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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".