Phosphorus and Nitrogen in Rainfall Simulation Runoff after Fresh and Composted Beef Cattle Manure Application
Bibliographic record
Abstract
Fresh beef cattle (Bos taurus) manure has traditionally been applied to cropland in southern Alberta, but there has been an increase in application of composted manure to cropland in this region. However, the quality of runoff under fresh manure (FM) versus composted manure (CM) has not been investigated. Our objective was to compare runoff quality under increasing rates (0, 13, 42, 83 Mg ha(-1) dry wt.) of FM and CM applied for two consecutive years to a clay loam soil cropped to irrigated barley (Hordeum vulgare L.). We determined total phosphorus (TP), particulate phosphorus (PP), dissolved reactive phosphorus (DRP), total nitrogen (TN), NH4-N, and NO3-N concentrations and loads in runoff after one (1999) and two (2000) applications of FM and CM. We found significantly (P < or = 0.05) higher TP, DRP, and NH4-N concentrations, and higher DRP and TN loads under FM than CM after 2 yr of manure application. The TP loads were also higher under FM than CM at the 83 Mg ha(-1) rate in 2000, and DRP loads were higher for FM than CM at this high rate when averaged over both years. Application rate had a significant effect on TP and DRP concentrations in runoff. In addition, the slope values of the regressions between TP and DRP in runoff versus application rate were considerably higher for FM in 2000 than for FM in 1999, and CM in both 1999 and 2000. Significant positive relationships were found for TP and DRP in runoff versus soil Kelowna-extractable P and soil water-extractable P for FM and CM in 2000, indicating that interaction of runoff with the soil controlled the release of P. Total P and DRP were the variables most affected by the treatments. Overall, our study found that application of CM rather than FM to cropland may lower certain forms of P and N in surface runoff, but this is dependent on the interaction with year, application rate, or both.
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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 | 0.001 | 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 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".