Biostimulants Influenced Growth And Productivity Of The Organic Strawberries
Bibliographic record
Abstract
Organic strawberry production is faced with several biotic and abiotic stresses that compromise crop productivity and berry quality. In order to improve yield and berry quality, we have compared the potential beneficial effects of seven biostimulant treatments 1- control without biostimulant (CONTROL), 2- seaweed extract (SEAWEED), 3- mycorrhiza Rhizoglomus irregular (MYC), 4- mix of three bacteria, Azospirillum brasilense, Gluconacetobacter diazotrophicus, and Bacillus amyloliquefaciens (BACT), 5- combination of MYC+BACT, 6- MYC+BACT with a low fertilization (MYC+BACT/LF), and 7- citric acid-based (CITRIC) within a complete randomized block design with five replicates. Our results showed that some biostimulants did impact the soil relative abundance of fungi and soil CO2 efflux, while no effect was observed for the microbial activity (FDA) compared with the control. Leaf chlorophyll content and the chlorophyll fluorescence were not significantly affected by biostimulants. MYC decreased the number of flowering stalks (-18%) compared with control plants, while citric acid increased their dry root biomass (+35%). However, biostimulants did not affect the mineral content of leaves. Little effect of biostimulants on crop productivity was observed compared with control plants. However, MYC+BACT increased ºBrix (+11%), total polyphenols (+40%) and anthocyanins (+26%) of the berries compared with control. The use of a lower fertilization reduced plant growth and yield.
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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".