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Record W2028657166 · doi:10.4141/s05-026

In-crop application effect of nitrogen fertilizer on grain protein concentration of spring wheat in the Canadian prairies

2006· article· en· W2028657166 on OpenAlexvenueaboutno aff
Ross H. McKenzie, E. Bremer, Cynthia A. Grant, Adrian Johnston, J. DeMulder, A. B. Middleton

Bibliographic record

VenueCanadian Journal of Soil Science · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsAgronomyFertilizerCropAnthesisAmmonium nitrateCultivarUreaNitrogenWinter wheatNitrateChemistryBiology

Abstract

fetched live from OpenAlex

Due to the price premium for high-protein wheat (Triticum aestivum L.), many producers are interested in the efficacy of in-crop application of low rates of N fertilizer for increasing grain protein concentration (GPC). We conducted field studies at 26 site-years in Alberta, Saskatchewan and Manitoba from 1998 to 2000 to determine if in-crop application (tillering, boot stage or anthesis) of N fertilizer [broadcast ammonium nitrate (AN) or foliar urea-ammonium-nitrate solution (UAN); 15 kg N ha -1 ] could economically increase GPC of a Canada Western Red Spring (CWRS) wheat cultivar (AC Barrie). Basal N fertilizer rates were 60 and 120 kg N ha -1 . The average increase in GPC due to in-crop N application was 3 g kg -1 . The increase in GPC was similar at basal N rates of 60 and 120 kg N ha -1 . Broadcast AN and foliar-applied UAN were generally equally effective at increasing GPC, but were not more effective than application at the time of seeding. Late application tended to increase GPC more effectively than early application. The increase in GPC due to application of in-crop N was not economic at most sites in this study, but might be greater if applied under more N deficient conditions. Key words: Split N application, foliar, timing

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.142

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.210
Teacher spread0.202 · 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 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

Citations29
Published2006
Admission routes2
Has abstractyes

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