The influence of biological and fungicidal seed treatments on chickpea (<i>Cicer arietinum</i>) damping off
Bibliographic record
Abstract
Damping off of chickpea (Cicer arietinum) can lead to stand loss and yield reduction. Fungicide seed treatments are able to successfully control chickpea damping off, but the effectiveness of many commercially available biological seed treatments has not been well tested. The objective of this study was to test the effect of commercially available biological and fungicide seed treatments on damping off of cultivars of kabuli and desi chickpea types. The biological seed treatments Bacillus pumilus GB34 (Yield Shield), Bacillus subtilis GB03 (Kodiak), B. subtilis MBI 600 (Subtilex), Streptomyces lydicus WYEC 108 (Actinovate), Streptomyces griseoviridis K61 (Mycostop), Trichoderma harzianum Rifai strain KRL-AG2 (T-22 Planter Box), the fungicide treatments fludioxonil (Maxim) and mefenoxam (Apron XL LS), as well as combined biological and fungicide seed treatments were tested in greenhouse and field experiments. The desi cultivar exhibited lower incidence of damping off in greenhouse and field trials than the kabuli cultivar. Several biological seed treatments inhibited germination and growth of the kabuli cultivar in the absence of pathogens in greenhouse experiments but not in field trials. In greenhouse experiments where soil was artificially infested with the damping off pathogen Pythium ultimum, the kabuli cultivar emergence was increased by the application of mefenoxam but not by biological seed treatments. Mefenoxam was the most effective seed treatment in field trials at three locations in Montana in the spring of 2007. Biological seed treatments were ineffective for reducing damping off and increasing plant growth measurements above untreated controls, even in combination with fungicides. These results indicate the use of mefenoxam is critical for controlling Pythium damping off in Montana and that biological seed treatments are not effective.
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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) | 0.001 | 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".