Transcript Sustainability of the Peruvian anchoveta

Sustainability of the Peruvian
anchoveta-based supply chains
from sea to plate
ANCHOVETA-SC PROJECT
status report
Angel Avadi, IRD, Université Montpellier II
Main project collaborators:
• Marilú Bouchon, IMARPE
• Camilo Cuba, UNFV
• Dr. Pierre Fréon, IRD
• Federico Iriarte, UNFV
DISCOH Scientific Workshop
29-31 March 2012
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Ana Medina, IMARPE
Jesus Nuñez, IRD
Jorge Tam, IMARPE
Rosa Vinatea, UNFV
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Outline
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2.
The ANCHOVETA-SC project
Supply chain modelling and evaluation
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3.
Modelling
Sustainability indicators
Initial LCA results
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ANCHOVETA-SC
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Project financed by IRD and project partners
Coordinator: Pierre Fréon, IRD
Location: Peru
Duration: 4 years (01.2010 - 12-2013)
Theme: Environmental and socio-economic
assessment of major international supply
chains consuming Peruvian anchoveta
(aligned to WP5 DISCOH)
• Outputs:
– Sustainability assessment
– Policy and sustainability suggestions
– PhD thesis (plus other theses)
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Focus
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Characterisation of biophisical flows
along the supply chains (SC)
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3.
DHC
Marine
Ecosystem
Extraction
Transformation
Aquaculture
Featuring ecosystem-SC interactions
Comparison of scenarios based on
different fishing intensities and
“fate” of landings (DHC vs IHC)
Harvest
• Anchoveta catches
• Predators catches
• Catches for DHC or IHC
IHC
Fate
• Final product
Sustainability comparison of
chains/scenarios based upon:
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Energy performance
Environmental impacts (LCA)
Seafood-specific impact categories
Nutritional value
Selected socio-economic indicators
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1) Simplified SC diagram
Inputs
Ecosystem dynamics
Anchoveta,
predators
Inputs
DHC
processing
Canned, cured,
frozen
Emissions
Consumption
Fisheries
Emissions
Inputs
Anchoveta
Inputs
(including
crops)
Inputs
Reduction
Aquafeed
Aquaculture
Emissions
Emissions
Emissions
Chinese finfish?
European salmon?
Shrimp?
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Modelling ecosystem-SC interactions
Ecopath with Ecosym
Trophic model
Umberto
Material and energy
flow model
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2) Scenarios and 3) Indicators
Harvest
Fate
Indicators rationale
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Status quo
(maximum anchoveta
stock exploitation)
Status quo
(1-2% DHC)
Status quo
(maximum anchoveta
stock exploitation)
Increase in DHC
(10-15% DHC)
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Diversification
(reduction of anchoveta
catches + increase of
predator catches)
Mixed model with
anchoveta DHC/IHC
and anchoveta
predators DHC
To compare feed ingredients,
feed formulations and seafood
products:
Gross energy content (MJ/kg)
Edible protein Energy Return
On Investment (%)
Biotic Resource Use (g C/kg)
Ecological Footprint (ha/t)
To compare intermediate and
final seafood products, and
competing supply chains:
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LCA impact categories
Socio-economic indicators (to
be defined)
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LCAs carried out
• Two fishmeal plants:
– a conventional one producing
only Fair Average Quality (FAQ)
fishmeal and using mainly
heavy fuel as energy source
– a more modern steam plant
producing both FAQ and prime
quality fishmeal and using both
heavy fuel and natural gas
• Detailed inventories of
industrial anchoveta fleet
under processing
– preliminary LCA of
representative “average“ 395
m3 vessel category
• Two aquafeed plants (Iquitos)
– A pilot facility and a working
commercial facility
• One aquaculture farm
(Iquitos)
– Peruvian Amazonian species
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Iquitos Colossoma farm
• Colossoma macropomum (Gamitana), a large Amazonian fish
• Farm: 30 ha, converted from rain forest, 11.2 ha of ponds (no
wastewater treatment), production: 100 t/a, feed: 150 t/a
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Network: Colossoma farm
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Fossil depletion
Metal depletion
Climate change
Human Health
Human toxicity
Particulate matter
formation
Natural land
transformation
Climate change
Ecosystems
Agricultural land
occupation
Ionising radiation
Terrestrial
ecotoxicity
Urban land
occupation
Ozone depletion
Photochemical
oxidant formation
Terrestrial
acidification
Freshwater
eutrophication
Freshwater
ecotoxicity
Marine
ecotoxicity
Characterisation: Colossoma farm
• Main impact contributors: feed and rain forest
transformation
FRY
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Iquitos Aquafeed plants
• 2 plants visited:
– 30 t/a IIAP plant
– 8 t/m commercial plant (competing with Purina, etc.)
• < 6% Peruvian fishmeal content in feeds
• > 33% Bolivian soymeal content
• > 45% local cornmeal content
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Network: Aquafeed plant (8 t/m)
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Fossil depletion
Metal depletion
Climate change
Human Health
Human toxicity
Particulate matter
formation
Natural land
transformation
Climate change
Ecosystems
Agricultural land
occupation
Ionising radiation
Terrestrial
ecotoxicity
Urban land
occupation
Ozone depletion
Photochemical
oxidant formation
Terrestrial
acidification
Freshwater
eutrophication
Freshwater
ecotoxicity
Marine
ecotoxicity
Characterisation: Aquafeed plant
• Main impact contributor: use phase
LCA FISHMEAL PLANT
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Fossil depletion
Metal depletion
Climate change
Human Health
Human toxicity
Particulate matter
formation
Natural land
transformation
Climate change
Ecosystems
Agricultural land
occupation
Ionising radiation
Terrestrial
ecotoxicity
Urban land
occupation
Ozone depletion
Photochemical
oxidant formation
Terrestrial
acidification
Freshwater
eutrophication
Freshwater
ecotoxicity
Marine
ecotoxicity
Aquafeed plant use phase
• Impact contributors in use phase:
– oil-powered electricity
– feed ingredients, mainly Bolivian soymeal (due to clearcutting in Bolivia)
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Network: Hypothetical trout feed plant
(43% fishmeal)
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Comparison of feed plants
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Network: Fishing vessel 395 m3
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Design remarks
• Key aquaculture products haven’t been characterised
for Peruvian conditions
– E.g. Peruvian rice and corn.
– Proxies were used and adaptations introduced when
possible (e.g. Bolivian soymeal adapted from Brazilian)
• Key industrial products haven’t been characterised,
yet it’s composition is known/estimated
– E.g. electric and combustion engines
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LCA results
• Construction and maintenance of (reduction, feed)
plants contributes negligibly
• Fuel use is the main contributor to impacts in all
activities (fishing, reduction, feed processing)
• Feed provision is the main contributor to impacts in
extensive Peruvian aquaculture
• The sourcing of feed ingredients is a critical factor for
associated environmental impacts of feeds
– E.g. Brazilian soymeal performing much worst than
Bolivian one, due to clear cutting of rain forest vs. of
shrublands.
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Further (ongoing) work
• EwE scenarios definition and integration with Umberto
• Selection of and data gathering for socio-economic
indicators
• Statistical processing of fleet inventories and
comprehensive LCA of fleet categories
• Further LCAs:
– Canning, curing and freezing plants
– Carnivore fish and shrimp aquaculture farms
• Gathering supply chains operative data and LCIs
– Background processes for provision of feed ingredients
– Published LCI/LCA data must be recalculated to ensure
consistency
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Gracias por su atención…
Preguntas, comentarios?
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