Sward responses of rhizoma peanut–bahiagrass mixtures and bahiagrass monocultures in contrasting on‐farm environments
Bibliographic record
Abstract
Abstract Incorporating forage legumes into grass swards has the potential to enhance the sustainability of pasture systems and reduce reliance on nitrogen fertilizers. The aim of this study was to assess the on‐farm performance of bahiagrass (BG; Paspalum notatum Flüggé)–rhizoma peanut (RP; Arachis glabrata Benth.) mixtures compared to BG monocultures in three diverse environments across Florida. Three forage treatments were employed at all locations: BG in monoculture (Bh), BG + Ecoturf RP (Eco), and BG + Florigraze RP (Flo). Significantly greater herbage accumulation rates were observed for BG + Ecoturf RP (37 kg DM ha−1 day−1, where DM is the dry matter) and BG with Florigraze RP (35 kg DM ha−1 day−1) in comparison with BG in monoculture (30 kg DM ha−1 day−1). Crude protein and in vitro digestible organic matter concentrations were greater for RP binary mixtures compared with monoculture BG across all locations. In North and South Florida, BG + Ecoturf RP exhibited greater RP belowground biomass than BG + Florigraze RP. Additionally, biological N2 fixation increased linearly as the proportion of RP increased. Integration of RP germplasm Ecoturf and the Florigraze cultivar into BG pastures in North, Central, and South Florida led to increased rates of herbage accumulation and improved herbage nutritive value compared to BG monoculture. Overall, North and South Florida exhibited more favorable responses to the inclusion of RP compared to Central Florida.
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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".