Phosphorus Losses in Simulated Rainfall Runoff from Manured Soils of Alberta
Bibliographic record
Abstract
Manure applied to agricultural land at rates that exceed annual crop nutrient requirements can be a source of phosphorus in runoff. Manure incorporation is often recommended to reduce phosphorus losses in runoff. A small plot rainfall simulation study was conducted at three sites in Alberta to evaluate the effects of manure rate and incorporation on phosphorus losses. Treatments consisted of three solid beef cattle manure application rates (50, 100, and 200 kg ha(-1) total phosphorus), an unmanured control, and two incorporation methods (nonincorporated and incorporated with one pass of a double disk). Simulated rain was applied to soils with freshly applied and residual (1 yr after application) manure at 70 mm h(-1) to produce 30 min of runoff. Soil test phosphorus (STP), total phosphorus (TP), and dissolved reactive phosphorus (DRP) concentrations in runoff increased with manure rate for fresh and residual manure. Initial abstraction and runoff volumes did not change with manure rate. Initial abstraction, runoff volumes, and phosphorus concentrations did not change with manure incorporation at Lacombe and Wilson, but initial abstraction volumes increased and runoff volumes and phosphorus concentrations decreased with incorporation of fresh manure at Beaverlodge. Phosphorus losses in runoff were directly related to phosphorus additions. Extraction coefficients (slopes of the regression lines) for the linear relationships between residual manure STP and phosphorus in runoff were 0.007 to 0.015 for runoff TP and 0.006 to 0.013 for runoff DRP. While incorporation of manure with a double disk had no significant effect on phosphorus losses in runoff from manure-amended soils 1 yr after application, incorporation of manure is still recommended to control nitrogen losses, improve crop nutrient uptake, and potentially reduce odor concerns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.001 | 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 teacher head, 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".