Impact of cultivar, row spacing and seeding rate on ascochyta blight severity and yield of chickpea
Bibliographic record
Abstract
Field trials to assess the impact of chickpea type (desi vs. kabuli), row spacing and seeding rate on ascochyta blight of chickpea were conducted over 2 yr at Brooks, Alberta. A compound-leaved desi chickpea cultivar and unifoliate kabuli cultivar were sown at 20, 30 and 40 cm row spacing, and at three seeding rates (20, 40 and 60 seeds per 3 m row). Most of the variation in disease severity was associated with differences between the cultivars. Seeding rate, row spacing and their interactions had substantially smaller effects on ascochyta blight in comparison with cultivar effects. Late in the growing season, blight severity was consistently lower in the desi than the kabuli cultivar. Wide row spacing and low seeding rate reduced ascochyta blight severity and increased seed yield per plant. Wide row spacing in the first year reduced the seed yield per hectare, but row spacing did not significantly affect yield in 2005. Low in-row seeding rates increased yield only in 2004. There was a positive linear relationship between plant density and blight severity, and a negative relationship between yield per plant and both plant density and disease severity. We conclude that reduced plant population density could be one tool in a program to manage ascochyta blight of chickpea. Key words: Cicer arietinum, plant population density, ascochyta blight, yield
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".