Does Rotational Grazing Based on Leaf Expansion Duration Modify Grazing Behavior and Feed Intake of Beef Heifers on Natural Grassland?
Bibliographic record
Abstract
Grasslands develop a multifunctional role to humanity, with unique fauna and flora, besides being the primary feed source for herbivores. However, grasslands are usually considered a low-efficiency production system, often converted into other land uses such as crops and forestation (e.g., south Brazil). This study aimed to evaluate the effect of two rest intervals between grazing occupations in rotational grazing on the grazing behavior and feed intake of beef heifers. Two grazing intervals, 375 and 750 DD (degree-days) were used; based on the cumulative thermal sum necessary for the leaf expansion of native grasses of two functional groups. The experiment was conducted as a completely randomized block design, with two treatments, three replications, and repeated measures over time. The grazing behavior was evaluated continuously for 18 hours (7 a.m. to 11h59 p.m.). Herbage intake was estimated using an external marker (Cr2O3) in four periods (one for each season). The green leaf mass was similar between treatments, with a mean of 40% of the pregrazing mass (kg DM ha-1). On average grazing, time was 50% of the period, and the bite rate was 38.7 bites min-1. The number of daily meals was 6.5, with an average of 84 minutes for each meal. The number of feeding times (feeding stations by minute) visited was 6.4. On average, there was a 2.23% difference in dry matter intake (% BW) among seasons. Neither ingestive behavior or forage consumption of heifers was affected by the treatments, both maintain similar chemical composition on natural grassland.
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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".