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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".