The economics of Integrated Multi‐Trophic Aquaculture: where are we now and where do we need to go?
Bibliographic record
Abstract
Abstract In integrated multi‐trophic aquaculture (IMTA), species from different trophic levels are raised in proximity to one another and the co‐products (organic and inorganic wastes) of one cultured species are recycled to serve as nutritional inputs for others. IMTA can reduce the ecological impacts near aquaculture operations, improve social perceptions of aquaculture and provide financial benefits for aquaculture producers via product diversification, faster production cycles and price premiums on IMTA products. We review aspects of IMTA’s economic potential and market acceptance and consider ways to address the current gaps in our understanding. We find that adopting IMTA raises the assimilative capacity of the farm and that IMTA substantively reduces the environmental cost of aquaculture. Moreover, integrating extractive species (e.g. invertebrates and/or seaweeds), with existing fed‐monoculture operations, can increase farm profits. The presence of positive public attitudes towards IMTA, as expressed by a willingness to pay a premium for its products, can further increase the profitability of adopting IMTA. Areas requiring more economic research include the development of comparative bioeconomic models of IMTA and the evaluation of competing production systems and their ability to internalize externalities to demonstrate the true value of IMTA to society. Further exploration of economic incentives, such as instruments needed to foster adoption of IMTA, and investigation of marketing opportunities, such as promoting the eco‐certification of IMTA products, are also needed. Our paper aimed to inform economists and non‐economists alike about the latest developments in IMTA economics, and spur further research on critical topics concerning this important subject.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".