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Record W2157305707 · doi:10.4236/jep.2012.328096

Impacts of Land Use, Fertilizer and Manure Application on the Stream Nutrient Loadings in the Salmon River Watershed, South-Central British Columbia, Canada

2012· article· en· W2157305707 on OpenAlexafffundabout
Zhanxue Zhu, Klaas Broersma, Asit Mazumder

Bibliographic record

VenueJournal of Environmental Protection · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Victoria
FundersAgriculture and Agri-Food CanadaMinistry of Environment
KeywordsEnvironmental scienceManureWatershedFertilizerNutrientHydrology (agriculture)Soil waterPhosphorusVegetation (pathology)ProductivitySurface runoffSWAT modelSTREAMSAgronomyEcologyBiologySoil scienceChemistry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.006
GPT teacher head0.162
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2012
Admission routes3
Has abstractyes

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