The effects of mycorrhizal colonization on phytophagous insects and their natural enemies in soybean fields
Bibliographic record
Abstract
The use of belowground microorganisms in agriculture, with the aim to stimulate plant growth and improve crop yields, has recently gained interest. However, few studies have examined the effects of microorganism inoculation on higher trophic levels in natural conditions. We examined how the diversity of phytophagous insects and their natural enemies responded to the field-inoculation of soybean with a model arbuscular mycorrhizal fungus (AMF), Rhizophagus irregularis , combined with a nitrogen-fixing bacterium, Bradyrhizobium japonicum , and a plant growth-promoting bacterium, Bacillus pumilus . We also investigate if the absence or presence of potassium fertilizer can affect this interaction. We found an increase in the abundance of piercing-sucking insects with the triple inoculant irrespective of potassium treatment, whereas there were no differences among treatments for other insect groups. A decrease in the abundance of the soybean aphid, Aphis glycines , with the double inoculant Rhizophagus + Bradyrhizobium was observed in potassium enriched plots and in the abundance of Empoasca spp. with potassium treatment independent of inoculation type. Although it was not possible to discriminate the mycorrhization realized by inoculum from that of the indigenous AMF in the field, we confirmed global negative effects of overall mycorrhizal colonization on the abundance of phytophagous piercing-sucking insects, phytophagous chewing insects, and the alpha diversity of phytophagous insects. In perspective, the use of AMF/Rhizobacteria inoculants in the field should focus on the identity and performance of strains to better understand their impact on insects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".