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Record W1967725906 · doi:10.4141/cjps07138

Long-term effect of placement of fertilizer nitrogen and phosphorus on barley yields

2008· article· en· W1967725906 on OpenAlexvenueno aff
R. E. Karamanos, J.T. Harapiak, N.A. Flore

Bibliographic record

VenueCanadian Journal of Plant Science · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsNitrogenPhosphorusFertilizerAnimal scienceCanolaAgronomySeedingYield (engineering)BiologyChemistryMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

An experiment that was established in 1990 to assess depth and method of placement of nitrogen (N) and phosphorus (P) fertilizer on the yield of continuous barley (with 1 yr interruption with canola in 1995) was continued to 2001. Annually, 80 kg N ha -1 were banded either at 7.5 to 10 or 15 to 17.5 cm depth alone or in combination with 40 kg P 2 O 5 ha -1 ; the latter was either seedrow placed or banded with the N (dual banding), or split 1/2 in the seedrow and 1/2 in the band. An unfertilized control was maintained in all years. Temperature after seeding had a marked effect on the effectiveness of depth of N and P placement as well as the method of P placement. Shallow (7.5 to 10 cm depth) placement resulted in greater yields in 8 of the 11 yr that barley was grown and was never inferior to deeper placement (15 to 17.5 cm); this advantage was directly related to cooler-than-normal temperature after seeding. Cooler-than-normal temperatures also resulted in benefits from seedrow placed P; however, benefits were not as frequent as those obtained by either dual banding or splitting P between seedrow and the band. It would appear that overall benefits from banding P together with N (dual band), independently of the depth of banding, are greater than those from seedrow placing, as those benefits from the latter are less frequent and of considerably less magnitude. Key words: Band, dual band, seedrow, shallow, deep

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 score0.017
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.211
Teacher spread0.193 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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