Surface and subsurface phosphorus export from agricultural fields during peak flow events over the nongrowing season in regions with cool, temperate climates
Bibliographic record
Abstract
In cool temperate regions with significant winter periods, midwinter thaws and the spring freshet are critical periods for annual phosphorus (P) loss from agricultural fields. The efficacy of best management practices during the winter period is not clear. This paper reports hydrologic and biogeochemical P losses in surface runoff and tile drainage from two fields in Ontario, Canada, during peak flow events (~5 per site) occurring throughout the nongrowing season (October to April). We relate inter-event variability in the quantity and speciation (dissolved or particulate) of P to event climatic drivers (e.g., rainfall, rain-on-snow, and snowmelt) and pre-event soil conditions (e.g., presence or absence of snow cover or presence of frozen ground). Runoff and P (dissolved and particulate) were lost via both surface and subsurface (tile) pathways; however, P concentrations were greater and more variable in surface runoff than in tile drain effluent. The total P load leaving the sites with both overland runoff and tile drainage during the October through April period observed ranged from 0.23 to 0.34 kg ha<sup>−1</sup> (0.21 to 0.30 lb ac<sup>−1</sup>). Particulate P concentrations, particularly in surface runoff, increased as the proportion of precipitation that fell as rainfall increased, and flow-weighted mean concentrations of particulate P were greatest when rain fell on thawed, bare soils. Particulate P represented between 70% and 90% of the total P loss in the observation period. In contrast, while dissolved reactive P loads were only 10% to 30% of the total P loss, dissolved reactive P concentrations were greatest in January and declined over the remainder of the season. This work provides insight for the design and implementation of suitable best management practices for the mitigation of P losses throughout the nongrowing season.
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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 | 0.000 | 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 | 0.001 |
| 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 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".