Herbage Responses and Grazing Performance of Mature Horses in Warm-Season Perennial Grass–Legume Mixed Pastures
Bibliographic record
Abstract
The pasture–animal interface of warm-season perennial grass–legume mixed pastures has never been investigated in forage-based equine systems. Therefore, this 2-year study investigated the herbage and animal responses under 84-day continuous stocking in mixed pastures of rhizoma peanut (RP, Arachis glabrata Benth) and bahiagrass (BG, Paspalum notatum Flüggé) with 30 kg nitrogen (N) ha−1 (BG-RP) compared to BG pastures fertilized with 120 kg N ha−1 (BG-N120) and no N (BG-N0). Measurements were taken every 14 days, except for intake and in vivo digestibility, which were measured every 28 days. BG-N120 had the highest stocking rate (p = 0.01; 3.7 AU ha−1) in 2019, while BG-N0 had the lowest (p = 0.01; 2.6 AU ha−1) in 2020. Crude protein and digestible energy were greatest (p < 0.05) for BG-N120 and BG-RP in some of the evaluation days in 2019 but similar across pastures in 2020. Crude protein digestibility was greatest (p < 0.05) for BG-RP in the late season. Intake was less (p = 0.03) for horses grazing BG-RP (3.2%BW) compared to BG-N0 (5.0%BW). Nonetheless, no differences (p > 0.05) were observed among pastures for body measurements. The results indicate that BG-RP pastures can improve forage nutritive value and maintain horse body condition while maintaining similar stocking rate to monoculture bahiagrass with high N fertilizer rates.
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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".