Review – Insect farming for food and feed in the Global South: Focus on black soldier fly production
Bibliographic record
Abstract
Clear differences exist between the Global South and the Global North with respect to economic development. The majority of small and medium-sized insect production farms are located in Africa, Asia, and Latin America, which face challenges to food production and organic waste management in general. These regions have a long history of insect use, as well as environmental advantages, making production of insects in general - and the black soldier fly in particular - a promising option for sustainable food production and organic waste management. This study aimed to identify the current state of black soldier fly (Hermetia illucens L.) production in the Global South. The results of a survey and a Strengths, Weaknesses, Opportunities, and Threats Analysis are presented; responses were obtained from 33 of the 100 insect producers contacted: 14 in Africa, 4 in Latin America, and 15 in Asia. Their responses indicate that although insects have great potential and can help meet many of the Sustainable development goals thanks to their ecosystem services they provide - which is one of the main reasons producers engage in their production, significant challenges exist to the sustainability of insect farming, including lack of an adequate regulatory framework, an unstable supply of raw materials, market instability, and lack of adequate technology for operational scaling. Global collaboration among all stakeholders is crucial to overcoming these challenges.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".