RESEARCH ON THE INFLUENCE OF ORGANIC AND MINERAL FERTILIZERS ON RAPESEED YIELD AND ASH COMPOSITION OF ITS OIL SEEDS
Bibliographic record
Abstract
The article presents results of a field experiment with application of mineral (NPK and zeolite) and organic fertilizers in the technology of spring rapeseed cultivation, as well as a comparative analysis of the ash composition of rape seeds of Rif variety, depending on the variants of the research. Six variants were studied: growing plants without fertilization (control); mineral fertilizer N60: P60: K60 alone and in combination with zeolite (5 t/ha); pure zeolite (5 t/ha); chicken manure (5 t/ha) alone and in combination with zeolite (5 t/ha). Accumulation of nine main elements (wt., %) contained in the ash of spring rapeseed seeds was studied by the method of energy dispersive spectrometry (X-Ray-analysis). The order of element accumulation was determined: P ≈ K>Mg ≥ Ca>Mo> S>Zn>Mn>Fe. The proportion of P is from 10.852 to 11.855 wt. %; the proportion of K is from 9.933 to 12.343 wt.%; Mg, Ca and Mo are present in rape seeds in similar concentrations within 4.0 -5.8 wt.%. Mutual usage of zeolite and organic fertilizer provided an increase of mineral accumulation in the seeds. Correlations between the elements were established. Higher level of macro- and microelements in seeds, necessary for humans, brings about good perspectives to create functional products based on the studied rape seeds for food enrichment. Positive effect of combined organo-mineral fertilizers on accumulation of mineral substances in rape seeds was established.
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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".