Canola rotation frequency impacts canola yield and associated pest species
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".