Microbial composition and bioremediation in frass fertilizers from insect-based agri-food waste valorization
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Bibliographic record
Abstract
Insect frass fertilizer is emerging as a sustainable and novel input for improving soil health and crop production; however, research attention on its safety and microbial properties remains limited. Here, we evaluated the levels of heavy metals, pathogens, diversity, abundance, composition and functional roles of bacteria and fungi in frass fertilizer produced by eight edible insect species. Our results revealed the absence of Salmonella spp. In the frass fertilizers produced by all insect species, while the levels of other pathogens and heavy metals were within permissible limits for organic fertilizers. We found that 79-86 % of the variations in bacterial and fungal communities in the frass fertilizers were influenced by the species of insects used in waste recycling. The highest richness of bacteria and fungi was recorded in the frass fertilizers generated from Oryctes rhinoceros and Pachnoda sinuata. Taxonomic classification revealed 36 bacteria phyla across the frass fertilizers, with most belonging to Firmicutes (43 %), Proteobacteria (23 %), and Actinobacteriota (18 %), whereas the main fungal phyla were Ascomycota (80 %) and Basidiomycota (10 %). Functional profiling revealed that most fungi were sapotroph-symbiotrophs, pathogenic saprotrophs, pathotrophs, and symbiotrophic saprotrophs, which are key in organic matter decomposition, nutrient recycling and pathogen suppression. In contrast, the bacteria were mostly associated with antibiotic and phytohormone production, biosynthesis of plant growth regulators, nitrogen metabolism, nitrification, nitrogen fixation, especially in frass fertilizers derived from P. sinuata, Schistocerca gregaria, and Hermetia illucens. Our findings demonstrate the potential of insects to recycle low-value organic wastes into hygienic organic fertilizer and highlight the role of beneficial microbes, which could be harnessed for bioremediation, sustainable soil health management, improved crop productivity and food security.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it