Combining Urease and Nitrification Inhibitors with Incorporation Reduces Ammonia and Nitrous Oxide Emissions and Increases Corn Yields
Bibliographic record
Abstract
Less than 50% of applied nitrogen (N) fertilizer is typically recovered by corn (Zea mays L.) due to climatic constraints, soil degradation, overapplication, and losses to air and water. Two application methods, two N sources, and two inhibitors were evaluated to reduce N losses and enhance crop uptake. The treatments included broadcast urea (BrUrea), BrUrea with a urease inhibitor (BrUrea+UI), BrUrea with a urease and a nitrification inhibitor (BrUrea+UI+NI), injection of urea ammonium nitrate (InjUAN), and injected with one or both inhibitors (InjUAN+UI, InjUAN+UI+NI), and a control. The BrUrea treatment lost 50% (64.4 kg N ha−1) of the applied N due to ammonia volatilization, but losses were reduced by 64% with BrUrea+UI+NI (23.0 kg N ha−1) and by 60% with InjUAN (26.1 kg N ha−1). Ammonia losses were lower and crop yields were greater in 2014 than 2013 as a result of the more favorable weather when N was applied in 2014. When ammonia volatilization was reduced by adding a urease inhibitor, N2O emissions were increased by 30 to 31% with BrUrea+UI and InjUAN+UI compared with BrUrea and InjUAN, respectively. Pollution swapping was avoided when both inhibitors were used (BrUrea+UI+NI, InjUAN+UI+NI) as both ammonia volatilization and N2O emissions were reduced, and corn grain yields increased by 5% with BrUrea+UI+NI and by 7% with InjUAN+UI+NI compared with BrUrea and InjUAN, respectively. The combination of two N management strategies (InjUAN+UI+NI) increased yields by 19% (12.9 t ha−1) compared with BrUrea (10.8 t ha−1). Core Ideas Ammonia volatilization resulted in 50% loss of applied urea over 2 yr. When urease inhibitors were added with urea, ammonia volatilization was reduced by 64%. Injection of UAN reduced ammonia volatilization by 60% compared with broadcast urea. N2O emissions were increased by 30 to 31% when urease inhibitors were applied. Pollution swapping was avoided when both urease and nitrification inhibitors were used.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".