Non‐Legume Cover Crops Can Increase Non‐Growing Season Nitrous Oxide Emissions
Bibliographic record
Abstract
Core Ideas Nitrous oxide emissions were greater in winter than spring or fall. Tillage radish increased over‐winter N 2 O fluxes. Non‐legume cover crops increased N 2 O fluxes under apparent NO 3 limiting conditions. Cover crops retain post‐harvest nutrients but how they impact non‐growing season nitrous oxide (N 2 O) emissions is unclear. Therefore, we quantified how cover crop type (fall rye [ Secale cereale L.] or oilseed radish [ Raphanus sativus L.]) and fertilizer source (compost or inorganic fertilizer) affected N 2 O emissions, soil water‐extractable organic C (WEOC) and nitrate (NO 3 ) dynamics over two non‐growing seasons. A treatment with no fertilizer or cover crop was also included. Weekly, N 2 O fluxes were determined using vented static chambers; soil WEOC and NO 3 –N concentrations were measured monthly. Each non‐growing season, mean N 2 O fluxes were 74 to 450% greater in the winter (21 December–20 March) than spring (21 March–20 June) or fall (22 September–20 December). In winter 2014–2015, oilseed radish increased the mean N 2 O flux by 39 and 323% compared with fall rye and no cover crop, respectively, while the mean N 2 O fluxes were strongly correlated to the pre‐winter (16 Dec. 2014) NO 3 concentrations ( r = 0.96; P < 0.001), indicating NO 3 levels < 6 mg NO 3 –N kg –1 limited N 2 O fluxes. In 2014–2015, fall rye and oilseed radish had 76 and 154% greater cumulative N 2 O emissions than amended soils with no cover crop, respectively. Across both winters, an exponential model explained 67% of variability between the pre‐winter WEOC to NO 3 ratio and N 2 O fluxes, indicating that organic C and NO 3 controlled over‐winter N 2 O fluxes. Non‐legume cover crops increased non‐growing season N 2 O emissions, suggesting that cover crops concentrate denitrification substrates in root‐associated soil to enhance N 2 O fluxes.
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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 | 0.001 | 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 | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".