Influence of Organic and Organo-Mineral Fertilizers on Growth and Fruit Yield of Eggplant on Acidic Soil
Bibliographic record
Abstract
Eggplant is a very important vegetable and economic resource crop for populations in urban areas of developing countries. Its cultivation, on acidic coastal soils of Côte d’Ivoire, presents several edaphic constraints. This study aims to assess the effects of organic fertilizers and an organo-mineral fertilizer, as compared to mineral fertilizers, on the growth and eggplant yield on an acid soil. The experiment was carried out in a randomized Fisher block, with 4 treatments and a control repeated 3 times. Treatments consisted of organic and organo-mineral fertilizers, a liquid organic fertilizer and a mineral fertilizer application. Application rates of organic and organo-mineral fertilizers were 17.5 t ha-1. The liquid organic fertilizer rate was 1 L per 200 L ha-1 of water. As for the mineral fertilizer, the formula 0-23-19 and urea (46-0-0) were used, bringing rates of 138 kg ha-1 (N), 65.35 kg ha-1 (P2O5) and 54 kg ha-1 (K2O). The organo-mineral fertilizer used produced the best effects. Very highly significant (p < 0.001) growth, such as plant height and collar diameter, were 52.69 and 1.49 cm, respectively, 90 days after transplanting. Concerning yield, statistical analysis showed highly significant differences (p < 0.01) between treatments. The highest fruit yield (20.87 t ha-1) was recorded in mineral fertilizer plot, but not significantly different from those of plots with organo-mineral (17.55 t ha-1) and organic (16.66 t ha-1) fertilizers. Organo-mineral fertilizers, based on highly enriching organic materials, are capable to contribute to a lasting improvement in the practice of this crop.
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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 | 0.001 |
| 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) | 0.000 | 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".