10%) grazing time and lower (4%) rumination time. AC results and VO results had strong associations with each other for grazing time (r = 0.91, CCC = 0.71) and rumination time (r = 0.89, CCC = 0.80). Regression analysis showed a significant linear relationship between AC and VO for grazing time (R2 = 0.83, p < 0.05) and rumination time (R2 = 0.78, p < 0.05). The relative prediction error (RPE) values for grazing time and rumination time were 0.17 and 0.40, respectively. Overall, the results indicated that AfiCollar is a reliable device to accurately monitor and record grazing and rumination behaviours of grazing dairy cows, although, some minor improvements can be made in algorithm calibrations to further improve its accuracy.