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Record W2605423820 · doi:10.1139/cjps-2016-0411

Effect of preceding crop and nitrogen application on malting barley quality

2017· article· en· W2605423820 on OpenAlexafffundvenueabout
John T. O’Donovan, Marta S. Izydorczyk, Breanne D. Tidemann, M. J. Edney, T. Kelly Turkington, Cynthia A. Grant, K. Neil Harker, Yantai Gan

Bibliographic record

VenueCanadian Journal of Plant Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsBrandon UniversityCanadian International Grains InstituteAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaAlberta Canola Producers CommissionCanola Council of CanadaSaskatchewan Canola Development Commission
KeywordsCanolaAgronomyCropLegumeHordeum vulgareFertilizerField peaBiologyCrop rotationEnvironmental sciencePoaceae

Abstract

fetched live from OpenAlex

As legume crops fix nitrogen (N) from the atmosphere, there is concern that soil residual N from legumes grown in rotation with malting barley may result in unacceptably high protein content and have negative effects on quality. However, little research has been conducted to investigate this. Field pea, lentil, faba bean [as seed or as a green manure (GM) crop], canola, and wheat were grown in 2009, canola in 2010, and malting barley in 2011. The objective was to determine the effects of crops grown in 2009 on the quality of barley grown in 2011. Crops were direct-seeded at Lacombe (Alberta), Swift Current (Saskatchewan), and Brandon (Manitoba). Fertilizer N (urea) was applied in 2010 and 2011 at 0, 30, 60, 90, and 120 kg ha −1 . The legumes had few negative effects on barley quality compared with canola or wheat. Exceptions occurred at Lacombe where the lentil and faba bean GM crops increased protein and decreased kernel plumpness. This was not evident at other locations. Increasing N fertilizer rate negatively affected almost all malt quality parameters at all locations. The results indicate that growing legume crops prior to malting barley is less likely to reduce malting barley quality than applying fertilizer N.

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)0.002
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 score0.562
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.030
GPT teacher head0.280
Teacher spread0.250 · 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
Published2017
Admission routes4
Has abstractyes

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