Application of biostimulant products and biological control agents in sustainable viticulture: A review
Bibliographic record
Abstract
Current and continuing climate change in the Anthropocene epoch requires sustainable agricultural practices. Additionally, due to changing consumer preferences, organic approaches to cultivation are gaining popularity. The global market for organic grapes, grape products, and wine is growing. Biostimulant and biocontrol products are often applied in organic vineyards and can reduce the synthetic fertilizer, pesticide, and fungicide requirements of a vineyard. Plant growth promotion following application is also observed under a variety of challenging conditions associated with global warming. This paper reviews different groups of biostimulants and their effects on viticulture, including microorganisms, protein hydrolysates, humic acids, pyrogenic materials, and seaweed extracts. Of special interest are biostimulants with utility in protecting plants against the effects of climate change, including drought and heat stress. While many beneficial effects have been reported following the application of these materials, most studies lack a mechanistic explanation, and important parameters are often undefined (e.g., soil characteristics and nutrient availability). We recommend an increased study of the underlying mechanisms of these products to enable the selection of proper biostimulants, application methods, and dosage in viticulture. A detailed understanding of processes dictating beneficial effects in vineyards following application may allow for biostimulants with increased efficacy, uptake, and sustainability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".