ECONOMIC IMPACT OF KEY OPERATIONAL FACTORS IN THE EUROPEAN INDUSTRY OF SEA BASS AND SEA BREAM FARMING

Introduction

European sea bass (dicentrarchus labrax) and gilthead sea bream (sparus aurata ) are both an economically important cultured fish species along the Mediterranean coast. In 2018 the two species represented  a 38 % of the total value of the European aquaculture sector (STECF, 2021) ranking the second and third species respectively after the Atlantic salmon. The EU is one of the largest producers of sea bass and sea bream in the world, being Greece the largest producer within the EU followed by Spain. Aquaculture of sea bass and sea  bream  is an industry with a high economic competitiveness in which the profitability of operations is very important and sensible to different operational factors and production scenarios .  Despite of its importance, there are few empirical studies about this topic so far. Hence, i t would be very interesting for this industry to analyze  how changes in different key operational factors affect the  economic performance of  production facilities . T he aim of this work , which is part of the MedAID project funded by the European Commission (H2020, GA727315), is to analyze these economic  effects or impacts in the European sea bass and sea bream aquaculture industry at the micro (farm) level.

Methodology

To carry out this analysis, we have design ed a deterministic static model ,  which we have named MedAID Model for Economic Simulation (MMES),  composed of a production sub-model and an economic sub-model (see Figure 1)  and programmed with the spreadsheet Excel to simulate the annual income statement of a standard (average) aquaculture farm. The model  can be employed to  simulate  the annual economic results of a grow-out facility for growing up sea bass and sea bream as well as a hatchery facility to culture sea bass and sea  bream fry and larvae in the Mediterranean Sea. To obtain the baseline values of  the  model parameters, we have used data from representative facilities from six European countries (Croatia, Cyprus, France, Greece, Italy, and Spain) collected in a survey conducted in MedAID’s WP1.

With the former model, a sensitivity analysis has been applied to identify the most important, highly sensitive, parameters in the model . Thus, w e  have examined  the  sensitivity of the baseline model results to different assumptions about some  key  model parameters  such  as the unit sales price, the survival rate, the harvested weight, the tank or cage density, the growth rate, the fingerling unit cost, the feed unit cost, or the feed conversion ratio (FCR). Each of these model parameters has been varied one by one maintaining the rest of parameters constant (ceteris paribus) by 10%, 20% and 30% above and below its baseline value , what allows us  to analyze the impacts of those variations on the indicators of economic performance  employed in this research such as the gross  operating profit, the net operating profit, the average operating cost, and the break-even point. F our different production scenarios have been designed to run the sensitivity analysis . These scenarios have been chosen based on actual data from different European facilities producing sea bass and sea  bream, which have been surveyed in the MedAID project. In Table 1 we summarize the four production scenarios.

Results

The economic impact of model parameters changes shows that the unit sales price (p) is the model parameter with the largest impact for all scenarios (i.e., hatcheries and grow-out facilities), followed by survival rate (s) and harvested weight (w1) parameters in hatcheries and by survival rate (s ), feed  unit cost (pf) and FCR (r ) in grow-out facilities. On the other hand, the feed unit cost (pf )  and feed conversion ratio (r )  are the model parameters with the lowest impact on profits of a hatchery facility (scenario 1), whereas the harvested weight (w1 )  and fingerling unit cost (pj )  are the model parameters with the lowest impact on profits of a grow-out facility (scenarios 2, 3 and 4). All these effects are briefly summarized in Table 2 for a standard hatchery facility (scenario 1 ) and Table 3 for a standard grow-out facility (scenarios 2, 3 and 4).

References

 Scientific, Technical and Economic Committee for Fisheries (STECF). The EU Aquaculture Sector – Economic report 2020 (STECF-20-12) . Publications Office of the European Union, Luxembourg, 2021, doi: 10.2760/441510.