Two decades of micrometeorological measurements show dynamic drivers of springtime N2O emissions
Bibliographic record
Abstract
Nitrous oxide (N2O) is a potent greenhouse gas with a global warming potential 265 times that of carbon dioxide and the ability to destroy stratospheric ozone. Agricultural soils contribute over two-thirds of anthropogenic N2O emissions. Field observations in temperate climates have commonly shown N2O emission peaks occurring in the spring during a period of snow melt and soil thaw. This freeze-thaw period typically accounts for approximately 35% of annual N2O emissions, however, current understanding of its drivers is based off of relatively short observation periods. The analysis of such data over decadal time spans is therefore needed to improve our understanding of key drivers. Here, we report on 21 years (2002-2022) of micrometeorological N2O flux data from a field site in Ottawa, Ontario. Correlation analyses were conducted between the N2O flux during the spring thaw period and variables that are commonly considered drivers. Little to no correlation was seen from linear or multilinear regressions across a range of meteorological, management, and soil variables. The non-linear response of non-growing season cumulative N2O emissions to cumulative freezing degree-days was consistent with previous studies (Wagner-Riddle et al., 2017). However, in isolating freezing degree-days we may be neglecting other possible controls. These results show that the relationship of N2O flux with environmental variables may be related to complex, potentially non-linear, interactions between agricultural practices, weather, soil quality, and other variables. We will further examine the data using other multivariate statistical methods to further investigate potential drivers of these non-growing season emissions with a focus on those emissions occurring specifically during the spring thaw. As these relationships are used in nitrification/denitrification models, improved understanding is still needed to accurately simulate these processes, which is imperative to improving nitrogen budgets and ultimately achieving climate goals.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".