University of Southern Denmark *** Management and Economics of Resources and the Environment (MERE) Optimal Quota Allocation in Multispecies Environment The Seventeenth International Conference of the International Institute of Fisheries Economics & Trade Towards Ecosystem Based Management of Fisheries: What Role can Economics Play July 8, 2014 Barbara Hutniczak bhu@sam.sdu.dk 2 Case background The Baltic Sea fishery in Poland Cod (Gadus morhua) 35% revenue Sprat (Sprattus sprattus) 20% revenue Herring (Clupea harengus) 19% revenue Barbara Hutniczak bhu@sam.sdu.dk 2 SDU 3 Case background Multispecies interactions Fishing fleet Harvest mortality (F) (Vessels) Environment Natural mortality (M) (Salinity index) Cod (recruitment) Cod (age >1) Sprat Herring (1 age class) (7 age classes) (8 age classes) (8 age classes) Predation mortality (P) Barbara Hutniczak bhu@sam.sdu.dk SDU 4 Case background Regulations - Individual Vessel Quotas (IVQ) and entry/exit system Total TAC is distributed between vessels according to: wn – redistribution coefficient based on vessel’s length Strict entry/exit regulations Length category number of vessels in 2012 w for cod w for herring w for sprat 8-9.99m 117 0.4 0.4 0.0 10-11.99m 106 0.69 0.4 0.0 12-14.99m 52 0.86 0.4 0.2 15-18.49m 60 1 0.6 0.4 18.5-20.49m 14 0.97 1 1 20.5-25.49m 31 0.97 2 2 25.5-30.49m 28 0.97 4 3.5 ≥30.5m 4 0.27 4 4 Barbara Hutniczak bhu@sam.sdu.dk SDU 5 Case background Polish TAC and vessels with cod harvest permit 2008 2009 2010 2011 2012 TAC cod 11 700 11 300 13 230 15 390 21 870 Harvest cod 10 082 11 154 11 752 11 835 14 836 TAC utilization 86% 99% 89% 77% 68% Harvest sprat 55 273 84 625 58 843 55 892 63 115 Harvest herring 17 023 22 528 24 747 29 763 27 121 2008 2009 2010 2011 2012 8-11.99m 219 86 87 132 248 12-14.99m 54 17 18 27 53 15-18.49m 94 30 26 22 65 18.5-25.49m 60 19 19 13 46 ≥25.5m 54 16 16 10 32 TOTAL 481 168 166 204 444 Barbara Hutniczak bhu@sam.sdu.dk SDU Research question 6 How is the TAC utilized? How the multispecies interactions affect the management? Barbara Hutniczak bhu@sam.sdu.dk SDU 7 Model structure Individual efficiency DI – input oriented distance from the frontier E – effort [days at sea] yi – harvest of species i {cod, herring, sprat, other species} Bi,t – harvestable biomass od species i {cod, herring, sprat} k – capital as power of main engine [kW] vn,t – stochastic error term un – time-invariant, firmspecific inefficiency term Properties: - symetry - homogeneity Barbara Hutniczak bhu@sam.sdu.dk Simplified version for vessels below 12m SDU Model strcture 8 Asymetry in efficiencies Efficiencies for vessels present in 2012 – 411 units Barbara Hutniczak bhu@sam.sdu.dk SDU Model structure 9 Economic incentives Substitution possibilities at the frontier πn,t – individual profit of firm n at time t ρ – discount rate pi – price of species i yi – harvest of species i cv – variable cost per unit of effort e – effort Y – vector of harvest u – inefficiency term cf – fixed costs if πn,t < 0 -> harvest activity not profitable Barbara Hutniczak bhu@sam.sdu.dk Red surface – maximum effort SDU 10 Simulation Two TAC scenarios BAU COD: - Current management plan - target F=0.3, critical F of 0.6 - TAC change +/- 15% PELAGIC SPECIES: - F constant - TAC change +/- 15% Alternative management ALL SPECIES: - Multispecies MSY (ICES 2013) - TAC change +/- 15% Starting values – based on 2012 ICES estimates Model scalled to Polish share based on EU TAC shares (~30%) Barbara Hutniczak bhu@sam.sdu.dk SDU Simulation 11 Two TAC scenarios Incorporating production feasibility and profitability Barbara Hutniczak bhu@sam.sdu.dk SDU Summary • • • • 12 Benefits from alternative management scenario (higher TACs): Conservative TAC plan in some cases may not be the optimum solution given the capacity is regulated and there are strongly established species interactions Better flexibility with respect to harvest choice implies higher pofits Importance of entry/exit regulations Adaptive management adjusting quota annually implies no risk of overexploitation (sensitivity analysis results) that may be a result of faster efficiency increase Barbara Hutniczak bhu@sam.sdu.dk SDU 13 THANK YOU Barbara Hutniczak bhu@sam.sdu.dk SDU 14 ADDITIONAL INFORMATION Barbara Hutniczak bhu@sam.sdu.dk Bioeconomic model SDU 15 Management Cod management Cod – Individual Vessel Quotas lower 2007 quotas not enforced 2008 EU punishment 2012 whole fleet back to fishing Three years plan 2009 ~1/3 fleet allowed to fish 2011 ~1/3 fleet allowed to fish 2010 ~1/3 fleet allowed to fish Barbara Hutniczak bhu@sam.sdu.dk 15 SDU 16 Discussion WHY VESSELS MAY HAVE LIMITED INCENTIVES TO EXIT? * Under succesfully carried cod management plan, IVQs are expected to be increasing * The model assumes very conservative technological change – 1% [efficiency increase by 1% per year] * Sunk cost in form of investment in vessel * Possibility to introduce ITQs in the future [if grandfathered, potentially valuable asset] * changing prices Barbara Hutniczak bhu@sam.sdu.dk SDU Simulation 17 SSB under full utilization of TAC Scalled to Polish fleet TAC share (~30%) Barbara Hutniczak bhu@sam.sdu.dk SDU Biological model 18 Fishing mortality Dividing harvest into age categories and stock in number prefi,a - harvest preference or selectivity for species i at age a Hi,t – total harvest of species i at time t Ni,a,t – number of species i at age a and time t Fishing mortality derived from nonlinear relationship: Barbara Hutniczak bhu@sam.sdu.dk Bioeconomic model SDU 19 Biological model Predation mortality Predation (functional responses according to Heikinheimo (2011) with preferences based on cod stomach content from Tomczak et al. (2012) pi,a,b,t - number of fish of species i at age a eaten by cod population of age b in year t λi,a,b - relative consumption preference of cod at age b over species i at age a xc,b,t - number of cod at age b at time t Cb - maximum consumption of herring and sprat by one cod at age b Dsh - half saturation constant n - functional response constant pi,a,t - number of fish of species i at age a eaten by cod population in year t Barbara Hutniczak bhu@sam.sdu.dk Bioeconomic model Predation mortality derived from nonlinear relationship: SDU 20 Methodology Distance function estimates Coefficients for log-values scaled to average of 1 simplified function for vessels 8-12m [minimum harvest of pelagics] coefficient SE cod 0.273 0.032 herring 0.177 0.027 sprat 0.160 0.028 other 0.098 0.018 capital -0.351 0.111 0.083 0.010 cod*herring -0.007 0.002 cod*sprat -0.007 0.002 cod*other -0.015 0.002 0.048 0.007 herring*sprat -0.013 0.003 herring*other 0.000 0.003 sprat*sprat 0.053 0.008 sprat*other -0.011 0.003 other*other 0.025 0.005 capital*capital -0.250 0.155 cod*capital -0.092 0.016 herring*capital -0.009 0.022 sprat*capital 0.021 0.021 other*capital -0.024 0.017 D2008 -0.026 0.041 D2009 -0.187 0.051 D2010 -0.230 0.051 D2011 -0.246 0.053 _cons -0.013 0.060 cod*cod herring*herring Barbara Hutniczak bhu@sam.sdu.dk estimate *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** SDU 21 Distance function At the frontier Days at sea per tone of cod 5 4 3 2 1 0 20 40 60 80 100 Harvest of cod t [at average vessel size ~270kW] Barbara Hutniczak bhu@sam.sdu.dk SDU 22 Distance function At the frontier Days at sea per tone of cod 2.0 Effort requirement as function of vessel’s size 1.5 1.0 0.5 100 200 300 400 500 600 700 Capital kW [at average cod harvest ~40t] Barbara Hutniczak bhu@sam.sdu.dk SDU 23 Distance function At the frontier Variable cost of effort as a function of capital: pe=0.338+0.0195*k [based on individual vessels reports] Marginal cost of harvesting 1 t of cod PLN 8 6 4 2 100 200 300 400 500 600 700 Capital kW [at average cod harvest ~40t] Barbara Hutniczak bhu@sam.sdu.dk SDU Distance function 24 At the frontier – MP from cod Red surface – marginal profit of zero [at average harvests of herring, sprat, other species and biomass] Barbara Hutniczak bhu@sam.sdu.dk SDU 25 Results Sensitivity analysis Dashed line indicates minimum SSB defined by ICES (2013) Barbara Hutniczak bhu@sam.sdu.dk SDU