94 President Oral Presentation Pick: Grazing diverse combinations of tanniferous and non-tanniferous legumes: Implications for beef cattle performance and environmental impact
Bibliographic record
Abstract
Abstract We tested the effect of increasingly diverse combinations of tanniferous legumes (birdsfoot trefoil-BFT, sainfoin-SF) and alfalfa (ALF) on cattle performance, methane (CH4) emissions and nitrogen (N) balance. Pairs of heifers (401 ± 49.6 kg) grazed three spatial replications of 7 treatments (n = 3/treatment): monocultures (BFT, SF, ALF) and all possible 2- and 3-way choices among strips of these legumes in a completely randomized block design of two 15-d periods during 2 consecutive years. Average daily gains (ADG) of heifers grazing the tanniferous legumes (1.05 kg/d) were 40% greater (P < 0.10) than of heifers grazing ALF (0.74 kg/d) during the first year. Heifers grazing the 3-way choice had greater intakes (10.4 vs 7.8 kg/d; P = 0.064) and ADG (1.21 vs. 0.95 kg/d, P = 0.054) than those grazing monocultures, suggesting a nutritional synergism among legumes. The average CH4 emissions for legume monocultures vs. 2- and 3-way choices was 222 vs. 202 and 162 g/kg BW gain (P > 0.10), respectively. For heifers grazing SF and BFT compared with ALF, blood urea N was less (14.3 and 16.8 vs 20.8 mg/dL; P < 0.05) as were urinary N concentrations (3.7 and 3.5 vs 6.0 g/L; P < 0.05), but fecal N concentrations were greater (34.5 and 35.5 vs 30.5 g/kg, respectively; P < 0.05). Combining both tanniferous legumes (SF-BFT) led to the greatest declines in urinary N (2.24 g/L) and urea-N (1.71 g/L) concentration, suggesting that different types of tannins in different legume species result in associative effects that enhance N economy. In addition, heifers grazing 3-way choices partitioned less N into urine (40.7 vs 50.6%; P = 0.037) and retained more N (36.1 vs 25.2%, P = 0.046) than heifers grazing monocultures. In summary, combinations of tanniferous legumes with alfalfa improved animal performance and reduced environmental impacts relative to monocultures, resulting in a more sustainable approach to beef production in pasture-based finishing systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.577 | 0.285 |
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".