Vermicompost utilization: A way to food security in rural area
Bibliographic record
Abstract
The increase in agricultural production as well as its nutritional quality at a cost bearable by producers is today a challenge in rural areas. Vermicompost is a low-cost organic amendment known for its effectiveness on agricultural productivity increase but little is diffused about its efficacy on nutritional quality. This study aimed to evaluate the benefits of vermicompost from cattle dung on Lagenaria siceraria yield and its edible parts content in mineral and in heavy metal compared to raw dung. The study was carried out in the region of Mankono the biggest area for L. siceraria production in Côte d'Ivoire during three cycle seasons. The experiment consisted of three treatments arranged in a complete randomized block design with four replicates. The agronomic parameters as yield, number of leaves and flowers per plant were evaluated. Also, mineral nutrients and heavy metal concentrations in roots, leaves and seeds were measured. Results showed that yield, number of leaves and flowers per plant were higher with the vermicompost than with the cattle manure and the control. The yield obtained with the vermicompost was 2.5 times and 20 times higher respectively than that with the cattle manure and the control. Mineral concentrations in roots, leaves and seeds were the highest with the vermicompost when heavy metal contents were the lowest. The present study indicates that vermicompost utilization improves the yield and the nutritional quality of the edible parts of L. siceraria and hence could be recommended to producers for increasing productivity with keeping the health and safety of human.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".