Impact of drainage type on simultaneous nitrogen losses in Atlantic Canada
Bibliographic record
Abstract
Nitrogen (N) losses from agricultural tile drainage systems are environmental and economic losses for producers. This field study quantified N losses from three reps of shallow (SD), deep/conventional (DD), and controlled drainage (CD) on farmland in Nova Scotia. Drainage systems were under corn and alfalfa–oats–clover production. Outflow water and gas samples were obtained and analyzed for nitrate and nitrous oxide. Nitrate-N loads were 5.0, 11.1, and 6.4 kg ha−1 in 2015; 1.8, 6.7, and 2.8 kg ha−1 in 2016; and 0.74, 1.8, and 1.6 kg ha−1 in 2017 for SD, DD, and CD, respectively. Controlled drainage reduced NO3−-N loading by 42.3%–58.2% when compared with the conventional/DD in 2 of 3 yr of study, whereas SD was found to reduce NO3−-N loading by 54.9%–73.1% compared with DD in all years studied. Total NO3−-N losses in this study were measured during the growing season (1 Apr. to 31 Oct.); the magnitude of NO3−-N losses and treatment effects may vary if studied year-round. Nitrous oxide fluxes were variable and low in magnitude throughout the study. Cumulated N2O losses were <1% of the applied N for all drainage types. Controlled drainage increased yields compared with SD and DD. The use of CD in the region could aid in reducing climate stresses, as well as overall NO3−-N loads exiting drainage systems and may enhance crop yields compared with conventional systems. Future studies on dissolved N2O losses from drainage water may provide important insight into whether dissolved N2O losses exceed surface emissions.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".