Impacts of Land Use, Fertilizer and Manure Application on the Stream Nutrient Loadings in the Salmon River Watershed, South-Central British Columbia, Canada
Bibliographic record
Abstract
Manure and fertilizer applications can increase soil productivity and land economic values, but the controversial result can be a decline of water quality due to the increased nutrient exports from soils to the streams. The impacts of landuse, manure and fertilizer application on nutrient exports from soils to the streams were analyzed using the SWAT (Soil Water Assessment Tool) model for the Salmon River watershed in south-central British Columbia, Canada. The results showed that the animal farms had the highest rates of nutrient exports from soils to the streams and the natural forested lands had the lowest. It was estimated that the whole Salmon River watershed would export approximately 11.52 t·yr-1 of organic nitrogen (ON), 8.05 t·yr-1 of nitrate nitrogen (NO3-N), 2.30 t·yr-1 of organic phosphorus (OP) and 1.36 t·yr-1 of soluble reactive phosphorus (SRP) if the whole watershed was covered by natural vegetation without human disturbance. Current landuse changes, by converting natural vegetation lands to agriculture and animal farms and associated manure and fertilizer applications, have in-creased approximately 53.30 t·yr-1 of ON, 9.68 t·yr-1 of NO3-N, 22.69 t·y-1 of OP and 6.23 t·y-1 of SRP exports to the streams in the whole watershed. The SWAT model predicted that a daily 100 kg·ha-1 of fresh manure deposit from grazing cows during grazing season from later spring to later fall could increase 2.57 kg·ha-1·yr-1 of ON, 0.39 kg·ha-1·yr-1 of NO3-N, 2.35 kg·ha-1·yr-1 of OP and 0.48 kg·ha-1·yr-1 of SRP export to the streams. Fertilization could increase 1.57 kg ha-1 yr-1 of ON and 4.02 kg·ha-1·yr-1 of NO3-N export to the streams if 100 kg·ha-1·yr-1 of nitrogen (NH4NO3) fertilizer was applied in spring. Also fertilization could increase 1.18 kg·ha-1·yr-1 of OP and 0.20 kg·ha-1·yr-1 of SRP export to the streams if 100 kg·ha-1 phosphorus (P2O5) fertilizer was applied in spring.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".