Effect of long-term conventional tillage and no-tillage systems on soil and water quality at the field scale
Bibliographic record
Abstract
No-tillage (NT) is becoming increasingly attractive to farmers because it clearly reduces soil erosion and production costs relative to conventional tillage (CT). However, the impacts of no-tillage on the quantity and quality of tile drainage water are less well known. Accordingly, two adjacent field scale on-farm CT and NT sites were established to compare the impacts of the two tillage systems on tile drainage and NO3-N loss in tile drainage water. The effect of the two tillage systems on soil structure, hydraulic conductivity, and earthworm population were also investigated. The total NO3-N loss in tile drainage water over the 5-yr period (1995-1999) was 82.3 kg N ha(-1) for the long-term NT site and 63.7 kg N ha(-1) for the long-term CT site. The long-term NT site had 48% more tile drainage (6,975 kL ha(-1)) than the long-term CT site (4,716 kL ha(-1)). The average flow weighted mean (FWM) NO3-N concentration in tile drainage water over the 5-yr period was 11.8 mg N L(-1) for the NT site and 13.5 mg N L(-1) for the CT site. For both tillage systems, approximately 80% of tile drainage and NO3-N loss in tile drainage water occurred during the November to April non-growing season. Long-term NT improved wet aggregate stability, increased near-surface hydraulic conductivity and increased both the number and mass of earthworms relative to long-term CT. The greater tile drainage and NO3-N loss under NT were attributed to an increase in continuous soil macropores, as implied by greater hydraulic conductivity and greater numbers of earthworms.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".