Phytohormonal basis for the plant growth promoting action of naturally occurring biostimulators
Bibliographic record
Abstract
There is increasing interest in the use of naturally occurring 'biostimulators' for enhancing the growth of agricultural and horticultural crops. Bacteria, fungi and protozoa, as well as marine algae-based seaweed extracts, can produce or contain biostimulators. The activity of biostimulators to promote plant growth is often attributed to their ability to directly or indirectly provide mineral nutrients (mostly N, but also P, S and other macro- and micro-nutrients) to plants. Alternatively, biostimulators are postulated to increase the plant's ability to assimilate these mineral nutrients, often in return for photo-assimilates (as occurs with certain bacteria and fungi associations). Although optimal growth of plants depends on the availability of adequate mineral nutritients, that growth (and also development, including reproduction) is also regulated by plant hormones (phytohormones), including gibberellins, auxins and cytokinins. This review describes and discusses the evidence that the presence or application of biostimulators also increases plant growth directly via phytohormone action and also influences the plant's ability to control its own hormone biosynthesis and homeostasis. Finally, it discusses the need for a better understanding of the role(s) that are played by the naturally occurring biostimulators associated with the plant in the crop field. It is suggested that better understanding will allow for optimal crop yield returns, since disruptions of phytohormone homeostasis in plant organs and tissues can yield either beneficial or sub-optimal outcomes.
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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 | 0.000 | 0.000 |
| 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".