Optimizing Black Soldier Fly Production: The Impact of Substrate on Growth, Survival Rate, and Nutritional Profile
Bibliographic record
Abstract
Black soldier fly larvae (BSFL) have emerged as a sustainable and efficient tool for bioconverting organic waste into valuable insect biomass, particularly for use in animal feed. We investigated the effects of three substrate types—fruit and vegetable waste (FVW), animal protein waste (APW), and a combination of both (FVW + APW)—on the growth performance and nutritional profile of BSFL under Ghanaian conditions. Five-day-old larvae were reared in 2-litre containers with 2 kg of each substrate, replicated three times, and monitored over 8 days. Larval development parameters were evaluated, including weight, size, feed conversion ratio (FCR), survival rate, and nutritional content. Substrate temperatures remained within the mesophilic range (30.46 °C to 37.83 °C), with the highest temperatures observed in the mixed-substrate treatment. BSFL fed the combined substrate exhibited significantly superior growth, achieving the highest weight (7.83 g), length (13.84 mm), and width (3.07 mm) compared to larvae reared on single substrates. Nutrient analysis revealed that larvae raised on APW had the highest crude protein content (27.78±2.12%), while those on FVW and FVW + APW had higher fat (5.09-5.89%) and carbohydrate contents (13.33% in FVW). Moisture content ranged from 72.24% in FVW to 76.21% in APW, with ash content peaking in APW (1.14±0.19%). Notably, although growth and nutritional parameters varied significantly among treatments (P < 0.05), survival rates were not significantly affected, suggesting all substrates supported larval viability. These findings highlight the crucial role of substrate composition in maximizing BSFL production. The mixture of fruit, vegetable, and animal protein waste offered the most favourable balance of temperature, nutrient availability, and larval development outcomes, making it the most suitable for large-scale rearing of black soldier flies (BSFLs). This study provides a practical foundation for sustainable organic waste valorization and insect-based feed production in tropical contexts.
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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 | 0.001 | 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".