Effects of Hardwood Biochar on Methane Production, Fermentation Characteristics, and the Rumen Microbiota Using Rumen Simulation
Bibliographic record
Abstract
Biochar is a novel carbonised feed additive sourced from pyrolyzed biomass. This compound is known to adsorb gases and carbon, participate in biological redox reactions and provide habitat biofilms for desirable microbiota proliferation. Therefore, biochar holds potential to modify rumen fermentation characteristics and reduce enteric CH4 emissions. The objective of this study was to investigate the effect of hardwood biochar supplementation on fermentation parameters, methane (CH4) production and the ruminal archaeal, bacterial, and fungal microbiota using the in vitro RUSITEC (rumen simulation technique) system. Treatments consisted of a control diet (oaten pasture: maize silage: concentrate, 35:35:30 w/w) and hardwood biochar included at 400 mg or 800 mg per day (3.6% and 7.2% of substrate DM, respectively), over a 15-day period. Biochar supplementation had no effect (P ≥ 0.37) on pH, effluent (mL/d), total gas (mL/d), dry matter (DM) digestibility or CH4 production (mg/d). The addition of 800 mg biochar per day had the tendency (P = 0.10) to lower the % of CH4 released in fermentation compared to 400 mg/d biochar and control treatments. However, no effect (P ≥ 0.44) was seen on total VFA, acetate, propionate, butyric, branched-chain VFA, valerate and caproate production and the ratio of acetate to propionate. No effect (P > 0.05) was observed on bacterial, archaeal or fungal community structure. However, biochar supplementation at 800 mg/d decreased the abundance of one Methanomethylophilaceae OTU (19.8-fold, P = 0.046) and one Lactobacillus spp. OTU (31.7-fold, P < 0.01), in comparison to control treatments. Two fungal OTUs classified as Vishniacozyma victoriae (5.4 × 107 increase) and Sporobolomyces ruberrimus (5.4 × 107 fold increase) were more abundant in the 800 mg/d biochar samples. In conclusion, hardwood biochar had no effects on ruminal fermentation characteristics and may potentially lower the concentration of enteric CH4 when included at higher dosages by manipulating ruminal microbiota abundances.
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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".