PSXI-31 Methane emissions by cattle grazing tannin-containing legumes.
Bibliographic record
Abstract
Tannin-containing legumes like birdsfoot trefoil (BFT) and sainfoin (SAIN) may enhance the efficiency of energy and protein use in ruminants relative to grasses and other legumes such as alfalfa (ALF). In addition, synergisms among diverse combinations of forages may further contribute to improve the efficiency of nutrient utilization, approaching values attained by feeding grain-based rations in confinement. We tested the effects of grazing monocultures of (1) ALF, (2) BFT, (3) SAIN or (4) a three-way choice of these legumes on cattle performance, intake and methane emissions. Twelve pairs of heifers (366 ± 46.2 kg) grazed three spatial replications of the aforementioned four treatments in a completely randomized block design. Animals grazed during 2 periods of 22-d each and composited forage and fecal samples were collected during five consecutive days at the end of each period to estimate apparent digestibility, using ADL as an internal marker. Fecal output was measured using an external marker (Cr2O3) dosed once a day for 9 days. Fecal output along with digestibility data were used to estimate dry matter intake (DMI). Methane emissions were assessed using the sulfur hexafluoride technique during each 5-d collection period. Results were analyzed as a completely randomized block design with spatial replication (random effect) nested within treatments, and day and period as the repeated measures. Heifers grazing the three-way choice showed greater DMI and ADG than animals grazing monocultures (P < 0.10; Table 1), suggesting a synergism among pastures. In addition, g methane emissions/kg of gain tended to be lower (14 to 24% range) for the three-way choice than for monocultures (pre-planned contrast three-way choice vs. monocultures; P = 0.12; Table 1). In summary, combinations of tannin-containing legumes and alfalfa yielded the compounded benefits of improved animal gains and reduced environmental impacts per kg of BW gain.
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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".