Cultivar Type, Plant Population, and Ascochyta Blight in Chickpea
Bibliographic record
Abstract
Integrated management strategies are required to minimize ascochyta blight, a fungal disease caused by Ascochyta rabiei (Pass.) Labrousse [teleomorph, Didymella rabiei (Kovachevski) v. Arx] in chickpea ( Cicer arietinum L.). This study determined the effect of cultivars varying in plant architecture and plant population density (PPD) on the severity of ascochyta blight. Four desi chickpea (with pinnate leaves) and four kabuli chickpea (two with pinnate leaves and two with unifoliate leaves) were grown at 25, 36, 44, 53, and 62 plants m −2 (actual counts 3 wk after initial seedling emergence) at Swift Current from 2002 to 2005 and at Saskatoon in 2004 and 2005. Site‐years had a significant effect on ascochyta blight epidemics, with the highest severity at Swift Current in 2005 and lowest at Saskatoon in 2004. Across site‐years, ascochyta blight was most severe on ‘Evans’, followed by ‘CDC Xena’, and lowest on ‘222B‐11’. Cultivars with pinnate leaves had lower blight severity than those with unifoliate leaves during all growth stages. At the late‐pod stage, severity in cultivars with pinnate leaves averaged 15% compared with 48% in unifoliate cultivars. Kabuli cultivars had higher severity than desi cultivars throughout the growing season, and at the late‐pod stage, severity was 13% for the desi and 33% for the kabuli. There was a significant interaction between cultivar and PPD for blight severity. Ascochyta blight increased as PPD increased for the majority of the cultivars tested, with a few exceptions. Site‐year accounted for the largest portion of the treatment variance in blight severity (69%), followed by cultivar type (25%), and then PPD (6%). Increasing PPD consistently increased seed yield per unit area, despite more disease on plants at higher PPD. Identifying optimum plant populations for groups of cultivars with similar plant architecture should be a component in an integrated strategy to minimize ascochyta blight in chickpea.
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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 | 0.000 |
| 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".