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 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".