Review: Ecosystem service indicators in insect farming − a novel One Health perspective
Bibliographic record
Abstract
The global agrifood system faces growing pressure to meet increasing food demands, driving the need for sustainable agricultural practices that improve the efficiency and resilience of food systems. Insects play diverse socio-ecological roles that can be explained through the lens of ecosystem services (ES). Insect farming offers a sustainable strategy that supports food security, ecosystem balance, and agricultural resilience. The One Health (OH) framework, which integrates human, animal, and environmental health perspectives, provides a valuable approach to understanding and managing these contributions. This review explores four categories of ES provided by insect farming-support, provisioning, cultural, and regulation-which reflect the broad contributions of insects to ecological balance, health, and agrifood systems. These services position insect farming as a multifunctional tool for improving food systems and enhancing human, animal, and environmental health. However, despite its benefits, insect farming also faces challenges such as regulatory complexities, disease transmission risks, and potential environmental impacts, necessitating careful management. To measure the ES provided by insect farming, we synthesised insights from the literature and proposed a structured set of indicators aligned with the OH framework. These indicators aim to assess the benefits and challenges of insect farming, providing a foundation for evidence-based policies that maximise positive contributions to human, animal, and environmental health while minimising risks.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".