Effects of Trichoderma asperellum BV10 and Bacillus amyloliquefaciens BV03 in Meloidogyne incognita Control Considering Three Different Management Systems
Bibliographic record
Abstract
Crop yield decrease is the main concern when a pathogen or plague is identified in an agriculture field. Thus, part of this issue can be attributed to plant-parasitic nematodes (PPNs), such as Meloidogyne species, due to, most of the time, the hard diagnosis, and non-specific symptoms. Its management is mainly based on chemical pesticides, followed by a few potential biological control agents, and the management system. Therefore, this study aimed to evaluate the effects of biological agents in Meloidogyne incognita control in different soil systems. For that, two biological products were chosen, Trichoderma asperellum BV10 and Bacillus amyloliquefaciens BV03, and soils were sampled from three different managements systems: (i) soybean no-tilled system at Goiás state, Brazil; (ii) forest soil at Goiás state, Brazil, and (iii) soybean conventional managed system at Mato Grosso do Sul state, Brazil. Biocontrol and growth promotion effects, volatile organic compounds (VOCs) and soil respiration were determined in vegetation house and laboratory, respectively. As a result, both BV10 and BV03 had significant nematode control activity, comparing to control treatment, in all the three soils systems. Plus, the number of immobile nematodes by potential VOCs had significant increase when BV03 was applied, while the application of BV10 agent raised the soil respiration rate. In conclusion, both biocontrol agents presented great efficiency in control M. incognita, with better performance of BV03. Lastly, more studies must be done to elucidate how the resident soil microbiome can influence on biocontrol agent establishment and performance, as well as the consequence of the application of biological products on soil microbiome network.
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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 | 0.000 |
| 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".