Combining measurements and modelling to reveal long-term effects of nitrogen fertilizer application timing on N2O emissions in corn
Why this work is in the frame
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Bibliographic record
Abstract
The impact of nitrogen fertilizer (N) application timing on nitrous oxide (N 2 O) emissions is inconsistent in the literature. This inconsistency is attributed to year-to-year weather variations, which affect soil conditions around N application time. Planting dates (PD) also vary year-to-year based on weather, and PD can influence N timing decisions. The study aims to evaluate: i) the long-term effects of different N application timings on N 2 O emissions and, ii) how variations in PD influence the relative performance of different N timing strategies. We used the DeNitirifcation-DeComposition (DNDC) model, calibrated with field measurements from Elora, Ontario, Canada, to simulate 39 growing seasons using historical weather data. Three N timing strategies were tested: spring application one day before planting, in-season application at the V6 growth stage, and a split-N strategy with N applied at both times. PDs were either dynamically adjusted each year based on rainfall or fixed to one of three typical corn ( Zea mays L.) planting dates in Ontario: April 25, May 5, and May 15. For the first objective, the long-term simulation found that average N 2 O emissions were greatest when N was applied at V6 (3.2 kg N ha −1 ) compared to when N was applied pre-plant (2.3 kg N ha −1 ) or split-applied (2.0 kg N ha −1 ). This was caused by slightly greater rainfall around V6 than planting. For the second objective, the relative performance of different N-timing strategies was affected by PD. Earlier PDs resulted in lower N₂O emissions compared to later PDs, primarily due to lower soil temperatures around the time of N fertilizer application. Earlier PDs also led to the largest differences in N 2 O emissions among the N timing strategies, with PD delays leading to smaller differences among N timing strategies. Large single N applications, particularly those applied in-season, resulted in greater N 2 O emissions than split and at-planting N applications in a long-term simulation. Early PDs consistently reduced N 2 O emissions by creating less favourable conditions for N 2 O production. Moreover, the relative performance of N timing strategies was mediated by PD. This study highlights the interconnected nature of cropping systems, where one management practice, PD, can influence a seemingly unrelated outcome, N 2 O emissions. Long-term climatic, social, economic, and technological changes that influence PD will also influence N 2 O emissions from spring and summer-applied N fertilizer. • Early planting dates reduce N 2 O emissions from nitrogen applications. • Split nitrogen applications result in the lowest average N 2 O emissions. • N 2 O emissions peak with large nitrogen applications at the V6 growth stage. • Effect of nitrogen timing on N2O emissions depends on planting date. • Long-term simulations can reveal the true effect of different fertilizer strategies for emission reduction.
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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 | 0.000 |
| 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 it