Expert knowledge–based system for risk assessment of the occurrence of Amyloodinium ocellatum in semi-intensive fish farms

Abstract

AbstractThe implementation of a system to assess the risk of Amyloodinium ocellatum occurrence in rearing ponds in fish farms located in southern Spain is a fundamental aspect to ensure the economic viability of these facilities. For this purpose, a computer program (called Amy) for Windows PCs and an application for mobile devices (AmyAPP), based on the Android operating system, were developed integrating transformation functions and weightings associated with environmental parameters and fish behavioural factors from which it is possible to estimate the level of risk of occurrence of A. ocellatum. The weights for each of the environmental parameters and behavioural factors were estimated from the responses of a panel of experts (the fish farmers) using a Delphi methodology. The results indicate that, under operational validation, Amy/AmyAPP responses were statistically sensitive to the occurrence of A. ocellatum outbreaks in sea bream (Sparus aurata) and sea bass (Dicentrarchus labrax) rearing ponds.