University of Southern Denmark
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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
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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