Intercropping of oat or mustard with legumes under organic management in the semiarid Canadian Prairie
Bibliographic record
Abstract
Abstract Intercropping, the growing of more than one crop at the same time within the same land area, could be a sustainable method of crop production in semiarid regions, which could increase biodiversity, and productivity and quality of crops compared to monocultures. This may be of significance under limited N, such as in organic agriculture, and could be an alternative to green manure. An organic study was conducted in the semiarid Canadian Prairie in drier than average years (2017–2018) to determine if intercropping legumes with non‐legumes could reduce weeds and increase grain yield and quality of crops at different seeding rate ratios. Intercrops examined were lentil ( Lens culinaris Medik.)–yellow mustard ( Sinapis alba L.), and field pea ( Pisum sativum L.)–oat ( Avena sativa L.), at three seeding rate ratios, and their respective monocultures. Weed density was lower in the pea–oat intercrop than the pea monoculture, while weed biomass was lower in the lentil–mustard intercrop than the lentil monoculture. Legumes, when intercropped even at monoculture ratios, had lower aboveground biomass and grain yield than their monocultures, with pea showing higher tolerance than lentil to competition with its companion. Total biomass and grain yield were accounted for mostly by the non‐legumes, which performed better than expected based on their seeding ratios. Mustard grown with lentil appeared to be more competitive than oat grown with pea. Grain weight of oat was higher in all intercrops with pea than in its monoculture, while grain protein of pea was higher when intercropped with oat than in its monoculture.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".