Changes in Maize (Zea mays L.) Performance and Nutrients Content with the Application of Poultry Manure, Municipal Solid Waste and Ash Composts
Bibliographic record
Abstract
This research was carried out at the Teaching and Research Farm of the University of Uyo, Nigeria to study the effect of applying compost made from poultry manure, municipal waste and ash on maize growth and yield. The composting of the organic wastes was done using a dry weight ratio of 3:1:0.5 of poultry manure, municipal solid waste (sorted) and ash, respectively. The finished compost was applied to the maize in polythene bags at the rates of 0, 10, 20 and 40 t ha-1. Each of these treatments was replicated four times. Eight maize seeds were planted per bag for six weeks during which soil samples were collected. Standard laboratory analysis was carried out on experimental soil, compost and maize plants samples. The mean plant height, root length and number of roots of maize planted on the control (soil only) were significantly (P<0.05) lower than those on all the compost fertilized treatments from 14 to 42 days after planting (DAP). Dry matter (%) of maize plant at 42 DAP indicated significant differences (P < 0.05) among all the treatments. Among the micronutrients Cr was not detected, Cd maintained a constant level of 2.60 mg kg-1 in both compost fertilized and unfertilized maize plants, while Fe, Mn and Co were found in lower amounts in plants fertilized with compost than in the soil only. Maize plants fertilized with various compost treatments performed better than those planted on soil only due to enhancement by the organic matter and various nutrients contained in the composts.
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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 | 0.001 |
| 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".