85 Automated Walk-Over Weighing System: Methods to Track Daily Body Mass and Growth in Grazing Steers

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.

175 GPS Tracking Collars and Accelerometers Provide Detailed Tracking of Foraging Behavior and Space Use in Grazing Steers in Bermudagrass and Tall Fescue Pasture

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.

Characterization of feeding behavior traits in steers with divergent residual feed intake consuming a high-concentrate diet

The objective of this study was to examine the differences in feeding behavior patterns of steers with divergent phenotypes for residual feed intake (RFI). Three trials were conducted with 508 Angus-based composite crossbred steers (body weight [BW] = 309 ± 57 kg) fed a high-concentrate diet in pens equipped with electronic feed bunks (GrowSafe System). Initial and final carcass ultrasound measurements (intra-muscular fat, backfat depth, and rib-eye area) were collected on days 0 and 70, and BW measured at 14-d intervals. Individual dry matter intake (DMI) and feeding behavior traits were collected for 70 d, and RFI calculated as the residual from the regression of DMI on average daily gain (ADG) and mid-test BW0.75. Steers were ranked by RFI and assigned to low-, medium-, and high-RFI classes based on ± 0.5 SD from the mean RFI within the trial. The feeding behavior traits evaluated in this study included frequency and duration of bunk visit (BV) and meal events, head-down (HD) duration, mean meal length, time-to-bunk interval, the maximum nonfeeding interval, and the day-to-day variation of these traits, defined as the root mean squared error (RMSE) from linear regression of each trait on the day of trial. Additionally, three ratio traits were evaluated: BV events per meal, HD duration per BV event, and HD duration per meal event. Low-RFI (feed-efficient) steers consumed 16% less (P < 0.01) DMI, while BW and ADG were not different compared with high-RFI steers. Low-RFI steers had 18% fewer and 21% shorter (P < 0.01) BV events, and 11% fewer and 13% shorter (P < 0.01) meal events per day compared with high-RFI steers. Furthermore, low-RFI steers exhibited less (P < 0.05) day-to-day variance in DMI, as well as in frequency and duration of BV and meal events and HD duration compared with high-RFI steers. Differences in feeding behavior traits due to RFI were minimally affected by covariate adjustment for DMI, indicating that steers with divergent RFI have distinct feeding behavior patterns that are largely independent of differences in DMI. These results suggest that feeding behavior traits may be useful biomarkers for the prediction of feed efficiency in beef cattle.

Efficacy of statistical process control procedures to identify deviations in continuously measured physiologic and behavioral variables in beef steers experimentally challenged with Mannheimia haemolytica

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.

31 Characterization of feeding-behavior patterns and application of chemometrics to predict residual feed intake based on feeding-behavior traits in growing Holstein heifers

Objectives of this study were to characterize feeding-behavior (FB) patterns in growing dairy heifers with divergent RFI phenotypes (±0.50 SD) and to evaluate the accuracy of partial least squares regression (PLSR) models to predict RFI based on FB traits. Performance, DMI, and FB traits were measured for 70 to 100 d in 15 trials with Holstein heifers (n = 611) fed a corn-silage based ration. Seventeen FB traits were evaluated: frequency and duration of bunk visit (BV) and meal events, head-down duration (HDD), meal length, maximum non-feeding interval, corresponding day-today variation (SD) of these traits, and ratios of HDD per BV duration and meal duration, HDD per meal duration, and BV events per meal event. Data was analyzed using a mixed model that included RFI group and trial. The PLSR model for RFI was developed using cross-validation procedures (Leave-One-Out) in JMP (SAS), with FB traits as independent variables. LowRFI heifers consumed 24% less (P < 0.01) DMI and had lower (P < 0.01) day-to-day DMI variation than high-RFI heifers. Distinct differences were observed in FB patterns between low- and high-RFI heifers (Table 1). Eight of 17 FB traits were included [selected based on variable of importance (VIP) score > 0.80] in the PLSR model that explained 33% of the variation in RFI. Head-down duration had the highest VIP score; accordingly, low-RFI animals had 44% lower HDD and 30 and 40% lower ratios of HDD per BV duration and meal duration, respectively. Additionally, low-RFI animals had 20 and 18% fewer BV and meal events per day, spent 21% less time eating during BV events, and had reduced day-to-day variation in HDD and meal frequency. For this study, distinctive differences were observed in the FB patterns of Holstein heifers with divergent RFI, which explained 33% of the between-animal variation in RFI.

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