Biostimulants Promote Plant Development, Crop Productivity, and Fruit Quality of Protected Strawberries
Bibliographic record
Abstract
Berries such as strawberries are recognized as a significant constituent of healthy human diets owing to their bioactive secondary metabolites. To improve crop sustainability, yield and berry quality, alternative production systems should be proposed such as organic farming and the use of biostimulants. Thus, we have compared within a complete randomized block design seven biostimulant treatments: 1-control, 2-seaweed extract, 3-Trichoderma, 4-mycorrhiza, 5-mixture of three bacteria, 6-combination of mycorrhiza + bacteria, and 7-citric acid. Strawberry plants were grown in conventional high tunnel (CH), conventional greenhouse (CG) and organic greenhouse (OG). Our results showed that biostimulants did not impact the soil microbial activity (FDA) when compared with the control. Leaf chlorophyll content and photosynthetic leaf performance were not affected by any studied biostimulants. Bacteria, citric acid, and the combination of mycorrhiza + bacteria increased the number of flowering stalks compared with the control in CH, while bacteria increased the crown diameter and all biostimulants increased fresh and dry shoot plant biomass. Citric acid increased leaf Ca content in CG, when all biostimulants increased leaf N content in CH. Studied biostimulants increased berry productivity in CH, while citric acid treatment had the highest yield in CG. The anthocyanins content increased with the use of biostimulants in CH, whereas Trichoderma (CG) and the combination of mycorrhiza + bacteria (OG) increased the Brix, total polyphenols, and anthocyanin contents of the berries compared with the control.
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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".