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Body weight (BW) is a critical component for monitoring animal weight gain, body condition, nutritional status. Remote animal weighing systems facilitate frequent collection of animal BW, however, datasets often contain spurious data. The objective of this study was to describe the utility of using a remote Walk-over-Weigh system and subsequent methods for data cleaning. Beef steers (n = 10) were tagged with Electronic RFID tags (EID) in an improved pasture (~12.1 hectares) containing Bermuda and Tall Fescue and inter-seeded with Annual Ryegrass and grazed from Feb. – Dec. 2020. Static chute weights (n = 80) were collected monthly, and a WOW system placed by the water to remotely collect BW (n = 5,466). Data were first loaded into Program R and scanned for spurious data using each of 2 primary approaches, 1) the whole herd and individual means ± 1 standard deviation (SD) calculated daily or over the entire trial and 2) each of 3 data smoothing algorithms, which included a quadratic growth model, cubic splines, and polynomial regression. Then, data with spurious observations removed were paired with static chute weights and fitted to a linear model to measure accuracy (mean bias) and precision (R2) of WOW data. Whole herd mean ± 1SD and individual daily mean ± 1SD identified 1,204 and 1,516 spurious data, with mean bias of -12.46 and -15.37 KG and R2 of 0.90 and 0.68, respectively. Smoothing functions identified 1,707, 4,684, and 4,776 spurious points, with a mean bias of 13.61, -19.78, and 12.58 KG, and R2 of 0.94, 0.70, and 0.87 for quadratic growth models, cubic splines, and polynomial regression, respectively. These results indicate the utility of using a simple WOW system to collect data for measuring growth curves and using weight data in a real-time fashion to make management and marketing decisions.

Animal behavior plays a crucial role as an indicator of animal health and nutritional status and serves as an indicator of animal growth. The objective of this study was to build an ethogram describing behavior in grazing cattle. We collected video and accelerometer data from crossbred steers (n = 10) used as part of a larger grazing study on the HH Leveck Animal Research Center, Mississippi State, MS. Daily Diary accelerometers (Wildbyte® technologies, Swansea) were programmed to collect magnetometer and accelerometer data at 40 Hz and attached to the GPS collars fitted on the animals prior to their release into a 10-hectare pasture of Tall Fescue and Bermudagrass, overseeded with Annual Ryegrass. Automated camera traps (Bushnell Essential®) were synced with UTC time and programmed to record 30-second video clips when triggered. Approximately 387,000 accelerometer signals representing 161 minutes of behavior from 10 animals were recorded, and behavior classified according to 1 of 5 categories: traveling, foraging, resting, ruminating, and grooming (Kilgour et al., 2012). Categorized accelerometer data was used to train a random forest model (Liaw and Weiner, 2002) in Program R (R Core Team, 2020), which resulted in a model sensitivity of 0.97, 0.93, 0.90, 0.87, and 0.80 for Traveling, Foraging, Resting, Ruminating, and Grooming, respectively, and an overall model accuracy of 0.95. Behaviors were aggregated into behavior bouts, and a daily ethogram was calculated for March 2019. This revealed that the steers spent the most amount of time traveling, an average of 1,026 minutes per day. This behavior was followed, in the average length of time, by foraging and resting for 205 ± 52.8 minutes and 31.8 ± 28.2 minutes per day, respectively. These results indicate the ability to accurately build a behavioral ethogram for grazing cattle and warrant further study in future research and livestock management.

Previous research in feedlot studies has demonstrated that cattle feeding behavior is driven by internal metabolic processes and external environmental stimuli and serves as an indicator of animal health, nutritional status, and growth and feed quality and availability. However, technology has only recently allowed measurement of foraging behavior in grazing cattle. Objectives of this study were to measure frequency and duration of foraging bouts, meals, and total distance traveled during meals in grazing steers. The study was conducted as part of a larger grazing study on a 10-hectare Bermudagrass and Tall Fescue pasture, overseeded with Annual Ryegrass, located at the HH Leveck Animal Research Center, Mississippi State, MS. Using tri-axial accelerometers and GPS information from 10 crossbred steers, we examined foraging and meal bout frequency and duration and distance and speed traveled per meal for the period of March 2019. Observed animal behavior was used to train a randomforest model to predict foraging behavior, with model accuracy and sensitivity of 0.95 and 0.93, respectively. We found individual foraging bouts occurred on average 2,849 bouts per day and took on average 5.0 ± 1.8 min (range: 3–9 min), and that steers fed on average 205 ± 52.8 min/day (range: 120–270 min/day). Steers had an average of 9.5 ± 2.9 meals/day, that took on average, 89.3 ± 93.9 min/meal (range: 0.5–938.5 min/meal). Steers traveled an average of 412.4 ± 93.9 meters per meal, with an average foraging speed between 0 and 0.63 m/s. Traveling distance while foraging was positively correlated with meal length (0.83, P < 0.01) and foraging speed (0.70, P < 0.01). These results show that cattle grazing behavior can be accurately quantified in grazing cattle and warrants further research to examine associations between animal efficiency and performance, forage quality, and pasture management.

It is often difficult to test hypotheses about how and why animal movement responds to environmental conditions, and at what spatial scales movement decisions are made, all of which are critical for sound management. We used fractal dimension (D) as a measure of tortuosity because it described animal movement patterns and was useful for testing hypotheses about effects of sex, home-range size, monthly rainfall, and reproductive phase on movement paths and for detecting changes in movement patterns of animals across a range of movement distances. We captured and fitted 33 (18 females and 15 males) white-tailed deer (Odocoileus virginianus) with global positioning system collars. We found that females moved more tortuously (D 5 1.75 6 0.035 SE) than males (D 5 1.549 6 0.025). These differences in movement were related to home-range size of females but not of males. Rainfall predicted D for females; thus, females may have been able to forage more intensively in a smaller area due to increased forage availability. Fractal D of females was greatest during the parturition period (1.468 6 0.02), likely due to restricted movements in smaller areas or increased foraging. Home-range size of males was similar in spring and rut, but D was lower during rut, indicating that deer changed movement patterns within previously established home ranges. Movement patterns were similar at path lengths related to foraging patches and home ranges, whereas movement patterns changed as path length approximated the size of habitat patches.