The influence of different fertilizer schemes on ‘Haschberg’ European elderberry growth and fruit production
Bibliographic record
Abstract
Due to their nutritional and therapeutic properties of the fruits, elderberry orchards started to gain more and more ground in Romania as well. Therefore, the purpose of this research was to improve soil quality and yield production in an elderberry orchard from Carastelec by applying both organic and mineral fertilizers. For this purpose, five fertilization schemes (FS) were set up with different ratios of cattle manure, mineral fertilizers and soil amendments. The field experiment was carried out in a randomised block design with three replicates. The best plant and yield performance among the four fertilizer schemes were recorded in FS_4 when mineral fertilizers -NPK -16:16:16 (0,8 kg/tree) + NHNO (0,8 kg/tree) + CaCO 3 (5 kg/tree) were applied providing a yield of 204,891,87 kg/row equal to 4.090.13 kg fruit/tree and 106.932.57cm average annual shoot length. Double-dose organic fertilization (50 kg cow manure/tree) increased the yield by 23,8% as compared to control and shoot length by 15,28%, while lower doses of mineral fertilizers and without soil amendments improved yield performance by 38,08% and growth by 11,85%. Therefore, the findings of this study reveal that large amounts of nitrogen are necessary to be applied in combination with soil amendments in order to improve soil quality and increase elderberry yield and growth performance up to 61%.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".