Nongrowing season soil nitrous oxide emissions as influenced by cover crops and fall tillage termination
Bibliographic record
Abstract
Cropland soil is a major driver of global nitrous oxide (N2O) emissions. In cold climates, nongrowing season (NGS) emissions can be significant due to high fluxes during freeze–thaw (FT) cycles. Cover crops can alter key soil conditions that govern N2O-producing microbial processes, with multiple potential pathways to either increase or decrease N2O production during FT cycles. Cultivating cover crops in the fall to terminate may further disrupt these processes and the overall impact of cover crops on N2O emissions. Yet, few studies have touched on how termination practices of cover crops impact FT emissions over the NGS. Using the flux gradient method to continuously measure N2O emissions from a conventional corn–soybean rotation, we investigated the effects of summer-established cover crops (perennial ryegrass and crimson clover) (with cover crops, +CC; without cover crops, −CC) when terminated by fall cultivation (with fall cultivation, +FC; without fall cultivation, −FC) over a six-month NGS that was characterized by several freezing and thawing periods. Crimson clover cover crop was completely winterkilled, while the ryegrass survived on the +CC−FC field. Total NGS (Nov–Apr) emissions varied nearly 2.5-fold among treatments from 395.1 (−CC−FC) to 978.1 (+CC+FC) g N2O-N ha−1. Compared with the control treatment (−CC−FC), fall cultivation alone (−CC+FC) and cover crops alone (+CC−FC) increased total NGS N2O emissions, and fall cultivation with cover crops (+CC+FC) increased N2O fluxes even more. Careful CC species selection and management are important to avoid elevated NGS emissions.
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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 | 0.001 |
| 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".