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Record W1969315032 · doi:10.1080/01904160009382024

Effect of swine manure and urea on soil phosphorus supply to canola

2000· article· en· W1969315032 on OpenAlexaffabout
Pei‐Yuan Qian, J.J. Schoenau

Bibliographic record

VenueJournal of Plant Nutrition · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCanolaLoamAgronomyManureChemistryUreaPhosphorusBrassicaSoil waterFertilizerNutrientAnimal scienceEnvironmental scienceBiologySoil science

Abstract

fetched live from OpenAlex

Abstract Limited information exists as to the effect of liquid swine manure on soil phosphorus (P) availability in Western Canadian soil. Swine manure is most often applied to meet additional requirements for nitrogen (N) and research to date has emphasized N effects. The effect of swine manure and urea on P supply to canola was investigated under controlled environment condition. Canola (Brassica napus) was grown in pots with manure or urea added to two Saskatchewan soils (sandy loam and clay loam) at 0 and 100 mg N kg‐1. Plants were grown to maturity, and yield and nutrient content were determined. Phosphorus supply rates in soils were measured in the pots using anion exchange resin membrane probes. Additions of swine manure and urea enhanced canola P accumulation and led to a higher proportion of P in seeds. This response was more evident in the manure treatment than with urea. Soil amended with manure significantly increased N and P supply rates in soils as the manure contains N and P. On the contrary, application of urea significantly increased N supply rate, but led to a slight decrease in the measured soil supply rate of available P. Despite the apparent decrease in soil supply of available P in urea treatment, canola maintained its N:P ratio by increasing P absorption, possibly due to a greater root mass.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.195
Teacher spread0.192 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations16
Published2000
Admission routes2
Has abstractyes

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