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Record W6959939573 · doi:10.1139/cjps2012-031

Nitrogen management of fallow crops in Canadian prairie soils

2012· article· en· W6959939573 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsCanolaFertilizerSoil waterNitrogenYield (engineering)CropCrop yieldNormalized Difference Vegetation IndexSummer fallow

Abstract

fetched live from OpenAlex

Karamanos, R. E., Selles, F., James, D. C. and Stevenson, F. C. 2012. Nitrogen management of fallow crops in Canadian prairie soils. Can. J. Plant Sci. 92: 1389-1401. The ability of fallow to supply nitrogen (N) to crops has been questioned, particularly for crops with greater N requirements. A study was conducted to determine canola (Brassica napus L.) and wheat (Triticum aestivum L.) responses to a range of N fertilizer rates (0-75 kg N ha-1 for canola and 0-50 kg N ha-1 for wheat) at 17 fallow sites across Saskatchewan and Alberta, Canada, from 2003 to 2005. Yield and N uptake responses to progressively greater N fertilizer rates were curvilinear for both crops. Maximum yield occurred with 76 kg N ha-1 for canola yield (2190 kg ha-1) and 47 kg N ha-1 for wheat (2910 kg ha-1). Maximum N uptake occurred at about 90 kg N ha-1 for both crops. Wheat grain yield and N uptake responses were mostly associated with normalized difference vegetation index (NDVI) at anthesis or flag leaf, whereas canola yields and N uptake were most associated with NDVI at five-leaf or bolting, or Cardy Nitrate meter at bolting. The preceding relationships were most apparent at the highest N fertilizer rates. Canola and wheat water use were not affected by N fertilizer rate, but water use efficiency increased linearly for both crops as N fertilizer rate was increased.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.921

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)0.0000.000

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 head0.220
GPT teacher head0.234
Teacher spread0.014 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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