PSXIV-7 Performance and environmental benefits from biochar supplementation in beef cattle grazing systems
Bibliographic record
Abstract
Abstract Reducing greenhouse gas (GHG) emissions from beef cattle production systems has continuously been recognized as an important strategy to help mitigate climate change. This experiment was conducted to determine the optimum inclusion level of biochar (Oregon Biochar Solution, White City, OR) in the diet to reduce enteric methane (CH4) emissions from beef cows. Biochar is a stable form of carbon produced through low-oxygen and high-temperature pyrolysis of organic matter (typically forestry waste). Using a 4 x 4 Latin square design, pregnant beef cows (n = 8) of similar weight and stage of pregnancy were supplemented with biochar daily at 0, 1, 2, or 3% of total dry matter intake (DMI). Biochar was added to a pellet containing 45% biochar, 42.5% wheat midds, 10% canola oil, and 2.5% dry molasses to facilitate ease of feeding and encourage biochar consumption. Each 21-day period consisted of 14 days for diet adaptation and 7 days for data collection. Enteric gas emissions were measured using C-Lock GreenFeed trailers (C-Lock Inc., Rapid City, SD, USA) and DMI was collected using Insentec feeders (Insentec, Voorsterweg, The Netherlands). Enteric CH4 emissions expressed in g CH4/day, g CH4/kg DM, and g CH4/kg BW were not affected by biochar supplementation (P ≥ 0.41), although the 3% inclusion was numerically lowest. For all parameters expressing CH4 emissions, linear and quadratic effects for inclusion rate were not significant (P ≥ 0.19). Dry matter intake and cow body weights were not affected by biochar supplementation (P ≥ 0.34). These results suggest that biochar was ineffective for reducing CH4 emissions from beef cows fed a high forage diet (50% haylage, 30% straw, 17% corn silage) with no effects on animal performance. Further research should investigate whether type of biochar or higher inclusion levels 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".