Effect of Summer Annual Forage and Type of Shade on Grazing Behavior of Beef Stocker Heifers
Bibliographic record
Abstract
Heat stress in beef cattle is still one of the issues affecting animal performance in the beef cattle industry. Our objective was to evaluate the effect of two summer annual forages such as alyceclover (Alysicarpus vaginalis L.), and pearl millet (Pennisetum glaucum) with natural (trees) or artificial shade (80% shade) on grazing behavior and on reducing the heat load of crossbred yearling heifers. On three consecutive years from mid-July to mid-September, 36 (Bos taurus × B. indicus) heifers (body weight [BW] = 321±11.3 kg) were randomly allotted (n = 3) and continuously stocked in 12-1.33 ha paddocks in a 2 × 2 factorial arrangement of treatments (2 forage types and 2 shade types) with three replicates. Heifers grazing on alyceclover gained more (p = 0.03) than those grazing pearl millet (0.94 and 0.80 kg, respectively). Grazing behavior variables were not affected (p > 0.05) by forage type and forage type x shade type interaction; however, shade type affected grazing and lying time (p < 0.05). Time of day (TOD) affected (p < 0.05) grazing and standing time, number of steps taken, respiration rate, and panting scores. These negative effects are related with the greatest temperature humidity index between 1100 and 1459 h. When data were analyzed by TOD, the negative effect on grazing behavior variables was not different for heifers with access to natural or artificial shades. Under the conditions of the present experiment, artificial shade provided protection for cattle. Grazing behavior parameters can be used to monitor heat load in grazing 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".