Delaying application and injecting nitrogen fertilizer with urease and nitrification inhibitors decreased nitrous oxide emissions and enhanced corn yields
Bibliographic record
Abstract
Abstract Early season nitrous oxide (N2O) emissions following nitrogen fertilizer application can be significant if spring rains lead to anaerobic conditions before the crop is established and able to utilize the applied N. However, delaying fertilizer application by 4‐5 weeks after planting usually results in warmer temperatures which promote ammonia (NH3) volatilization losses. This 3‐year study on a clay loam soil compared NH3 losses, N2O emissions, and corn (Zea mays L.) grain yields for pre‐plant urea ammonium nitrate (UAN) injection versus side‐dress UAN injection using no inhibitors, a urease inhibitor, or a urease and nitrification inhibitor. Side‐dress N‐application resulted in 13% greater corn grain yields compared to pre‐plant N application when averaged over the inhibitor treatments. Pre‐plant UAN with a urease inhibitor had 59% greater N2O emissions (2.15 kg N ha−1) than pre‐plant injected UAN with a urease and nitrification inhibitor (1.35 kg N ha−1); pre‐plant UAN injection with no inhibitors produced intermediate N2O losses (1.89 kg N ha−1). Delaying UAN application to side‐dress in 2015, the year with above‐normal spring precipitation, decreased N2O emissions (1.24 kg N ha−1) by 52% compared to pre‐plant UAN (2.56 kg N ha−1); however, side‐dress application had minimal impact on N2O in the subsequent 2 years which had drier spring conditions. The dual urease and nitrification inhibitor treatment reduced yield‐scaled N2O emissions by 37% compared to urease only when averaged over the timing treatments. Side‐dress N application reduced yield‐scaled N2O emissions by 28% compared to pre‐plant application when averaged over the inhibitor treatments. Urease plus nitrification inhibitors combined with side‐dress UAN application increased corn yields and decreased N2O losses, whereas urease inhibitors alone increased N2O 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) | 0.001 | 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".