Functional differentiation of industrial hemp rhizosphere microbiome along environmental gradients
Bibliographic record
Abstract
The southwestern, central, and northeastern regions of China are the primary cultivation areas for industrial hemp. Microorganisms within the soil-root continuum play a crucial role in plant health. However, the mechanisms by which these microbial communities respond to environmental gradients remain unclear. This study aimed to explore how bacterial and fungal communities in the bulk soil and rhizosphere of industrial hemp respond to environmental gradients across diverse climatic zones. We collected soil samples from eight representative regions and analyzed microbial diversity, composition, and assembly mechanisms using DNA metabarcoding. Results showed that microbial diversity in the rhizosphere was lower than in bulk soil, likely due to selective root filtering. The lowest diversity occurred in the temperate continental climate, highlighting the effects of precipitation, soil nutrients, and organic carbon. Climate type was the main factor influencing microbial community structure, with annual precipitation and temperature as key environmental constraints. Bacterial communities were mainly assembled by deterministic processes, while fungal communities were driven by stochastic processes. Additionally, geographic variation in rhizosphere microbial function reflected a co-adaptive mechanism between hemp and its microbial community under varying environmental pressures. These findings enhance our understanding of industrial hemp soil microbiomes and provide insights for optimizing cultivation management.
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 | 0.001 | 0.001 |
| 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".