Spiral nematodes, soil microbiome and micronutrients increase chickpea drought susceptibility but do not induce symptoms of the emerging health issue
Bibliographic record
Abstract
Abstract In 2019, an emerging health issue was noted in chickpea in Saskatchewan, Canada. Symptoms included apical wilting, branch chlorosis and necrosis. The causes remain unclear. In 2023 these symptoms appeared on one side (“unhealthy”, UH), but not the other (“healthy”, H), of a dry field in Redvers, Saskatchewan. To test the hypothesis that Helicotylenchus , or spiral nematodes, and differences in soil microbiome and nutrients, in combination with drought, contribute to these symptoms, chickpea were grown in H and UH soil, and well-watered, or exposed to drought. Plant height, number of nodes and pods, chlorophyll fluorescence (Fv/Fm), biomass, foliar and root-rot symptoms, soil nutrients, nematodes and soil microbiome were assessed. Symptoms more consistent with drought than the emerging health issue developed. When chickpea was exposed to drought, symptoms were more severe in UH soil. Height and Fv/Fm were lower in UH soils. Foliar symptoms were more severe and spiral nematodes more abundant in UH soils. All parameters were affected by drought. Concentrations of K + and Mg + were higher in H soil; Ca + concentration was higher in UH soil. Microbiome community composition, including bacteria, fungi and oomycetes, differed between H and UH soils, however, no pathogens that could be responsible for symptoms were more abundant in UH soils. Nematodes and other soil factors increase the impacts of drought but are not sufficient to induce the emerging chickpea health issue in pots. In the field, the root growth restrictions due to nematode feeding may lead to chickpea emerging health symptoms.
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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 | 0.000 |
| 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 | 0.001 |
| 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".