Bibliographic record
Abstract
Semi-leafless field peas (Pisum sativum L.) do not compete well with weeds and synthetic herbicides are prohibited in certified organic crop production. Therefore, significant interest has been placed on the development of alternative strategies of weed management in organic field pea production. Pea intercropping has recently become popular in the Canadian Prairies as a means of suppressing weeds. However, little research has been completed on additive pea intercrops under organic management. The objectives of this research project were to evaluate different yellow pea (cv. CDC Amarillo) intercrop mixtures to examine their effect on weed suppression, grain yield and quality, plant growth, and profitability. In 2019 and 2020 three separate pea intercrop experiments were completed in Carman Manitoba, Canada, under organic management. The three experiments were separated by companion crop species. Peas were planted in both monoculture and additive intercropping designs with low, medium, or high seeding rates of barley (Hordeum vulgare L.), mustard (Brassica juncea L.), or oats (Avena sativa L.). When averaged across site-years weed biomass suppression varied between the three experiments. The medium and high seeding rates reduced (P<0.05) weed biomass by 17% to 44% and resulted in a significant (P<0.05) pea yield penalty from 8% to 26%. Pea-cereal intercropping appeared to provide a greater level of weed biomass suppression compared to pea-mustard intercropping. Furthermore, pea-mustard intercrops were unstable across the three site-years suggesting that pea-mustard intercropping in organic production may result in unpredictable outcomes. In most instances, across all three experiments, pea growth and grain quality were not affected by intercropping. Intercropping did not increase net returns across a wide range of market conditions. In conclusion, while all three intercrop mixtures exhibited the ability to suppress weed biomass, substantial reductions in pea yields were observed and the contribution of the non-pea companion crop grain yield did not benefit net returns significantly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".