Greenhouse gas emissions from onion fields cultivated on organic soils under sprinkler irrigation in Quebec
Bibliographic record
Abstract
Agricultural practices contribute to greenhouse gas emissions. A four year field study was conducted to quantify and compare CO2, N2O and CH4 fluxes from sprinkler irrigated and non-irrigated onion fields in southern Quebec, Canada. Irrigation practices influence GHG emissions by changing the soil moisture content and thus impacting the soil microbial activity. The experimental plots were located on three organic soils with different degrees of stabilization. The static chamber method was used to obtain in-situ gas fluxes. Meteorological and soils data were also collected. Results for CO2, N2O and CH4 fluxes ranged from 1 to 268 mg CO2-C m-2*hr-1, -1.06 x 10-4 to 0.566 mg N2O-N m-2 * hr-1 and -0.00628 to 0.00760 mg CH4-C m-2 *hr-1, respectively. Results showed that sprinkler irrigation had minimal effects on N2O and CH4 gas fluxes, however, the CO2 fluxes increased within 24 hours of an irrigation event. In fact, CO2 fluxes were found to be more prominently influenced by the growth stage of the plant. Higher CO2 fluxes were observed, both, earlier and later in the season when root and leaf growth, respectively, were at their maximum. For N2O, higher fluxes were observed primarily in the spring after snow melt and fertilizer application. As well, N2O fluxes were influenced by heavier rainfalls (>10 mm) and wetter soils (WFPS between 70 and 100%). Organic soils for this research were predominantly methane sinks with slight increases in CH4 flux observed following fertilizer application and soil tillage. Since greenhouse gas fluxes were sporadic and seldom linked to irrigation events, it is concluded that sprinkler irrigation had a limited impact on greenhouse gas emissions from the organic soils in this study.
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 | 0.000 | 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".