Effects of Pesticides on the Arbuscular Mycorrhizal Symbiosis
Bibliographic record
Abstract
Substantial amounts of pesticides, used in agricultural production to control pests, diseases, and weeds, and thereby attain high product quantities and quality, can severely affect the ecosystem and human health. The amounts of pesticides used depend on the specifics of the current production system but also exhibit large effects of past practices. Pesticides do not act only on the target organisms but also on organisms for which the chemicals were not specifically formulated, constituting hazardous molecules for humans and the environment. Pesticides, therefore, also influence soil microbial communities including organisms that engage in mutualistic plant symbioses that play a crucial role in its mineral nutrition, such as arbuscular mycorrhizal fungi. In this review, we summarize the current knowledge on the effects of synthetic and natural (‘green’) pesticides (fungicides, herbicides, and insecticides) on arbuscular mycorrhizal symbiosis. We deal with both the direct effects (spore germination and extraradical and intraradical growth of the mycelium) and indirect effects on the agroecosystem level. Such indirect effects include effects through the spread of herbicide-resistant crops and weeds to neighboring ecosystems, thereby modifying the mycorrhizal inoculum potential and altering the plant–plant interactions. We also briefly discuss the possibility that mycorrhizal plants can be used to enhance the phytoremediation of organic pesticides.
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".