Indian Ocean Science

Zanzibar Fisheries and Marine Resources Research Institute (ZAFIRI), Tanzania

 

Title: Seaweed industry evolution, status and recent development

 

Abstract: Zanzibar recognised globally as a hub in cultivation, production and exportation of seaweed products. It's positioning a second leading export crop after cloves and, her third-most earner of foreign earnings after tourism and cloves. Seaweed marks the single most important aquaculture sub sector in Tanzania, playing a significant role in the development of the blue economy particularly in Zanzibar. Seaweed farming in Zanzibar started early 1989's introduced seeds from Philipines. Seaweed sector employs nearly 26,000 people with almost 80 percent of them being women. Two species commonly farmed in Zanzibar are Kappaphycus alvarezii and Eucheuma denticulatum. Commercially recognised as Cottonii (Mwani mnene) and Spinosum (Mwani mwembamba). Seaweed playing a significant role in Zanzibar Blue Economy which is among the five priorities of Zanzibar Blue Economy area. Zanzibar archipelago is the first in Africa and among top five seaweed producer globally. Production, exports and earnings of seaweed in Zanzibar have fluctuated over time due to social and environmental factors. The maximum production was 2015 and 2023 respectively reported export 16,653 and 16,514 metric tons. The recent establishment Ministry of Blue Economy and Fisheries in Zanzibar fuel the development of seaweed industry as it support seaweed farmers various tools (ropes, boats, seeds), training and construction of seaweed factory to add value, attractive price ideally encourage seaweed farmers.

 

Indian National Centre for Ocean Information Services, Ministry of Earth Sciences, Hyderabad, India

 

Title: Sea-surface pCO₂ maps for the Bay of Bengal based on advanced machine learning algorithms

 

Abstract: Understanding the variations of the partial pressure of carbon dioxide (pCO₂) for the Bay of Bengal (BoB) has been limited due to the unavailability of a sufficient number of observations. Using a significant number of open and coastal ocean pCO₂ measurements and collocated variables controlling pCO₂ variability in the BoB, an machine learning (ML)-based high-resolution (1/12°) climatological data product (INCOIS-ReML) has been developed, which provides sea-surface climatological pCO₂ maps for the BoB. The capability of INCOIS-ReML has been demonstrated by comparing it with sea-surface pCO₂ data from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) mooring and gridded Surface Ocean CO₂ Atlas (SOCAT) data product. INCOIS-ReML is performing better than six widely used ML-based pCO₂ data products. The high-resolution INCOIS-ReML significantly captures the spatial variability of pCO₂ compared to other ML products in the coastal and the northern BoB. This data product is expected to help the researchers distinguish the source/sink behavior of the BoB, which essentially improves the Indian Ocean carbon budget in a changing environment.