The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Fish to 2030: The Role and Opportunity for Aquaculture
Abstract
Seafood sector can contribute to the global food supply in an important way, and provide an important source of animal protein. Based on observed regional trends in seafood production and consumption and using a global, partial-equilibrium, multi-market model, this study investigates what the global seafood market may look like in 2030. The model projects that the total fish supply will increase from 154 million tons in 2011 to 186 million tons in 2030, with aquaculture entirely responsible for the increase. The fastest aquaculture growth is expected for tilapia and shrimp, while the largest expansion is expected in India, Latin America and Caribbean and Southeast Asia. Fast-growing seafood demand in China and elsewhere represents a critical opportunity for global fisheries and aquaculture to improve their management and achieve sustainable seafood economy.
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Cites
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Cited by
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A Global Blue Revolution: Aquaculture Growth Across Regions, Species, and Countries
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Metadata
- Authors
- Kobayashi, M., Msangi, S., Batka, M., Vannuccini, S., Dey, M. M., & Anderson, J. L.
- Venue
- Aquaculture Economics & Management
- Publisher
- Informa UK Limited
- Published
- 2015-07-03
- Volume
- 19
- Issue
- 3
- Pages
- 282-300
- ISSN
- 1365-7305 (ISSN-L)
- 1551-8663
- OpenAlex
- W1857143922
- Cited by
- 416
- References
- 33
- FWCI
- 83.70
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kobayashi, M., Msangi, S., Batka, M., Vannuccini, S., Dey, M. M., & Anderson, J. L. (2015). Fish to 2030: The Role and Opportunity for Aquaculture. Aquaculture Economics & Management. https://doi.org/10.1080/13657305.2015.994240
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
