Canola cultivar mixtures and rotations do not mitigate the negative impacts of continuous canola
Bibliographic record
Abstract
Harker, K. N., O'Donovan, J. T., Turkington, T. K., Blackshaw, R. E., Lupwayi, N. Z., Smith, E. G., Dosdall, L. M., Hall, L. M., Kutcher, H. R., Willenborg, C. J., Peng, G., Irvine, R. B. and Mohr, R. 2015. Canola cultivar mixtures and rotations do not mitigate the negative impacts of continuous canola. Can. J. Plant Sci. 95: 1085–1099. High-frequency canola (Brassica napus L.) rotations increase canola production risks. From 2008 to 2013, direct-seeded experiments involving several variations of continuous canola were compared with wheat (Triticum aestivum L.) and field pea (Pisum sativum L.) rotated with canola at five western Canada locations. Continuous canola rotations involved sequences of different herbicide-resistant canola and two-cultivar mixtures of herbicide-resistant canola from different sources in the same year. Fertilizers, herbicides, and insecticides were applied as required for optimal production of all crops. Rotating herbicide-resistant canola types over years or mixing two cultivars of the same herbicide-resistant type provided no pest management, yield or seed quality advantages compared with planting the same herbicide-resistant cultivar type each year. In 2013, weed biomass was lower in canola preceded by other crops than most continuous canola treatments. Compared with continuous canola, when 1 or 2 yr of wheat or field pea and wheat were inserted into 3-yr rotation cycles, 2010 root maggot damage was reduced 6% and 2013 blackleg [Leptosphaeria maculans (Desmaz.) Ces. & De Not.] incidence and severity were reduced 53 and 54%, respectively. Furthermore, yields were 22% higher when canola was grown only once in 3 yr compared with continuous canola and the wheat–canola–canola rotation. The most important mitigation strategy to ensure long-term sustainable canola production is to rotate canola with other crops.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".