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Predicting dry matter intake (DMI) for beef cattle on extensive rangelands presents a significant challenge to determining stocking rates. Traditionally, DMI is estimated by taking full body weight (BW) multiplied by a percentage selected based on animal class, production phase, and forage quality, which introduces tremendous levels of accumulated error at the herd level. Animal Unit Months (AUMs) are utilized to simplify the determination of stocking rate (animal units per area per a specific period of time) of pastures. This challenge represents a tremendous opportunity to leverage precision technology to account for individual animal variation in BW and growth, with subsequent impacts on herd-level decisions. Therefore, the objective of this study was to utilize precision livestock technology (PLT) collected data to build a precision system model (PSM) to evaluate the differences in predicted DMI using either initial BW, expected mid-season BW, or PLT measured BW. The PSM model was built utilizing BW data measured using SmartScale (C-Lock Inc.) for 60 days during the summers of 2021 and 2022 on Angus yearling steers (average initial BW 393.71 ± 39.01 and 315.23 ± 53.91 kg, n = 130 and 124, for year 2021 and 2022, respectively) on native pastures at the South Dakota State University Cottonwood Field Station. The PSM evaluated total forage consumption and deterministically estimated hectares of pasture required to meet the herd forage demands relative to available biomass (kg/ha). the PSM estimated 4.49% and 6.94% more DMI at the herd level compared with using initial BW for years 2021 and 22, respectively, and 1.64% less DMI than mid-season BW in 2021. This resulted in an additional 14.03 and 17.95 ha required for years 2021 and 22, respectively, according to PSM estimates compared with initial BW, while animals were understocked by 5 and 0.3 ha for 2021 and 2022, respectively, using mid-season BW. Individuals expressed a divergent growth rate, resulting in greater misalignment between static and PSM predicted DMI as the grazing period progressed (Menendez et al., 2023), indicating greater opportunities for targeted grazing management in long grazing periods. Therefore, applying precision data provides more precise DMI estimates and demonstrates the advantages and disadvantages of herd-level estimates. The use of PSM helps to identify high-leverage precision tools to minimize a performance gap like overgrazing extensive rangeland systems and demonstrates the critical need to develop robust data-collection and processing steps to leverage continuously collected PLT data.

Attaining sustainability of livestock production and understanding environmental synergies requires in-depth knowledge of grazing animal growth and behavior. A pilot study (2021-22) was conducted at the South Dakota State University Cottonwood Field Station as part of a broader project to evaluate precision livestock technology and environmental synergies on native rangelands. Yearling Angus steers (n = 262) were fitted with virtual fencing collars (Vence), weighed on a traditional chute scale, and allocated to one of six native grass pastures equipped with individual weighing (SmartScale) scales at the water source. Each pasture was assigned either Rotational (RG) or Continuous (CG) grazing strategy and one of three stocking rates (Low, Medium, and High, 0.3, 0.42, and 0.7 AUMs, respectively) in a 2x3 factorial design. The data were downloaded, (Rcore Team, 2023), and spurious weights identified and removed using Robust Regression (hwts > 0.99, Parsons et al. 2023). Effects of stocking rate and grazing strategy were analyzed on water visits, time spent drinking, and growth using a linear mixed effects model to calculate effect sizes. Differences between BW and ADG calculated using the smart-scale vs. traditional chute weights were evaluated using a paired t-test. Steers visited the smart-scale 3.44 ± 2.79 visits per day, with significant effect of RG vs. CG strategies (3.65 ± 0.09 vs. 2.89 ± 0.10 visits per day respectively, P< 0.05). Total water visit duration averaged 5.04 ± 7.58 minutes per day, with most visits occurring between 0600 and 2000 hours and no observations occurring between 2000 and 0000 hours. RG managed steers exhibited significantly higher water visit duration (5.28 ± 0.308 vs 4.16 ± 0.317 minutes/day, P< 0.05) compared with CG managed steers. Smartscale measured weights were significantly heavier than chute weights at the beginning and end of the study period, (7.47 ± 24.54 and 9.77 ± 46.25 kg respectively, P< 0.05). No differences in overall ADG were found between smartscale and traditional chute weights(P> 0.58), however, significant temporal interactions occurred (P <0.05), which demonstrates that ADG varied over the trial duration. This created significant discrepancies in predicted vs. actual stocking rates in heavy stocked pastures (0.84 AUM, P< 0.05), indicating pastures exceeded their target stocking rate. Overall, we found steers readily acclimated to smart-scale weighing systems, and creates a viable technology to monitor watering behavior, real-time body weight, and ADG in extensively managed cattle. Further, we showed RG vs CG grazing strategies significantly affect animal drinking behavior, while stocking rate resulted in inequalities between expected vs. actual assigned animal unit months. Precision livestock technologies offer a vital solution towards enhancing sustainable livestock management practices and improving nutrition and modeling in extensive rangeland systems.

Energy budgets in grazing cattle are notoriously difficult to study given complex environmental and physiological interactions between plants and animals, each operating according to individual grazing budgets. However, these synergistic energy pathways can be modeled using a series of mechanistic relationships describing forage resource distribution and animal behavior, each which can be observed with high levels of accuracy and precision using biometric sensors and remote sensing technology. The objective of this study is to demonstrate the coordination of data inputs into a coherent framework utilizing biometric and remote sensing technology. Crossbred steers (n = 10) were fitted with GPS and video tracking collars (Vectronic) and accelerometers (Wildbytes technologies), then managed for 286 days as part of a broader research grazing ecology project on pasture (~11.54 hectare) comprised of bermudagrass (Cynodon dactylon) and tall fescue (Festuca arundinacea) and equipped with an automated walk-over-weigh (TruTest) scale system. Distribution of forage quality was sampled monthly with a ½ m2quadrat using a 20 x 20-meter grid (n = 297) and remotely assessed via an unmanned aerial vehicle (Matrice 100) mounted hyperspectral camera (MicaSense) at 8 cm resolution. Animal behavior was predicted utilizing a trained randomforest prediction algorithm, and linked to geo-location and forage quality at the spatial-temporal scale in Program R. We then constructed animal ethogram, distribution of forage quality, and spatial-temporal distribution of animal behavior across the grazing landscape for a test period. We found steers allocated the greatest proportion of time to grazing and resting behaviors, 30 and 60% respectively, with the remainder dedicated to ruminating and walking behaviors. A diurnal, circadian rhythm in animal behavior demonstrates that steers grazed between 0900 and 1000 in the morning, and again between 1300 and 2000 in the evening, while resting behavior dominated the hours between 0300 and 0900, with a slight increase in grazing behavior noted at 0600, or approximately sunrise. Animal behavior linked forage availability showed steers selectively grazed areas with higher NDVI signatures, while seeking sources of water and cover to practice resting behavior. Given the need to develop robust models predicting forage intake and energy in grazing animals to improve sustainability and environmental metrics, animal tracking and biometric technologies offer a vital solution to improving management of extensively managed grazing livestock.

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.

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.

The objective of this experiment was to determine if statistical process control (SPC) procedures coupled with the remote continuous collection of feeding behavior patterns, accelerometer-based behaviors, and rumen temperature can accurately differentiate between animals experimentally inoculated with Mannheimia haemolytica (MH) or PBS. Thirty-six crossbred steers (BW = 352 ± 23 kg) seronegative for MH were randomly assigned to bronchoselective endoscopic inoculation with MH (n = 18) or PBS (n = 18). Electronic feed bunks were used to measure DMI and feeding behavior traits, accelerometer-based neck collars measured feeding- and activity-behavior traits, and ruminal thermo-boluses measured rumen temperature. Data were collected for 28 d prior to and following inoculation. Steers inoculated with MH exhibited elevated (P < 0.02) levels of neutrophils and rumen temperature indicating that MH challenge effectively stimulated immunologic responses. However, only nine of the MH steers exhibited increased serum haptoglobin concentrations indicative of an acute-phase protein response and one displayed clinical signs of disease. Shewhart charts (SPC procedure) were used for two analyses, and sensitivity was computed using all MH-challenged steers (n = 18), and a subset that included only MH-challenged haptoglobin-responsive steers (n = 9). Specificity was calculated using all PBS steers in both analyses. In the haptoglobin-responsive only analysis, DMI and bunk visit (BV) duration had the greatest accuracy (89%), with accuracies for head-down (HD) duration, BV frequency, time to bunk, and eating rate being less (83%, 69%, 53%, and 61%, respectively). To address the diurnal nature of rumen temperature, data were averaged over 6-h intervals, and quarterly temperature models were evaluated separately. Accuracy for the fourth quarter rumen temperature was higher (78%) than the other quarterly temperature periods (first = 56%, second = 50%, and third = 67%). In general, the accelerometer-based behavior traits were highly specific ranging from 82% for ingestion to 100% for rest, rumination, and standing. However, the sensitivity of these traits was low (0% to 50%), such that the accuracies were moderate compared with feeding behavior and rumen temperature response variables. These results indicate that Shewhart procedures can effectively identify deviations in feeding behavior and rumen temperature patterns to enable subclinical detection of BRD in beef cattle.