Effects of Planting Pattern and Fungicide Application Systems on Ascochyta Blight Control and Seed Yield in Chickpea
Bibliographic record
Abstract
Improved cultural practices can be used to manage ascochyta blight in chickpea (Cicer arietinum L.), a disease caused by Ascochyta rabiei (Pass.) Labrousse. This study examined the effect of planting pattern, seeding rate, and fungicide application systems on ascochyta blight severity and crop yield of chickpea at Swift Current (50°25′ N, 107° 44′ W), SK, Canada, in 2004 and 2005. Crop was grown in uniform row (25 cm) and paired‐row spacing (25 cm within each pair and 75 cm between pairs), at 44 and 31 plants m−2, and with 1× and 0.67× the recommended fungicide application rates. Area under disease progress curve (AUDPC) averaged 1580 units for the susceptible cultivar CDC Xena, and significantly greater than 573 for the partially resistant cultivar Amit. The AUPDC value reduced from 750 at one fungicide application to 400 at four applications for Amit, and from 1907 to 1250 for CDC Xena. Seed yield was 2.24 Mg ha−1 with four applications and 1.73 Mg ha−1 with one application for Amit, and 1.04 and 0.18 Mg ha−1, respectively, for CDC Xena. Paired‐row spacing reduced AUDPC by 12% for Amit and 14% for CDC Xena from uniform row spacing. With paired‐row spacing, blight severity did not differ between the 1× and 0.67× fungicide application rates or between the plant densities of 44 and 31 plants m−2. Altered planting patterns, in combination with improved fungicide application systems, can potentially enable growers to reduce fungicide rates by 30% without decreasing efficacy of ascochyta blight control or seed yield for chickpea in northern Great Plains.
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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 | 0.001 |
| 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 | 0.000 |
| 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".