Humalite shapes the wheat rhizosphere soil microbiome by altering microbial community structure, diversity, and network stability
Bibliographic record
Abstract
The application of humic substances to enhance soil health and crop yield has gained considerable interest in recent years, mainly due to their organic origin and capacity to improve the physicochemical and biological properties of the soil. Humalite, a rich source of humic substances found in southern Alberta, Canada, is particularly valuable due to its low ash and heavy metal levels. Despite its agricultural potential, its effects on the soil microbiome have yet to be evaluated. This study utilized 16S rRNA gene and ITS2 region amplicon sequencing to examine bacterial and fungal communities in rhizosphere soil collected from wheat plants cultivated at five Humalite application rates (0, 200, 400, 800, and 1600 kg/ha) in combination with nitrogen, phosphorus, and potassium (NPK) at recommended levels based on soil test under controlled greenhouse conditions. Results indicated that Humalite application influenced microbial community composition by increasing the abundance of beneficial bacterial ( Flavisolibacter , Gaiella , Geomonas and Sphingomonas ) and fungal ( Solicoccozyma , Clonostachys, Trichoderma ) genera while reducing pathogenic and harmful taxa (Bedellovibrionota and Fusarium ). The Humalite application reduced bacterial diversity while increasing fungal diversity specifically at 800 and 1600 kg/ha, and increased the co-occurrence network stability. Notably, the abundance of various taxa involved in reducing N 2 O emissions (Methylomirabilota, Gemmatimonadota, Terrimonas ) was higher in Humalite-treated soils. Overall, Humalite application modulated rhizosphere microbial communities, enhancing beneficial taxa and network connectivity while suppressing pathogenic and harmful taxa. These changes suggest that Humalite creates a more balanced, resilient, and health-promoting soil microbiome.
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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 | 0.000 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 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".