Ammonia volatilization, nitrous oxide emissions, and corn yields as influenced by nitrogen placement and enhanced efficiency fertilizers
Bibliographic record
Abstract
Abstract Ensuring sufficient fertilizer nitrogen (N) for crops while minimizing N losses requires best management practices optimized for climate, crop, soil, and root zone hydrology. In Ontario, pre‐plant N fertilization of corn ( Zea mays L.) is common; however, this practice extends the time between application and significant root interception of N by the plant, potentially increasing the risk of N loss through soil nitrous oxide emissions, ammonia (NH 3 ) volatilization, and nitrate leaching. These losses contribute to greenhouse gas emissions, affect air quality (NH 3 ), and are a substantial financial loss. This study compared three N placement methods (broadcast urea [BrUrea], broadcast incorporated urea [BrIncUrea], and injected urea ammonium nitrate [InjUAN]) and the presence or absence of N metabolite inhibitors (urease inhibitor [UI], urease plus nitrification inhibitor [UI+NI]). Fertilizer N was applied immediately before planting (150 kg N ha −1 ) to all treatments except for the control. Averaged over 3 yr (2015–2017), NH 3 losses were reduced by 34% from BrIncUrea, by 42–55% from BrUrea+UI+NI and BrIncUrea+UI+NI, and by 99% from InjUAN relative to BrUrea (21 kg N ha −1 ). On average, N application increased corn grain yields by 83% relative to the control (6 t ha −1 ). There were no annual yield differences among N placement methods. It was concluded that incorporation or injection of N in soil and use of urease and nitrification inhibitors reduced NH 3 emissions when N fertilizer was applied pre‐plant.
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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 | 0.001 |
| 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 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".