Application of biostimulant products and biological control agents in sustainable viticulture: A review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it