Impacts of humic-based products on the microbial community structure and functions toward sustainable agriculture
Bibliographic record
Abstract
Humic-based products (HPs) are carbon-rich organic amendments in the forms of extracted humic substances from manure, compost, and raw and extracted forms of lignites, coals and peats. HPs are widely used in agriculture and have beneficial effects on plants. While the agronomic benefits of HPs have been widely reported, information on their impact on the soil microbial community composition and functions is lacking, despite claims made by companies of humic substances as biostimulants. In this review, we explored published research on microbial responses with HPs application in an agronomic context. Although research data are sparse, current results suggest indirect impacts of HPs on microbial community composition and activities. HPs application changes the physico-chemical properties of the soil and influence root exudation, which in turn impact the microbial structure and function of the soil and rhizosphere. Application of HPs to the soil as biostimulants seemed to favor plant/soil beneficial bacterial community composition. HPs impacts on microbial activities that influence soil biogeochemical functioning remain unclear; existing data are also inconsistent and contradictory. The structural properties of HPs caused inconsistencies in their reported impacts on soil properties and plants. The sources of HPs and forms (whether extracted or raw), soil type, geographic location, crop species, and management strategies, among others, affect microbial communities affecting HPs efficacy as biostimulants. A more holistic approach to research encompassing multiple influential factors and leveraging the next-generation sequencing technology is needed to unravel the impacts of HPs on the soil microbiome. Addressing these knowledge gaps facilitates sustainable and efficient use of HPs as organic agricultural amendments reducing the use of chemical fertilizers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".