A SYSTEMS APPROACH TO IMPROVING PRODUCTIVITY AND QUALITY IN THE AQUACULTURE INDUSTRY Harkaitz Eguiraun1,2,*, Gregor Bwye1, Karmele Lopez de Ipiña2, and Iciar Martinez1,3,4 1
Research Center for Experimental Marine Biology and Biotechnology - Plentziako Itsas Estazioa (PIE), University of the Basque Country, Plentzia, Spain; 2 Department of Systems Engineering and Automatics, University of the Basque Country, Spain; 3 IKERBASQUE Basque Foundation for Science, Bilbao, Spain; 4 Norwegian College of Fishery Science, Faculty of Biosciences, Fisheries and Economics, University of Tromsø, Tromsø, Norway. E-mail:
[email protected]
The productivity, quality and safety of farmed species depends to a very large extent on the pre-harvest activities: selection of eggs based on their genetic makeup, optimization of breeding facilities, location, conditions and feed, which will affect the phenotype, and the veterinary treatments including vaccination programs. There are abundant data on each one of these aspects, but no systematic approach to improving aquaculture practices by integrating all these variables and their interactions. We propose the application of a systems approach to aquaculture farming practices in order to (1) integrate and optimize all the inputs, practices and methodologies of any given plant, (2) identify deviations from the norm with real time requirements and (3) predict the outputs, such as the expected yield, quality and potential hazards. This approach will also permit to integrate the costs, carbon footprint of the process, expected price of the product and other multiple variables in the model. The implementation of a systems approach methodology will, in addition, make it easier the introduction of control mechanisms to identify and correct deviations as soon as they happen. Aquaculture production as a system A system is a set of components that interact with each other and where there is a causeeffect relationship between them: a variation in the state of a single component affects the state of the other components. A system is made up of inputs, outputs and disturbances. Inputs are the functions of the independent variables, for example, genetic makeup, water quality, feed composition, exercise, location of the plant, its environment. Outputs are the dependent variables of the system´s response (quality, safety, yield, price). Disturbances are random and unexpected inputs from the outer world. By studying complex systems, one may wish to establish how relationships between each of its parts give rise to the collective behavior of the system and how this in turn interacts and forms relationships with its environment. The key issues with complex systems are their formal modelling and simulation. Considering aquaculture as a whole system, we need to define its components. We can have as many as we wish: it may only be one cage, all the cages in one location, all the installations of a particular company; it may comprise only one species, or type of product or several. The next step is the selection of a mathematical model that permits to monitor and control the system in real time. Modelling aquaculture practices A suitable mathematical model must contain all the relevant characteristics of the system, it is driven through a series of hypotheses and approximations, being a partial representation of the reality, it is suitable only for the specific purpose it is designed for (thus it must be formulated to be useful to achieve that objective) and it requires a compromise between simplicity and the need to contain all the essential aspects of the system. Theoretical modelling uses hypothesis and physics laws to model real system behavior, it is mostly used when it is easy to rule with equations and the system dynamics are no very complex. Experimental modelling on the other hand is used when it is difficult to get information about the system. Then, sequences of inputs are used and the corresponding output data for that system are noted. A third type of modelling, which is the most common for complex models, uses both theoretical and experimental
modelling techniques and it is used when there is no physical interpretation about the model parameters and very little knowledge about the system´s behavior. The aquaculture industry has invested large sums of money in improving the yield, quality and safety of the products, but the research has seldom been performed in a holistic manner, i.e., considering all the variables of the model. For example, the study of variations in feed formulations refer usually to one or a few components (fatty acid composition and origin, amino acids and other nutrients), but they seldom include the genotype of the individuals used, their exercise pattern, a full analysis of the feeds, the location, temperature, type and design of cages, fish behaviour, etc. Changes in the outputs (such as bad texture or contaminants in the fillets) are usually not detected until all the production has been slaughtered, and sometimes even sold. Changes in the outputs can be due to disturbances (per definition unpredictable, but can be modelled), but also to changes in the inputs that have not been properly registered, or that remain unknown. Either cause indicates that the model needs improvements and/or that relevant inputs for the model remain unknown. Working Framework A systems approach, suitable to identify and apply the most relevant variables that may impact on the yield, composition and safety of the fish that would help breeders to optimize farming equipment, facilities, locations, fish stocks and selection of fishes for slaughtering with optimized nutritional value and quality. Such a model will require inputs from, at least; engineers, biochemists, experts in fish biology, nutrition and behavior, veterinaries and food safety experts. A schematic representation of such a framework is shown in Figure 1. References Close Ch. M., Frederick D.K., Newell J.C. (2001); Modeling and analysis of Dynamic Systems. John Wiley and Sons Ed. Dhar PK, Zhu H, Mishra SK (2004). Computational approach to systems biology: from fraction to integration and beyond. IEEE Trans Nanobioscience, 3:144-152. 2. Dumas A, Frane J, Bureau D (2010) Modelling growth and body composition in fish nutrition: where have we been and where are we going? Aquacult Res 41:161–181.
Figure 1. Inputs and interrelationships in a systems model.
