Influence of Organic Fertilization on Agro-morphological Traits and Mineral Nutrient Content in Bean Grains
Bibliographic record
Abstract
Plant residues such as animal feed weeds and bean straws are excellent sources of raw material for the production of organic fertilizers. In view of this, the objective of this work was to evaluate the effect of organic fertilization on the agromorphological and nutritional aspects in bean grains. The experiment was implemented in the field, in a randomized block design, with three replications, in a split plot scheme, with two types of organic compound (elephant grass enriched with cattle manure) and (bean straw enriched with cattle manure) applied in six increasing doses (0.0, 33.32, 66.65, 100.00, 133.32 and 166.65%) control treatment (recommended mineral fertilization). It was possible to identify interaction between the type of compound and the doses applied only to PL. With a yield of 2.8 t ha-1, mineral fertilization did not differ from treatment with 0.0% organic fertilizer. The dose of 166.65% organic fertilizer increased grain yield reaching 3.8 t ha-1 compared to 2.4 t ha-1 obtained in the treatment with 0.0%. In addition, this treatment increased the K content in the grains. On the other hand, the application of 133.32% of organic fertilizer can be indicated for the increase in mg content in the grains.
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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".