Sustainability of the Peruvian anchoveta -...

Thesis defense

Sustainability of the Peruvian anchovetasupply chains from sea to shelf: towards a new strategy for optimal use of resources

25.03.2014Angel AVADÍ

SIBAGHE ‐ UM2

Mme. Catherine MARIOJOULS, AgroParisTech Rapporteur/Président

Mme. Friederike ZIEGLER, SIK Rapporteur

Mr. Arnaud HÉLIAS, Montpellier SupAgro Examinateur

M S l i PERRET CIRAD E i t

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Mr. Sylvain PERRET, CIRAD Examinateur

Mr. Pierre FRÉON, IRD Directeur de thèse

Mr. Peter TYEDMERS, Dalhousie University Co-directeur de thèse

The ANCHOVETA‐SC project

• Project financed by IRD and project partners• Coordinator Pierre Fréon IRD• Coordinator: Pierre Fréon, IRD• Location: Peru• Duration: 4 years (01.2010 – 12.2013)• Theme: Environmental and socio‐economic

assessment of major Peruvian supply chains consuming anchoveta

• Outputs: – Sustainability assessment– Policy and sustainability suggestions– PhD thesis financed by IRD/DSF– Other works

2/44Angel Avadí ‐ Sustainability of the Peruvian anchoveta supply chains from sea to shelf

http://anchoveta‐sc.wikispaces.com/

Publications

1. Avadí A, Fréon P (2013) Life cycle assessment of fisheries: A review for fisheries scientists and managers. Fish Res 143:21–38.

2. Avadí, A., Vázquez‐Rowe, I., & Fréon, P. (2014a). Eco‐efficiency assessment of the Peruvian anchoveta steel and wooden fleets using the LCA+DEA framework Journal of Cleaner Production in presssteel and wooden fleets using the LCA+DEA framework. Journal of Cleaner Production, in press. doi:10.1016/j.jclepro.2014.01.047

3. Avadí, A., Fréon, P., & Quispe, I. (2014b). Environmental assessment of Peruvian anchoveta food products: is less refined better? The International Journal of Life Cycle Assessment, in press. doi:10.1007/s11367‐014‐0737‐y/ y

4. Fréon, P., Avadí, A., Amelia, R., & Chavez, V. (2014). Life cycle assessment of the Peruvian industrial anchoveta fleet: boundary setting in life cycle inventory analyses of complex and plural means of production. The International Journal of Life Cycle Assessment, in press. doi:10.1007/s11367‐014‐0716‐3

5. Avadí, A., Pelletier, N., Aubin, J., Ralite, S., Núñez, J., & Fréon, P. (2014). Comparative environmental performance of artisanal and commercial feed use in Peruvian freshwater aquaculture. Aquaculture, in review.

6. Fréon, P., Avadí, A., Marín, W., & Negrón, R. (2014). Environmentally‐extended comparison table of large‐vs. small‐ and medium‐scale fisheries: the case of the Peruvian anchoveta fleet. Canadian Journal of Fi h i d A ti S i i iFisheries and Aquatic Sciences, in review.

7. Avadí, A., & Fréon, P. (2014). A set of sustainability performance indicators for seafood: direct human consumption products from Peruvian anchoveta fisheries and freshwater aquaculture. Ecological Indicators, submitted.

8 Avadí A Fréon P & Tam J (2014) Coupled ecosystem/supply chain modelling of fishfood products

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8. Avadí, A., Fréon, P., & Tam, J. (2014). Coupled ecosystem/supply chain modelling of fishfood products from sea to shelf: the Peruvian anchoveta case. PlosOne, submitted.

Angel Avadí ‐ Sustainability of the Peruvian anchoveta supply chains from sea to shelf

Outline

1. Introduction and research overviewresearch overview

2. Literature review3 P d f k P d t ti i3. Proposed framework4. Case study

Papers and presentations in conferences and other scientific and political venues

5. Uncertainty management

6. Conclusions and recommendations


Why sustainability assessment of anchoveta?Intro Literature review Framework Case study Uncertainty Conclusions

• Need for sustainability of food and fishfood systems towards sustainable developmentsystems towards sustainable development

• Peru: – 2nd largest fishing country; first producer of fishmeal

– Nutritional deficiencies and other socio‐economic issues

i l i l d fi h l h i– Environmental issues related to fish supply chains


Objectives and main outcomesIntro Literature review Framework Case study Uncertainty Conclusions

• Evaluate the CURRENT sustainability performance of Peruvian seafood supply chainsof Peruvian seafood supply chains→ Framework for sustainability assessment and

comparison of target systems→ Case study (papers)

• Evaluate the FUTURE sustainability performance f P i f d l h iof Peruvian seafood supply chains→ Scenario modelling→ Case study (synthesis paper)→ Case study (synthesis paper)

• Advise decision‐making→ Management and policy recommendations

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→ Management and policy recommendations


Literature reviewIntro Literature review Framework Case study Uncertainty Conclusions

• Sustainabilityd ll f f d• Modelling of seafood systems

– Marine ecosystem modelling– Supply chain modelling– Combined ecosystem/supply chain modelling

• Sustainability assessment– Application: macro, micro and meso levelspp ,– Indicators– Tools

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Tools


SustainabilityIntro Literature review Framework Case study Uncertainty Conclusions

EcosystemEcosystem servicesEnvironment EnvironmentEnergy useResource depletionResilience

Vi blEco-efficient

Sustainable

ViableEco efficient

SocietyEconomy Equitable

EmploymentProfits and profit distribution


Community wellbeing

Sustainability indicatorsIntro Literature review Framework Case study Uncertainty Conclusions


Azapagic and Stichnothe (2009)

Sustainability assessment toolsIntro Literature review Framework Case study Uncertainty Conclusions

Procedural frameworks Focus/Level EN EC SOEnvironmental Impact Assessment (EIA) Micro (project) X X Strategic Environmental Assessment (SEA) Meso, macro (policy) X X Sustainability Assessment (SA) , Life Cycle Sustainability ( l )

Ideal, but still under Sustainability Assessment (SA) , Life Cycle Sustainability Assessment (LCSA) Macro, micro (policy, project) X X X

Multi‐Criteria Decision Analysis (MCDA) Micro, meso, macro (project, policy) X X X Analytical frameworks Focus/Level EN EC SOMaterial Flow Assessment/Analysis/Accounting (MFA)

( ) Macro (policy, plan) X

development

CritiquesSubstance Flow Analysis (SFA) Macro (policy, plan) X

Material Input per Service Unit (MIPS) Micro (product, service) X Energy/Exergy/Emergy Analysis (EA)Energy Return On Investment (EROI) Micro (process, product, service) X

Risk Analysis/Assessment (RA) Micro (project, chemicals) X

Critiques (e.g. Brown and Herendeed, 1996; Ayers, 1998; Hau et al., 2004; Herendeen, 2004; Sciubba and Ulgiati, 2005)

Lacks socialRisk Analysis/Assessment (RA) Micro (project, chemicals) X Eco‐Efficiency (EE) Analysis Micro (product, service) X X Life Cycle Assessment (LCA), Carbon Footprint (CFP), Ecological Footprint (EF) , Water Footprint (EF)

Micro (process, product, service) Macro, Meso (footprints) X

Environmental (Extended) Input‐Output Analysis (E(E)IOA) Meso macro (policy product service) X

Lacks social dimension

Hybrid LCA Meso, macro (policy, product, service) X

Life Cycle Costing (LCC) Micro (product, service) X Social Life Cycle Assessment (SLCA) Micro (product) X Cost–Benefit Analysis (CBA) Micro, meso, macro (project, policy) X Total Cost of Ownership (TCO)

Lack of data: selected socio‐economic indicators instead


Total Cost of Ownership (TCO)Total Cost Accounting (TCA) Micro (product, service) X

Sustainability dimensions: EN ‐ Environmental, EC ‐ Economic, SO ‐ Social.

Life Cycle Assessment (LCA)Intro Literature review Framework Case study Uncertainty Conclusions

• An accounting framework for calculating some environmental impacts associated to a productive activity over its life cycle

• Biophysical flows, no judgements• Two main schools of thought:

attributional and consequentialattributional and consequential• Key methodological issues

– co‐product allocationb d i d ff– system boundaries and cut‐off

• Deepening and broadening in various directions (e.g. LCSA) ISO 14040:2006

ISO 14044:2006

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• Integration with other methods


ISO 14044:2006

Life cycles nestingIntro Literature review Framework Case study Uncertainty Conclusions

Extraction Construction Extraction Construction

Recycling Use bRecycling Use Distribution Use

Cradle‐to‐gate

Final disposal

C dl


Cradle‐to‐grave

Proposed frameworkIntro Literature review Framework Case study Uncertainty Conclusions

1. Characterisation and modelling of the biophysical flows and socio‐economic aspects associated to the seafood systems under study (excluding the consumption phase)under study (excluding the consumption phase).

2. Definition and calculation of a set of sustainability indicators(spanning energy, nutrition, ecological, environmental, social and economic aspects)and economic aspects).

3. a) Comparison of sustainability of supply chains. b) Definition and simulation of alternative policy‐based exploitation scenarios and fates of anchovetaexploitation scenarios and fates of anchoveta.

→ Scope of application: whole supply chains, without detailing value chains but aggregating sectors (via generalisation asvalue chains, but aggregating sectors (via generalisation, as opposite to the EwE + value chain approach)

→ Intended users: researchers, decision makers


Assessment phasesIntro Literature review Framework Case study Uncertainty Conclusions

Supply chain link Supply chain

Phase 1 Modelling:EwE (trophic)Umberto (material

characterisation modelling

Phase 3a

Comparison of competing

flow)

PRESENTsupply chains

Environmental (LCA, BRU)Energy (EROI)Nutrition (GEC, NRF)Socio economic (profit

PRESENT

Phase 2

Phase 3b

Definition ofscenarios

Comparison of scenarios

Socio‐economic (profit, employment)

EwEPolicy FUTURE

Definition ofsustainabilityindicators

Calculation of sustainability indicators

Phase 2

IndicatorsIndices


indicators indicators

Supply chain characterisation (Phase 1)Intro Literature review Framework Case study Uncertainty Conclusions

+ Socio‐economic characterisation+ Policy environment

Additional inputs: materials, energy

d d

U b t

Value chain

GEC, CED, BRU,

Intermediate product

Intermediate product

Umberto

DHC

selected LCA indicators

EwE LCA, etc.

Fisheriesprocessing

ReductionConsumption

Distribution,

Ecosystem dynamics Aquafeed

Consumption


Aquaculture

Sustainability indicators (Phase 2)Intro Literature review Framework Case study Uncertainty Conclusions

Sust. dimension Indicator (unit) Reference publications

E l i l

IBNR,sp (years)IBNR,eco (years)

Langlois et al. (2014) (details)

A TLEcological Average TLlandShin et al. (2010) (details)Proportion of predatory fish (%)

Inverse fishing pressure (ratio)

Ecological/ BRU (g C/kg) Pauly and Christensen (1995)environmental BRU‐based discard assessment Hornborg (2012), Hornborg et al. (2012b, a)

Environmental

LCA/ReCiPe midpoint indicatorsLCA/ReCiPe single score (Pt)

Goedkoop et al.(2009)

LCA/CED (MJ) Hischier et al. (2010)LCA/CML[USES‐LCA] (kg 1,4‐DB eq) Guinée et al. (2002), van Zelm et al. (2009)LCA/USEtox (CTU) Rosenbaum et al. (2008)

NutritionalGEC (MJ/kg) Tyedmers (2000)Nutritional profile (NRF index) Drewnowski and Fulgoni (2008) (details)Nutritional profile (NRF index) Drewnowski and Fulgoni (2008) (details)

Energy efficiencygross edible EROI (%) Tyedmers (2000), Tyedmers et al. (2005), Hall

(2011)edible protein EROI (%)Production costs (USD)


Socio‐economicKruse et al. (2008)Employment (USD)

Value added (USD)Gross profit (USD) Accounting concept

Indicators: ReCiPe single scoreIntro Literature review Framework Case study Uncertainty Conclusions


Environmental MechanismsEnvironmental MechanismsMidpoints Endpoints

Design of LCA studies (details)Intro Literature review Framework Case study Uncertainty Conclusions

• Functional Unit: one tonne of fish (net weight) in final product, which accounts for process losses, dehydration, and edible portions (including edible anchoveta bones)

• Attributional LCA (biophysical allocation, averages)LCIA h d R CiP C l i E D d USE• LCIA methods: ReCiPe, Cumulative Energy Demand, USEtox, additional impact categories

• SimaPro, ecoinvent 2.2SimaPro, ecoinvent 2.2• Own modelling of:

– anchoveta fisheries and reduction into fishmeal and oil (applying ll i b )allocation by gross energy content)

– the Peruvian energy mix and specific fuel types (Diesel+2% biodiesel)– ice production (consumed by SMS fleet)

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– Distribution until retail of all products modelled as an additional layer


Definition of scenarios (details)Intro Literature review Framework Case study Uncertainty Conclusions

Scenarios

ExplorativePredictive NormativeExplorative(What can happen?)

Predictive(What will happen?)

Normative(How can a target

be reached?)

Forecasts What-if External Strategic Preserving Transforming

E li tiE.g.: policy-

E.g.: climatic events,

changes in international

d d f fi h

g po cyinduced changes

in fish fate(DHC vs IHC)

E.g.: climatic events

(e.g. ENSO)

E.g.: consequences of changes in

stock

E.g.: preservation

of landing volumes

E.g.: introduction of mother-

factorydemand for fish products,

changes in ecosystem

i

E.g.: policy-induced changes in landings and

landing

managementvolumes

but increase in aquaculture production

factory ships


Typology by Börjeson et al. (2006)

regime composition

Intro: The Peruvian anchoveta supply chainsIntro Literature review Framework Case study Uncertainty Conclusions

• The Humboldt Current System (HCS) is the most productive fishing ground in the worldproductive fishing ground in the world– Currently dominant small pelagic: anchoveta (Engraulis ringens)

• The largest national fleet targeting a single species, > 1300 vessels landed 6.5 million tonnes

(2001 2010)per year (2001‐2010)• Large reduction industry, producing mostly Prime fishmeal but also FAQ and residual fishmealfishmeal, but also FAQ and residual fishmeal– The bulk is exported to Asia and Europe – Third contributor of foreign exchange (~8%) for Peru


Third contributor of foreign exchange ( 8%) for Peru

A heterogeneous fleetIntro Literature review Framework Case study Uncertainty Conclusions

269

200

306 340 329

266

185

107131

200

250

300

350

400

800 000

1 000 000

1 200 000

1 400 000

andings (tonn

es) Steel industrial

4

94 94 107 78

35 18 2 7

‐ 50

0

50

100

150

0

200 000

400 000

600 000

Average an

nual la

Small‐scale Medium‐scale Vikingas Steel industrial No. of vessels

2935 e

15 17

28 29

15 12

26

10 16 15 15 16 16 17 15

20 21

510 15 20 25 30

uel per land

ed to

nn

« Vikingas » SMS

‐5

kg fu


Fuel use efficiency (kg/t)

Catches and fates of landingsIntro Literature review Framework Case study Uncertainty Conclusions

Steel industrial

81% Fishmealtonn

es

Fish oil

18% Canning~ 6.5 mio.

« Vikingas »8% Canning

SaltingCuring00

5‐2010)

Freezing~1‐2%

~0 1%andings (20

SMSFresh0.1%

Annu

al la

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Peruvian anchoveta supply chains. Red lines represent flows under 1% contribution, including illegal (Fréon et al., 2010).


A large reduction industryIntro Literature review Framework Case study Uncertainty Conclusions

• 43 conventional plants fair average quality (FAQ) fishmeal (~64% protein), direct heat ( p ),drying

• 74 high protein content plants indirect (steam, hot air) drying; high quality fishmeal (67%‐70% protein)

• 43 residual plants process fish residues (in theory) lower graderesidues (in theory), lower grade fishmeal (up to 55% protein)

• High quality fishmeal Fish:FMratio = 4.2; Fish:FO ratio = 21.3

• Residual fishmeal Fish:FMratio = 5.5


A homogenous DHC industryIntro Literature review Framework Case study Uncertainty Conclusions

• Canning, 60 plants• Freezing, 117 plantsg p• Curing, 18 plants


A growing aquaculture sectorIntro Literature review Framework Case study Uncertainty Conclusions

Iquitos: rain forest Titicaca lake: highlands

Features: • Average 30% annual growth over the past 20 years.P d i i d i d b i i ll (50%)• Production is dominated by marine species: scallops (50%) and shrimps (23%)

• Main freshwater species are trout (22%), tilapia (3%) and, more recently black pacu (1%)

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more recently, black pacu (1%) • Freshwater aquaculture is mainly semi‐intensive (10‐15 t/ha)


Fish consumption in Peru (details)Intro Literature review Framework Case study Uncertainty Conclusions

98 594 106,565 120,209120,000

140,000nn

esAnchoveta DHC apparent consumption

43 502

75,102

98,594 ,

88,794

60,000

80,000

100,000

120,000

To

43,502

0

20,000

40,000

2006 2007 2008 2009 2010 Average

Canned Frozen Fresh fish Cured Exports

• Per capita fish consumption in Peru: 22.5 kg/y• Per capita anchoveta consumption is marginal: 2.3‐3.3 kg/y• Consumer preferences (Fréon et al 2013)


• Consumer preferences (Fréon et al., 2013)

Policy environment (details)Intro Literature review Framework Case study Uncertainty Conclusions

• SMS fleets allowed to land anchoveta for DHC only (excluding fresh fish for final consumers)only (excluding fresh fish for final consumers)

• Industrial fleets allowed to land anchoveta for lIHC only

• Issues:– Illegal, unreported and unregulated landings are tolerated (e.g. SMS landing for reduction)

– 40% of landings nominally for DHC are allowed to be redirected to IHC


Economic performance (details)Intro Literature review Framework Case study Uncertainty Conclusions

1,6001,8002,000

SD/ton

ne

8001,0001,2001,400U

200400600800

0FMFO Canning Curing Salting

(artisanal)Freezing

P d ti t V l dd d G fit

Fréon et al. (2013):Occasionally IHC plants pay more to SMS vessels than DHC plants, per landed tonne.

Production costs Value added Gross profit


Even when not the case, larger volumes when destined to IHC overcompensate and represent better income for SMS vessels

Nutritional profiling (NRF) (details)Intro Literature review Framework Case study Uncertainty Conclusions

• Nutrients: Omega‐3 fatty acids (EPA + DHA), other non‐saturated lipids (including Omega‐6 fatty acids) vitamins A B 12 and D; Ca K P and Fe

Overall nutritional ranking, from best to worst (NRFn.3) acids), vitamins A, B‐12 and D; Ca, K, P and Fe.

• Nutrients to limit (LIM): saturated fat, Na• Anchoveta products feature higher contents of protein Omega 3 and vitamins B 12 than other

1. Canned anchoveta2. Fresh/frozen anchoveta3. Salted anchoveta (desalted)4 Trout protein, Omega‐3 and vitamins B‐12 than other

fish • Salted and cured products feature excess Na• Trout features higher levels of proteins vitamins

4. Trout5. Hake6. Eggs (hen’s)7. Tilapia

• Trout features higher levels of proteins, vitamins and minerals than other cultured species

• Black pacu features a high content of saturated fat.

8. Black Pacu9. Beef10. Shrimp11 Chicken

• Main source of animal protein in Peru is chicken, due to competitive prices, easier conservation and efficient distribution

11. Chicken12. Milk13. Pork14. Cured anchoveta

h h


15. Fresh cheese

Fleets environmentally‐extended Thomson tableIntro Literature review Framework Case study Uncertainty Conclusions

Criteria Industrial fleet (steel + Vikinga) SMS fleet (SS, MS) g

Number of fishers per landed 1000 t

4 19

Number of fishers per landed 1 Million USD

19 96

Landings for DHC per year (t) 0 132 000Landings for DHC per year (t) 0 132 000

Landings for IHC per year (t) 5.2 Million 324 000

T l l d d lTotal landed value per year (USD) b

1 025.7 Million 91.4 Million

Landed tonne per t of fuel d ( )

70 40used (t)

Weighted LCIA score per landed t (Pt∙t‐1)

14 23


CED per landed t (MJ∙t‐1) 1 890 6 810

Anchoveta products environmental comparison Intro Literature review Framework Case study Uncertainty Conclusions

100.0%Climate change

Terrestrial acidificationBiotic Resource

(at plant gate, midpoints, fish in product)

1.0%

10.0% Terrestrial acidificationUse (incl. discards)

0.0%

0.1%Freshwater eutrophicationCumulative

Energy Demand

Agricultural land occupationToxicity (average USES‐LCA/USEtox)

Water depletionMetal depletion

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Fresh anchoveta Frozen anchoveta

Salted anchoveta

Canned anchoveta (production average)

Cured anchoveta (production average)


Environmental contribution of distributionIntro Literature review Framework Case study Uncertainty Conclusions

+6%1600

1800

score (Pt)

+3% +11%

+9%

1000

1200

1400

CiPe

single s

+3% +11%

600

800

1000

Re

+91% +250% +36%200

400

0Fresh/chilled Frozen Salted Canned Trout Black pacu Tilapia

Anchoveta DHC products Aquaculture products


Production Production + distribution

Sustainability comparison of seafood products (Phase 3a)Intro Literature review Framework Case study Uncertainty Conclusions

‐3 ‐2 ‐1 0 1 2 3 4

Fresh anchoveta (HGT)

Fresh hake (fillets)Fresh

prod

ucts

(at plant gate)

Average canned anchoveta product (HGT)

Average frozen anchoveta product (HG)

Average salted anchoveta product (HGT)

Average cured anchoveta product (fillets)Processed

seafoo

d

Average cured anchoveta product (fillets)

Trout (semi‐intensive, artisanal feed, fillets)

Trout (semi‐intensive, commercial feed, fillets)

Trout (semi‐intensive, commercial salmon feed, fillets)

rodu

cts

Black pacu (semi‐intensive, artisanal feed, fillets)

Black pacu (semi‐intensive, commercial feed, fillets)

Tilapia (semi‐intensive, artisanal feed, fillets)

Tilapia (intensive artisanal feed fillets)Aquacultu

re p

Tilapia (intensive, artisanal feed, fillets)

Tilapia (intensive, commercial feed, fillets)

A

Contribution to aggregated score

BRU incl. Discards ReCiPe single score Toxicity (2‐method average)


BRU incl. Discards ReCiPe single score Toxicity (2 method average)Nutritional value index Gross edible EROI EmploymentGross profit generation

Alternative exploitation scenarios (Phase 3b)Intro Literature review Framework Case study Uncertainty Conclusions

Landings Fates

Status quo(maximum anchoveta stock

exploitation)

Status quo(1.5% DHC)

Scenario 1Status quo p )

+DHC( i h t t k Increase in DHCScenario 2 (maximum anchoveta stock

exploitation)

Increase in DHC(10% DHC)

Scenario 2Increased DHC

Diversification(reduction of anchoveta catches

50% + increase of predator catches, i.e. hake: 22%)

Mixed model with anchoveta DHC (3.6%) +

IHC and anchoveta predators DHC

Scenario 3Diversification


p

Scenario 3 – changes in biomasses (details)Intro Literature review Framework Case study Uncertainty Conclusions

1.40

~300 t/y2001‐2010

~30K‐40K t/y

1 10

1.20

1.30

/y2002‐2010No stats

0.90

1.00

1.10

0.80 Anchoveta

Benthic elasm

Butter fish

Catfish

Cetaceans

Characteristic

Chondrichthy

Conger

Detritus

Diatoms

Dino‐and sili

Flatfish

Gelatinous zo

Horse macke

Jumbo squid

Large hake

Macrobentho

Macrozoopla

Medium

dem

Medium

sciae

Mesopelagics

Mesozooplan

Microzooplan

Other Cepha

Bonito

Other sm

all p

Pinnipeds

Prionotus ste

Sardine Sardi

Seabirds

Small dem

ers

Small hake Mm

ob

c yans

c oop

rel

Dos

os

ankto

mersal

enid

s Li

nkton

nkto

lopo

pel

eph

ino

sals

Merl

Changes in biomasses of all modelled species from 2004 to 2031, after a 50% d ti f h t fi hi t lit


50% reduction of anchoveta fishing mortality

Scenarios – changes in indicators (details)Intro Literature review Framework Case study Uncertainty Conclusions

112%

434%

53%

150%

Sub‐totals: only studied species

3% %

22%

7% %

65%

0% %

0%

100%

‐2% 0%

1 3

‐3%

10%

1% 0%

5 7

‐4%

39%

‐40% 8%

‐30

‐40%

‐24%

50%

‐ ‐50%

0%

‐50%Mass

sub‐totals (1000 t)

Mass total commercial(1000 t)

ReCiPe single score

(Pt)

BRU (kt C∙kt‐1)

Available protein

for DHC (t)

Gross profit (1000 USD)

Employment, full time (No.)


Scenario 1: Status quo (2021) Scenario 2: +DHC (2021) Scenario 3: Diversification (2021)

Scenarios – ecological indicatorsIntro Literature review Framework Case study Uncertainty Conclusions

3 50

4.00

4.50 10 000

ndicator

tonn

es)

2 00

2.50

3.00

3.50

100

1 000 In

gs (m

illionn

0 50

1.00

1.50

2.00

10

nual land

ing

‐

0.50

1Current situation (2011) Scenarios 1 and 2 (2021) Scenario 3 (2021)

Ann

Anchoveta annual landings (t) Hake annual landings (t) Anchoveta IBNR,sp (years)

Hake IBNR,sp (years) TL of landings Inverse fishing pressure (ratio)

f


Proportion of predators in commercial biomass (%)

Which future scenario is more sustainable?Intro Literature review Framework Case study Uncertainty Conclusions

• Scenario 2 (+DHC) maximises socio‐economic performance. It seems to be the best alternative among the three scenarios modelled

• Scenario 3 (Diversification) improves ecosystem health but decreases socio‐economic performance.

• Scenario 3 seems less preferable than Scenario 1 (Status quo) and• Scenario 3 seems less preferable than Scenario 1 (Status quo) and Scenario 2, despite some ecological and environmental improvements

Any future scenario modelling featuring reduction of anchovetamortality should feature sensitivity analyses to estimate the optimal level of reduction according to the response of all species in the ecosystem, including seabirds and mammals, and their usefulness and potential use (e.g. tourism)


Uncertainty management (details)Intro Literature review Framework Case study Uncertainty Conclusions

Issues category

Description Treatments applied

Data issues associated to cut‐off criteria and multiple data sources

Use of weighted averages to harmonise multiple data sourcesmultiple data sources. data sources. Sensitivity analyses of input data

Toxicity calculation issues arise from diverse implementations featured in existing toxicity

Comparing various toxicity methods, namely ReCiPe/USES‐LCA vs. USEtox (scientific consensus

LCA related

methods. method)Allocation issues occur in multi‐species fisheries and multi‐function fishfood processes.

Contrasting diverse allocation methods, as practiced in literature, was NOT done. For fishfood co‐products, energy‐based allocation

issues was practiced, and other allocation criteria for other situations

Impact assessment issues due to the fact that fishfood‐specific categories are not implemented

A variety of fishfood‐specific impact categories were computedfishfood specific categories are not implemented

in LCIA methodswere computed

Differences in system boundary setting and cut‐off criteria among LCA , inclusion of capital goods d l d/ id ti

Whole cradle to gate LCAs were modelled, when data was available. as well as distribution/retailing for fi l d t


and land/sea use considerations. final products

Uncertainty managementIntro Literature review Framework Case study Uncertainty Conclusions

Issues category

Description Treatment

The EwE model features data issues due to No treatment, uncertainties in the EwE model are

Trophic model related issues

availability and pedigree levels. discussed onlyOversimplification is inherent to population modelling, for instance, regarding number and interaction among trophic groups (species).

No treatment, uncertainties in the EwE model are discussed only

issuesAdditional omissions and simplification in the model such as the exclusion of climatic dynamics.

The model features both ENSO and non‐ENSO years as steady states

Simulation results are sensitive to scenario design parameters, such as DHC:IHC ratio, changes in

Due to time constraints, the selection of DHC:IHC ratios and fishing mortality was subjective, to

Supply chain

modelling

parameters, such as DHC:IHC ratio, changes in fishing mortality and Capture Per Unit of Effort (CPUE) over time, etc.

ratios and fishing mortality was subjective, to represent expected/desired future exploitation strategies. CPUE was adjusted over time and expressed as dj t d f l i t iti ti l t hadjusted fuel use intensities proportional to changes in biomass.

Model‐coupling

One‐way model coupling: the coupled model cannot directly recalculate the ecosystem

“Snapshots” in specific time periods were obtained from the EwE model to be connected to


related issues

changes, but those need to be modelled in EwE alone.

scenarios of the material flow model set in different time periods

Conclusions directions for further researchIntro Literature review Framework Case study Uncertainty Conclusions

Environmental performance of steel industrial fleets, is superior per FU, but this fleet provides the bulk of landings

The best opportunities for improving the sustainability performance of the anchoveta fisheries lie on the improvement of this fleet and the reduction industry

Consumers’ preferences do not favour anchoveta products

and the reduction industry

Further research consumers’ preferences and anchoveta placing strategies

There is no DHC product which is the “best” in all dimensions of analysis

Decision‐making needs to set priorities

Feasibility of a national refrigerated

Freshwater aquaculture has potentials for improvement and expansion

distribution chain

Better farm management and feed formulations

Scenarios represent a clear multi‐criteria decision‐making tool

More detailed and up to date policy‐based scenarios need to be explored, including a dynamic linking between ecosystem and

41/44

material flow models


Management adviceIntro Literature review Framework Case study Uncertainty Conclusions

• Improve insulation of holds and enforce ice use (or alternative preservation techniques) for vessels landing for DHC

• Improve awareness of fishermen and landing points controlling personnel on sanitary issuescontrolling personnel on sanitary issues

• Build/optimise landing infrastructure and public wharfs for small‐ and medium‐scale fisheries

• Engage in best practices for aquaculture– Sustainable intensification

Prefer less environmentally burdened (less energy– Prefer less environmentally burdened (less energy intensive/refined) feed inputs

• Provide all reduction plants access to the gas grid


Policy adviceIntro Literature review Framework Case study Uncertainty Conclusions

A policy reform is necessary for fisheries, to eliminate evil incentives for illegal landings andeliminate evil incentives for illegal landings and reduction, and to improve control, compliance, stock management and socio‐economic gperformance

• Assign flexible tolerances for discards from different DHC processes, based upon their inherent quality requirements

• Allow the development of fresh anchoveta supply chain


Lessons learntIntro Literature review Framework Case study Uncertainty Conclusions

• Fisheries, reduction and DHC industries are so tightly interlinked that a coherent policy environment must p yaddress them as a whole

• The reduction industry is too important, so reducing it would encounter resistance yet it needs to be better

regulated and regulation properly enforced• The anchoveta fleet is very heterogeneous and requires re‐The anchoveta fleet is very heterogeneous and requires re‐

structuring regarding management and policy• Anchoveta (and other wild caught fish) and aquaculture

DHC production and consumption have plenty of room for improvement


Thank youMerciG iGraciasTackTack


Marine bio‐economic modelling spectrumIntro Literature review Framework Case study Uncertainty Conclusions

AuthorsCriteria

a) Gordon (1954)b) Schaefer (1954), Fox (1970)

a) Smith (1968)b) Clark (1985)

a) Csirke and Caddy (1983)

b) Caddy and Defeo

a) Beverton and Holt (1957)

b) Seijo and Defeo (1970)

(1996) (1994b)

TypeStatic production surplus a) linear

Dynamic production surplus) li l i l

Yield‐mortality a) logistic

Age‐structured a) static

b) exponentiala) linear, polynomialb) exponential

b) exponential b) dynamic

Exploitation i

Open access, restricted i l

Open access, restricted access, Open access Open access

scenarios access, single ownersingle owner

Logistic biological th t t h t

Static parameters plus) St k d fi hi T t l t lit (i

Growth, recruitment,

Parametersgrowth, constant harvest price, constant unit cost of effort, harvest

a) Stock and fishing effortb) Capital discount rate

Total mortality (in lieu of fishing effort)

mortality, age‐specific parameters


Marine ecosystem modelling spectrumIntro Literature review Framework Case study Uncertainty Conclusions


Plagányi (2007)

Supply chain modelling spectrumIntro Literature review Framework Case study Uncertainty Conclusions

Min and Zhou (2002)

Porter (1985)


Min and Zhou (2002)

Coupled ecosystem/supply chain modellingIntro Literature review Framework Case study Uncertainty Conclusions

We use our own approach, due to data limitations and facility of use


Christensen et al. (2011, 2013)

Indicators: formal definitionIntro Literature review Framework Case study Uncertainty Conclusions

• IBNR,sp (years) = reference flow ∙ 1/MSY• IBNR,eco (years) = BRUland / (A ∙ NPPecosys)

• ReCiPe single score– AoP: Human health,

E os stems Reso r es• Inverse fishing pressure = 1/(Landings/Biomassecosys)

• BRU = PPR (g C∙kg‐1) =

Ecosystems, Resources– ReCiPe E/A:

• Egalitarian cultural perspective• Average weighting set HumanBRU = PPR (g C kg ) =

(catch/9) ∙ 10(TL‐1)

• Edible Protein EROI (%) =

Average weighting set Human health: 40%, Ecosystems: 40%, Resources: 20%.

– No weighting within AoP(P ∙ Penergy ∙ EY) / CED

– Gross edible EROI (%) = (GEC ∙ EY) / CED

• NRF 3 = NRF – LIM

– Excludes marine eutrophication and water use

NRFn.3 NRFn LIM– NRFn = (∑1‐n ((Nutrient /DV) ∙100/n) / ED (ED = GEC)LIM = (∑ (Nutrient/MRV)/2)∙100/Q

• Value added = revenues ‐purchased inputs

• Gross profit = revenues costs

50/44

– LIM = (∑1‐2(Nutrient/MRV)/2)∙100/Q


• Gross profit = revenues ‐ costs

ReCiPe cultural perspectivesIntro Literature review Framework Case study Uncertainty Conclusions


LCA: no thresholdsIntro Literature review Framework Case study Uncertainty Conclusions


Potting et al. (1999)

Ecological indicators: inverse fishing pressureIntro Literature review Framework Case study Uncertainty Conclusions

“1/(landings/biomass)” measures the inverse level of exploitation or total fishing pressure on an ecosystem. This indicator is considered as a measure of resource

i l b i fl h f h i d i d di d fi hipotential because it reflects the part of the community production dedicated to fishing. It is more commonly expressed as landings/biomass (as a proxy for exploitation rate), but it was inverted here so that it should decrease under increasing fishing pressure, hence varying theoretically in the same direction as the other indicators in the selected y g ysuite. Notwithstanding, care needs to be taken in interpreting trends in this indicator because variations in total biomass and catch are not only the result of fishing (Bundy et al., 2010). Further, it is influenced by changes over time in the selectivity of fishing gear and in the species targeted b fishing sectors as ell as b inconsistencies in reportedand in the species targeted by fishing sectors, as well as by inconsistencies in reported catches (Bundy et al., 2010).

Retained species (landings): these are species caught in fishing operations, although not necessarily targeted by a fishery (i.e. including by‐catch species), and which are retained because they are of commercial interest, i.e. not discarded once caught, although this does not imply that sometimes certain size classes of those species may not be discarded A non‐retained species is considered to be one that would never be retained


discarded. A non‐retained species is considered to be one that would never be retained for consumptive purposes.

Ecological indicators: TL of landingsIntro Literature review Framework Case study Uncertainty Conclusions

“TL of landings” measures the weighted mean TL of species exploited by the fishery, representing the trophic position of the whole catch, and is expected to decrease in response to fishing because fisheries tend to target species at higher TLs first (Paulyresponse to fishing, because fisheries tend to target species at higher TLs first (Paulyet al., 1998). Initially, catches increase as the foodweb is fished down and because lower TLs are ecologically less expensive (production is greater at lower TL and there is less loss of productivity by trophic transfer up the foodweb), catches may ultimately stabilize or decline (Pauly et al., 1998). Fishing can change the structure of marine foodwebs by reducing the mean TL and potentially also ecosystem functioning by shortening the length of food chains and releasing predation on low‐trophic‐level organisms TL is considered to be a measure of ecosystem structuretrophic level organisms. TL is considered to be a measure of ecosystem structure and functioning and is used to measure state and trend.

TL can vary with fish age because fish are life‐history‐dependent omnivores. By considering TL of landings (vs. TL of the community), however, we focus on the recruited/adult stages of the populations to which the species TL estimates better correspond


correspond.

Ecological indicators: Proportion of predatory fishIntro Literature review Framework Case study Uncertainty Conclusions

“Proportion of predatory fish” in the biomass is a measure of the di i f fi h i h i d fl h i l ff f

(back)

diversity of fish in the community and reflects the potential effects of fishing on the functioning of marine foodwebs. The resilience of predator species is particularly threatened by intense exploitation (Hutchings 2000; Christensen et al 2003; Myers and Worm 2003)(Hutchings, 2000; Christensen et al., 2003; Myers and Worm, 2003), but their role in the ecosystem is essential because they act as dampeners of the whole foodweb (Sala, 2006), and their depletion can lead to trophic cascades (Frank et al., 2005, 2006; Daskalov et al.,can lead to trophic cascades (Frank et al., 2005, 2006; Daskalov et al., 2007). Restoring the declining abundance of predator functional groups should be a target of EAF implementation (Daskalov, 2008). The indicator is used to measure state and trend. For its calculation here, predatory fish were considered to include all fish species surveyed that are piscivorous or feed on invertebrates >2 cm.


Ecological indicators: IBNRIntro Literature review Framework Case study Uncertainty Conclusions

IBNR,sp (years) = reference flow ∙ 1 / MSYIBNR,eco (years) = NPPuse / [Aecosys ∙ NPPecosys]

NPPecosys = fishery BRU (t C)yNPPecosys = annual net primary productivity of the ecosystem (t C∙km‐2)

Impacts of biotic‐resource extraction are

In the case of overexploitation, the impacts of Biotic Natural Resource (BNR) depletion at the species level IBNR sp2 should express that the capture resource extraction are

expressed as a maximal potential regeneration time (in years), i.e. the time required to restore

( ) p p BNR,sp2 p pof a given mass of an overexploited stock has higher impacts than the capture of the same mass of a sustainably exploited stock with the same MSY. To perform this, the IBNR,sp1 is multiplied by a factor that reflects the gap between current fish catch and MSY for overexploited or recovering time required to restore

a given uptake of a particular species assuming equilibrium conditions

g p p gstocks. A factor that is equal to the ratio of MSY to Ct is proposed; it varies from 1 to infinity for catch rates varying from MSY to zero (i.e. when the stock is overexploited close from MSY level or when it is severely depleted respectively). Thus, IBNR sp becomes:conditions. p y) , BNR,sp

where the first member m is the inventory flow (in t of wet weight) and the second member 1/MSY is the characterization factor, with Ct representing the average fish catches during the last five years prior to impact


the average fish catches during the last five years prior to impact assessment, in order to approximate the equilibrium situation.

IBNR,sp for Peruvian commercial speciesIntro Literature review Framework Case study Uncertainty Conclusions

1,000.0

10,000.0

d tonn

es

100.0

illion land

ed

1.0

10.0

Years p

er m

i

0.1

Y

IBNRsp = m / Ct(5‐years) IBNReco = NPPuse/NPPecosys


Landings for 2006‐2010 used for calculating Ct

Critiques to IBNRIntro Literature review Framework Case study Uncertainty Conclusions

The impact of fishing in a stock that has a lower MSY will then always be larger than in one with a higher MSY, i.e. fishing

ll t k i d fi iti l

Yes, per FU and in case of stock under‐ or fully exploited. Indeed, if one has to catch 1 t of fish, it better to extract it from a large stock than from a small onesmaller stocks is per definition less

sustainable than fishing larger stocks

th l th t t l l di th l th

from a small one.

Yes, per FU also but here in case of stock over‐exploited and, ideally, at equilibrium. The

the larger the total landings, the lower the "impact", i.e. if a fishery is rapidly being developed, fishing over MSY, this will be mirrored in the indicator as a very low i t

higher is the amount of fish that the stock can still afford, the lower the impact. The issue of fast growing fishery (non‐equilibrium), rightly pointed here, is attenuated by the use of a 5‐y

impact

it i t t d th t it l k t th i l lThis is true. Species level is used in a biodiversity/LCA framework and opposed to

average of catches.

it is stated that it looks at the species level, but MSY values are not defined per species, but per stock

biodiversity/LCA framework and opposed to ecosystem level (Langlois et al. 2013 caption of they Table 1)The considered North‐Centre anchoveta stock includes ~90% of the total biomass of the


includes 90% of the total biomass of the species

Critiques to IBNRIntro Literature review Framework Case study Uncertainty Conclusions

(back)

If an MSY value with a range has been presented, that would be more valuable to use to get an idea about the uncertainty

MSY is used when the stock is under‐ or fully exploited and when necessary an averageuse, to get an idea about the uncertainty

around the values, instead a five year average of landings was used, but landings, as just mentioned, do not reflect the impact a fishery has on a stock

exploited, and when necessary an average MSY value or a range of value can be used. 5‐y catches average is used when the stock is over‐exploited.

a fishery has on a stock.

Instead of the interpretation in the results

Although this characterization methods suffers, as most other such methods, of limitations due to oversimplification, we still

section that "these values represent the time in years necessary to rebuild, at the species level, the production of one tonne of fish" , I would say that the value represents

limitations due to oversimplification, we still pretend that the unit here is a relevant time unit of stock rebuilding. Indeed, at equilibrium (a simplification of reality) one year is the time used for the stock to rebuilt

the average time (part of a year) used to fish one tonne of Anchoveta, which is not a measure with any kind of ecological relevance.

year is the time used for the stock to rebuilt the biomass extracted and (1 UF / Total catch). However, we admit that this is less true for (1 UF / MSY). In the revised version of IBNR (Helias et al. LCA Food 2014) this issue


of IBNR (Helias et al. LCA Food 2014) this issue is properly addressed.

Ecological indicators: Nutrient Rich IndexIntro Literature review Framework Case study Uncertainty Conclusions

(back)NRFn.3 = NRFn – LIM

h NRF t d f N t i t Ri h F d i th b f iti t i twhere NRF stands for Nutrient Rich Food, n is the number of positive nutrients assessed and LIM is a measure of the nutrients to limit delivered by the seafood product compared to Maximum Recommended Values (MRV).

NRFn = (∑1‐n ((Nutrient /DV )∙100/n)/ED

where DV represents the recommended daily values for each nutrient assessed (n = 10), and ED is the energy density of the food item, in kcal. Included nutrients,10), and ED is the energy density of the food item, in kcal. Included nutrients, expressed together with their DV per 100 g of the food item, are protein, Omega‐3 fatty acids (EPA + DHA), other non‐saturated lipids (including Omega‐6 fatty acids), vitamins A, B‐12 and D; calcium, potassium, phosphorus and iron.

LIM = (∑1‐2(DA/MRV)/2)∙100/Q

where DA is the daily amount, in g, provided by the seafood item in a portion of Q =


y , g, p y p Q100 g; and DI represents the daily intake of food (in g).

Design of LCA studies (more details)Intro Literature review Framework Case study Uncertainty Conclusions

• Cradle‐to‐gate, including construction, use and maintenance phases; excluding decommissioning/site remediation

• By‐products (fresh fish residues) were modelled as their equivalent in whole fresh fish for reduction, applying mass allocationallocation

• In multi‐product systems (e.g. canning), production weighted averages were used

• Fish reduction products used in aquafeeds were modelled, as well as other agricultural and animal inputs, applying mass‐weighted gross energy content allocationweighted gross energy content allocation

• Sensitivity analyses of critical inputs were performed


Definition of scenariosIntro Literature review Framework Case study Uncertainty Conclusions

Scenarios

Explorative(What can happen?)

Predictive(What will happen?)

Normative(How can a target

be reached?)

Forecasts(What will happen,

on the condition th t th lik l

What-if(What will happen, on the condition of

ifi d

External(What can happen to the development of

t l f t ?)

Strategic(What can happen if certain strategy is

d t d?)

Preserving(How can the target

be reached, by dj t t t th

Transforming(How can the target be reached, when

th ilithat the likely development

unfolds?)

some specified events?)

external factors?) adopted?) adjustments to the current situation?)

the prevailing structure blocks

necessary changes?)

E.g.: climatic events, changes in international

demand for fish products, changes in ecosystem regime

E.g.: policy-inducedchanges in fish fate

(DHC vs IHC)

E.g.: policy-induced

E.g.: climatic events

(e.g. ENSO)

E.g.: consequences of changes in stock

management

E.g.: preservation of landing volumes

but increase in aquaculture production

E.g.: introduction of mother- factory

ships


Typology by Börjeson et al. (2006)

ecosystem regime g p ychanges in landings

and landing composition

production

Fish consumption in PeruIntro Literature review Framework Case study Uncertainty Conclusions

ProductConsumption a

(kg∙person‐1∙y‐1) Area of consumption

Main species2005 2007 2009 2011

Fresh fish 11 6 13 8 13 2 11 7 Coastal areas Jack mackerel Mahi mahi jumbo squidFresh fish 11.6 13.8 13.2 11.7 Coastal areas Jack mackerel, Mahi mahi, jumbo squidCanned fish 3.1 4.2 4.3 6.1 National level Jack mackerel, tuna, anchovetaFrozen fish 2.8 2.4 3.5 3.8 Major cities South Pacific hake, jumbo squidCured (salted) fish 1.1 1.0 1.1 0.9 Provinces Chub mackerel, jack mackerel, anchovetaTotal 18 6 21 4 22 2 22 5Total 18.6 21.4 22.2 22.5a Figures expressed in whole fish‐equivalent volumes. National consumption of freshwater aquaculture products is marginal (0.52 kg∙person‐1∙y‐1), and mostly limited to the producing communities and regions.

2006 2007 2008 2009 2010 Average ContributionAnchoveta for DHC (t) 43 502 75 102 98 594 106 565 120 209 88 794 100%Anchoveta for DHC (t) 43 502 75 102 98 594 106 565 120 209 88 794 100%Estimated national

consumption (t)

Canned 18 700 45 844 58 051 62 557 72 634 51 557 58%Frozen 68 2 486 7 332 9 517 11 693 6 219 7%Fresh 538 401 336 293 223 358 <1%Salted 6 058 1 459 942 2 962 3 979 3 080 3%(t) Salted 6 058 1 459 942 2 962 3 979 3 080 3%

Estimated exports (t)

Canned 12 300 16 100 20 800 22 400 21 600 18 640 21%Frozen 1 200 2 800 4 933 2 000 3 467 2 880 3%Cured, salted 4 600 6 000 6 200 6 800 6 600 6 040 7%

C i f t ith t t f h fi h d 0 50 f 0 75 d d 0 25


Conversion factors with respect to fresh fish : canned = 0.50, frozen = 0.75 and cured = 0.25.Source: PRODUCE statistics, exports: PromPeru (2010)

Policy environment (more details)Intro Literature review Framework Case study Uncertainty Conclusions

• Vertically integrated companies (top 6 dominate ~50% of the market)

• Gross overcapacity pervades anchoveta fisheries (2‐3 fold) and reduction industries (2.5‐3.3 fold)

• Individual vessel quota system since 2009Individual vessel quota system since 2009• SMS fleets have access to exclusive fishing grounds (currently under legal battle)

• Issues:– Policy environment: complex, lobbied, conflictive and poorly enforced; yet anchoveta stock is fairly well managed

– Fishing rights, taxes and other payments are not balanced with indirect subsidies to reduction fisheries (C. Paredes)

– SMS fleets do not pay anything to the State

64/44

p y y g


Economic performanceIntro Literature review Framework Case study Uncertainty Conclusions

Landings Reduction Direct Human Consumption Aquaculture

Indicator Unit Steel fleetVikinga fleet

SMS fleet FMFO Canning CuringSalting

(artisanal)Freezing Trout

Black pacu

Tilapia

Production t ∙ y‐1 5 043 916 939 588 341 476 1 617 497 95 589 9 772 3 450 43 985 12 817 564 1261

Revenues 103 USD ∙ y‐1 683 444 115 356 44 392 1 675 995 101 224 24909 13 370 81 006 49 146 2 153 3 331

Employment(direct)

jobs ∙ y‐1 10 744 6 361 7 144 12 550 8 032 2 515 338 1827 13 024 492 672

jobs ∙ t‐3 2 7 21 8 84 257 98 42 1016 872 533jobs t 2 7 21 8 84 257 98 42 1016 872 533

Production costs

103 USD ∙ y‐1 514 984 86 226 19 089 1 136 332 78 955 17 492 8 815 66 318 32 594 1132 2 232

USD ∙ t‐1 102.1 91.8 55.9 702.5 826.0 1790 2 555 1508 2 543 2 007 1770

Value added 103 USD ∙ y‐1 120 901 20 906 39 065 491 029 60 734 14 945 4 145 13 365 25 695 1192 1758

USD ∙ t‐1 24.0 22.3 114.4 303.6 635.4 1529 1201 303.9 2 005 2 113 1394

Gross profit 103 USD ∙ y‐1 164 460 29 130 25 303 539 663 22 269 7 417 4 327 14 689 16 553 1021 1099

USD ∙ t‐1 33.4 31.0 74.1 333.6 233.0 759.0 1245 334.0 1291 1811 871.7

V l dd d h d i G fi Al d i fi f PRODUCE i i h SMS flValue added = revenues ‐ purchased inputs, Gross profit = revenues ‐ costs. Al production figures are from PRODUCE statistics, the SMS fleet production figure adjusted for illegal, unreported and unregulated fishing landings.

Fréon et al. (2013):Occasionally IHC plants pay more to SMS vessels than DHC plants, per landed tonne.


Occasionally IHC plants pay more to SMS vessels than DHC plants, per landed tonne. Even when not the case, larger volumes when destined to IHC overcompensate and represent

better income for SMS vessels

Nutritional profiles (NRF)Intro Literature review Framework Case study Uncertainty Conclusions

Edible portionEnergy

kcal∙100‐1 g

Basic profile (%) Vitamins (μg∙100‐1 g) Minerals (mg∙100‐1 g)Ranking 1=bestProtein

Lipids (total, Omega‐3, SFA)

Water Ash A B‐12 D Ca Na K P Fe

a Fresh/frozen (gutted)465.8 a

19 1 8 8 2 5 1 3 70 8 1 2 15 0 0 6 <0 1 77 1 78 0 241 4 174 0 3 0 2 2

Overall

Anchoveta

prod

ucts

Fresh/frozen (gutted)188.2 b

19.1 8.8, 2.5, 1.3 70.8 1.2 15.0 0.6 <0.1 77.1 78.0 241.4 174.0 3.0 2

Canned (HGT) c 166.0 21.3 9.0, 2.6, 2.7 59.8 3.5 18.5 11.2 6.4 365.0 408.0 380.5 400.5 2.5 1Salted (HGT) c 126.1 18.4 5.9, 1.7, 2.2 43.0 6.2 12.0 0.9 1.7 232.0 1 223 544.0 252.0 4.6 3Cured (fillets) c 155.8 30.0 4.0, 1.2, 2.2 48.1 17.6 12.0 0.9 1.7 232.0 3 668 544.0 252.0 4.6 7

esh

sh

Cultured rainbow trout d 171.1 18.4 7.6, 0.7, 1.4 73.8 1.2 84.0 4.3 15.9 25.0 51.0 377.0 226.0 0.3 4Cultured black pacu e 196 8 15 0 12 0 0 4 4 8 71 6 2 1 6 0 2 2 2 9 35 0 35 3 164 9 631 8 0 5 6

2

131448

Fre fis Cultured black pacu 196.8 15.0 12.0, 0.4, 4.8 71.6 2.1 6.0 2.2 2.9 35.0 35.3 164.9 631.8 0.5 6

Cultured red tilapia f 108.6 18.3 1.9, 0.1, 0.6 80.5 1.4 0.0 1.6 3.1 10.0 52.0 302.0 170.0 0.6 5

Beef (lean) a,b 105.0 21.3 10.0, 0.04,4.1 75.9 1.1 0.0 2.7 0.0 16.0 59.0 271.0 208.0 3.4 3Chicken (lean) a,b 119.0 21.4 9.3, 0, 2.7 75.5 1.0 16.0 0.3 3.3 12.0 64.0 144.0 173.0 1.5 5Eggs a,b 141 0 13 5 8 4 0 6 3 1 75 4 0 9 140 0 0 9 2 1 34 0 142 0 138 0 194 0 1 1 2

87

9116Eggs , 141.0 13.5 8.4, 0.6, 3.1 75.4 0.9 140.0 0.9 2.1 34.0 142.0 138.0 194.0 1.1 2

Fresh cheese a,b 264.0 17.5 20.1, 0.05, 13.7 55.0 4.1 420.0 1.8 0.7 783.0 704.0 126.0 375.0 1.3 8Hake (edible portion) c,d 102.3 16.6 1.2, 0.5, 0.3 82.1 1.2 7.3 0.5 1.0 14.7 64.0 403.7 180.0 0.0 1Milk a,b 63.0 3.1 7.6, 0, 4.6 87.8 0.7 28.0 0.2 0.2 106.0 106.0 303.0 94.0 1.3 6Pork (carcass) a,b 198.0 14.4 15.1, 0.01, 7.9 69.2 1.2 2.0 0.6 0.0 12.0 42.0 253.0 238.0 1.3 7

Shrimp a,f 71.0 13.6 1.0, 0.1, 0.1 83.0 1.9 54.0 1.1 0.1 54.0 566.0 113.0 244.0 0.2 4

6155121310p

• Overall nutritional ranking, from best to worst (NRFn.3): canned, fresh/frozen and salted anchoveta, trout, hake, eggs, tilapia, black pacu, beef (lean), shrimp, chicken (lean), milk, pork (lean), cured anchoveta and fresh cheese.

• Main source of animal protein in Peru is chicken (17.4 kg∙person‐1∙y‐1), due to competitive prices, easier


Main source of animal protein in Peru is chicken (17.4 kg person y ), due to competitive prices, easier conservation and efficient distribution

Aquaculture products environmental comparison Intro Literature review Framework Case study Uncertainty Conclusions

(at farm gate, midpoints)

Live weight Edible portionWheat

100%

Acidificationpotential(CML 2)

Agricultural l dTo icit

100%

Acidificationpotential(CML 2)

Agricultural l dT i it

0%

50%

land occupation(ReCiPe)

Biotic resource

Water depletion

Toxicity(CML 2)

0%

50%

land occupation(ReCiPe)

Biotic resource

Water depletion

Toxicity(CML 2)

0% resource use

Cumulative energy demand

Global warming potential

depletion(ReCiPe)

0% resource use

Cumulative energy demand

Global warming potential

depletion(ReCiPe)

demand(CED)

Eutrophication potential(CML 2)

potential(CML 2)

demand(CED)

Eutrophication potential(CML 2)

potential(CML 2)


TrArtS1 GaArtS1 TiArtS1 TrArtS1 GaArtS1 TiArtS1

Scenarios – trends in biomassesIntro Literature review Framework Case study Uncertainty Conclusions


Scenarios ‐massesIntro Literature review Framework Case study Uncertainty Conclusions

Scenario 3


Scenarios ‐massesIntro Literature review Framework Case study Uncertainty Conclusions

5

6

10 000 000

100 000 000

+8%

2

3

4

5

10 000

100 000

1 000 000

10 000 000

es)

‐40%

‐1

0

1

10

100

1 000

10 000

ousand

tonn

e

‐3

‐2

0

1

10

IHC

ards FM FO

DHC

ning

resh

ting

DHC

ards

rout

apia

acu

otal

rcialM

ass (Th

o

Land

ings

Disca

Land

ings D

Cann

Freezin

g/fr

Curin

g/salt

Land

ings D

Disca Tr Tila

Black p

Sub‐t o

Total

commer

A h t H k A lt Bi

70/44

Anchoveta Hake Aquaculture Biomass


Scenarios – subtotalsIntro Literature review Framework Case study Uncertainty Conclusions

685,469

7 94

608,056

6

13%

%

10%57%

%

39%‐30%

%

‐24%

500%

1 000 000

10 000 0009,232

88,607

26,9

44,546

‐2%

‐3%

112%

22%

1%

‐4%

434 %

65%

‐40%

‐40% 53%

0%

300%

400%

100 000

9 ‐ ‐

200%

1 000

10 000

0%

100%

10

100

‐100%1Mass

(1000 t)ReCiPe

single score (Pt)BRU

(kt C∙kt‐1)Available protein

for DHC (t)Gross profit (1000 USD)

Employment, full time (No.)


Current situation (2011) Scenario 1 (2021) Scenario 2 (2021) Scenario 3 (2021)

Fate of 1 t of anchovetaIntro Literature review Framework Case study Uncertainty Conclusions

21.7

20

25

oces

sed

15

20

anch

ovet

a pr

oe

of p

rodu

ct

3.5 4.1 3.4

0 7 0 9

4.2 4.2

1 32.9

1 02.1

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Aquaculture Reduction DHC

Umberto material flow modelIntro Literature review Framework Case study Uncertainty Conclusions

P15:Auxiliaryenergy

P13:Othermaterials

<== Ecosystem Supply Chain ==>

P18:Indicators

P14:Bioticmaterials

T3:Reduction

T5:Aquafeedprocessing

T6:Aquaculture

T8:Transportationagricultural inputs

P4:FishmealP5:Aquafeed

P7:Natural

P8:Anchovetabiomass (stock)

P10:Agriculturalinputs

P12:Fish oil

P13:Other

P13:Othermaterials

P15:Auxiliaryenergy

P18:Indicators

P15:Auxiliaryenergy

P18:Indicators P15:Auxiliaryenergy

P18:Indicators

P23:Hakebiomass (stock)

P24:Nutrients

P22:Otherpray for hake

T1:Ecosystemdynamics(NHCS)

T2:Fishing(anchoveta)

P1:Anchovetacaptures

P3:Landedanchovetafor IHC

P7:Naturalenergy

P13:Othermaterials

P15:Auxiliaryenergy

P18:IndicatorsP20:Planktonbiomass

T7:Exports and other domestic use

P29:FMFO for export andother domestic use

P15:Auxiliaryenergy

P30:FM export

P31:FO export

P18:Indicators

( )

T4 Seafood P6:Seafood

P13:Othermaterials

P15:Auxiliaryenergy

P18:Indicators

P2:Anchoveta T10 Distrib tion P21:Seafood products

P27:Plankton mortalities

P13:Othermaterials

P15:Auxiliaryenergy

P19:Seafoodfor processing

P11:Landed

P18:Indicators

T4:Seafoodprocessing

P6:Seafoodfor distribution

P2:Anchovetamortalities

T10:Distribution(national and exports)

P21:Seafood products for consumption

P25:Hakecaptures

P26:Hakepredation andmortalities

T14:Fishing(hake)

P15:Auxiliaryenergy

P18:Indicators

P28:Landedpredators

anchoveta for DHC

Notes:* National transportation included in Reducttion, Production agricultural inputs, Aquafeed processing and Aquaculture* Modelled suply chains include anchoveta for DHC and IHC, hake fishery and processing for DHC, feed and aquaculture

d ti f til i G it d t t


P9:Waste production of tilapia, Gamitana and trout.

Umberto material flow modelIntro Literature review Framework Case study Uncertainty Conclusions

P15:Auxiliaryenergy

P13:Other

P18:Indicators

P14:Bioticmaterials

T8:Transportationagricultural inputs

P4 Fi h l

P10:Agriculturalinputs

P13:Othermaterials

P15:Auxiliaryenergy

P18:Indicators

P15:Auxiliaryenergy

P18:Indicators

materials

P15:Auxiliaryenergy

P18:Indicators

T3:Reduction

T5:Aquafeedprocessing

T6:Aquaculture

P4:FishmealP5:Aquafeed

P12:Fish oil

P15:Auxiliaryenergy

P18:Indicators

ing P3:Landedh t

P18:Indicators

T7:Exports and other domestic use

P29:FMFO for export andother domestic use

energy

P30:FM export

P31:FO export

gveta) anchoveta

for IHC

P13:Othermaterials

P15:Auxiliary P18:Indicators P15:Auxiliary

P19:Seafoodfor processing P18:Indicators

74/44Angel Avadí ‐ Sustainability of the Peruvian anchoveta supply chains from sea to shelfT4:Seafoodprocessing

P6:Seafoodfor distribution

P15:Auxiliaryenergy

P18:Indicators

T10:Distribution(national and exports)

P21:Seafood products for consumptionP18:Indicators

P15:Auxiliaryenergy

p g

P11:Landedanchoveta for DHC

P18:Indicators

Uncertainty managementIntro Literature review Framework Case study Uncertainty Conclusions

Issues category


Data issues associated to cut‐off criteria and multiple data sources. The use of weighted averages to harmonise multiple data sources may reduce deviations in results due to uncertainty.Sensitivity analyses of input data should be applied to various ranges of key contributors to environmental impacts (e.g. fuel use). Both treatments were applied

Toxicity calculation issues arise from diverse implementations featured in existing toxicity methods.

Comparing various toxicity methods, for instance ReCiPe/USES‐LCA vs. USEtox(scientific consensus method), would clarify toxicity contribution of studied systems. Relevant LCIA methods are described in Appendix C: A comparison of

LCA related

current Life Cycle Impact Assessment methods. This treatments was applied

Allocation issues occur in multi‐species fisheries and multi‐function fishfood processes, as extensively discussed in fishfood LCA literature.

Contrasting diverse allocation methods, as practiced in literature, would highlight sensitivity of results to allocation. A specific approach for allocation in fisheries LCA studies is described in section 2.4.1. For fishfood co products energy based allocation was practiced and other

issues* For fishfood co‐products, energy‐based allocation was practiced, and other allocation criteria for other situations

Impact assessment issues due to the fact that fishfood‐specific categories are not implemented in LCIA methods, namely species removal and seafloor damage (for fisheries) and biotic resource use (for fishfood products in general).

Calculation of those impact categories contributes to more complete and relevant LCA studies. Moreover, additional impact categories and LCIA methods should be included, when relevant, to offer a more multi‐criteria comparison (i.e. BRU including discards, complemented with energy efficiency and ( p g ) ( g p gy ynutritional indicators). A variety of fishfood‐specific impact categories were computed

Differences in system boundary setting and cut‐off criteria among LCA studies make difficult the nesting of studies to cover large segments of a fishfood supply chain (e.g. integration of studies on

System boundaries and cut‐off criteria must be clearly described and justified. All life cycle stages of the system under study should be included, despite any perceived negligibility in contribution to environmental impacts (e.g. in


fisheries and reduction industries). Other methodological sources of uncertainty include the inclusion of capital goods and land use considerations.

fisheries, both construction and end of life of vessels should be included).Whole cradle to gate LCAs were modelled, as well as distribution/retailing for final products

Uncertainty management (back)Intro Literature review Framework Case study Uncertainty Conclusions

Issues category


Trophic

The EwE model features data issues due to availability and pedigree levels.


Trophic model related issues

Oversimplification is inherent to population modelling, for instance, regarding number and interaction among trophic groups (species).


Additional omissions and simplification in the model such as the exclusion of climatic dynamics.

The model features both ENSO and non‐ENSO years as steady statesexclusion of climatic dynamics. states

Supply chain

Simulation results are sensitive to scenario design parameters, such as DHC:IHC ratio, changes in fishing mortality and Capture Per Unit Effort (CPUE) over time, etc.

Due to time constraints, the selection of DHC:IHC ratios and fishing mortality was subjective, to represent expected/desired future exploitation strategies. Alternative EwE simulations featuring variations in fishing mortality for anchoveta and hake

d d t l d d f lt d t d i dmodelling

were produced, yet excluded from results due to undesired effects (e.g. collapse of other commercial species stocks). CPUE was adjusted over time and expressed as adjusted fuel use intensities proportional to changes in biomass.

Challenges arising from the proposed model coupling (one‐way To fully overcome this model linking constraint would be possible

Model‐coupling related issues

forcing) approach are due mostly to the complex nature of the EwE model. The base model is static, which is later dynamically modelled over time. The material flow model is static, so steady states of the dynamic EwE model are required for coupling steady state instances Thus the coupled model cannot directly

by developing a software interface, which exceeds the scope of this research. “Snapshots” in specific time periods were obtained from the EwE model to be connected to scenarios of the material flow model set in different time periods


issues steady state instances. Thus, the coupled model cannot directly recalculate the ecosystem changes, but those need to be modelled in EwE alone.

model set in different time periods

Additional policy adviceIntro Literature review Framework Case study Uncertainty Conclusions

• Reduce fleet overcapacity How? – By enforcing prohibition of new vessels, by applying quotas for SMS y g p , y pp y g q

vessels, by fully transferrable individual quotas (outside scope)– Reducing overcapacity has an economic justification, rather than

environmental

• Other analysts suggest:– Deploy a quota system for SMS fleets (management challenge)?

All ll fl l d f i h DHC IHC l i i– Allow all fleets to land for either DHC or IHC, as long as minimum requirements for each activity are fulfilled

– Improve calculation of fishing rights and generalise the requirement of i h f h i h f fi hi ll flpaying the state for the right of fishing to all fleets

– Implement a truly scientific fisheries management under the ecosystems approach to fisheries (clear of special interest influences)


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