Softwood biochar and greenhouse gas emissions: a field study over three growing seasons on a temperate agricultural soil
Bibliographic record
Abstract
Agricultural activities contribute to greenhouse gas emissions, but agroecosystems can also mitigate emissions by adding recalcitrant carbon sources such as biochar to soil. Our goal was to understand if greenhouse gas emissions decrease in soil amended with manure and biochar (MN) or with manure, nitrogen fertilizer, and biochar (MNB) than soil amended with manure and N fertilizer (MN) over the longer term. We hypothesized that biochar reduces the release of labile carbon and reactive nitrogen from organic amendments and nitrogen fertilizer, thereby reducing CO2 and N2O emissions and that soil temperature, moisture, and nitrogen availability are the strongest predictors for intra- and inter-annual variation in greenhouse gas emissions. Over three growing seasons, biweekly measurements of CO2 and N2O emissions were similar (P < 0.05) among treatments. Although input of labile carbon and reactive nitrogen was highest in MN and MB, cumulative CO2 emissions were lowest (P < 0.05) in MNB. The availability of nitrogen from fertilizer caused greater cumulative N2O emissions (P < 0.05) in MN and MNB. We found that soil temperature, moisture, and nitrogen availability regulated intraannual variability (P < 0.05) of CO2 and N2O emissions in all treatments, where emissions were greatest (P < 0.05) in the spring followed by summer and autumn. We accepted our hypothesis and concluded that, for cumulative emissions, biochar reduces the release of labile carbon and reactive nitrogen, but we rejected this hypothesis for biweekly emissions. We also concluded and accepted our hypothesis that soil temperature, moisture, and nitrogen availability regulated intraannual variation of CO2 and N2O in all treatments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".