Plant Disease Management: Leveraging on the Plant-Microbe-Soil Interface in the Biorational Use of Organic Amendments
Bibliographic record
Abstract
Agriculture is faced with many challenges including loss of biodiversity, chemical contamination of soils, and plant pests and diseases, all of which can directly compromise plant productivity and health. In addition, inadequate agricultural practices which characterize conventional farming play a contributory role in the disruption of the plant-microbe and soil-plant interactions. This review discusses the role of organic amendments in the restoration of soil health and plant disease management. While the use of organic amendments in agriculture is not new, there is a lack of knowledge regarding its safe and proper deployment. Hence, a biorational approach of organic amendment use to achieve sustainable agricultural practices entails the deployment of botanicals, microbial pesticides, and organic minerals as organic amendments for attaining plant fitness and disease suppression. Here, the focus is on the rhizosphere microbial communities. The role of organic amendments in stimulating beneficial microbe quorum formation related to the host-plant-pathogen interactions, and its role in facilitating induced systemic resistance and systemic-acquired resistance against diseases was evaluated. Organic amendments serve as soil conditioners, and their mechanism of action needs to be further elaborated to ensure food safety.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".