Plant and soil responses to tillage practices change arbuscular mycorrhizal fungi populations during crop growth
Bibliographic record
Abstract
Background Tillage practices can substantially affect soil properties depending on crop stage. The interaction between tillage and crop growth on arbuscular mycorrhizal fungi (AMF) communities remains unclear. We investigated the interactions between four tillage treatments (CT: conventional tillage, RT: reduced tillage, NT: no tillage with mulch, and SS: subsoiling with mulch), maintained for 25 years, and two wheat growth stages (elongation stage and grain filling stage) on AMF diversity and community composition. Results The AMF community composition strongly changed during wheat growth, mainly because of changes in the relative abundance of dominant genera Claroideoglomus, Funneliformi, Rhizophagu, Entrophospora, and Glomus. Co-occurrence network analysis revealed that the grain filling stage had a more complex network than the elongation stage. Redundancy analysis results showed that keystone genera respond mainly to changes in soil organic carbon during elongation stage, whereas the total nitrogen content affected the keystone genera during grain filling. Compared with CT, the treatments with mulch, i.e., NT and SS, significantly changed the AMF community composition. The change of AMF communities under different tillage practices depended on wheat biomass and soil nutrients. NT significantly increased the relative abundances of Glomus and Septoglomus, while RT significantly increased the relative abundance of Claroideoglomus. Conclusion Our findings indicate that the relative abundance of dominant genera changed during wheat growth stages. Proper tillage practices (e.g., NT and SS) benefit the long-term sustainable development of the Loess Plateau cropping systems.
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) | 0.000 | 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".