Enriched cocoa pod composts and their fertilizing effects on hybrid cocoa seedlings
Bibliographic record
Abstract
Purpose Composting has the potential to recycle wastes as a means of conserving natural resources. The study was aimed at examining feasibility of producing nutrient-enriched composts from pest infested cocoa pods with chemical amendments and using manure composts as a fertilizing material in cocoa seedling nursery. Methods Cocoa pod waste was composted in static vessels, aerobically, with chemical enrichments (triple super phosphate charged at 0.4% P or urea charged at 0.8% N or poultry manure charged at 22%) along with a control at the Cocoa and Coconut Institute, Papua New Guinea. The reaction (pH) of the composting mixtures (pH) and macro-nutrients dynamics was monitored at periodic intervals. Effect of soil incorporation of cocoa pod manure composts at 10 g kg−1 was assessed on the growth and foliar concentration of macro-nutrients in hybrid cocoa seedlings. Results In the finished manure composts, dry matter loss ranged from 30.6 to 63.3%; greatest in composting mixtures charged with super phosphate and poultry manure. Besides, super phosphate enriched mixture lost small fraction of initial N (6.6%) compared to un-enriched cocoa pod waste (30.2%). Composting mixtures with greater pH values during composting process showed higher losses of N. Super phosphate charged manure compost outperformed the control, in terms of C/N ratio and concentration of macro-nutrients (P, K, Ca, Mg and S). Quality parameters for all the manure composts conformed to the Canadian Compost Guidelines indicating satisfactory standards. Waste cocoa pods enriched with superphosphate did not show any deleterious effects on cocoa seedlings’ growth, rather, improved plant height, dry matter production and foliar N concentration. Conclusion Waste cocoa pods, co-composted with triple super phosphate and poultry manure, produced composts of desirable quality and can be effectively used to fertilize the cocoa seedlings.
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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".