Increased N2O emissions by cover crops in a diverse crop rotation can be mediated with dual nitrification and urease inhibitors
Bibliographic record
Abstract
Agriculture significantly contributes to global soil nitrous oxide (N2O) emissions. Crop rotation diversification and cover cropping are feasible agronomic strategies to reduce nitrogen losses to the environment. However, input of cover crop residues could potentially increase soil N2O emissions. Dual nitrification and urease inhibitors (NUI) administered after cover crop termination at the time of nitrogen fertiliser addition could reduce emissions, but this has not been widely evaluated in field studies. A 4-year crop rotation study was conducted to determine the effect of crop diversification and use of NUI on N2O emissions, crop yield and N2O intensity. Nitrous oxide flux was measured year-round using a micrometeorological method deployed on four 4-ha fields. Two fields were managed with a conventional crop rotation (CONV) (corn – soybean – soybean) and two fields were managed with a diverse crop rotation (DIV) (corn – soybean – winter-wheat plus cover crops either as 2-species mixture under seeded to corn or 4-species mixture after winter-wheat harvest). The effect of a NUI [N(-n-Butyl) thiophosphoric triamide and Pronitridine] was tested in corn in the fourth year. The DIV rotation resulted in 43 % lower annual N2O emissions when winter wheat was grown instead of soybean and 18–26 % increase in annual N2O emissions for corn. The DIV rotation increased N2O intensity by 15 % in Year 1 and 36 % in Year 4 compared to corn in the CONV rotation. The use of NUI in DIV rotation resulted in 15 % lower total N2O emissions over 3 years of the rotation cycle. The application of NUI resulted in a 19 % reduction in N2O intensity within the DIV rotation, with no observable effect on corn yield. Further research should focus on optimising the N application rates according to NUI use, considering available nitrogen from crop residues and cover crops when integrated into the crop rotation.
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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 | 0.000 | 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 teacher head, 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".