The objective of this study was to determine the role of terrain in the foraging behaviour of beef cows grazing hilly Mediterranean grasslands. The study was conducted in eastern Galilee, Israel, during 2002 and 2003 on two similar 28-ha paddocks encompassing distinct terrain types in terms of slope and rock cover. The paddocks were continuously grazed by cows from mid winter (January–February) to autumn (September) at two different stocking rates (1.1 and 0.56 cows per ha). From early June to the end of September the cows were offered poultry litter ad libitum as a supplementary feed. The location and activity of cows were monitored with GPS collars during four periods in each year: early spring (February–March), late spring (April), early summer (June) and late summer (August). Herbage mass was measured at the beginning of each of these periods. During early and late spring, when the herbage mass and nutritive value of herbage were high, the cows spent 40–50% of the day grazing, with peaks in the morning and afternoon. In the dry, late summer period (August), grazing of the herbage was 20–22% of the day, occurring only in the early morning and late afternoon with sporadic bouts of grazing until midnight. In all periods the cows tended to prefer the flattest terrain sites. As the herbage mass declined to 1000–1500 kg ha–1, the exploitation of the pasture during grazing became increasingly similar among the different terrains. Even on relatively small paddocks, where grazing pressure was close to the full potential of the site, free-ranging cows tended to prefer less sloping and rocky sites. It is concluded that the grazing strategy of beef cows is determined by the interaction between terrain, the distribution of the herbage mass and the nutritive value of the herbage. As herbage mass declines during the growing season, the distribution of grazing becomes uniform and all terrain types are exploited.

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.