Biological control properties of microbial plant biostimulants. A review
Bibliographic record
Abstract
Plant biostimulants, sometimes referred to as biofertilisers or plant enhancers, are ingredients stimulating plant nutrition processes independently of the product’s nutrient content with the sole aim to improve the agronomic performance of a plant. Many of these biostimulants contain microorganisms. Although most of these microorganisms are supposed to only promote plant growth, some have plant protection properties. We reviewed commercial microbial plant biostimulants with regard to their potential effects on insects. This revealed 483 different products and 245 microorganisms registered as microbial plant biostimulants in Hungary, Switzerland, Spain, France, Indonesia, and/or Canada (181 ± 157 products, 64 ± 27 species per country). Among the products, 82% contained bacteria (133 ± 106 products), 63% contained fungi (77 ± 59) and 14% contained protista including algae (23 ± 24). About 1/3rd of products contained mixes of either bacteria, fungi, and/or protista; and 48% contained more than one microorganism. About 53% of products (137 ± 121) contained microorganims that had been reported to have insecticidal properties and 36% of species (23 ± 9), although the underlaying mechanisms often remain unknown. About 67% of products (149 ± 133) contained microorganisms reported to defend a plant from insects, and 54% of species (35 ± 10). The most common biostimulant microorganisms with reported insecticidal effects were strains of <i>Rhizophagus irregularis</i>, followed by <i>Bradyrhizobium japonicum</i>, <i>Rhizobium leguminosarum</i>, <i>Bacillus megaterium</i>, <i>B. subtilis</i>, <i>B. amyloliquefaciens</i>, <i>B. licheniformis</i>, <i>Penicillium bilaiae</i>, <i>B. pumilus</i> and <i>Ascophylum nodosum.</i> In conclusion, growers may profit from, but should be made aware of the multiple effects of microbial plant biostimulants.
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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 | 0.000 | 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) | 0.092 | 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".