Nitrous oxide emissions from northern barley croplands after injections of liquid manure and nitrification inhibitors
Bibliographic record
Abstract
Abstract Increasing contributions of nitrous oxide (N2O) from agriculture to the atmosphere is a concern. We quantified N2O emissions from barley fields after repeated injections of liquid manure in Central Alberta, Canada. Manure alone was injected in the fall or spring, and we also evaluated two nitrification inhibitors (NIs: nitrapyrin and DMPP) admixed with the manure. Flux measurements were done with surface chambers from soil thawing to freezing. Soil moisture, ammonium and nitrate were repeatedly measured. Across all manure treatments, annual N2O emissions ranged broadly from 1.3 up to 15.8 kg N2O–N ha− 1, and likewise, the direct emission factor (EFd) varied widely from 0.23 up to 2.91%. When comparing the manure injections without NIs, spring-manure had higher annual N2O EFd than fall-manure. The effectiveness of NIs on reducing emissions manifested only in moist soils. The spring thaw after the last manure injections was very wet, and this generated high N2O emissions from soils that had received repeated manure injections in the previous years. We interpreted this result as an increased differential residual effect in soils amended with spring-manure in the previous growing season. This outcome supports the need to account for emissions in succeeding springs when estimating N2O EFd of manure injections. Neglecting this residual spring-thaw N2O emission would lead to a substantial underestimation of year-round EFd. Across all treatment combinations, increased spring-thaw N2O emissions were associated with increases in both moisture and postharvest nitrate in these heavily-manured soils.
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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) | 0.000 | 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".