Sward structure and forage intake rate of elephant grass cv. Napier subjected to strategies of intermittent stocking management
Bibliographic record
Abstract
Context Grazing management strategies affect sward structure, changing patterns of foraging and intake, and consequently animal performance. Aims The objective of this study was to evaluate the relationship between sward structure and forage intake rate by cattle grazing elephant grass (Pennisetum purpureum Schumach) cv. Napier subjected to strategies of rotational stocking management. Methods The experiment was conducted in Piracicaba, SP, Brazil, from January 2011 to April 2012. Treatments corresponded to all combinations between two post-grazing conditions (post-grazing heights of 35 and 45 cm) and two pre-grazing conditions (95% and maximum canopy light interception during regrowth; LI). The response variables evaluated at both pre- and post-grazing conditions were: (1) vertical distribution of morphological components; (2) bite rate; (3) bite mass; (4) intake rate; and (5) forage nutritive value (morphological and chemical composition of oesophageal extrusa samples). Key results Bite mass was smaller and bite rate was greater for LI95% swards at pre- and post-grazing, resulting in greater rate of forage intake. The post-grazing height targets affected the morphological and the chemical composition of the forage consumed. Conclusions In general, pastures managed with targets LI95% (pre-grazing) and 45 cm (post-grazing height) resulted in greater leaf percentage and nutritive value of the consumed forage. Implications Adequate grazing management strategies allow for high residual leaf area in pastures, ensuring rapid recovery after grazing. For grazing elephant grass cv. Napier, this corresponded to the combination between the LI95% pre-grazing target and the 45 cm post-grazing height.
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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".