Yield and Agronomic Advantages of Pea Leaf Type Mixtures under Organic Management
Bibliographic record
Abstract
Field pea (Pisum sativum L.) is an important organic crop due to its contribution to soil fertility and other rotational benefits. Leafed (wild‐type) pea cultivars tend to be more weed suppressive, but their poor standing ability limits yield compared with semi‐leafless cultivars. Growing mixtures of leafed and semi‐leafless cultivars may improve weed suppression and yield compared with monocultures of the same cultivars by altering canopy morphology. To test this hypothesis, replicated field experiments were conducted under weedy, organic conditions in Saskatchewan, Canada, in 2011 and 2012. Mixtures of a leafed and semi‐leafless cultivar, CDC Sonata and CDC Dakota, were sown in ratios of 0:100, 25:75, 50:50, 75:25, and 100:0 leafed to semi‐leafless pea, at target seeding rates of 88 and 132 plants m−2. Mixtures that included 50% or more semi‐leafless pea had similar lodging resistance and weed biomass suppression to the agronomically superior semi‐leafless cultivar grown alone. The strong competitive ability of the semi‐leafless cultivar was unexpected based on previous accounts. The combined yield of the two cultivars grown in a 75% semi‐leafless mixture exceeded the seed and biomass yield of either single cultivar by at least 18 and 12%, respectively. Yield enhancement was attributed to the leafed cultivar, whose seed yield increased by more than two and a half times in this mixture relative to its monoculture. Results suggest that breeding of leafed cultivars specifically for mixture with semi‐leafless pea may be a future source of yield gains in organic and low‐input systems.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".