Intensive measurement of nitrous oxide emissions from a corn–soybean–wheat rotation under two contrasting management systems over 5 years
Bibliographic record
Abstract
Abstract No‐tillage (NT), a practice that has been shown to increase carbon sequestration in soils, has resulted in contradictory effects on nitrous oxide (N 2 O) emissions. Moreover, it is not clear how mitigation practices for N 2 O emission reduction, such as applying nitrogen (N) fertilizer according to soil N reserves and matching the time of application to crop uptake, interact with NT practices. N 2 O fluxes from two management systems [conventional (CP), and best management practices: NT + reduced fertilizer (BMP)] applied to a corn ( Zea mays L.), soybean ( Glycine max L.), winter‐wheat ( Triticum aestivum L.) rotation in Ontario, Canada, were measured from January 2000 to April 2005, using a micrometeorological method. The superimposition of interannual variability of weather and management resulted in mean monthly N 2 O fluxes ranging from − 1.9 to 61.3 g N ha −1 day −1 . Mean annual N 2 O emissions over the 5‐year period decreased significantly by 0.79 from 2.19 kg N ha −1 for CP to 1.41 kg N ha −1 for BMP. Growing season (May–October) N 2 O emissions were reduced on average by 0.16 kg N ha −1 (20% of total reduction), and this decrease only occurred in the corn year of the rotation. Nongrowing season (November–April) emissions, comprised between 30% and 90% of the annual emissions, mostly due to increased N 2 O fluxes during soil thawing. These emissions were well correlated ( r 2 = 0.90) to the accumulated degree‐hours below 0 °C at 5 cm depth, a measure of duration and intensity of soil freezing. Soil management in BMP (NT) significantly reduced N 2 O emissions during thaw (80% of total reduction) by reducing soil freezing due to the insulating effects of the larger snow cover plus corn and wheat residue during winter. In conclusion, significant reductions in net greenhouse gas emissions can be obtained when NT is combined with a strategy that matches N application rate and timing to crop needs.
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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 | 0.000 |
| 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 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".