Field surveys of chickpea plant damage and association with biotic and abiotic factors in Saskatchewan during 2021–2023
Bibliographic record
Abstract
A chickpea health issue was first noted in July 2019 in southwest Saskatchewan, Canada. Surveys of commercial chickpea fields were undertaken in 2021, 2022, and 2023 to investigate the potential roles of herbicides, fungicides, fertilizer, seed treatments, inoculants, nutrients, nematodes, drought stress, and chickpea cultivars on severity of plant damage. Fields to which herbicide containing the active ingredient metribuzin had been applied had more severe symptoms compared to fields to which it had not. Seed treatment with Apron Advance (active ingredients thiabendazole, fludioxinil, and metalaxyl) was associated with more severe symptoms than treatment with Vibrance Maxx (sedaxane, fludioxonil, and metalaxyl). No effects of rhizobial inoculation of chickpea seed was observed. Fields in which lentil or canola preceded chickpea had higher severity of aboveground plant damage compared to fields in which chickpea followed barley or durum. No significant correlation was found between severity of plant damage and concentrations of nitrogen (N), phosphorus (P), and chloride in plant tissues. A significant negative correlation ( P < 0.05) was found between potassium (K) concentration and severity, especially in 2023. There was a significant positive correlation ( P < 0.05) between severity of plant damage and the amount of P fertilizer applied. Plant ectoparasitic nematodes Paratylenchus and Helicotylenchus species were detected. Paratylenchus species abundance was very high in some fields. Although no clear diagnosis of the causal agent(s) of plant damage was made, crop rotation, metribuzin herbicide application, seed treatments, plant K concentrations, P fertilization, and co-occurrence of stressors may exacerbate the plant and foliar symptoms observed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".