Nitrous oxide soil emissions from an organic and conventionally managed cropping system in Manitoba
Bibliographic record
Abstract
In recent decades the knowledge of nitrous oxide (N2O) emissions after the application of nitrogen (N) fertilizers in agriculture soil has improved. However the understanding of emissions of N2O from Canadian organic agricultural systems has not been developed. The Glenlea Long Term Crop Rotation is the longest running organic conventional comparison study in western Canada and was used here to compare N2O emissions between the systems. In organic cropping systems forage legumes such as alfalfa are incorporated into the soil as an N source. The amount of N2O that is emitted after the incorporation and during the subsequent crop is not well known. The wheat and legume phases (alfalfa (Medicago sativa) in organic system and soybean (Glycine max L.) in the conventional) of the rotation were monitored for N2O. In 2014, 2015, and spring 2016 (data still being analysed) emissions of N2O were monitored using the vented static chambers method as well, soil conditions (temperature, moisture, inorganic N and extractable carbon) and yields were measured. Typical N2O emissions from spring applied urea were observed after application in the conventional system however no emission episode was seen after the fall alfalfa plough down or during spring thaw in the organic system. Greater NO3- accumulation was observed in the organic treatments however low emissions were observed. The organic system resulted in lower yields for both years, but still resulted in lower emissions per amount of grain produced (yield-scaled emissions) than the conventional system. This study adds to the knowledge that N2O emissions from organic systems do differ from conventional however yields need to be improve to fully exploit the benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".