Data collection and analysis tools for food security and nutrition

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 

---

2 
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 
[
29

---

30 
]
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.

---

2 
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.
[
31

---

32 
]
DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION
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
).

---

2 
A REVIEW OF EXISTING FSN DATA COLLECTION AND ANALYSIS INITIATIVES
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 
[
33

---

34 
]
DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION
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.

---

2 
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.
[
35

---

36 
]
DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION
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
).

---

2 
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