The State of World Fisheries and Aquaculture 2020

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PART 3 
OUTLOOK AND EMERGING ISSUES
In line with the vision of the SDGs, which 
anticipates benefits from innovation in 
information technologies, the fisheries and 
aquaculture sector is rapidly introducing these 
technologies to improve economic, social and 
environmental sustainability along value chains. 
This will result in fully monitored fisheries 
and precision aquaculture, with vessels and 
farms connected to multiple-sensor networks 
generating big datasets that can be used for all 
management purposes. 
Automatic Identification System, artificial 
intelligence and machine learning
With advances in satellite technology, the 
tracking of vessel movements around the globe 
is well within the realms of technical possibility. 
One tracking technology designed for 
navigation safety is AIS. Every 10–30 seconds, 
it transmits a vessel’s position, identity, course 
and speed. The tracking of the movements 
of tens of thousands of industrial fishing 
BOX 23
SMARTFORMS AND CALIPSEO – FAO’S NEW TOOLS TO HELP ADDRESS WEAKNESSES IN 
NATIONAL DATA SYSTEMS
While emerging technologies are expected to cause 
significant disruption of existing monitoring and 
management frameworks, there is an immediate 
need to address weaknesses in existing data systems. 
Data collection in small-scale fisheries is typically poor 
because the fishing activity is usually dispersed along 
coasts, and data systems are complex and costly. 
The data that are collected are often scattered and 
in different formats. The lack of integration remains a 
major challenge to sector monitoring and management. 
Countries face increasing difficulties in coping with 
multiple reporting to international bodies. To help 
countries address these issues, FAO has developed two 
innovative tools: SmartForms, and Calipseo. 
SmartForms is a multilingual application to collect 
and review fishery data. The platform allows users to 
design forms according to survey needs, to install a 
mobile app that implements the forms, and to store, 
review and analyse data in a portable database. This 
database can be exchanged with any authorized 
third-party system such as Calipseo (below). 
SmartForms is built on a participatory approach where 
stakeholders, such as fishers, scientific observers, 
national institutions and intergovernmental 
organizations, can share the same app and collect 
data under international standards with linkages to 
national and regional standards. Conversely, each 
survey is autonomous and collects data in a secure and 
confidential environment. This new FAO app has also 
been released as an open-source application, and 
interested organizations are welcome to join and 
contribute. SmartForms is expected to enhance data 
collection capacity, including by applying international 
standards, and should therefore facilitate 
harmonization of datasets among data collection 
schemes. SmartForms also constitutes an innovative 
approach to data collection for sectors that are poorly 
documented and monitored (e.g. recreational fisheries, 
and socio-economic information).
Calipseo
 
is an IT solution to integrate and 
streamline fisheries data along the national data 
supply chain. It is a web-based multilingual 
application that can be deployed in the cloud or on 
local servers. It has been designed to collect and 
manage the various typologies of fisheries data, 
including fisheries administrative data (vessel, fisher 
and fishing companies records or registries), fishing 
activities data (landing forms, logbooks, and purchase 
orders from processing plants), statistical survey data 
collected through sampling, and biological data 
(crucial for stock assessment). The data-processing 
engine is customizable and produces reports and 
statistics according to the needs of national fisheries 
authorities. Data and information can be also shared 
according to the standard reporting templates or 
models with regional fisheries management 
organizations and with international organizations 
with a priority for FAO. Following a pilot developed 
for the Bahamas, the system has now been deployed 
in Trinidad and Tobago.
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THE STATE OF WORLD FISHERIES AND AQUACULTURE 
2020
vessels, analysed jointly with vessel registers 
by machine-learning algorithms enables 
predictions of the type of fishing activity, and 
quantification of fishing intensity by fishing 
gear. Thus, it is possible to create a global 
database of fishing effort by gear type with 
unprecedented spatial and temporal resolutions. 
To this end, FAO and its partners are promoting 
the potential of AIS to assist fisheries 
management and research around the globe, 
and highlight its strengths, limitations and gaps 
(Taconet, Kroodsma and Fernandes, 2019). 
In 2017, AIS started to be considered a valid 
technology for estimating fishing indicators. 
It can track most of the world’s large fishing 
vessels (those longer than 24 m), especially 
distant-water fleets and vessels on the high 
seas from upper- and middle-income countries. 
However, these larger vessels represent only 
2 percent of the world’s total of 2.8 million 
motorized fishing vessels (Taconet, Kroodsma 
and Fernandes, 2019), and only a small fraction 
of the smaller and more coastal fleets carry 
AIS. The performance of AIS in tracking 
fishing activity varies significantly by fishing 
areas. For example, in Europe, where almost all 
vessels of more than 15 m in length have AIS, it 
provides a good estimate of fishing activity in 
the Northern Atlantic. However, in Southeast 
Asia, where the proportion of small vessels is 
large, where very few of them have AIS, and 
where reception quality is poor, AIS reports 
only a small fraction of the fishing activity. 
The largest discrepancy between AIS-based 
information and other fishing data occurs for 
fishing activity in the Eastern Indian Ocean. 
Although AIS can provide information on 
fishing activity much more rapidly than can 
logbooks or official assessments via a vessel 
monitoring system (VMS), its level of detail 
(e.g. number of fishing gear or species captured) 
could be insufficient for many other uses, and 
compared with a VMS, vessels can easily turn 
off their AIS or broadcast incorrect identity 
information. Many benefits can be derived from 
combining AIS with VMS and logbook data.
The ability of AIS to differentiate gear is 
improving, although progress is still needed. 
Longliners, with a wide presence on the high 
seas worldwide, are the type of vessels best 
captured by AIS-based algorithms, to the 
point that this technology can be considered 
for providing metrics of fishing effort for 
stock assessments. The system also captures 
well other main fishing vessel types, such 
as purse seiners and trawlers, but tends to 
under-represent their importance compared 
with longliners. However, AIS is still limited in 
its ability to discriminate fishing activities for 
multi-gear vessels. 
Overall, AIS can begin to be considered a 
viable technology for near-real-time estimates 
of fishing effort and marine spatial planning, 
provided it is supported by human verification 
(given the variable accuracy of AIS). Many actors 
see AIS as a technology that can track illegal 
fishing. However, AIS was originally designed 
for maritime security purposes – so that ships 
are aware of other ships’ positions – and its use 
for another purpose is likely to lead to problems 
and is not recommended. That said, AIS data 
could be used to provide statistical estimates of 
illegal fishing in certain situations.
In the future, AIS should be able to support 
fisheries management in the face of uncertainty 
and changing climate. It, or similar technologies, 
should be able to provide near-real-time 
monitoring of catch volume by fishery 
together with fishing effort. This step requires 
improved algorithm performance to integrate 
additional data sources, including VMS and 
logbooks, and comprehensive knowledge 
on species biology, fishing techniques, and 
the physical and jurisdictional environment. 
Generating intelligence and accurate estimates 
of fishing effort and catch of this big data 
assemblage will increasingly require AI and 
machine learning. Moreover, new infrastructures 
will be necessary to fill in the missing data 
of currently undetectable fleet segments. 
These include low-cost devices installed on 
small vessels to transmit their position, which 
are already being tested, and newer satellites 
that will be capable of detecting smaller 
transponders, detecting vessels using radio 
frequencies, or combining synthetic-aperture 
radar with AIS to identify vessels not using AIS 
or a VMS.
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PART 3 
OUTLOOK AND EMERGING ISSUES
can play a leading role by contributing to the 
development of standards, guidelines and best 
practices through standard-setting bodies such 
as the Coordinating Working Party on Fishery 
Statistics (CWP), United Nations Centre for Trade 
Facilitation and Electronic Business, and the 
Research Data Alliance.
Blockchain 
Blockchain has considerable potential to improve 
traceability, accuracy and accountability along 
fisheries value chains, although significant 
constraints remain. It can provide an online 
traceability infrastructure for the permanent 
storage and sharing of key data elements (e.g. 
catch area, species and product type, production 
or expiry date) along with critical tracking events 
(e.g. fishing vessel operations, landing, product 
splits and processing). Blockchain is already used 
as a digital ledger for recording transactions of 
products between supply chain actors. 
Blockchain consists of a linked chain that stores 
auditable data in units called blocks (FAO and 
ITU, 2019). It can be used to record, track and 
monitor physical and digital assets in fish supply 
chains. It offers opportunities to integrate and 
manage, in real time, processes, product attributes 
and transactions that are added by supply-chain 
actors and the IoT, i.e. sensors and other devices. 
Table 22
illustrates a fish supply chain supported by 
blockchain where the end-user (consumer) will be 
able to retrieve the full history of the product as 
well as its attributes. Data stored in the blockchain 
are secure, decentralized and immutable. 
Applications of blockchain in food supply chains 
can address a wide array of issues (FAO and ITU, 
2019; Nofima, 2019; Bermeo-Almeida 
et al.
, 2018). 
These include: improving food safety, traceability 
and transparency; and enhancing performance, 
revenue, accountability, data security, and brand 
protection. From an operational perspective, 
blockchain in fish value chains could provide 
incentives for different stakeholders in the 
industry. For the private sector, it could improve 
operational efficiencies and bolster brands in the 
marketplace, while for governmental authorities 
it could be a means to verify and validate catch 
reports and to document that export market 
requirements are met.
Precision aquaculture and
monitoring technologies 
In aquaculture, sensors increasingly collect 
optical (e.g. by video camera) and physical data to 
monitor, for example, fish growth, health and feed 
loss reduction. While past innovations focused 
on hardware and data collection, the problem 
is now the pressure on farmers to consistently 
interpret the large amount of data. Here, AI and 
data processing can help by identifying patterns 
in feeding activities and presenting strategies to 
farmers, ranging from cost-efficient use of feed to 
maintaining fish welfare. 
Genomics is rapidly impacting many facets of 
life. In the fisheries and aquaculture sector, 
DNA technology has become important in: fish 
breeding; the detection of pathogens; early 
warning systems for detecting plankton-borne 
threats to aquaculture based on environmental 
DNA; and fish authentication and provenance, 
especially for fish products in international 
trade. Moreover, DNA can be used to confirm the 
authenticity of specific products, with data also 
being stored in a blockchain structure (
Table 22
). 
However, there is no regulatory standard for 
DNA-based authentication of fish products, 
and an international collaboration based on 
industry-agreed systems is needed in order to 
make this innovation accessible. 
The knowledge needed for developing 
aquaculture systems under a blue growth 
paradigm requires innovations in monitoring. 
This is achievable through intensive data 
integration across various scales. Satellites, with, 
for example, normalized difference vegetation 
index products, can elucidate the location, 
number, surface of cages or ponds, and even the 
type of aquaculture practised. The IoT provides 
this interconnectedness among systems and 
across sensors, and enables managers to analyse 
data generated by satellite observations jointly 
with those provided from electronic fish tags. 
The key challenge with all these innovations 
is to combine data across data providers and 
countries and analyse them in a consistent 
way. Cloud computing and AI will benefit if 
data are consistent and follow standards for 
their collection and processing. Here, FAO