WP2 ECONOMIC PERFORMANCE AND PRICES PAUL STEINAR VALLE KONTALI ANALYSE AS Annual meeting, Vilanova, 2017
1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 A reminder; Europe in a global perspective EU versus Non-EU 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 250.000.000 200.000.000 150.000.000 100.000.000 Fisheries and Aquaculture by continent 30.000.000 25.000.000 20.000.000 15.000.000 Fisheries and Aquaculture, EU 50.000.000 0 10.000.000 5.000.000 Fisheries_Africa Fisheries_Americas Fisheries_Asia Fisheries_Europe Fisheries_Oceania Aquaculture_Africa Aquaculture_Americas Aquaculture_Asia Aquaculture_Europe Aquaculture_Oceania 0 Europe_Fisheries_EU Europe_Fisheries_Non_EU Europe_Aquaculture_EU Europe_Aquaculture_Non_EU
volume value Seafood Export/Import Imbalance and Self-Sufficiency 7,00 6,00 5,00 4,00 3,00 2,00 1,00 0,00 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 23,00 22,00 21,00 20,00 19,00 18,00 17,00 16,00 15,00 volumes (million tonnes) values (billion euro) The EU Fish Market 2016, EUMOFA FAO-data
Economic perfomance The seafood markets Volume & Price = Value
SeaBass and SeaBream sector a relevant example?
Some key observations Low ex EU import prices challenge the profitability of European (EU) B&B sector (Turkish export prices being 20 %) below Greek Forcing the European closer to their producing costs The innovative (future) developments i.e. sectors ability to meet the future consumer demands and/or emerging markets in Europe...? Processed products fillets and added value products are currently not found profitable within EU with the current raw material (whole fish) price/cost AND labour cost. WHILE extra EU competitors (Turkey) do! The situation is a challenge for the competing strategies for European B&B sector both in short perspective - and in the long run.? However, is B&B unique i.e. an odd case or representative also for other seafood sectors?
Economic performance Historical and Future price (and volume) perspectives
Seafood price studies & Boom and Bust Boom and Bust Identification of price systematics through structural time series models (the Kalman filter) (uninformed modelling) Decompose a time series into elementary components: Trend Seasonality Cycle Irregular component (unexplained price changes)
The trend analysis and the cycles are based on the factorization of the phenomenon observed in various components (e.g. price level, increasing or decreasing long term trend, seasonality -fluctuations within the year which tend to repeat, cyclical -deviation from long-term trends-, and irregular component - exceptional events - outliers). The Graph reporting Forecast include a Confidence charts. User can choose what type of confidence level based on risk aversion. The range of the confidence help the user to understand the precision of the prediction. For every chart and table, showing the decompositon of time series, a short comment has been reported by the authors. However, the chart itself should be quite explanatory of the price trend in the observed period. The items Slope, Seasonal, Cycle and Irregular yield have values in the interval [0,1]. The unit of the item Period refers to month.
Market, Market levels and Species canada iceland norway spain Cod na e f r f r Investigations Herring e f f f na f r uk denmark faroe germany italy turkey greece vietnam Salmon e r na Trout f w r (1) f r f r na (2 ) na Seabas s r f r (2) na Seabrea m f w r (1 f r (2) na Pangasi us Caption: f = first sale, w = wholesale, r = retail, i = import, e=export, na=not available na
Denmark first sale trout: an excellent case 6 5 4 3 2 0.50 0.25 0.00 0.25 0.50 first sale Level first sale Seasonal 0.5 0.0 0.5 1.0 2010 2015 2010 2015 first sale Cycle 1 first sale Irregular 0.5 0.0 0.5 Denmark first sale trout market shows a growing trend along all period in which prices increase by about 2. Data shows a good regularity in seasonality with the lowest price in november/december. The amplitude of cycle is about 12 months. The B&B cycle are detected in: 01/10/07 01/01/08Bust 01/03/09 01/08/09Boom 01/09/10 01/01/11 Bust 01/03/12 01/08/12Boom 01/09/13 01/01/14Bust 01/03/15 01/08/15Boom 01/09/16 01/12/16Bust 2010 2015 2010 2015
Denmark first sale trout: an excellent case 6.5 first sale Realised first sale 6.0 5.5 5.0 4.5 4.0 Forecast first sale +/ SE Forecast for Denmark first sale trout market shows stationary prices till July a decreasing trend till november then a new growth of prices. 3.5 2015 2016 2017 2018
Boom and Bust Time-series price models extrapolating the history Versus 1 400 000 1 200 000 Estimated juvenile production Explanatory models explaining the future prices i.e. based on expected production, exhange rates Future harvest estimations Currently model for salmon production (Kontali) Simple estimation (expert inputs) for seabass and seabram) (Kontali) 1 000 000 800 000 600 000 400 000 200 000 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Greece Turkey Italy France Spain Others Source: Kontali Analyse
WHAT S IMPORTANT FOR STRENGTHENING THE SEAFOOD SECTOR? Stakeholder day, April 6th, 2017
This Project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No. 635761 Thank you all for your attention!