Greenhouse Gas Emissions from Forest Soils Reduced by Straw Biochar and Nitrapyrin Applications
Bibliographic record
Abstract
Forestlands are widely distributed in the dominantly agricultural landscape in western Canada, and they play important ecological functions; such forestlands (e.g., shelterbelts) accumulate soil organic matter and may receive a substantial amount of nitrogen in the form of surface and subsurface runoff from adjacent croplands and become a significant source of emissions of greenhouse gases (GHGs) such as CO2, N2O, and CH4. Biochar and nitrapyrin applications could potentially mitigate GHG emissions, but their co-application in forest soils has not been studied. We investigated the effect of the application of biochars produced at low (300 °C; BC300) and high temperatures (700 °C; BC700) using canola (Brassica napus L.) straw and the effect of their co-application with nitrapyrin on GHG emissions and soil properties in a 35-day laboratory incubation experiment using forest soils collected from five shelterbelt sites. Results showed no significant interaction effect of biochar and nitrapyrin on the global warming potential (GWP) of the GHG emissions, and the GWP was 15.8% lower in the soil with nitrapyrin than without nitrapyrin application treatments. The GWP was significantly enhanced by BC300 addition due to a 26.9% and 627.1% increase in cumulative CO2 and N2O emissions, respectively, over the 35-day incubation. The GWP significantly decreased by BC700 addition due to a 27.1% decrease in cumulative CO2 emissions. However, biochar addition did not affect CH4 emissions, while nitrapyrin decreased CH4 uptake by 50.5%. With BC300 addition, soil-dissolved organic carbon and microbial biomass carbon increased by 26.5% and 33.9%, respectively, as compared to no biochar addition (CK). Soil pH increased by 0.16 and 0.37 units after the addition of BC300 and BC700, respectively. Overall, the effect of biochar and nitrapyrin was independent in mitigating GHG emissions and was related to the type of biochar applied and changes in soil properties.
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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) | 0.001 | 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".