Evaluating management practices to limit phosphorus losses from agricultural fields in the Castor watershed using the WEND model
Bibliographic record
Abstract
The objective of this study was to apply the WEND model, a nutrient mass balance model, to the Castor watershed in southern Quebec to evaluate phosphorus movement, storage and export over time. The WEND model was customized to run on a field-scale and then individually applied to 266 fields on the watershed for a 30-year simulation period. Field-specific information for the period of 1997-1999, was used as basic inputs to the model. Climatic information was obtained from local sources. The additional information required to run the model was derived from the literature. Model outputs were analysed at three different levels: (i) the overall watershed impacts, (ii) by cropping system, and (iii) for field management practices presenting a high risk of P losses. Specific outputs examined were: soil test Mehlich-III P, soil P saturation with aluminium, RUSLE soil loss potential and TP export. The model was used to examine the impacts of crop rotations, fertilizer application and tillage management on TP export. For the Castor watershed, the soil test P increased at a mean rate of 3.71 kg Mehlich-III P ha -1 yr-1, equivalent to a mean input of about 32 kg P2O5 ha-1 yr-1 in excess of plant requirements, assuming current field management practices remain constant. If TP export is considered the most important parameter in terms of P contamination, crop rotations are a good alternative to continuous corn monocropping under which losses could reach as high as 3.36 kg TP ha-1 yr -1. Crop rotations were shown to be an important management practice that should be more carefully examined when establishing field management practices. Just one year of grassland within a rotation can greatly improve the overall environmental health of a watershed. The management of P inputs is also an important target for improvement, as fertiliser inputs often surpassed plant requirements by two- or three-fold.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".