Search

Spatial utilization of pasture landscapes by grazing animals is often heterogeneous and driven by complex environmental and physiological interactions between plants and animals. Synergistic energy pathways can be modeled using mechanistic relationships describing resource distribution, animal behavior, metabolic energy rate, and pasture utilization using biometric sensors. The objective of this study was to demonstrate the ability to measure the energy landscape by mapping fine-scale contributions to animal energy expenditure, nutrient acquisition, growth, and overall economic profitability. We utilized animal trajectories and location fixes recorded by virtual fencing collars (Vence) daily body weight (BW) using precision livestock scales (Smartscale, C-Lock Inc.) and oxygen consumption (GreenFeeds, C-Lock Inc.) on steers (n = 127), managed as part of a broader project to evaluate environmental synergies in extensive grazing systems. Steers were allocated to one of six native grass pastures assigned to either rotational or continuous grazing strategies, and one of three stocking rates (low, moderate, high) in a 2 x 3 factorial arrangement. Space utilization was quantified as the frequency of fixes located within each cell of a 5 x 5-meter Grid and the distance between grid cells and water was calculated using the Pythagorean theorem. Movement behavior was quantified using Program R to calculate step length, the distance between two temporally continuous fix points utilizing the Pythagorean Theorem, and turn angles calculated as the cosine of the pre- and pro-ceeding step lengths where larger values indicated greater deviation from a straight trajectory. A mixed linear regression model was fitted using the lmer function in the lme4 package. Space utilization decreased with increased distance from water (P < 0.05), with rotational grazing strategies increasing concentration around water sources (P < 0.05). This indicates an opportunity to utilize precision livestock technology to facilitate pasture management and increase space utilization in grazing cattle. [ABSTRACT FROM AUTHOR]

Virtual fencing (VF) has the capacity to transform the landscape of livestock management within extensive rangeland systems. The objective of this study was to evaluate the effect of Vence (Merck & Co., Inc., Rahway, NJ) VF technology on yearling steer behavior and performance. Steers were allocated into one of two grazing systems [continuous grazing (CG) or rotational grazing (RG) managed using VF] across three different stocking rates (light, moderate, and heavy, 0.32, 0.40, and 0.72 animal unit months, respectively) for three grazing seasons. Based on initial body weight, steers were randomly blocked into six pastures (346 ± 39, 273 ± 34, and 319 ± 29 kg, respectively) at the South Dakota State University Cottonwood Field Station (n = 127, 135, and 127, in 2021, 2022 and 2023, respectively). Steers assigned to the CG treatment had free access to the entire pasture for the duration of the grazing season. Steers assigned to the RG treatment were rotated among virtual ‘paddocks’ for the duration of the grazing season. The days spent grazing in each paddock for the RG treatment were determined based on bi-weekly clip plots for biomass estimation. The VF collars were used to track steer location at 5-min intervals; only steers assigned to the RG treatment were managed with VF boundaries and exposed to auditory and electrical cues from the collars. Raw data was downloaded from Vence Herd Manager through an automatic programming interface and cleaned by removing messages that failed to transmit correctly or were outside the bounds of study site pastures. Animal behavior metrics of daily distance traveled (DDT), daily grazing time, daily resting time, and daily walking time were calculated for each individual animal and averaged by pasture using Program R. Steer weights were collected using SmartScales (C-Lock Inc, Rapid City, SD). Average daily gain (ADG) was calculated using a linear model to develop a regression equation for each individual animal using weight as the dependent variable and day of trial as the independent variable. The slope of the regression line was used to estimate ADG. Individual DDT, behavior, and performance metrics were analyzed using a mixed model analysis of variance (ANOVA) to compare the impact of grazing treatment and stocking rate (fixed effects) with year as the random effect. Treatment did not influence (P > 0.05) grazing, walking, or resting behavior. Similarly, no differences were detected in DDT between treatment groups or across different stocking rates. Furthermore, there were no differences (P > 0.05) in ADG across treatment groups. Implementation of VF had no impact on animal behavior or performance, justifying its use in grazing settings; however, financial and cattle finishing aspects still need to be evaluated to determine its overall benefits and costs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Body weight is a critical component for monitoring body mass, body condition, in grazing animals, but traditional methods are stressful, costly, and impractical to conduct frequently in extensive landscapes. • Remote systems automatically collecting body mass measurements often collect spurious weight measurements. • Multiple methods to identify and remove spurious weight points exist, each with a varying degree of accuracy, and it is important for researchers to identify and use the appropriate means which best fits their research objectives. • Robust regression provides a repeatable means of identifying and removing potentially spurious weight points in an automated fashion, with a high degree of precision and accuracy observed in the final body weights. Body weight is a critical component for monitoring animal body mass, body condition, nutritional status, and health. However, traditional methods of collecting body weight are stressful, costly, and logistically impractical with cattle on extensive landscapes. Remote systems capable of collecting individual animal body weight provide a potential solution, but automatic systems may collect spurious weight measurements. The objectives of this study were to (1) describe the utility of using a remote weight capture system, (2) develop methods for identifying and removing spurious measurements, and (3) cross-validate automatically collected weights with static chute collected weights. Beef steers (n = 10) were tagged with electronic Radio Frequency Identification tags and grazed for 286 days in an improved pasture (±11.54 ha) containing Bermuda (Cynodon dactylon) and Tall Fescue (Festuca arundinacea) and inter-seeded with Annual Ryegrass (Secale cereale). A walk-over-weigh (WOW) system was designed from a platform scale connected to a TruTest® scale head and paired with a computer to save data files. In total, 5,466 raw weights associated with individuals were collected (0 kg < weight < 1,000 kg). We assessed the use of whole herd and individual means ± 1 standard deviation calculated either daily or over the entire trial to identify potential outliers. Additionally, 3 data smoothing methods were tested: a linear quadratic model, cubic splines, and polynomial regression. Each method was evaluated for total weight events recorded, event frequency, measured body mass and average daily gain. Further, accuracy and precision were calculated using cross-validation with paired chute weights (n = 8). Whole herd means ± 1 SD identified the fewest spurious data points and showed a stronger relationship with paired chute weights (R2 = 0.90) whereas individual daily means ± 1 SD identified the most and exhibited a weaker relationship (R2 = 0.68). Repeated quadratic regression models provided the best results, identifying 3,759 clean weight points and providing an R2 = 0.96 between paired chute and WOW measurements. A real-time simulation analysis found that both individual and the herd means ± 1 SD performed similarly with an average of 30% of data classified as spurious and removed from the analysis, resulting in agreement (R2 = 0.93 for both individual and herd means) between chute and WOW weight measurements. These results indicate the utility of using a simple WOW system to collect data for measuring individual animal body mass and associated metrics, which has implications on making management and marketing decisions in near real-time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]