Organic production tools for field peas: are cultivar mixtures more competitive with weeds?
Bibliographic record
Abstract
Within Saskatchewan’s organic industry there is a need for improved tools to minimize yield \nlosses due to weeds. Cultivar mixtures may improve the ability of organic pulse crops to \nsuppress weeds and maintain yields in the presence of weeds. While semileafless peas are \nknown for their lodging resistance and high yield potential in the absence of weeds, conventional \npeas may provide better weed suppression and yield stability in the presence of weeds. A \nreplicated field experiment was conducted at two organic field sites to test the hypothesis that \ncultivar mixtures of conventional and semileafless field pea would differ in weed suppression \nand yields. The experiment tested factorial combinations of five ratios of semileafless pea \ncultivar CDC Dakota and conventional cultivar CDC Sonata (0:100, 25:75, 50:50, 75:25, and \n100:0, respectively), and two seeding rates (conventional and organic recommended). Plots were \nmonitored for crop and weed emergence, biomass, and yields. Significant differences were \nobserved among the different ratios of semileafless and conventional field pea. Results indicate \nthat the semileafless cultivar was more competitive with weeds than the conventional. As the \ncanopy composition progressed from a pure conventional canopy towards increasing percentages \nof semileafless pea in the mixture, total weed biomass decreased, and total crop yields increased. \nIt was concluded that while no additional weed suppression or yield benefits were seen compared \nwith growing the more strongly competitive semileafless cultivar alone, cultivar mixtures \nreduced the risk associated with growing unfamiliar or less competitive cultivars by stabilizing \nweed suppression and crop yields at a level between the two components of the mixture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".