Species Choice Influences Weed Suppression, N Sharing and Crop Productivity in Oilseed Rape–Legume Intercrops
Bibliographic record
Abstract
Increasing crop trait diversity in oilseed rape (OR, Brassica napus L.) cropping systems by introducing frost-sensitive legume species could improve weed suppression and crop productivity. Intercrops and sole crops were compared over two years in the field in Western France. Winter OR was intercropped simultaneously with either spring faba bean (Vicia faba L.) or common vetch (Vicia sativa L.) in a row replacement design without herbicides. Each species was sown at 50% of the recommended sole crop density in alternate rows. Due to the high values of faba bean aboveground traits (height, leaf area, and biomass) and the strong competitive ability for soil N of OR, both species appeared complementary in resource utilization, and thus less soil N and light were available for weeds. The OR–faba bean intercrop was able to reduce weed biomass by 41% compared to the OR–common vetch intercrop. Furthermore, growth and competitive ability of OR for soil N were increased when intercropped with faba bean. Both grain yield and number per plant were three times higher in OR–faba bean intercrops compared to OR sole crops. Under high weed infestation, the presence of faba bean with OR reduced weed aboveground biomass by 35% and weed N accumulation by 11% compared to the OR sole crop. No change was observed in the weed community composition. We observed that a level of aboveground biomass greater than 2 t ha−1 and a soil N uptake at approximately 80 kg ha−1 was needed to reduce biomass and N content of weeds.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".