Forage Yield and Nutritive Value of Turf Bermudagrasses Managed to Simulate a Horse Pasture Management Scheme in the U.S. Upper Transition Zone
Bibliographic record
Abstract
Turf‐type bermudagrasses [Cynodon dactylon (Pers.) L.] with improved cold tolerance could have potential use in horse pastures of the U.S. Upper South for minimizing the damage to grass stands due to heavy trampling in these pastures; however, the dry matter (DM) yield potential and nutritive values of these bermudagrasses are not known. A small‐plot experiment was conducted for three growing seasons with monocultures of two turf‐type bermudagrasses (‘Yukon’ and ‘Riviera’), one forage‐type (‘Wrangler’), and four mixtures of the three (Wrangler × Yukon, Wrangler × Riviera, Yukon × Rivera, and Wrangler × Yukon × Riviera) to evaluate and compare DM yield, crude protein (CP), in vitro true digestibility (IVTD), neutral detergent fiber (NDF), and acid detergent fiber (ADF). Although DM yield, averaged across seasons, for Wrangler was 31 and 40% greater than for Yukon and Riviera, yields were similar between Wrangler and Wrangler × Yukon mixture, and the three mixtures provided greater DM yields than either turf type alone. Crude protein, averaged across seasons, was similar between the turf types, and both had greater CP than Wrangler and the mixtures. Across treatments, IVTD, NDF, and ADF were similar among bermudagrass cultivars. Results of the experiment indicated that Yukon and Riviera bermudagrasses have acceptable nutritive value for horse pastures, but the Wrangler × Yukon mixture could serve as an option for heavier stocked pastures.
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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) | 0.000 | 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 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".