The distance from tree legumes in silvopastoral systems modifies the litter in grass-composed pastures
Bibliographic record
Abstract
Abstract The use of silvopastoral systems with tree legumes is a viable alternative to recover and develop pastures, as they add N to the system influencing pasture growth. This study hypothesized that the herbage and litter of signalgrass (Urochloa decumbens Stapf) is affected by legume trees in the pasture. Treatments were composed of (1) signalgrass + Mimosa caesalpiniifolia Benth.; (2) signalgrass + Gliricidia sepium Jacq.; and (3) signalgrass monoculture. The 3-year experiment followed a randomized complete block design with three replications. Tree legumes were planted in double rows (15 × 1 × 0.5 m), in 1 ha paddocks. Litter samples were taken in five distance points (0, 1.8, 3.7, 5.6 and 7.5 m) perpendicular to tree legume rows. Signalgrass was taller at longer distances from the trees (P < 0.05). Signalgrass height differed between treatments, with taller signalgrass found in pastures mixed with G. sepium (15.6 cm) compared to M. caesalpiniifolia (9 cm) (P < 0.05). Herbage N content decreased with increasing distance from tree rows (P < 0.05). Litter N content followed a similar pattern, ranging from 23 g/kg under the trees to 12 g/kg at 7.5 m away from tree rows. Signalgrass did not grow under the tree crown (0–1.8 m), especially when intercropped with M. caesalpiniifolia. The findings of this study suggest that the type of legume trees used in the silvopastoral system has the potential to modify the pattern of grass growth and content of N in pasture litter.
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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".