particularly problematic in terms of updating and
refining food and agriculture policies, considering
the rapid transformation of the agricultural sector
in most LMICs. Paramount among the gaps in
information is the lack of availability of agrifood
data and statistics. Globally, annual agricultural
survey data are available approximately for 60
percent of the countries (Committee on World
Food Security, 2021). The availability of data to
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A REVIEW OF EXISTING FSN DATA COLLECTION AND ANALYSIS INITIATIVES
compute indicators of productivity and income
of smallholders, of food loss, food waste and
secure right over agricultural land is currently
sufficient for less than 4 percent of the countries
(Committee on World Food Security, 2021). There
is also a lack of improved agricultural forecasting
and other techniques that can augment traditional
agricultural surveys. For developing countries,
this poses a huge challenge, as agriculture and
food production data are important to understand
the links between food security, livelihoods and
poverty (Committee on World Food Security, 2021).
Gaps also exist, for example, in understanding the
contribution of fisheries and aquaculture to FSN
and the sustainability of these operations (
SEE BOX 3
).
BOX 3:
IMPROVING THE ANALYSIS OF FISH DATA
Several studies (see Hicks et al., 2019 and Vaitla et al., 2018) have highlighted the potential importance of fish as a
source of micronutrients, especially in middle- and low-income countries. Despite this, little information is available
regarding the nutrient values of fish.
To fill this data gap, GitHub (2022) developed the Fishbase Nutrient Analysis Tool, a Bayesian hierarchical model
that uses both phylogenetic information (which considers the relationships between fish species) and trait-based
information (which considers key aspects of fish diet, thermal regime and energetic demand) to estimate the
concentration of calcium, iron, omega-3, protein, selenium, vitamin A and zinc in marine and inland fish species. The
FishNutrients component of Fishbase estimates the specific nutritional content of a vast array of aquatic species
caught around the world (see
https://www.fishbase.in/Nutrients/NutrientSearch.php
).
While recognizing the potential of fish as a source of key nutrients, FAO also recognises the need to monitor the
sustainability of fishing activities. In an effort to address the sustainability of fishing, FAO has developed a definition
for illegal, unreported and unregulated (IUU) fishing – a broad term that captures a wide variety of fishing activities.
IUU fishing is found in all types and dimensions of fisheries and is reported to occur both on the high seas and in
areas within national jurisdiction (
https://www.fao.org/iuu-fishing/background/what-is-iuu-fishing/en/
).
Several initiatives aim to further our understanding of the sustainability of global fishing activities, their yields and
their contribution to livelihoods. Illuminating Hidden Harvests is an upcoming FAO, WorldFish and Duke University
study that seeks to quantify and standardize the immense contribution of small-scale fisheries to global fishery
yields and livelihoods:
https://sites.nicholas.duke.edu/xavierbasurto/our-work/projects/hidden-harvest-2/
.
Another initiative, the Global Fishing Watch platform, is being designed to enable the use of multiple open-source
technologies and data sources to evaluate and manage fisheries:
https://globalfishingwatch.org/news-views/
mapping-a-new-world/
.
Some of the data gaps are partially filled by
efforts led by international organizations or
other institutions, mostly operating in high-
income countries, which collect country-level
information to guide their operations and
make their data and information available for
other uses. Particularly relevant in this area
are the
Global Information and Early Warning
System
(GIEWS) on food and agriculture,
managed by FAO; the activities coordinated by
the
Vulnerability
,
Analysis and Mapping
(VAM)
team at the World Food Programme (WFP)
and those of the
International Production
Assessment Division
(IPAD) of the Foreign
Agricultural Service (FAS) at the U.S. Department
of Agriculture (USDA) (
SEE BOX 4
). Through their
data dissemination portals, these initiatives
make available country briefs, country profiles
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DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION
and other periodic reports on crop production
and forecasts, food prices and food security.
Extremely important in his context is the timely
information on local food prices available through
the GIEWS
Food Price Monitoring and Analysis
portal, which contains the latest available
information and analysis on the domestic prices
of basic foods in developing countries.
BOX 4:
GIEWS AND OTHER INFORMATION SYSTEMS
FAO’s
Global Information and Early Warning System on Food and Agriculture
(GIEWS) continuously monitors food
supply and demand and other key indicators for assessing the overall food security situation in all countries. It issues
regular analytical and objective reports on prevailing conditions and provides early warning of impending food crises
at country or regional level. At the request of national authorities, GIEWS supports countries in gathering evidence
for policy decisions or planning by development partners, through its Crop and Food Security Assessment Missions
(CFSAMs), fielded jointly with the WFP. Through the use of tools for earth observation and price monitoring at the
country level, GIEWS also strengthens national capacities in managing food security-related information.
To guide its operations, the WFP requires large amounts of data, some of which is accessible to others through
“DataViz”, a web-based platform (see
https://dataviz.vam.wfp.org/
).
The International Production Assessment Division of the Foreign Agricultural Service at USDA offers a rich set of
data products, including reports and brief, geospatial data, crop calendars and production maps, easily accessible
through their web portal at
https://ipad.fas.usda.gov/Default.aspx
.
Though very useful, these initiatives should not
substitute national data systems, and efforts
should be made to ensure they are fully “owned”
by national governments and to avoid that they
crowd out national capacities. To that end, the
United Nations Statistics Division
plays an
important role developing standards and norms
for statistical activities and supporting efforts
to strengthen national statistics systems in
many countries. It must be noted here that the
continued evolution of data technologies is rapidly
changing the information landscape on crop
production conditions, yield forecasts, etc. (see
further discussion of data-related technologies
in Chapter 4), allowing for much more frequent
and rich data generation. However, this trend
widens the divide that already exists between
LMIC and high-income countries (Kitchin, 2014a;
2021). Notable efforts to fill these gaps are
ongoing.
FAO’s Hand-in-Hand Initiative
(
BOX 5
)
supports national policymaking by facilitating
easier access to relevant geospatial and other
disaggregated available data on all dimensions
of agriculture and FSN. The
50x2030 initiative
aims to close the food and agricultural data gap
in 50 countries
(
BOX 6
). An additional initiative,
the
Global Strategy to improve agricultural and
rural statistics
, a large technical support and
capacity development programme established
in 2015 with important implications for data
governance, is discussed in Chapter 5.
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A REVIEW OF EXISTING FSN DATA COLLECTION AND ANALYSIS INITIATIVES
BOX 5:
FAO HAND-IN-HAND INITIATIVE
The
FAO Hand-in-Hand Initiative
(HiHI) was launched by the FAO Director-General in September 2019. FAO Member
Nations expected to be facing challenges(*) were invited to participate in this initiative, which aims to accelerate
agricultural transformation and sustainable rural development, through an evidence-based, country-led and
country-owned process supported by FAO. As of today, 48 countries have joined.
The initiative is designed as an inclusive process that builds partnerships, alliances and synergies among public and
private sectors, and with international development partners. The objective is to identify investments that could have
the highest impact on agrifood system and rural transformations and to achieve SDG goals of eradicating poverty and
hunger and reducing inequalities. It aims to channel resources – technical, financial, institutional and human – to
where they are needed most and where the potential for reaching the SDG 1, SDG 2 and SDG 10 targets is greatest.
Data are at the core of HiHI. Situation analyses needed to identify intervention opportunities in areas with high levels of
poverty and malnutrition and extensive inequalities may call for complex analyses on cross-domain data, aggregating
and enriching existing information from geospatial and socioeconomic data, as well as information gathered from
non-conventional sources. HiHI emphasizes timely information and sophisticated analysis of data on biophysical
phenomena and agroecological and livelihood conditions, at all levels – from highly aggregated global data to the
most granular local data. This requires analytic tools and capacities that do not exist yet in all participating countries.
To support these situation analyses, HiHI offers its
Geospatial Platform
, described as the world’s largest and most
capable platform for geospatial data and information exchange and analysis (
https://www.fao.org/hand-in-hand/
en/
). The platform brings together over 20 technical units from multiple domains across FAO, from animal health to
trade and markets, integrating data from across FAO departments focusing on soil, land, water, climate, fisheries,
livestock, crops, forestry, trade, social and economic statistics, among others. In addition, the platform continuously
and increasingly integrates vast amounts of georeferenced data in specialized domains (maritime food trade, climate
risks and other vulnerabilities for small island developing nations and other at-risk nations) gathered from partners in
academia and other public and private entities, making them available free of charge to users at large.
Another initiative, the
Data Lab for Statistical Innovation
supports HiHI by addressing specific challenges related
to timeliness, granularity, data gaps and automation of analysis for faster in-depth analyses. To achieve these
objectives the Data Lab:
•
promotes the use of non-official, unstructured data and data science methods to fill in data gaps in domains and
geographical areas where official data are scarce;
•
validates official data reported by countries in order to identify areas of future collaboration and technical assistance;
•
identifies relevant data sources and appropriate analysis techniques to produce evidence and build insights;
•
develops geospatial tools and tagging systems at subnational level, to increase data granularity, especially in
tropical and dryland areas where the most vulnerable populations live;
•
builds data systems for HiHI that will facilitate the identification of target areas and highlight aspects of their
agricultural potential;
•
provides tailored text-mining tools to extract, summarize and categorize information on effective policy
interventions that can be applied in similar situations.
Note: (*) Eligible countries include countries classified as Least Developed Countries (LDC), Landlocked Developing
Countries (LLDS), Small Island Development States (SIDS) and countries included in the group of Food Crisis
Countries covered in the Global Report on Food Crises.
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BOX 6:
THE 50X2030 INITIATIVE TO CLOSE THE AGRICULTURAL DATA GAP
The
50x2030 initiative to close the agricultural data gap
was launched in 2019 by FAO, IFAD and the World Bank to
improve country-level data in 50 countries in Africa, Asia, the Middle East and Latin America by 2030, by building
strong nationally representative survey programmes. Depending on the conditions in each country, this may take
some time. But while new data are being generated, it is also important to demonstrate the usefulness of this
information by making the best possible use of the available evidence from farm surveys, even if scattered, including
by integrating existing data with data and information from other sources, or by devising creative ways of analysing
the data. The 50x2030 initiative builds on the
Global Strategy to Improve Agriculture and Rural Statistics
(GSARS),
and promotes research, for example by offering data research grants to local researchers.
FSN DATA AND INFORMATION AT THE
MICRO (IMMEDIATE) LEVEL
There are essentially two types of data and
information relevant to FSN at the micro level
–
supply-side data and household level demand-
side data. Data and information on the supply side
should address dimensions of food availability,
stability, sustainability and accessibility (to the
extent that they include food prices). A variety of
sources of such data are needed at the micro level
including farms; fisheries; production, processing
and distribution operations; retail distributors
and restaurants. These may be local or regional
businesses (from micro to large businesses)
or local affiliates of national or multinational
companies. Micro level data on these dimensions
of FSN would capture some elements of the food
environment, which has been described in previous
HLPE-FSN reports (HLPE, 2017; 2020) as the point
of interaction of the individual with the food system.
The analogy is not perfect however, even for what
has been described by some as the external food
environment (availability, price, market and vendor
properties, and marketing and regulation related
to food) (Turner
et al., 2020). In our conceptual
framework, marketing and regulation, for example,
would sit at the meso or even macro level as it may
have an influence on availability, price and market
and vendor properties.
Regardless of whether the food environment
framing is used or not, there are enormous gaps
in the availability of FSN-relevant data at the micro
level (Turner
et al., 2020). Key among these are
data on the operation of local markets. The highly
diverse local and national food markets that are
embedded in territorial food systems have been
defined by the Committee on World Food Security
(CFS) as
territorial markets (
CFS, 2016
). Despite
their importance in linking food supply and demand
at the territorial level, data on territorial markets
are seldom included in national data collection
systems (FAO, 2022;
CSM, 2016
;
CFS, 2016
), a gap
that FAO is trying to fill with a recent initiative (
SEE
BOX 7
).
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BOX 7:
FAO’S APPROACH TO MAPPING TERRITORIAL MARKETS
To address the evidence gap in the contribution of territorial markets to food availability and to other factors that
may influence consumers’ food purchasing and consumption, in 2017, FAO, together with several academic and
civil society organizations, began developing a methodology for the collection of reliable and comparable data on
territorial markets (FAO, 2022). The methodology consists of a set of guidelines and questionnaires for consumers
and for retailers, and uses a harmonized approach for collection and analysis that permits comparisons across
contexts and over time. It is designed to inform policy and market-level interventions aimed at improving the food
offering (from nutritional, safety and environmental perspectives) of the market environment and fostering healthier
food choices among consumers.
Based on existing evidence at the time, the expert group developing the methodology identified several key aspects
of markets, retailers and consumers that should be captured through the questionnaires: (i) women retailers’
inclusion in markets
http://www.fao.org/3/a-i3953e.pdf
; (ii) enabling/disabling aspects of the business environment;
(iii) length of the supply chain; (iv) food diversity; and (v) contribution of the market to healthy and diversified diets.
The main criteria used to identify these aspects included: their degree of influence on the foods on offer and on
consumer choice and their degree of influence on market inclusivity and responsiveness to interventions. These
aspects are represented by five multidimensional and synthetic indicators, which were created as part of the
methodology, in order to evaluate market performance on these particular aspects.
The methodology has been piloted in two countries, one in Africa and one in Latin America, and implemented in
six additional countries. To date, data have been collected on 60 markets and is available on
FAO’s Hand-in-Hand
geospatial platform
. In each country, the mapping process followed the same steps: 1) joint selection of the markets
by stakeholders and policymakers, based on the perceived relevance of these markets for the local communities;
2) adaptation of the questionnaires for the local context; 3) training of enumerators, including a field trial of the
questionnaire; 4) data collection; 5) data processing and analysis; 6) reporting on the findings; and 7) a final validation
workshop focusing on reviewing the findings to understand whether they resonate with current knowledge, and
exploring the potential implications of the findings for policy and programmatic interventions to promote healthy
food market environments and healthier food choices among market consumers. For the data collection itself, a
user-friendly, open-source questionnaire (the
KoBoToolbox
, adapted for online and offline use), was developed to aid
in standardized data collection and analysis approaches.
Another area in which data are lacking is the
extent of food losses along the supply chain, which
has important implications for food security and
nutrition policy (FAO, 2019a). Data relating to the
food systems such as consumer behaviour and
its drivers, impact of household interventions
to reduce food water/loss for instance, food-
utilization data or dietary diversity data are notably
scarce (Committee on World Food Security, 2021;
Deconinck
et al., 2021).
There are important challenges to improving the
availability of these data, including no consensus
on key data types needed and, therefore, no
harmonization of data standards; no repository into
which such data are regularly channelled; and little
to no incentive for businesses to publicly share data
related to local production, price, sales, market
characteristics and other relevant aspects. With
regard to the concept of a food environment, this
continues to evolve and there is still little clarity
of the core constructs for which data are required
to inform FSN policies and programmes. In the
area of food losses and waste, countries may need
to ensure cost-effective data generation, improve
the reliability of existing data by benchmarking
international standards in terms of methods and
metadata, enhance the accessibility of information
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for policymaking and encourage transfer of
innovative practices among countries and improve
transparency (Fabi
et al., 2021). Unfortunately, no
examples could be found of good practices from
a country, region or globally in addressing these
challenges.
The other type of data at the micro level – framed
as demand-side data – includes data generated at
the household level. These data may capture the
FSN dimensions of accessibility, utilization and even
agency, provided they are appropriately designed. It
may include relevant data on food purchases, gifts
and home production; income; assets and social
protection benefits; but also water; sanitation;
health services and many other aspects relevant
for FSN. Most of these data come from population-
based surveys. As such, the collection of such
data tends to be
resource-intensive and has been
plagued by a lack of stability in the availability of
resources needed to maintain the data up-to-
date. Infrequent data impede the adjustment of
policies based on changing circumstances of the
population. Data-related technologies and big
data are rapidly evolving and may help change
this in some contexts. (This is discussed further in
Chapter 4).
As discussed previously, granularity and
disaggregation at the subnational level is also
a challenge in many contexts given sample size
and thus, resource implications to implement
sufficiently large population surveys. Several
standardized survey platforms collect relevant
data at this level, including income and budget
surveys, household consumption surveys,
Living Standards Measurement Surveys (LSMS),
Demographic and Health Surveys (DHS) and
Multiple Indicator Cluster Surveys (MICS), among
others. These have done much to overcome
different barriers, including data harmonization
and the provision of technical support to
countries where capacity gaps exist.
The recent proposal to include agency as one
of the dimensions of FSN has an immediate
application in the data domain (Clapp
et al.,
2021), from the macro to the micro levels
of decision-making. In this context, agency
means the ability to identify one’s own data
needs, to undertake analysis and share data
and knowledge to address these needs and to
guide individual and collective decision-making
regarding food production and consumption and
other aspects concerning food systems. Agency
also means having access to and using local
data at the local level to make informed choices,
enhancing the two-way flow of data.
Data can indeed be a strategic instrument
of empowerment, just as lack of data and
information is a driver of vulnerability. This is
true for FSN as it is for other domains of policy
and decision-making affecting people’s well-
being. Examples on the importance of data
for agency abound: accurate information on
producer prices (and price forecasts) would
enable smallholders to decide what to cultivate,
when and where best to sell; data on markets
and prices can be used by smallholders to build
a credit or sale history so as to be able to access
bank loans or procurements by government or
private urban wholesalers; rain gauge data at
the local level can be instrumental to predict
rainfall or to claim rainfall insurance; soil quality
measures, traceability of inputs (such as certified
seeds) and what become of their produce will
empower farmers; forest conservation can be
monitored with drones, etc. Indigenous peoples
and grassroots organizations are collecting,
analysing and disseminating data, using new
technology, to mobilize collective action in food
systems. In India, the POSHAN (Partnerships
and Opportunities to Strengthen and Harmonize
Actions for Nutrition in India) initiative has
mobilized citizens as data generators and users
to improve nutrition (WHO and UNICEF, 2020)
(
SEE BOX 16
).
Despite these advances there is still a paucity
of data on many considerations critical for
policymaking, such as the interests and values
of individuals and stakeholders at all levels
(Deconinck
et al., 2021). These and other data
may not be amenable to the largely quantitative
orientation of most, if not all, of the data sources
described thus far.
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A REVIEW OF EXISTING FSN DATA COLLECTION AND ANALYSIS INITIATIVES
BOX 8:
DATA COLLECTION IN CONFLICT SETTINGS
Armed conflict and other situations of violence have remained one of the primary drivers of food insecurity,
malnutrition and famine in many countries. All five famines declared over the last decade in Ethiopia, Nigeria,
Somalia and twice in South Sudan were essentially driven by the consequences of armed conflict and violence.
Hotspots for violence tend to be blind spots for information, especially for survey and household data, which are
necessary to ascertain the severity of the situation and determine whether famines should be declared and the
responses required. Challenges in this regard are multiple and concurrent: data may be impossible to collect, it
may be collected but not released, or it may be collected but lacking in completeness, quality or timeliness. Remote
methods are increasingly viable to support data collection in areas that cannot be reached in person, but the
usefulness and accuracy of the data collected are still limited.
In these contexts where complete and reliable data cannot be collected, to the extent possible, it is recommended
that a combination of sources of evidence be used (IPC Global Partners, 2021). For example, useful data can include
those collected at assistance distribution points, those collected from people arriving at camps and those collected
in accessible areas that share similar conditions to inaccessible areas. Because of the limited reliability of these
data (as adequate sampling cannot be executed) it is necessary to carefully process and interpret these data. For
example, information gathered from new arrivals at camps needs to carefully consider origin and travel time of the
displaced populations. Whenever possible, data collected in conflict settings should be supported by quantitative
and qualitative data collected at the community level during missions to the areas affected by conflict. Helicopter
missions, for example, were crucial to classify the 2016 Famine in South Sudan.
In conflict situations, there is also likely an entire ecosystem of data collection and analysis unique to the given
context. Data on the extent of the conflict itself (number of people involved, causalities, etc.) may be more available
than data on the food security and nutritional status of the affected population. Many conflict contexts have a range
of publicly accessible reporting by various UN bodies, including Panels of Experts mandated by the UN Security
Council, Joint Mission Analysis Centres or Human Rights Divisions within UN peacekeeping operations, and other
analysis by specialized agencies, such as the International NGO Safety Organisation (INSO) and the Nigeria Security
Tracker. A variety of academic and other research institutions also provide conflict analysis and other analysis
directly relevant to the conflict, such as the Rift Valley Institute’s work across the Greater Horn of Africa. Regular
media reporting can also supplement these sources.
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The main lessons we derive from this overview
of existing FSN data, and data gaps, are the
following:
1)
There exists already an abundance of
data across several levels of our conceptual
framework and dimensions of FSN. In order to
effectively use these data and information for
FSN decision-making, continued efforts must
be made to
ensure harmonized data standards
and availability (as illustrated by examples of
FAOSTAT), to improve data access, to transform
data into relevant insights and to build capacity
to capture and use data (as illustrated by the
HiHI and AMIS). The abundance of data at
several levels offers an opportunity to reflect on
its utility and to explore areas where data can be
streamlined and prioritized, ensuring efficient
and effective use of scarce resources.
2)
There are, however, notable gaps in the
availability and accessibility of data. While it is
difficult to provide a universal list of high-priority
data gaps, as the gaps are country-specific, it
is a fact that relevant FSN data are particularly
scarce in most low-income countries. Even
where data exist, their
frequency and granularity
are often insufficient to track progress over
time, guide needed policy reform, or adjust
programmatic responses to the changing reality
of local contexts. It would be extremely helpful
to compile lists of FSN data priorities by country,
with technical and financial assistance from
international organizations and donors. The
50x2030 initiative (
SEE BOX 6
) seeks to address
this for many types of agricultural data, which
are relevant for FSN, but more needs to be
done, especially in terms of timeliness and
completeness of information at the household
and individual levels, covering people’s ability to
access food and the actual diets they consume,
which are crucial to guide effective FSN policy.
CHALLENGES AND
OPPORTUNITIES FOR FSN
DATA-INFORMED DECISION-
MAKING
In the previous section we highlighted several
strengths and weaknesses of extant data
across the levels of our conceptual framework
and across the dimensions of FSN. This
section explores how those strengths, gaps
and limitations may influence data-informed
decision-making for FSN, by reviewing each
of the steps in the data for decision-making
cycle (
FIGURE 2
). The gaps and limitations are
translated into the primary challenge(s) that may
impede each step in the cycle and good practice
examples and opportunities to overcome those
challenges are identified. Due to the growing
interest in food systems transformation and the
recognition of the centrality of diets to many
health outcomes, there are many efforts and
examples to draw on.
Before moving to the data cycle, however, let us
explore the role of target setting to motivate the
data generation and utilization for FSN. Target
setting for internationally agreed upon goals,
and the resulting tracking of progress towards
their achievement, has been an enormous
stimulus for data collection and dissemination.
Such data provide a tool for accountability and
supports evidence-informed advocacy for FSN.
International agreement on common goals is a
powerful incentive to bring together stakeholders
from across multiple sectors. This was indeed
one of the overarching objectives of the
United
Nations Sustainable Development Goal Indicator
Platform
(
BOX 9
).
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A REVIEW OF EXISTING FSN DATA COLLECTION AND ANALYSIS INITIATIVES
BOX 9:
FSN AND THE SDG MONITORING FRAMEWORK
Food security and nutrition is now high on the development agenda thanks to the deliberations of the World Food
Summit held in 1996 (FAO, 1996); the commitment to end hunger by 2015, included in the United Nations Millennium
Declaration in 2000 (
A/RES/55/2
) which established the Millennium Development Goals (MDGs); and – most recently
– the 2030 Agenda for Sustainable Development, endorsed by the UN General Assembly in 2015 (
A/RES/70/1
).
This emphasis on food security and nutrition, and the accompanying commitments, have created incentives for the
production of data on FSN globally and in most countries.
The 17 Sustainable Development Goals (SDGs) represent some of the most urgent and universal needs of the world
today, and for over a decade have formed the backbone of nearly every development initiative in the world. As a
mechanism to facilitate the implementation of the 2030 Agenda, on 6 July 2017, the UN General Assembly officially
adopted a framework composed of 169 targets and 241 indicators to monitor progress towards the 17 SDGs and to
inform policy and ensure accountability of all stakeholders towards their achievement (
A/RES/71/313
).
The monitoring framework has been of enormous importance to raise awareness regarding the importance of data
and statistics in all areas covered by the SDGs. Agriculture and FSN feature directly as the focus of SDG 2: “End
hunger, achieve food security and improved nutrition and promote sustainable agriculture”, but are relevant to many
other goals as well, including SDGs 1, 3, 10, 12 and 16.
The Inter-Agency and Expert Group on SDG indicators, established under the UN Statistical Commission, supports
coordination among Member Nations towards the harmonization of data, indicators and reporting, and has created
a dedicated web-based platform (
https://unstats.un.org/sdgs/
). Specialized UN agencies have been assigned
as custodians of SDG indicators in their respective areas of competence. The role involves the responsibility to
establish and maintain standard definitions of the indicators, to provide capacity development and technical support
to countries for the production of the indicators, and to collate and report on the indicators produced by countries.
FAO has been nominated the custodian agency for 21 of the 241 SDG indicators. Of particular note in response to
this responsibility is the annual publication by a consortium of five UN agencies of The State of Food Security and
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