Effects of Microbial Consortia, Applied as Fertilizer Coating, on Soil and Rhizosphere Microbial Communities and Potato Yield
Bibliographic record
Abstract
The use of biological inputs in crop production systems, as complements to synthetic inputs, is gaining popularity in the agricultural industry due to increasing consumer demand for more environmentally friendly agriculture. An approach to meeting this demand is the inoculation of field crops with beneficial microbes to promote plant growth and resistance to biotic and abiotic stresses. However, the scientific literature reports inconsistent results following applications of bio-inoculant to fields. The effects of inoculation with beneficial microbes on bulk soil and rhizospheric microbial communities is often overlooked as precise monitoring of soil microbial communities is difficult. The aim of this research was to use Illumina high throughput sequencing (HTS) to shed light on bulk soil and rhizospheric microbial community responses to two commercial microbial inoculants coated onto fertilizer granules, applied to potato fields. Bulk soil samples were collected 4 days before seeding (May 27th), 7 days after seeding (June 7th), at potato shoot emergence (June 21st) and at mid-flowering (July 26th). Rhizospheric soil was collected at the mid-flowering stage. The Illumina MiSeq HTS results indicated that the bulk soil microbial community composition, especially prokaryotes, changed significantly across potato growth stages. Microbial inoculation did not affect bulk soil or rhizospheric microbial communities sampled at the mid-flowering stage. However, a detailed analysis of the HTS results showed that bulk soil and rhizospheric microbial community richness and composition were different for the first treatment block compared to the other three blocks. The spatial heterogeneity of the soil microbial community between blocks of plots was associated with potato tuber yield changes, indicating links between crop productivity and soil microbial community composition. Understanding these links could help in production of high-quality microbial inoculants to promote potato productivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".