Soil–strain compatibility: the key to effective use of arbuscular mycorrhizal inoculants?
Bibliographic record
Abstract
Consistency of response to arbuscular mycorrhizal (AM) inoculation is required for efficient use of AM fungi in plant production. Here, we found that the response triggered in plants by an AM strain depends on the properties of the soil where it is introduced. Two data sets from 130 different experiments assessing the outcome of a total of 548 replicated single inoculation trials conducted either in soils with a history of (1) high input agriculture (HIA; 343 replicated trials) or (2) in more pristine soils from coffee plantations (CA; 205 replicated trials) were examined. Plant response to inoculation with different AM strains in CA soils planted with coffee was related to soil properties associated with soil types. The strains Glomus fasciculatum-like and Glomus etunicatum-like were particularly performant in soil relatively rich in nutrients and organic matter. Paraglomus occultum and Glomus mosseae-like performed best in relatively poor soils, and G. mosseae and Glomus manihotis did best in soils of medium fertility. Acaulospora scrobiculata, Diversispora spurca, G. mosseae-like, G. mosseae and P. occultum stimulated coffee growth best in Chromic, Eutric Alluvial Cambisol, G. fasciculatum-like and G. etunicatum-like in Calcaric Cambisol and G. manihotis, in Chromic, Eutric Cambisols. Acaulospora scrobiculata and Diversispora spurca strains performed best in Chromic Alisols and Rodic Ferralsols. There was no significant relationship between plant response to AM fungal strains and soil properties in the HIA soil data set, may be due to variation induced by the use of different host plant species and to modification of soil properties by a history of intensive production. Consideration of the performance of AM fungal strains in target soil environments may well be the key for efficient management of the AM symbiosis in plant production.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".