Microbial consortia as an option for biocontrol of <i>Stromatinia cepivora</i>
Bibliographic record
Abstract
Stromatinia cepivora, the causal agent of white rot, is responsible for 60–80% of economic losses in onion and garlic crops. This work aimed to select biological control agents (BCAs) to control white rot. Ten microorganisms were tested for hyperparasitic activity on S. cepivora sclerotia in garlic (Allium sativum). Bioassays consisted of pots filled with sterile soil and 50 sclerotia in a plastic tulle bag. Four microorganisms were selected to compare their capability for degrade sclerotia on garlic. Our results showed that increasing degradation happened when Th034, Th035, Th003, and Bs006 were added to the pots containing garlic. Subsequently, three application techniques (seeds, seedlings at transplant, and seeds and transplant) were evaluated. Seven BCAs applied singly and in mixtures were evaluated in semi-field experiments for their ability to reduce white rot symptoms in onion plants in soil inoculated with 300 sclerotia per kilogram. The results indicated that efficacy was dependent on microrganism, mixture, and technique of application. The synergy factor showed that only two treatments have synergistic effects. In both cases, the mixture consisted of a strain of Bacillus and two species of Trichoderma (T. koningiopsis, T. atroviride) applied twice. In most cases, antagonistic interactions among BCAs were observed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".