Cropping, soil type and manure application effects on phosphorus export and bioavailability
Bibliographic record
Abstract
A simulated rainfall study was conducted on an array of 36 runoff plots (6 m2 each) deployed on three benchmark soil series of the Missisquoi region in southwestern Québec. The split-plot experimental design tested the effects and interactions of pig slurry treatment as main plots (check vs. 59 kg P ha-1) and cropping (tilled vs. hay) as subplots, on runoff volume and concentrations in total suspended sediment (TSS), tot al phosphorus (TP), dissolved reactive soluble P (DRP), particulate P (PP) and bioavailable P (BioP). TP concentration in runoff ranged from a low of 1.17 mg L-1 average on non-amended Bedford hay, to a peak concentration average of 9.55 mg L-1 on manured and tilled Saint-Sébastien plots. Variance analysis indicated significant contributions of treatments in explaining TP concentration in runoff as follows: Soil type effect > Cropping effect > Soil × Cropping interaction > Manure effect. Erosion and sediment transport processes were identified as prime vectors of TP export . Manure effect alone accounted for 35% of overall DRP variability, while soil type alone accounted for 70% of variability in particulate P bioavaibility (BioP/PP). Among practical implications of this study is the importance of assessing PP bioavaibility to adequately predict the adverse impact of runoff on aquatic ecosystems. The high level of interaction among cropping and manure treatment and the site- specific influence of soil physical and chemical properties also calls for a holistic approach to nonpoint P risk assessment and management, that focusses on timely manure P management, control of soil P build-up and agricultural practices minimizing surface runoff. Key words: Phosphorus, P-index, rainfall simulation, runoff, erosion, bioavailability, sediment
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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 | 0.000 |
| 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 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".