Effects of simulated hail damage and foliar-applied recovery treatments on growth and grain yield of wheat, field pea, and dry bean crops
Bibliographic record
Abstract
Hailstorms can be responsible for significant economic loss to the agricultural sector in Alberta, Canada. Foliar applications of certain fungicides and nutrient blends have been advocated to promote recovery and yield of hail-damaged crops. Proper understanding of different crop and hail-related factors is required for an accurate assessment of hail damage to crops and for evaluations of hail-recovery product claims. This study was undertaken at three locations in Alberta during three growing seasons (2016–2018) to determine the effects of two levels of simulated hail severity at three different crop developmental stages, including the early growth (BBCH 30 for wheat; BBCH 14–16 for pulses), mid-growth (BBCH 39 for wheat; BBCH 60 for pulses), and late growth (BBCH 60 for wheat; BBCH 71 for pulses) stages. Plant growth and yield parameters of wheat (Triticum aestivum L.), field pea (Pisum sativum L.), and dry bean (Phaseolus vulgaris L.) crops were measured. Simulated hail damage led to reductions in height, biomass, NDVI, grain yield, and kernel weight of all three crops. Average yield decreased by 24% and 35% for wheat, 17% and 35% for dry beans, and 37% and 45% for field peas for light and heavy hail severity, respectively. Hail timing was a critical factor influencing the extent of crop damage, with hail damage during the early growth stage leading to a lesser yield reduction compared with hail damage at the mid-growth and late growth stages. Fungicides and nutrient blends applications did not significantly improve crop recovery, grain yield, or kernel weight for any of the crops in this study.
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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) | 0.001 | 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".