Impacts of herbicides and potassium fertilizer or seed treatments and seaweed extract on chickpea health in Saskatchewan
Bibliographic record
Abstract
An emerging health issue, with symptoms including leaflet edge bleaching, apical or upper branch chlorosis or necrosis, wilting, and plant death, has been seen in chickpea in Saskatchewan. Pre-emergence group 14 herbicides and/or post-emergence metribuzin are both suspected contributors, whose impacts may be additive. Field trials in 2022 and 2023 near Swift Current and Hodgeville, Saskatchewan, sought to test the above theory and the hypothesis that potassium chloride (KCl) fertility could reduce symptoms. Separate experiments at the same sites and years aimed to test the hypothesis that synthetic or natural product seed treatments, or seaweed extracts, would reduce this health issue, potentially synergistically. However, symptoms consistent with the chickpea emerging health issue—leaflet or upper plant discoloration, wilting, or death—were not observed, suggesting that sulfentrazone and metribuzin herbicides are insufficient to induce this issue. However, the impacts of the treatments in both experiments on emergence, ascochyta blight, weed control, and yield were evaluated. Metribuzin improved weed control only in combination with KCl and increased ascochyta blight. Sulfentrazone controlled weeds better than an alternative, ethalfluralin, only in Swift Current in 2022. KCl, as a main effect, did not alter any parameter measured. Synthetic seed treatment increased stand density, weed control, and yield, but also increased ascochyta blight. Saponin seed treatment increased stand density in one site-year. Seaweed extract had no impacts. While this study does not pinpoint cause(s) of the chickpea emerging health issue, it provides valuable insights into the impacts of management options for chickpea growers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".