Conventional and Conservation Tillage: Influence on Seasonal Runoff, Sediment, and Nutrient Losses in the Canadian Prairies
Bibliographic record
Abstract
Conservation tillage has been widely promoted to reduce sediment and nutrient transport from agricultural fields. However, the effect of conservation tillage on sediment and nutrient export in snowmelt-dominated climates is not well known. Therefore, a long-term paired watershed study was used to compare sediment and nutrient losses from a conventional and a conservation tillage watershed in the Northern Great Plains region of western Canada. During the treatment period, dissolved nutrient concentrations were typically greater during spring snowmelt than during summer rainfall events, whereas concentrations of sediment and particulate nutrients were greatest during rainfall events. However, because total runoff was dominated by snowmelt, most sediment and nutrient export occurred during snowmelt. Overall, conservation tillage reduced the export of sediment in runoff water by 65%. Similarly, concentrations and export of nitrogen were reduced by 41 and 68%, respectively, relative to conventional tillage. After conversion to conservation tillage, concentrations and exports of phosphorus (P) increased by 42 and 12%, respectively, with soluble P accounting for the majority of the exported P, especially during snowmelt. Our results suggest that management practices designed to improve water quality by reducing sediment and sediment-bound nutrient export from agricultural fields and watersheds can be less effective in cold, dry regions where nutrient export is primarily snowmelt driven and in the dissolved form. In these situations, it may be more appropriate to implement management practices that reduce the accumulation of nutrients in crop residues and the surface soil.
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 | 0.001 | 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".