Nutrient and Sediment Losses Under Simulated Rainfall Following Manure Incorporation by Different Methods
Bibliographic record
Abstract
Incorporation of manure into cultivated soils is generally recommended to minimize nutrient losses. A 3-yr study was conducted to evaluate sediment and nutrient losses with different tillage methods (moldboard plow, heavy-duty cultivator, double disk, and no-incorporation) for incorporation of beef cattle manure in a silage barley (Hordeum vulgare L.) cropping system. Runoff depths, sediment losses, and surface and subsurface nutrient transfers were determined from manured and unmanured field plots at Lethbridge, Alberta, Canada. A Guelph rainfall simulator was used to generate 30 min of runoff. Sediment losses among our tillage treatments (137.4-203.6 kg ha(-1)) were not significantly different due to compensating differences in runoff depths. Mass losses of total phosphorus (TP) and total nitrogen (TN) in surface runoff were greatest from the no-incorporation (NI) treatments, with reductions in TP loads of 14% for double disk (DD), 43% for cultivator (CU), and 79% for moldboard plow (MP) treatments. Total N load reductions in 2002 were 26% for DD, 70% for CU, and 95% for MP treatments compared to the NI treatments. Nutrient losses following incorporation of manure with the DD or CU methods were not significantly different from the NI treatments. Manure treatments generally had lower runoff depths and sediment losses, and higher phosphorus and nitrogen losses than the control treatments. Subsurface concentrations of NH4-N, NO3-N, and TN were greatest from the MP treatments, whereas subsurface phosphorus concentrations were not affected by tillage method. Tillage with a cultivator or double disk minimized combined surface and subsurface nutrient losses immediately after annual manure applications.
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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) | 0.000 | 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".