Friends and Foes: Bacteria of the Hydroponic Plant Microbiome
Bibliographic record
Abstract
Hydroponic greenhouses and vertical farms provide an alternative crop production strategy in regions that experience low temperatures, suboptimal sunlight, or inadequate soil quality. However, hydroponic systems are soilless and, therefore, have vastly different bacterial microbiota than plants grown in soil. This review highlights some of the most prevalent plant growth-promoting bacteria (PGPB) and destructive phytopathogenic bacteria that dominate hydroponic systems. A complete understanding of which bacteria increase hydroponic crop yields and ways to mitigate crop loss from disease are critical to advancing microbiome research. The section focussing on plant growth-promoting bacteria highlights putative biological pathways for growth promotion and evidence of increased crop productivity in hydroponic systems by these organisms. Seven genera are examined in detail, including Pseudomonas, Bacillus, Azospirillum, Azotobacter, Rhizobium, Paenibacillus, and Paraburkholderia. In contrast, the review of hydroponic phytopathogens explores the mechanisms of disease, studies of disease incidence in greenhouse crops, and disease control strategies. Economically relevant diseases caused by Xanthomonas, Erwinia, Agrobacterium, Ralstonia, Clavibacter, Pectobacterium, and Pseudomonas are discussed. The conditions that make Pseudomonas both a friend and a foe, depending on the species, environment, and gene expression, provide insights into the complexity of plant–bacterial interactions. By amalgamating information on both beneficial and pathogenic bacteria in hydroponics, researchers and greenhouse growers can be better informed on how bacteria impact modern crop production systems.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".