Effects of microbial inoculants on the biomass and diversity of soil microbial communities: a meta-analysis
Bibliographic record
Abstract
Abstract Microbial inoculants, transplanting microorganisms from their natural habitat to improve plant performance, hold promise for sustainable agriculture and ecological restoration but also raise a potential concern as a purposeful invasion to alter soil resident communities. Current studies have mainly focused on the impact of microbial inoculants on altering soil microbial communities in various single soil conditions. However, the comprehensive impact of microbial inoculants on soil microbial community (biomass, diversity, structure, and network) under a large scale of soil resource conditions remains unknown. Through a meta-analysis of 335 studies, we found significant and positive effects of microbial inoculants on microbial biomass. More importantly, we discovered that environmental stress weakened their positive effects, while fertilizer application and the use of native microbial inoculants enhanced them. Moreover, increased initial soil nutrients amplified the positive impact of microbial inoculants on fungal biomass, actinomycete biomass, microbial biomass carbon, and microbial biomass nitrogen. Although microbial inoculants did not significantly alter microbial diversity, they induced changes in microbial community structure and bacterial composition significantly. Lastly, we showed a reduction in the complexity of bacterial networks induced by microbial inoculants, along with increased stability. Our study highlights the overall positive impact of microbial inoculants on soil microbial biomass, emphasizing the benefits of native inoculants and the importance of considering soil nutrient levels and environmental stress.
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.028 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".