Cropping practices modulate the impact of glyphosate on arbuscular mycorrhizal fungi and rhizosphere bacteria in agroecosystems of the semiarid prairie
Bibliographic record
Abstract
A growing body of evidence obtained from studies performed under controlled conditions suggests that glyphosate use can modify microbial community assemblages. However, few studies have examined the influence of glyphosate in agroecosystems. We examined 4 wheat-based production systems typical of the Canadian prairie over 2 years to answer the following question: Does preseeding of glyphosate impact soil rhizosphere microorganisms? If so, do cropping practices influence this impact? Glyphosate caused a shift in the species dominating the arbuscular mycorrhizal fungal community in the rhizosphere, possibly through the modification of host plant physiology. Glyphosate stimulated rhizobacterial growth while having no influence on saprotrophic fungi, suggesting a greater abundance of glyphosate-tolerant 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) in bacteria than in fungi. Glyphosate stimulated rhizosphere bacteria in pea but not in urea-fertilized durum wheat, which is consistent with inhibition of EPSPS tolerance to residual glyphosate through high ammonium levels. Mitigation of the effects of glyphosate on rhizosphere bacteria through tillage suggests a reduction in residual glyphosate activity through increased adsorption to soil binding sites upon soil mixing. The influence of glyphosate on Gram-negative bacteria was mitigated under drought conditions in 2007. Our experiment suggests that interactions between soil fertility, tillage, and cropping practices shape the influence of glyphosate use on rhizosphere microorganisms.
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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) | 0.000 | 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 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".