Economic and ecological values of frass fertiliser from black soldier fly agro-industrial waste processing
Bibliographic record
Abstract
The sustainable utilisation of black soldier fly (BSF) for recycling organic waste into high-quality protein feed and organic fertiliser with a low environmental footprint is gaining momentum worldwide. Although BSF farming is becoming a rapidly growing agribusiness, studies on the BSF farming’s economic aspects are limited. This study analysed the economic benefits of farming BSF for animal feeds and composted frass, called frass fertiliser (FF) production using experimental data. The BSF larvae were fed on brewery spent grain amended with sawdust, biochar, and gypsum to determine the cost-effective feed and other by-products production. The agronomic performance of FF on the maize crop was assessed using field experiments. Our results demonstrated that sourcing and preparing the waste substrate for rearing the BSF larvae accounts for 81-90% of the total BSF production cost. The utilisation of FF as an additional value-added product would increase farmer’s net income by 5-15 folds compared to BSF farming alone. Feedstock amended with 20% biochar increased net income by 10-64% for BSF larvae and FF production than other feedstocks. Production of one megagram (Mg) of dried BSF larvae (USD 900) would generate 10-34 Mg of FF worth USD 3,000-10,200. Maize grown on plots treated with FF yielded 29-44% higher net income than maize harvested from plots amended with commercial organic fertiliser. Furthermore, smallholder insect farmers’ direct use of FF for maize production would generate 30-232% higher net income than farmers purchasing similar FF. Our results demonstrate for the first time the role of insect farming in circular economy and justify the opportunities for future investments that would lead to enhanced sustainability for agricultural and food systems, especially for smallholder farmers in low- and middle-income countries.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".