Seaweed-Based Compounds and Products for Sustainable Protection against Plant Pathogens
Bibliographic record
Abstract
Sustainable agricultural practices increasingly demand novel, environmentally friendly compounds which induce plant immunity against pathogens. Stimulating plant immunity using seaweed extracts is a highly viable strategy, as these formulations contain many bio-elicitors (phyco-elicitors) which can significantly boost natural plant immunity. Certain bioactive elicitors present in a multitude of extracts of seaweeds (both commercially available and bench-scale laboratory formulations) activate pathogen-associated molecular patterns (PAMPs) due to their structural similarity (i.e., analogous structure) with pathogen-derived molecules. This is achieved via the priming and/or elicitation of the defense responses of the induced systemic resistance (ISR) and systemic acquired resistance (SAR) pathways. Knowledge accumulated over the past few decades is reviewed here, aiming to explain why certain seaweed-derived bioactives have such tremendous potential to elicit plant defense responses with considerable economic significance, particularly with increasing biotic stress impacts due to climate change and the concomitant move to sustainable agriculture and away from synthetic chemistry and environmental damage. Various extracts of seaweeds display remarkably different modes of action(s) which can manipulate the plant defense responses when applied. This review focuses on both the similarities and differences amongst the modes of actions of several different seaweed extracts, as well as their individual components. Novel biotechnological approaches for the development of new commercial products for crop protection, in a sustainable manner, are also suggested.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.005 | 0.002 |
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".