Biochar and Manure from Cattle Fed Biochar as Agricultural Amendments Alter CH4 Oxidation in a Gray Luvisol
Bibliographic record
Abstract
Greenhouse gases (GHG) emissions from agricultural practices contribute 14% of anthropogenic emissions to the atmosphere, and novel practices to reduce these emissions, including feeding cattle a modified diet, are of interest. This study examines how additions of manure from cattle fed a regular diet or a diet supplemented with 2% biochar, and biochar at 5 or 10 Mg ha−1, impact GHG emissions in a Gray Luvisol agricultural field experiment. Emissions of CH4 and N2O were monitored, and soil samples were collected to analyze exchangeable NPKS, microbial biomass, total C and N, electrical conductivity, and pH. Wheat (Triticum aestivum) was planted, and grain yield measured. We calculated the yield-based emission factor (EFyield) and cumulative area-based GHG emissions emission factor (EFarea). The results showed an up to 98.5% change in the inhibition of CH4 oxidation from biochar-manure + biochar at 5 and 10 Mg ha−1 compared to the CT. The biochar in biochar-manure may have acted as a biocide to methanotrophs, causing a reduction in the release of CH4 over time. Yet, there were no significant differences in N2O emissions amongst treatments. Therefore, biochar-manure + biochar at 5 and 10 Mg ha−1 applications may impact total GHG emissions and improve grain productivity and protein content compared to BM alone.
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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 | 0.000 | 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".