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Behavior is one of the most important indicators for assessing cattle health and well-being. The objective of this study was to develop and validate a novel algorithm to monitor locomotor behavior of loose-housed dairy cows based on the output of the RumiWatch pedometer (ITIN+HOCH GmbH, Fütterungstechnik, Liestal, Switzerland). Data of locomotion were acquired by simultaneous pedometer measurements at a sampling rate of 10 Hz and video recordings for manual observation later. The study consisted of 3 independent experiments. Experiment 1 was carried out to develop and validate the algorithm for lying behavior, experiment 2 for walking and standing behavior, and experiment 3 for stride duration and stride length. The final version was validated, using the raw data, collected from cows not included in the development of the algorithm. Spearman correlation coefficients were calculated between accelerometer variables and respective data derived from the video recordings (gold standard). Dichotomous data were expressed as the proportion of correctly detected events, and the overall difference for continuous data was expressed as the relative measurement error. The proportions for correctly detected events or bouts were 1 for stand ups, lie downs, standing bouts, and lying bouts and 0.99 for walking bouts. The relative measurement error and Spearman correlation coefficient for lying time were 0.09% and 1; for standing time, 4.7% and 0.96; for walking time, 17.12% and 0.96; for number of strides, 6.23% and 0.98; for stride duration, 6.65% and 0.75; and for stride length, 11.92% and 0.81, respectively. The strong to very high correlations of the variables between visual observation and converted pedometer data indicate that the novel RumiWatch algorithm may markedly improve automated livestock management systems for efficient health monitoring of dairy cows.

Feeding behaviour is an important parameter of animal performance, health and welfare, as well as reflecting levels and quality of feed available. Previously, sensors were only used for measuring animal feeding behaviour in indoor housing systems. However, sensors such as the RumiWatchSystem can also monitor such behaviour continuously in pasture-based environments. Therefore, the aim of this study was to validate the RumiWatchSystem to record cow activity and feeding behaviour in a pasture-based system. The RumiWatchSystem was evaluated against visual observation across two different experiments. The time duration per hour at grazing, rumination, walking, standing and lying recorded by the RumiWatchSystem was compared to the visual observation data in Experiment 1. Concordance Correlation Coefficient (CCC) values of CCC=0.96 for grazing, CCC=0.99 for rumination, CCC=1.00 for standing and lying and CCC=0.92 for walking were obtained. The number of grazing and rumination bouts within one hour were also analysed resulting in Cohen's Kappa (κ)=0.62 and κ=0.86 for grazing and rumination bouts, respectively. Experiment 2 focused on the validation of grazing bites and rumination chews. The accordance between visual observation and automated measurement by the RumiWatchSystem was high with CCC=0.78 and CCC=0.94 for grazing bites and rumination chews, respectively. These results indicate that the RumiWatchSystem is a reliable sensor technology for observing cow activity and feeding behaviour in a pasture based milk production system, and may be used for research purposes in a grazing environment.

Observation of ingestive and rumination behaviors of dairy cows may assist in detecting diseases, controlling reproductive status, and estimating intake. However, direct observation of cows on pasture is time consuming and can be difficult to realize. Consequently, different systems have been developed to automatically record behavioral characteristics; among them is the RumiWatch System (RWS; Itin and Hoch GmbH, Liestal, Switzerland). Until now, the RWS has not been thoroughly validated under grazing conditions. The aim of the current study was to validate the RWS, against direct observation, in measuring ingestive and rumination behaviors of dairy cows during grazing and supplementation in the barn. A further objective was to examine whether it is possible to refine the algorithm used by the evaluation software RumiWatch Converter 0.7.3.2 to improve the accuracy of the RWS. The data were collected from an experiment carried out with 18 lactating Holstein cows in a crossover block design including 3 treatments and 3 measuring periods. All cows grazed night and day, 19 h/d, and were either unsupplemented or supplemented, with chopped whole-plant corn silage, or chopped whole-plant corn silage mixed with a protein concentrate. During the measuring periods, cows were equipped with the RumiWatch Halter, and their ingestive and rumination behaviors were recorded concurrently by the RumiWatch Halter and by direct observation (690 × 10 min). Comparison of concurrently measured data shows that the RWS detected jaw movements reliably, but classification errors occurred. A low relative prediction error of ≤0.10 for the number of rumination boluses, rumination chews, and total eating chews was found. A high relative prediction error of >0.10 was found for the number of prehension bites and time spent in prehension and eating. Both converter versions performed equally well in differentiating ingestive and rumination behaviors when cows were supplemented in the barn or when grazing and supplementation activities were combined. For grazing cows, with no supplementation, more reliable results for the total number of eating chews, rumination chews, prehension bites, and time spent in these activities were obtained, by using the RumiWatch Converter 0.7.3.11. In light of these findings, further research is warranted to improve the accuracy of the RWS and to allow a differentiation between mastication chews and prehension bites while eating.

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