Biological control of Phytophthora capsici root rot of pepper (Capsicum annuum) using Burkholderia cepacia and Trichoderma harzianum
Bibliographic record
Abstract
Objective: Study the antagonistic capacity of a combination of two compatible microorganisms, the bacterium Burkholderia cepacia and the fungus Trichoderma harzianum against the pathogen Phytophthora capsici, the causal agent of rot in pepper. Methodology and results: Evaluation of the two antagonists against the pathogen and between themselves was carried out by dual in vitro interactions in several cultural media, at different pH and temperature conditions. Both antagonists affected the survival and development of the pathogen P. capsici, through a variety of mechanisms. B. cepacia showed a high degree of antibiosis while T harzianum showed greater competition for space and nutrients, and a tendency to mycoparasitis m and enzyme lysis. Biomass production of the antagonists was optimised in an Oat-Vermiculite medium, which proved to be efficient, cheap and rapid. The optimal doses of the antagonists were 3.5×10 8 spores/ml for T. harzianum and 10 9 CFU/ml for B. cepacia in a pH range of 3.5 - 5.6 and temperature range of 23 - 30 °C. In vivo treatment with the combination of B. cepacia + T. harzianum reduced the incidence of wilt caused by the pathogen P. capsici on pepper by up to 71%. Conclusion and application of findings : One of the reasons for the success obtained is that we have obtained total compatibility between the two antagonists used, which belong to different genera with a wide antifungal spectrum. The use of such antagonistic microorganisms in biological control of plant pathogens provides an alternative to chemical products such as methyl bromide (MB) whose use is prohibited by the Montreal protocol, due to its environmentally harmful effects, especially thinning of the ozone layer.
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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) | 0.001 | 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) | 0.001 | 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".