Effect of Biochar Supplementation on Grazing Beef Cow and Calf Performance, Enteric Methane and Carbon Dioxide Emissions, Fecal Egg Counts and Fecal Nutrient Composition
Bibliographic record
Abstract
A three-year beef cow grazing study (2020-2022) was conducted to evaluate the effects of biochar supplementation on grazing cow performance, ruminal fermentation, fecal egg and oocyst count, fecal nutrient composition, and enteric methane (CH4) and carbon dioxide (CO2) emissions. Each year, forty-eight spring-calving Angus beef cows (BW = 669 ± 95 kg; mean ± STD) stratified by BW, were randomly assigned to 1 of 2 treatments. Treatments included either (i) pelleted supplement with biochar (Biochar; 405 g/d) inclusion at 2.2% of DMI or (ii) control pellet (Control; base fiber - no biochar). Twenty-four ha meadow bromegrass (Bromus riparius Rehm.)-alfalfa (Medicago sativa L.) paddocks (8.6% CP, 51.1% TDN) were assigned to each treatment group for the 92-d grazing trial. Enteric CH4 and CO2 emissions were measured using SF6 tracer gas technique. Cow and calf performance, rumen fluid parameters were not (p > 0.05) affected by biochar supplementation. During trial biochar supplementation reduced fecal oocyst (Eimeria spp.) count (p < 0.011; 3.9 vs. 15.0 count/g DM) but increased carbon:nitrogen ratio (p < 0.029; 26.1 vs. 21.3) relative to initial measurements. The enteric methane emission reduction in response to biochar supplementation at 2.2% of DMI, was negligible (~6.0% reduced compared to control).
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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 | 0.001 | 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".