A bioenergetic and protein flux model to simulate fish growth in commercial farms: Application to the gilthead seabream |
| |
| Institution: | 1. CIMAR/CIIMAR – Centro Interdisciplinar de Investigação Marinha e Ambiental, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, S/N, 4450-208 Matosinhos, Portugal;2. ICBAS – Instituto de Ciências Biomédicas de Abel Salazar, Universidade do Porto, Rua Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal;3. CCMAR, Centro de Ciências do Mar, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal;4. SPAROS Lda, Área Empresarial de Marim, Lote C, 8700-221 Olhão, Portugal;5. Faculty of Biosciences and Aquaculture, Marine Genomics Research Group, Nord University, 8049 Bodø, Norway;6. Instituto de la Grasa (CSIC), Universidad Pablo de Olavide, Edificio 46 Ctra. de Utrera km. 1, 41013 Sevilla, Spain;1. CIIMAR Interdisciplinary Centre of Marine and Environmental Research, Terminal de cruzeiros de Leixões. AV. General Norton de Matos S/N, 4450-208 Matosinhos, Portugal;2. ICBAS Institute of Biomedical Sciences Abel Salazar, University of Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal;3. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, Jiangsu Province, China;4. ALGAplus, Tv. Alexandre da Conceição, Ílhavo, Portugal |
| |
| Abstract: | A model for fish growth simulation based on the bioenergetic factorial approach is presented. This work presents a novel approach that extends the traditional bioenergetic model by explicitly including the Energy and Protein fluxes (EP model). This is a valuable feature that allows the dynamic simulation of fish proximate composition. For the aquaculture industry it represents a trade-off between detailed process simulation and feasibility of model implementation, namely regarding data gathering on an operational setting. The EP model is targeted to simulate fish production in commercial farms. Farm data for feed intake, feed composition (energy and protein content), temperature over time and the initial fish body weight are the only required data to run the model. Furthermore, apparent digestibility coefficient (ADC) values of the feed used in the farm must be known or else ADC values of feeds with similar composition can be used. The EP model implementation is illustrated for the gilthead seabream (Sparus aurata) based on published experimental data. The model was validated (r = 0.997, p < 0.05, n = 12) using a published experimental data set for gilthead seabream reared in a range of temperatures that reproduce the conditions in most countries producing this species. For the entire growth period (488 days) the estimated mean absolute error (MAE) is 8.8 g.fish?1 and the mean absolute percentage error (MAPE) is 8.3%. Simulation of fish growth in real operational conditions is evaluated with three datasets from a commercial farm that operates in earthen ponds with temperatures ranging between 12.6 °C and 24.8 °C. Overall the model outputs match well with the production data in the 3 batches. Initial weight ranged between 2.8 g and 3.7 g. The deviation between the data and simulated final weights is below 15 g, for a final weight around 435 g. The maximum absolute error is 21.1 g per fish (MAE) and in percentage 8.3% (MAPE). |
| |
| Keywords: | Bioenergetic model Dynamic protein mass balance Aquaculture production model Fish farming Gilthead seabream |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
