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Record W6922320448 · doi:10.1139/cjps-2014-289

Canola rotation frequency impacts canola yield and associated pest species

2015· article· en· W6922320448 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsnot available
Fundersnot available
KeywordsCanolaBrassicaRapeseedCrop rotationCrop yieldPEST analysis

Abstract

fetched live from OpenAlex

Harker, K. N., O'Donovan, J. T., Turkington, T. K., Blackshaw, R. E., Lupwayi, N. Z., Smith, E. G., Johnson, E. N., Gan, Y., Kutcher, H. R., Dosdall, L. M. and Peng, G. 2015. Canola rotation frequency impacts canola yield and associated pest species. Can. J. Plant Sci. 95: 9-20. Canola (Brassica napus L.) production has been steadily increasing in western Canada. Here we determine the effect of canola rotation frequency on canola seed yield, quality and associated pest species. From 2008 to 2013, direct-seeded experiments involving continuous canola and all rotation phases of wheat (Triticum aestivum L.) and canola or field pea (Pisum sativum L.), barley (Hordeum vulgare L.) and canola were conducted at five western Canada locations. Fertilizers, herbicides, and insecticides were applied as required for optimal production of all crops. Canola rotation frequency did not influence canola oil or protein concentration or the level of major (composition>1%) seed oil fatty acids. High canola yields were associated with sites that experienced cooler temperatures with adequate and relatively uniform precipitation events. For each annual increase in the number of crops between canola, canola yield increased from 0.20 to 0.36 Mg ha-1. Although total weed density was not strongly associated with canola yield, decreased blackleg disease and root maggot damage were associated with greater canola yields as rotational diversity increased. Long-term sustainable canola production will increase with cropping system diversity.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.177
GPT teacher head0.249
Teacher spread0.072 · 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.

Study designBench or experimental
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
Published2015
Admission routes1
Has abstractyes

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