Nitrogen Source, Application Time, and Tillage Effects on Soil Nitrous Oxide Emissions and Corn Grain Yields
Bibliographic record
Abstract
A 3-yr study was conducted to determine the effectiveness of N fertilizer application times (at planting vs. sidedress) and N fertilizer source (regular urea vs. coated urea) on N2O emissions and corn (Zea mays L.) grain yields from soil under conventional tillage (CT), zone tillage (ZT), or no-tillage (NT) in southwestern Ontario. On average, 4.19 kg N ha−1 yr−1 were lost from the CT treatments as N2O and these losses were reduced by 16.6% with NT (3.50 kg N ha−1 yr−1) and by 43.8% with ZT (2.35 kg N ha−1). There were some significant effects of N source and application time on N2O emissions; however, these were inconsistent among tillage treatments and years. In 2004, there was a significant interaction between N source and tillage with regular urea (6.49 kg N ha−1) producing 2.7 times more N2O than coated urea (2.40 kg N ha−1) with CT. However, N source did not significantly affect N2O emissions in either 2005 or 2006. In CT treatments, the 3-yr average N2O emissions using N applied at planting (5.02 kg N ha−1 yr−1) were, on average, 49% greater than when sidedress urea (3.37 kg N ha−1 yr−1) was applied. The 3-yr average corn grain yield for CT (9.38 t ha−1) was significantly greater than for NT (8.35 t ha−1), while the yield for ZT (9.01 t ha−1) was not significantly different from either CT or NT. The influence of tillage, N application time and N source on N2O emissions and corn grain yields appeared to be partially related to the amount and timing of precipitation and the root zone soil moisture content. Polymer-coated urea was most effective in 2004 under CT when the soil moisture was high in the first month after planting due to antecedent soil moisture conditions and rainfall. Under these wetter conditions, N2O emissions were reduced by either delaying urea hydrolysis with polymer-coated urea or by using ZT instead of CT.
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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 | 0.001 | 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 | 0.001 |
| 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".