The advent of the Global Positioning System (GPS) has transformed our ability to track livestock on rangelands. However, GPS data use would be greatly enhanced if we could also infer the activity timeline of an animal. We tested how well animal activity could be inferred from data provided by Lotek GPS collars, alone or in conjunction with IceRobotics IceTag pedometers. The collars provide motion and head position data, as well as location. The pedometers count steps, measure activity levels, and differentiate between standing and lying positions. We gathered synchronized data at 5-min resolution, from GPS collars, pedometers, and human observers, for free-grazing cattle (n = 9) at the Hatal Research Station in northern Israel. Equations for inferring activity during 5-min intervals (n = 1,475), classified as Graze, Rest (or Lie and Stand separately), and Travel were derived by discriminant and partition (classification tree) analysis of data from each device separately and from both together. When activity was classified as Graze, Rest and Travel, the lowest overall misclassification rate (10%) was obtained when data from both devices together were subjected to partition analysis; separate misclassification rates were 8, 12, and 3% for Graze, Rest and Travel, respectively. When Rest was subdivided into Lie and Stand, the lowest overall misclassification rate (10%) was again obtained when data from both devices together were subjected to partition analysis; misclassification rates were 6, 1, 26, and 17% for Graze, Lie, Stand, and Travel, respectively. The primary problem was confusion between Rest (or Stand) and Graze. Overall, the combination of Lotek GPS collars with IceRobotics IceTag pedometers was found superior to either device alone in inferring animal activity.

Grazing shapes rangelands globally, but it is difficult to study. Acoustic monitoring enables grazing to be described in terms of jaw movements, which are fundamental to how herbivores interact with their foraging environment. In an observational study on Mediterranean herbaceous rangeland, 10 beef cattle cows were monitored continuously over multiple days in two seasons. The algorithm used to analyze the acoustic signal furnished (without classification) a data sample of ≈5 M ingestive and ruminatory jaw movements. These were analyzed as between-event intervals and as minutely rates. The rumination displayed a consistent, strong rhythm and pattern of jaw movements. In contrast, there was no single “signature” jaw movement pattern for grazing (i.e., non-rumination). Although the underlying natural rhythm of rumination dominated non-rumination, it was intermittently and irregularly interrupted by longer intervals, whose size scaled logarithmically. There was evidence of further substructure, with a degree of separation between “grazing” and “resting” in the conventional sense. Three broad grazing styles emerged. In the “intense” style, animals sustained long runs of jaw movements in the natural rhythm, with relatively few interruptions. In the “regular” style, comprising the majority of non-rumination jaw activity, the natural rhythm still dominated, but was punctuated at irregular intervals by eruptions of somewhat longer intervals. The “diffuse” style comprised shorter runs in the natural rhythm, punctuated by highly erratic intervals spanning orders of magnitude. When the jaw movement events were viewed as minutely rates, the non-rumination population showed strong bimodality in the distribution of non-zero rates, with peaks at ≈60 and ≈15 jaw movements min⁻¹, suggesting two modes of grazing. The results strongly support the notion of behavioral grazing intensity and call into question the approach of viewing grazing as a binary state or expecting measures of grazing time to be strongly indicative of intake rate. Rate- and interval-based analyses of information at the jaw movement level can yield a penetrating profile of how an animal interacts with its foraging environment, epitomized in a graphical formulation termed the time accumulation curve. These results strengthen the case for the further development of this sensor technology.

Precision livestock farming (PLF) technologies have been heavily promoted in the past, but the implementation of these technologies is not easy. Numerous technical challenges need to be solved befo...