Harnessing Pseudomonas spp. for sustainable plant crop protection
Bibliographic record
Abstract
This review examines the role of Pseudomonas spp. bacteria as biocontrol agents against crop diseases, focusing on their mechanisms of action, efficacy, and potential applications in sustainable agriculture. Pseudomonas spp., ubiquitous in soil ecosystems and root microbiomes, have attracted attention for their ability to suppress phytopathogens and enhance plant health through various mechanisms. These include direct competition for nutrients, production of antimicrobial compounds and volatile organic compounds, competition using type VI secretion systems, and indirect induction of systemic resistance. Our review shows that Pseudomonas strains effectively control a wide range of diseases across diverse plant species, with some strains demonstrating efficacy comparable to chemical fungicides. However, the review also highlights challenges in achieving consistent performance when using Pseudomonas inoculants under field conditions due to various biotic and abiotic factors. Strategies to optimize biocontrol potential, such as formulation techniques, application methods, and integration with other management practices, are discussed. The advantages of Pseudomonas -based biocontrol for sustainable agriculture include reduced reliance on chemical pesticides, enhanced crop productivity, and improved environmental sustainability. Future research directions should focus on understanding the complex interactions within the plant microbiome, optimizing delivery systems, and addressing regulatory hurdles for commercial deployment. This review underscores the significant potential of Pseudomonas spp. in sustainable crop protection while acknowledging the need for further research to fully harness their capabilities in agricultural 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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".