Vermicomposting for improved N availability and the potential of vermicompost sources on N mineralization and soil biological properties on Nitisols
Bibliographic record
Abstract
Vermicomposting converts organic wastes into high-quality nutrient-rich compost and has been considered as a suitable technology for developing countries, especially at the household and community level as it is a simple and natural technology that does not require sophisticated machinery and frequent process monitoring. In addition, attempts to address SOM and nutrient losses in a sustainable manner in sub Saharan Africa countries require careful optimization of use of organic amendments such as through vermicomposting. We performed a vermicomposting experiment using three-earthworm species, exotic (Eisenia fetida and Eisenia andrei) and indigenous (Eudrilus eugeniae), fed on six different substrates made from agricultural wastes (cow and donkey manure separately spiked with three types of equally mixed crop residues (maize with soybean (MS), maize with banana (MB) and soybean with banana (SB)) to assess the nutrient stabilization in the resulting compost; and performed an incubation experiment using a selection of these vermicomposts to study the net N mineralization of the composts, and how they affected soil microbial properties such as microbial biomass C, dehydrogenase and β-glucosidase activities, in Nitisols. Earthworm activities during vermicomposting of agricultural wastes accelerated the rapid decomposition of mixed substrates and resulted with a significant reduction of TOC (C mass loss > 65%) and C:N ratios (twofold compared to the control without earthworm) and an increase in N mineralization and the total concentration of P, K and S. The effect of vermicompost on N availability and soil microbial quality significantly varied depending on vermicompost sources. The vermicompost made from cow manure mixed with soybean and banana residues using Eudrilus eugeniae had the highest net N mineralization (32.4 µgNg-1 soil, 75% of N added). Regardless of vermicompost type, amended soils had significantly higher microbial and enzymatic activities than unamended control. Our results suggest that vermicompost sources tested could reliably use for long-term improvement of soil nutrient mineralization and microbial activity under Nitisols of Ethiopia due to their greatest microbial activities and nutrient dynamics during vermicomposting.
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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) | 0.001 | 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) | 0.000 | 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 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".