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dc.contributor.authorLi, Hui
dc.contributor.authorChatzifotis, Stavros
dc.contributor.authorLian, Guoping
dc.contributor.authorDuan, Yanqing
dc.contributor.authorLi, Daoliang
dc.contributor.authorChen, Tao
dc.contributor.illustrator
dc.date.accessioned2022-03-22T10:46:14Z
dc.date.available2023-09-21T00:00:00Z
dc.date.available2022-03-22T10:46:14Z
dc.date.issued2022-03-22
dc.identifier.citationLi H, Chatzifotis S, Lian G, Duan Y, Li D, Chen T (2022) 'Mechanistic model based optimization of feeding practices in aquaculture', Aquacultural Engineering, 97 (May 2022), 102245.en_US
dc.identifier.issn0144-8609
dc.identifier.doi10.1016/j.aquaeng.2022.102245
dc.identifier.urihttp://hdl.handle.net/10547/625356
dc.description.abstractFish feed accounts for more than 50% of total production cost in intensive aquaculture. Feeding fish with low quality feed or adopting inappropriate feeding strategies causes not only food waste and consequent loss of income but also lead to water pollution. The aim of this study was to develop a mechanistic model based optimization method to determine aquaculture feeding programs. In particular, we integrate a fish weight prediction model and a requirement analysis model to establish an optimization method for designing balanced and sustainable feed formulations and effective feeding programs. The optimization strategy is necessary to maximise the fish weight at harvest, while constraints include specific feed requirements and fish growth characteristics. The optimization strategy is re-solved with new available fish weight measurement by using the error between measurement and model prediction to adjust the requirement analysis model and update feeding amount decision. The mechanistic models are parameterised using the existing nutritional data on gilthead seabream (Sparus aurata) to demonstrate the usefulness of proposed method. The simulation results show that the proposed approach can significantly improve aquaculture production. This particular simulation study reveals that when “Only prediction” method is considered as benchmark, the average improvement in fish weight of proposed method would be 13.25% when fish weight is measured once per four weeks (mimicking manual sampling practice), and 38.43% when daily measurement of fish weight is possible (e.g. through automatic image-based methods). Furthermore, if feed composition (460 g protein.kg feed−1 ; 18.9 MJ kg feed−1 ) is adjusted, the average improvement of proposed method could reach 46.85%. Compared with traditional feeding methods, the improvement of proposed method could reach 36.36% of the final fish weight at harvest. Further studies will consider improving the quality of feed plus executing more appropriate mathematical prediction models to optimize production performance.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/abs/pii/S0144860922000218
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectaquacultureen_US
dc.subjectfishen_US
dc.subjectfeed optimizationen_US
dc.subjectgrowth predictionen_US
dc.subjectrequirement analysisen_US
dc.subjectbioenergetic modelen_US
dc.subjectgilthead seabreamen_US
dc.subjectSubject Categories::D435 Aquacultureen_US
dc.titleMechanistic model based optimization of feeding practices in aquacultureen_US
dc.typeArticleen_US
dc.contributor.departmentUniversity of Surreyen_US
dc.contributor.departmentHellenic Centre for Marine Researchen_US
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.contributor.departmentChina Agricultural Universityen_US
dc.contributor.departmentBeijing Agriculture Internet of Things Engineering Technology Research Centeren_US
dc.identifier.journalAquacultural Engineeringen_US
dc.date.updated2022-03-22T10:40:02Z
dc.description.note18m embargo from pub date when known


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