PSVIII-A-3 Activity Behavior and Growth Performance During Summer of Grazing Beef Heifers with Divergent Residual Feed Intake
Bibliographic record
Abstract
Abstract Concerns about sustainability of beef production systems generate an interest in improved feed efficiency; however, there is a lack of research evaluating cattle activity budgets in response to the interaction between residual feed intake (RFI) and environment while grazing. Lying behavior and activity can provide insight into how animals interact with the environment and serve as an indicator of animal comfort. Furthermore, exposure to severe environmental factors can change behavioral patterns and impair animal performance. Therefore, this study evaluated activity budgets and performance in grazing beef heifers with divergent residual feed intake (RFI) during the summer season. From July to August 2022, forty-four crossbred beef heifers [358 ± 4.78 kg of body weight (BW); approximately 14 months of age] previously tested for RFI in drylot and classified as more (n = 21; LOW-RFI = -0.9 ± 0.70) or less feed efficient (n = 23; HIGH-RFI = 1.3 ± 1.00) were grazed at 2.72 AUM/ha over 7 wk in Alberta, Canada. IceRoboticTM pedometers (IceQube+) were used to track 24-hr heifer activity budgets [n = 43; total steps and lying and standing time (min/d and min/h) for 36 d]. Full BW was obtained on d -1, 0, 14, 28, 42, and 43 while fat scan on rib and rump were measured by ultrasound (Aloka 500 V diagnostic real-time) on d 0 and 42. Air temperature, relative humidity, wind speed and solar radiation information were collected within 1 km of the grazed area to calculate the Comprehensive Climate Index (CCI). Based on CCI, weather conditions were considered to impose risk to cause mild, moderate, severe, and extreme stress for 5, 18, 7, and 1 days, respectively. For BW, average daily gain (ADG), rump (RF) and rib fat (RiF), the data were analyzed as a completely randomized design, while behavior activity included repeated measures. An RFI x day interaction was observed for lying and standing times (P = 0.02) and total steps (P = 0.001). Greater number of steps (P < 0.01) and an increased standing time (P < 0.01) were observed in HIGH-RFI heifers. RFI × hour interaction was observed for lying and standing times (P = 0.006), where LOW-RFI heifers spent more time lying at 10:00 am (P < 0.01). Furthermore, LOW-RFI heifers had decreased number of steps per hour throughout the study (P = 0.03; 178 vs 191 ± 4.1). No effects were observed for ADG, BW, RF, and RiF (P > 0.24). In summary, selected activity behaviors differed between beef heifers with divergent residual feed intake while summer grazing. Further studies are needed to investigate the effects of continuous changes in weather conditions to better understand the environmental impacts on animal behavior while selecting for more efficient beef cattle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".