Phosphorus concentrations in subsurface water as influenced by cropping systems and fertilizer sources
Bibliographic record
Abstract
Over-fertilization of agricultural soils with P has been linked to water pollution, and it is becoming apparent that subsurface P losses can be substantial. The silty-loam Gleysol chosen for this study contained 146 mg Mehlich-3 P kg-1, which exceeds the critical limit (66 mg Mehlich-3 P kg-1) for Québec. Equal quantities of fertilizer P (45 kg P ha-1) were applied from inorganic (triple superphosphate) and organic (composted cattle manure) sources to corn-corn and corn-soybean rotations. We evaluated crop production, the Mehlich-3 P and degree of soil P saturation levels, and the P concentration in subsurface water. Crop yields were unaffected by the fertilizer source, and only 31 to 67% of the P applied over a 2-yr period was exported, suggesting that the P fertilizer rate, chosen to match the P requirements of the corn crop, was excessive. After 2 yr, surface (0 to 15 cm) soils contained 149 to 199 mg Mehlich-3 P kg-1 and the Mehlich-3 (P/Al) saturation ratio (DSPSM3) ranged from 0.10 to 0.14. Subsurface water from piezometers installed to a 60-cm depth contained between 0.3 and 1.7 mg total P L-1, and particulate P was the dominant P form at most sampling dates. Mehlich-3 P was positively correlated with dissolved reactive P, whereas the DSPSM3 was positively correlated with the total P and particulate P concentrations in subsurface water. We conclude that these soil test parameters could be useful indicators of subsurface P losses from Québec soils. Key words: Soil test P, degree of soil P saturation, composted cattle manure, triple superphosphate, P leaching
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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".