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Record W2140720281 · doi:10.4141/cjps06013

Efficacy of high seeding rates to increase grain yield of winter wheat and winter triticale in southern Alberta

2007· article· en· W2140720281 on OpenAlexvenueaboutno aff
Ross H. McKenzie, E. Bremer, A. B. Middleton, P. G. Pfiffner, Robert F. Dunn, Brian L. Beres

Bibliographic record

VenueCanadian Journal of Plant Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsTriticaleSeedingAgronomyGrain yieldYield (engineering)Winter wheatBiologyEnvironmental scienceSowingCultivar

Abstract

fetched live from OpenAlex

Field trials were conducted for 3 yr (2002/2003 to 2004/2005) at three locations in southern Alberta to determine the impact of seeding rate and opener type on plant stand and grain yield of winter wheat (Triticum aestivum L.) and winter triticale (× Triticosecale Wittmack). Responses were determined for an optimum date of seeding in early to mid-September and for a late seeding in early to mid-October. Conditions were generally favourable for crop establishment, winter survival and growth, and average site yields ranged from 4.5 to 8.9 Mg ha -1 . The disc opener increased spring plant density by 12.5% compared with the hoe opener, but did not affect grain yield. Spring plant density was 23% lower for winter triticale than winter wheat and 20% lower for October-seeded cereals than September-seeded cereals. Late seeding reduced grain yields of winter wheat and winter triticale by an average of 18 and 11%, respectively. Increases in target seeding rates from 150 to 350 plants m -2 (approximately 70 to 160 kg ha -1 ) did not affect grain yield and quality of September-seeded cereals, but increased grain yield of late-seeded crops by an average of 5 kg per kg increase in seeding rate. High seeding rates did not fully compensate for yield losses caused by late seeding. Key words: Triticum aestivum, × Triticosecale, plant stand density, yield

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.223
Teacher spread0.205 · 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

Citations15
Published2007
Admission routes2
Has abstractyes

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