Soil-surface carbon dioxide emission following nitrogen fertilization in corn
Bibliographic record
Abstract
Improvement in use efficiency of N fertilizers can potentially better sustain agriculture by reducing N2O emissions from soils, but little is known about its impact on soil CO2 emissions. A study, involving both a field experiment and a laboratory incubation, was conducted in eastern Canada to determine the N fertilization effect on soil CO2 emissions. In laboratory, we incubated nine different types of soil with and without 150 kg N ha−1 as KNO3 or (NH4)2SO4. The N-fertilized soils had lower CO2 emissions compared with the no-N control soils for six of them. Among fertilizer sources, emissions of CO2 were on average 22% lower with KNO3 than with (NH4)2SO4. The field experiment conducted on a clay soil included three sources of N (urea-NH4NO3, CaNH4NO3, and aqua NH3) at 0–200 kg N ha−1 band-incorporated at the six-leaf corn stage. Under field conditions, most CO2 was emitted between N application and grain maturity with cumulative seasonal soil emissions greater in the control (4.9 Mg C ha−1) than in the N treatments (average of 4.0 ± 0.3 Mg C ha−1). Evidence suggested that both heterotrophic and autotrophic respiration seemed affected, whereas the NO3-based source had a more depressing effect on CO2 emissions than did the NH4 source.
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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".