Canola–Wheat Intercrops for Improved Agronomic Performance and Integrated Pest Management
Bibliographic record
Abstract
Intercropping can enhance yields and reduce pest infestations, but investigations of intercropping regimes using crop species common to the large‐scale monoculture production systems of western Canada have not examined these diverse elements. Intercrops of canola (Brassica napus L.) and wheat (Triticum aestivum L.) were established at three sites in Alberta, Canada in 2005 and 2006 to determine interactions between intercropping regimes and crop grain and biomass yield, crop quality characteristics, soil microbial community biomass and diversity, flea beetles (Phyllotreta spp., Coleoptera: Chrysomelidae), and wheat leaf diseases. The study also investigated effects on flea beetles and soil microbial communities of a canola seed treatment containing a neonicotinoid insecticide and fungicides. Crop yields were comparable between intercrops and monocultures of canola and wheat. Crop quality characteristics and flea beetle feeding damage to canola seedlings had variable responses to intercropping. Flea beetle feeding was reduced with the inclusion of the seed treatment by between 1 and 20% damaged leaf area, although only one site‐year had damage levels great enough to overcome plant compensatory abilities in untreated plots. The first true‐leaf stage of canola development experienced the greatest flea beetle damage. Proportions of pathogen‐infected wheat leaf tissue were up to 2.5 times greater in intercrops than wheat monocultures but tended to decrease as the proportion of canola in the intercrops increased. Soil microbial parameters were unaffected by factors investigated. Although intercrop yields approximated those of monocultures, additional benefits of canola–wheat intercrops determined in this study appear insufficient to recommend this system for widespread adoption.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".