Enhancing Composting Efficiency and Nutrient Retention through Zeolite Amendment: Implications for Sustainable Soil Management and Plant Growth
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Composting is an effective waste management technique that not only reduces potentially harmful wastes but also generates valuable products for agricultural applications. This study aimed at producing useful agricultural materials from wastes by investigating the influence of natural and Mg-modified zeolite additives on the composting of chicken manure and sawdust as well as the impact of produced compost on the growth and yield of Hordeum vulgare (barley). Three different levels of natural zeolite and Mg-modified zeolite (0, 10%, and 15%) were co-composted with a mixture of chicken manure, sawdust, and dried leaves to produce five different composts (C, CNZ10, CNZ15, CMZ10, CMZ15). These composts, including 100% compost (C) as a control, were then added to sandy soil at a ratio of 1:3 (compost/soil). Our results revealed that the addition of zeolites enhanced the composting process, especially the 15% Mg-modified zeolite composts (CMZ15), which exhibited a lower electrical conductivity and greater NH 4 + and P retention compared to the other modified and unmodified composts. The NH 4 + and P retention increased by 30% and over 52%, respectively, when the Mg content was increased from 10% to 15% in the modified zeolite. Furthermore, soil amending with CMZ10 and CMZ15 (SCMZ10 and SCMZ15) and used in pot experiments under greenhouse conditions resulted in higher shoot biomass of barley plants, measuring 7.67 and 7.24 g, respectively, compared to SCNZ10, SCNZ15, and SC (6.19, 6.38, and 5.99 g shoot biomass, respectively). This research demonstrates that co-composting chicken manure and sawdust with zeolite, particularly Mg-modified zeolite, improves compost quality and consequently the yield of agricultural products.
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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".