Evaluation of simulated hail damage on seed yield and agronomic traits in canola (<i>Brassica napus</i> L.)
Bibliographic record
Abstract
Natural hail can cause significant damage on seed yield and yield contributing traits of canola (Brassica napus L.). Hail damage can be assessed by (i) type of damage such as stand reduction, stem cut-off, and leaf defoliation, (ii) level of damage, and (iii) plant growth stage. In this research, a simulated hail study was performed by applying nonuniform stand reduction treatments on canola grown in North Dakota, USA, over 5 site-years, in 2017 and 2018. The experiment was a randomized complete block design 4 × 5 factorial arrangement with four growth stages, rosette, bolting, 50%, and 90% flowering, when five stand reduction treatments were applied at 0 (control), 25%, 50%, 75%, and 90%. Growth stage and stand reduction were significant for seed yield where yield decreased as stage of treatment progressed and level of stand reduction increased. Regression equations were developed to estimate the seed yield reduction at each growth stage as stand reduction increased. Stand reduction also affected other traits where plant height was reduced as stand reduction increased, whereas 1000-seed weight, primary branches plant−1, secondary branches plant−1, pods plant−1, seed yield plant−1, plant biomass plant−1, and harvest index plant−1 increased as stand reduction increased. As growth stage progressed the number of primary branches plant−1, secondary branches plant−1, pods plant−1, and harvest index plant−1 decreased, whereas 1000-seed weight increased. The findings of differential yield losses by stand reduction will help producers and crop adjusters to assess the severity of hail damage in canola.
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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 | 0.000 | 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 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".