PSVII-13 Performance and environmental benefits from biochar supplementation in beef cattle grazing systems
Bibliographic record
Abstract
Abstract Worldwide, beef production systems represent a significant source of greenhouse gas (GHG), and enteric methane (CH4) emissions are the primary concern. The objective of this experiment was to determine whether biochar (Oregon Biochar Solution, White City, OR) supplementation can reduce CH4 emissions from grazing beef cows. Biochar is a stable form of carbon produced through the pyrolysis of organic matter (typically forestry waste). Sixty-four cows and their calves were blocked by cow body weight and calf age, and randomly allocated to 8 paddocks, each with 8 cow-calf pairs. Using a crossover design, each paddock was assigned to one of two treatments: (1) biochar supplemented at approximately 3% of estimated dry matter intake (DMI) or (2) control (no biochar). Biochar was incorporated into a pellet containing 45% biochar, 42.5% wheat midds, 10% canola oil, and 2.5% dry molasses and fed in a portable trough once daily. Each period consisted of 28 days: 21 days for biochar adaptation and 7 days for data collection. Enteric gas emissions from each paddock were measured using C-Lock GreenFeed trailers (C-Lock Inc., Rapid City, SD, USA) with pasture DMI estimated using paddock entry/exit quadrats during each sampling week. Enteric CH4 emissions expressed as g CH4/d were 249 and 260 ± 50.3 g (P ≥ 0.37) for control and biochar, respectively. Similarly, g CH4/kg DM and g CH4/kg BW were not affected (P ≥ 0.44) by biochar supplementation on pasture. Biochar supplementation did not affect estimated DMI or cow/calf body weights (P ≥ 0.15). Results suggest that biochar was ineffective for reducing methane emissions from grazing beef cows; however, measures of animal performance were not affected by biochar consumption. Further work is required to determine if type or higher inclusions of biochar can reduce CH4 emissions from beef cattle.
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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 | 0.001 | 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".