Effect of New Environmentally Friendly Fertilizers in a Commercial Vineyard
Bibliographic record
Abstract
In this study the effectiveness of the environmentally friendly fertilizers AXION RED, BIO.L.A and QUATTRO to improve soil fertility and the nutrition status of vines was examined. Application of above three fertilizers increased significantly the level of organic matter, K, P, Mn and Fe in soil. AXION RED also improved the concentration of Ζn, Β, and Cu. Tissue analyses showed that the leaf P content was significantly increased in the vines treated with the fertilizers in comparison to control in 2012. The fertilizer QUATTRO increased the leaf concentrations of Fe and Mn contents in 2012. It was found that the fertilizer BIO.L.A significantly increased the leaf N content, while the fertilizer QUATTRO increased the leaf B content in 2013. Although there was no statistically difference, the leaf P, K and Fe contents were increased in vines treated with each one of the fertilizers tested.The results also showed that all fertilizers increased the fruit N content in comparison to control. The percentage of fruit rot was significantly higher on bunches treated with each one of the fertilizers tested than the untreated control. Finally, the results of this study showed that only the fertilizer QUATTRO significantly increased the bunch weight in 2012. Generally, the above fertilizers improved the nutrition status of vine plants and also improved the soil fertility. However, the increased fruit N content in the grapes treated with each one of the fertilizers increased the susceptibility of fruits to pathogens causing fruit rots and therefore growers should consider its use carefully.
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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".