Nitrous oxide and carbon dioxide emissions from surface and subsurface drip irrigated tomato fields
Bibliographic record
Abstract
Irrigation practices change the soil moisture in agricultural fields and influence emissions of greenhouse gases (GHG). A 2 yr field study was conducted to assess carbon dioxide (CO2) and nitrous oxide (N2O) emissions from surface and subsurface drip irrigated tomato (Solanum lycopersicum L.) fields on a loamy sand in southern Ontario. Surface and subsurface drip irrigation are common irrigation practices used by tomato growers in southern Ontario. The N2O fluxes were generally ≤50 μg N2O-N m−2 h−1, with mean cumulative emissions ranging between 352 ± 83 and 486 ± 138 mg N2O-N m−2. No significant difference in N2O emissions between the two drip irrigation practices was found in either study year. Mean CO2 fluxes ranged from 22 to 160 mg CO2-C m2 h−1 with cumulative fluxes between 188 ± 42 and 306 ± 31 g CO2-C m−2. Seasonal CO2 emissions from surface drip irrigation were significantly greater than subsurface drip irrigation in both years, likely attributed to sampling time temperature differences. We conclude that these irrigation methods did not have a direct effect on the GHG emissions from tomato fields in this study. Therefore, both irrigation methods are expected to have similar environmental impacts and are recommended to growers.
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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 | 0.000 |
| 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) | 0.001 | 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".