Increased Nitrogen Retention and Reduced Methane Emissions of Beef Cattle Grazing Legume vs. Grass Irrigated Pastures in the Mountain West USA
Bibliographic record
Abstract
Grazing studies were carried out over a 5-year period using pregnant cows, yearling calves and 2-year-old heifers to investigate the influence of diet on intake, methane (CH4) emissions and retention of nitrogen (N). Monoculture legume (birdsfoot trefoil, BFT and cicer milkvetch, CMV) or grass (meadow bromegrass, MBG) pastures were rotationally stocked, and during year 4 and year 5, treatments were contrasted with total mixed rations (TMR) fed in confinement. The sulfur hexafluoride (SF6) method was used to continuously measure enteric CH4 emissions. Intake was greater on legume pastures and on TMR than on grass pastures, and enteric CH4 emissions per unit of intake were lower on legumes compared with grass pastures. Legume pastures had elevated non-fiber carbohydrate (NFC) concentrations (400 g kg−1 dry matter; DM) typical of perennial legumes cultivated in the Mountain West. A N balance calculated in 2017–2018 demonstrated that N retention was greater for TMR and legume than grass pastures. Enteric CH4 emissions of grazing cow herds account for the majority of greenhouse gas (GHG) emissions from beef production and can be significantly reduced by using highly digestible forage legumes. The N retention of legumes can potentially enhance the efficiency of N use, thereby increasing the sustainability of grasslands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".