Conversion of Conservation Tillage to Rotational Tillage to Reduce Phosphorus Losses during Snowmelt Runoff in the Canadian Prairies
Bibliographic record
Abstract
In a preceding study, converting conventional tillage (ConvT) to conservation tillage (ConsT) was reported to decrease nitrogen (N) but to increase phosphorus (P) losses during snowmelt runoff. A field-scale study was conducted from 2004 to 2012 to determine if conversion of ConsT to rotational tillage (RotaT), where conservation tillage was interrupted by a fall tillage pass every other year, could effectively reduce P losses compared with ConsT. The RotaT study was conducted on long-term paired watersheds established in 1993. The ConvT field in the pair has remained under ConvT practice since 1993, whereas tillage was minimized on the ConsT field from 1997 until 2007. In fall 2007, RotaT was introduced to the ConsT field, and heavy-duty cultivator passes were conducted in the late fall of years 2007, 2009, and 2011. Runoff volume and nutrient content were monitored at the edge of the two fields, and soil and crop residue samples were taken in each field. Greater soil Olsen P and more P released from crop residue are likely the reasons for the increased P losses in the ConsT treatment (2004-2007) relative to the ConvT treatment (2004-2007). Analysis of covariance indicated that, compared with ConsT (2004-2007), RotaT (2008-2012) increased the concentrations of dissolved organic carbon (DOC) by 62%, total dissolved N (TDN) by 190%, and total N (TN) by 272% and increased the loads of DOC by 34%, TDN by 34%, and TN by 60%. However, RotaT (2008-2012) decreased soil test P in surface soil, P released from crop residue, and duration of runoff compared with ConsT (2004-2007) and thus decreased the concentrations of total dissolved P (TDP) by 46% and total P (TP) by 38% and decreased the loads of TDP by 56% and TP by 42%. In the Canadian Prairies, where P is a major environmental concern compared with N, RotaT was demonstrated to be an effective practice to reduce P losses compared with ConsT.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".