Herbage responses and animal performance of nitrogen-fertilized grass and grass-legume grazing systems
Bibliographic record
Abstract
Abstract The study evaluated forage and livestock performance in different grazing systems over two years. Treatments were three contrasting grazing systems: (I) N-fertilized bahiagrass (Paspalum notatum Flüggé) in the summer overseeded during the winter by N-fertilized ryegrass (Lolium multiflorum) and oat (Avena sativa L.) (Grass + N); (II) unfertilized bahiagrass during the summer overseeded with ryegrass + oat and a blend of clovers (Trifolium spp.) in the winter (Grass + Clover); (III) unfertilized bahiagrass and rhizoma peanut (RP; Arachis glabrata Benth.) mixture during summer, overseeded during winter by ryegrass + oat + clovers mixture (Grass + Clover + RP). Average daily gain (ADG), gain per area (GPA), and stocking rate (SR) in the winter did not differ across treatments and averaged 0.87 kg/d (P = 0.940), 303 kg/ha, and 2.72 AU/ha. In the summer, Grass + Clover + RP had greater ADG than Grass + N (0.34 vs. 0.17 kg/d, respectively). During the summer, the GPA of Grass + Clover + RP was superior to Grass + N (257 vs. 129 kg/ha, respectively), with no difference in SR among treatments at 3.19 AU/ha. Over the entire year, ADG and GPA tended to be greater for Grass + Clover + RP. Annual SR differed between treatments, where Grass + N was greater (3.37 AU/ha) than the other treatments, which averaged 2.76 AU/ha. Integration of legumes into pasture systems in the summer and winter contributes to developing a sustainable grazing system, reducing N fertilizer use by 85% while tending to increase livestock productivity even though SR was decreased by 18%.
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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".