Intercropping organic field peas with barley, oats, and mustard improves weed control but has variable effects on grain yield and net returns
Bibliographic record
Abstract
Interest in intercropping semi-leafless field peas (Pisum sativum L.) is increasing as a means of weed control in organic production. We evaluated field pea (cv. CDC Amarillo) grown alone or intercropped with three seeding rates of either barley (Hordeum vulgare L.), mustard (Brassica juncea L.), or oat (Avena sativa L.). A full seeding rate of field pea was used in each instance, resulting in an additive intercropping design. Each crop combination was conducted in a separate experiment, three times over two years (2019 and 2020) in Carman, MB. Measurements included crop and weed biomass production, grain yield and quality, and net return. Intercrops reduced weed biomass at maturity from 17% to 44% with barley and oat being more suppressive than mustard. Intercrops also reduced field pea yield from 6% to 26%, but increased field pea seed mass. Barley at the high seeding rate provided the most weed suppression per unit of field pea yield loss (2.62 kg of weed suppression per kg of field pea yield loss) compared with oat (1.29) and mustard (0.87). Barley and mustard intercrops decreased net return compared with monoculture field pea. Under low weed pressure (1150 kg·ha−1 weed biomass at maturity) and earlier seeding, oat intercrops reduced net return. However, under weedy conditions (2649 kg·ha−1) and later seeding, field pea-oat intercrops significantly increased net return. In conclusion, while all three intercrop mixtures reduced weed biomass, reductions in field pea yields were observed, and net return benefits were observed only in certain circumstances.
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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".