Plant spatial arrangement to maximize dry bean (Phaseolus vulgaris L.) yield in Manitoba
Bibliographic record
Abstract
Manitoba accounts for a large proportion of dry bean hectarage in Canada, yet current production recommendations have not been validated for this region. The objective of these experiments was to determine the combinations of row spacing and plant densities in pinto and navy bean varieties that maximize seed yield. Field experiments were conducted at Carman and Portage la Prairie, Manitoba in 2015 and 2016. In each market class, two varieties were planted at row widths of 19, 38, 57, and 76 centimeters. Navy bean seeding densities ranged from 20 - 60 plants m-2 while pinto bean seeding densities ranged from 10 - 50 plants m-2. Planting at narrow row widths of 19 cm significantly increased dry bean seed yield, while increasing plant densities did not influence seed yield consistently in navy and pinto bean. Despite concerns of increased white mould disease pressure with narrow-row plantings, white mould severity was the lowest in beans planted at 19 cm row widths. This may have been due to the increased distance between plants at the same densities within the row in narrow-row compared to wide-row spatial arrangements. White mould severity increased significantly with greater seeding densities and type I growth habits. Further research is needed to explore the plant density-yield relationship in dry bean in Manitoba and the influence root rot diseases may have on this relationship. While narrow-row dry bean production has been proven to result in increased yields, there are other barriers preventing producers from adopting this system. Exploring producer constraints may increase adoption and improve production.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".