Seed treatment and N rate do not impact dry bean (<i>Phaseolus vulgaris</i> L.) plant growth or grain yield in Ontario
Bibliographic record
Abstract
Application of nitrogen fertilizer and commercial seed treatments are two strategies dry bean growers in Ontario utilize to manage root rot and maximize yields. However, data solidifying these practices is absent or outdated. Experiments were conducted at the Huron Research Station, near Exeter, Ontario, from 2008 to 2010, to measure plant growth and yield responses for navy, kidney, and cranberry dry bean market classes (cv. T9905, Pink Panther, and Etna, respectively) to seed treatment (NST, no seed treatment; CMBD, Cruiser Maxx Bean + Dynasty) and N fertilizer rates of 0, 35, 70, 105, 140, and 175 kg N·ha−1. Plant measurements taken included plant emergence, vigour, height and maturity, plant and seed mass, and grain moisture and yield. Over three years, plant growth and yield responses to seed treatment and nitrogen fertilizer were very scarce. Plant emergence and vigour either improved or were not affected by seed treatment or N rate, while increasing N rates increased plant height of only Pink Panther in 2008. Plant mass was unaffected by either factor while seed treatment decreased days to maturity and grain moisture of T9905 in 2008. Nitrogen fertilizer improved the grain yield of Etna, with the highest yields occurring with 70 kg N·ha−1 or more, while other cultivar site–years did not respond. Overall, these results demonstrate dry beans rarely respond to N fertilizer or seed treatment at the Huron Research Station. Additional testing over multiple years and locations would assist in predicting these responses.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".