Skip to main content

Green Aquaculture Intensification in Europe

Deliverables

Data Management Plan

This report will include a detailed plan for the management of all data collected and produced in GAIN, based on the approach detailed in Section 2.2.1 of the Part B of the DoA.

Report on the formulation of eco-efficient feed

Report on the formulation of eco‐efficient feed to allow the testing on organism level.

Innovative processes for mortality disposal in aquaculture

Report on the application of drying technology based on mechanical fluidization and superheated steam to sanitize and stabilize sludge and mortalities.

Report on EU consumption and production mass balance, and trade and market of different species

Investigation of species level consumption within the EU compared to production at a country and regional level using FAO and trade statistics. Analysis of trends and projections.

Draft Plan for the exploitation and dissemination

Details the main exploitation and dissemination actions and schedules them from month 6 to month 18.

Report on value chain mapping for key species /systems, with SWOT analysis of key informants

Through mapping the spatial and organisational division of labour in key species value chains we will document how value is created and captured at different locations and assess the public and private institutional and regulatory processes that underpin them. We will also consider their role in the wider cultural economy influencing economic organization.

Update of the Data Management Plan

This report will review the DMP and, if needed, will amend it, on the basis of requirements unforeseen at an earlier stage of the project.

Intermediate plan for the exploitation and dissemination

Reviews the draft Plan for exploitation and dissemination, presents the results obtained from month 1 to month 18 and schedules exploitation and dissemination activities from month 18 to month 40.

Methodologies for Big Data mining in aquaculture

Report on methodologies used for extracting information from Big Data collected at GAIN pilot sites and harvested from web portals.

Innovative processes for valorizing shellfish by-products

The report identifies sustainable processes for valorising shellfish by-products with focus on their use as bio-filters in RAS and in the cement industry.

Fish by products as feed ingredients

Feed ingredients and their production procedures to be used in WP1.

Report on legislation, regulation, and certification of aquaculture within the circular economy

Systematic review of EU legislation, third party standards and other guidelines which either encourage or limit the adoption of technologies investigated under GAIN. Identifying what would need to change to allow for better adoption.

Innovative methodologies for reusing aquaculture side streams

Report on the application of magnetic particle separation and sono-electro-flocculation for capturing particulate and dissolved matter in aquaculture side streams.

Report on instrumentation of GAIN pilot sites

Report on instrumentation of GAIN pilot sites to allow the testing of sensors for real time monitoring.

Plan for External communication

Details and schedules the main communication actions, including videos, newsletters, social media, press releases.

Selection of algal strains: preliminary results

Report detailing the methodology used for selecting the microalgal strains to be used as fish feed multifunctional components and presenting the preliminary results.

Project website and social media toolset

This deliverable includes the website, GAIN logo, a first project leaflet, templates for deliverables, poster presentations, and presentations at internal and public events, electronic newsletters, and press releases.

Searching for OpenAIRE data...

Publications

An integrated framework that combines machine learning and numerical models to improve wave-condition forecasts

Author(s): Fearghal O’Donncha, Yushan Zhang, Bei Chen, Scott C. James
Published in: Journal of Marine Systems, Issue 186, 2018, Page(s) 29-36, ISSN 0924-7963
DOI: 10.1016/j.jmarsys.2018.05.006

Ensemble model aggregation using a computationally lightweight machine-learning model to forecast ocean waves

Author(s): Fearghal O’Donncha, Yushan Zhang, Bei Chen, Scott C. James
Published in: Journal of Marine Systems, Issue 199, 2019, Page(s) 103206, ISSN 0924-7963
DOI: 10.1016/j.jmarsys.2019.103206

Statistical and machine learning ensemble modelling to forecast sea surface temperature

Author(s): Stefan Wolff, Fearghal O'Donncha, Bei Chen
Published in: Journal of Marine Systems, Issue 208, 2020, Page(s) 103347, ISSN 0924-7963
DOI: 10.1016/j.jmarsys.2020.103347

Drag coefficient parameter estimation for aquaculture systems

Author(s): Scott C. James, Fearghal O’Donncha
Published in: Environmental Fluid Mechanics, Issue 19/4, 2019, Page(s) 989-1003, ISSN 1567-7419
DOI: 10.1007/s10652-019-09697-7

Precision Aquaculture

Author(s): Fearghal O'Donncha, Jon Grant
Published in: IEEE Internet of Things Magazine, Issue 2/4, 2019, Page(s) 26-30, ISSN 2576-3180
DOI: 10.1109/IOTM.0001.1900033