Evaluating Rhizobacterial Antagonists for Controlling Cercospora beticola and Promoting Growth in Beta vulgaris
Bibliographic record
Abstract
Cercospora beticola Sacc. is an ascomycete pathogen that causes Cercospora leaf spot in sugar beets (Beta vulgaris L.) and other related crops. It can lead to significant yield losses if not effectively managed. This study aimed to assess rhizosphere bacteria from sugar beet soil as a biological control agent against C. beticola and evaluate their effect on B. vulgaris. Following a dual-culture screening, 18 bacteria exhibiting over 50% inhibition were selected, with 6 of them demonstrating more than 80% control. The bacteria were identified by sequencing the 16S rRNA gene, revealing 12 potential species belonging to 6 genera, including Bacillus, which was represented by 4 species. Additionally, the biochemical and molecular properties of the bacteria were characterized in depth, as well as plant growth promotion. PCR analysis of the genes responsible for producing antifungal metabolites revealed that 83%, 78%, 89%, and 56% of the selected bacteria possessed bacillomycin-, iturin-, fengycin-, and surfactin-encoding genes, respectively. Infrared spectroscopy analysis confirmed the presence of a lipopeptide structure in the bacterial supernatant filtrate. Subsequently, the bacteria were assessed for their effect on sugar beet plants in controlled conditions. The bacteria exhibited notable capabilities, promoting growth in both roots and shoots, resulting in significant increases in root length and weight and shoot length. A field experiment with four bacterial candidates demonstrated good performance against C. beticola compared to the difenoconazole fungicide. These bacteria played a significant role in disease control, achieving a maximum efficacy of 77.42%, slightly below the 88.51% efficacy attained with difenoconazole. Additional field trials are necessary to verify the protective and growth-promoting effects of these candidates, whether applied individually, combined in consortia, or integrated with chemical inputs in sugar beet crop production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".