Impact of fungicide, IMI-herbicide, and cultivar on ascochyta blight severity and yield of chickpea in Saskatchewan, Canada
Bibliographic record
Abstract
Ascochyta blight, caused by Ascochyta rabiei, is a serious constraint to chickpea production, managed primarily via foliar fungicides and genetic resistance. Imidazolinone (IMI) herbicides, used for in-crop weed management, can injure susceptible chickpea cultivars and so increase ascochyta risk. The impact of IMI application on susceptible and tolerant cultivars was assessed in combination with blight management options (starting fungicide application prior to symptom development, two versus four fungicide applications, genetic resistance) in small plot trials in Saskatchewan in 2019, 2021, and 2022. Two kabuli-type chickpea cultivars (CDC Orion, CDC Orkney) and two desi-types (CDC Vanguard, CDC Cory) were grown with or without IMI herbicide. CDC Orion and CDC Vanguard were susceptible to IMI herbicides, CDC Orkney and CDC Cory were tolerant. The severity of ascochyta blight (0–9 scale) was very low in 2019 and 2021 (1.3 in 2019, 0.5 in 2021) and generally did not differ among treatments. In 2022, the desi cultivars had less disease at the end of the season than kabuli cultivars (mean 3.1 vs. 4.3). In 2022, two applications of fungicide (starting before or after symptoms appeared) reduced severity relative to the control (3.5 vs. 4.6). Four applications did not provide additional reduction. IMI herbicides resulted in <15% injury on IMI-susceptible and none on IMI-tolerant cultivars. Waiting for ascochyta symptoms, rated 1, to apply fungicide was just as effective as applying fungicide before symptom development. Fungicides provide yield and disease management benefits when disease is moderate, but not when weather is hot and dry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".