Data collection and analysis tools for food security and nutrition

particular perspective, they are only illustrative 
examples and can be re-shaped through many 
different perspectives or country contexts.
Example 1: How to increase population-level 
fruit and vegetable (FV) consumption based on 
local FV supply chains?
As previously indicated, the first step to be 
undertaken, prior to data collection, is to identify 
evidence priorities and related questions, 
ideally. This example is based on the following 
question that could apply to any country: How to 
increase population-level fruit and vegetable (FV) 
consumption based on local FV supply chains. 
The matrix is used to respond to this question.
Once the question is framed, the first step is to 
review, consolidate and analyse existing data, 
identifying potential additional data that could 
be collected. In the first column of the table, 
we listed the types of data that we imagined 
might be useful for answering the question, also 
identifying data systems and sources for said 
data and indicating the levels (from macro to 
individual outcomes) to which those data apply. 
Basic suggestions regarding the specific data 
to analyse are presented in the second column, 
entitled Analyse data using appropriate analysis 
tools. The next question is, how will the specific 
data, such as fruit/vegetable yield and fruit/
vegetable supply per capita be analysed? One 
suggestion is to use statistical packages to rank 
such data in order of fruit and vegetable with 
the greatest yield as well as compare with the 
greatest per capita supply. Perhaps in some 
countries the fruit and vegetable products 
with the greatest yield are not those with the 
greatest per capita supply as the high-yield 
products may be used primarily for exports. 
Such analyses comparing between and across 
levels are imperative to better understand how 
population-level fruit and vegetable consumption 
can be increased based on local fruit and 
vegetable supply chains. As a third step (in 
the third column) we listed the examples of 
the kinds of results, insights and conclusions 
that might be garnered from the data reviewed 
and/or collected, again, by differentiating the 

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22 
]
DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION
level to which they refer. In this example, data 
on FV availability, infrastructure and access 
could be incorporated into a policy brief (e.g. 
FAO and Ministry of Social Development and 
Family of Chile, 2021). In the fourth column, we 
included related examples of how said results, 
insights and conclusions might be shared or 
disseminated and with whom. Finally, in the 
fifth column, we suggest how the information 
disseminated might be used to facilitate (or 
not) the development of a policy related to the 
question prioritized from the outset. When 
applicable, all information entered in the matrix 
was colour-coded according to the primary FSN 
dimension of the cross-cutting FSN dimensions. 
It is important to note that each stakeholder 
has different objectives, or priorities; as well as 
related indicators of success or failure that may 
occur at each step of the data cycle, or in the 
case that not all steps are followed, in relation 
to the specific data cycle steps performed by 
a respective stakeholder. In many countries, 
the conclusions from this type of question are 
different per stakeholder. For example, the 
interests of the Ministry of Health are different 
from those of the Ministry of Agriculture, which 
might be more interested in agro-export, as 
those from the Ministry of Education can differ 
from those of the Ministry of Finance.
As alluded to in Section 1.1, data come in many 
flavours and – as will be mentioned in subsequent 
sections – multitudes of resources, both human 
and financial, as well as appropriate institutional 
arrangements, are necessary for data collection. 
The goal of this report, however, especially in 
terms of what follows, is to provide guidance for 
many FSN stakeholders, independent of their 
knowledge or expertise, to understand how both 
data collection and analysis tools can be more 
efficiently utilized, including, in some cases, 
through innovative techniques and technologies. 
Taken together, the conceptual framework and 
the data-informed decision-making cycle are 
meant to be used in tandem, while thinking 
about existing data (Chapter 2). And with regard 
to the collection or consolidation of new and 
existing data for FSN outcomes, given both the 
disadvantages (constraints in Chapter 3) and 
advantages of new digital technologies (chapter 
4); while always being aware of the central role of 
effective data governance (Chapter 5).

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1 
SETTING THE STAGE
FIGURE 4:
EXAMPLE OF HOW TO USE THE CONCEPTUAL FRAMEWORK (THEORETICAL GUIDANCE) AND DATA-
INFORMED DECISION-MAKING CYCLE (METHODOLOGICAL GUIDANCE) FOR FSN
Action along the data cycle
Level in the 
conceptual 
framework
Review, consolidate, 
collect, curate data
Analyze data using 
appropriate analysis 
tools
Translate data into 
results, insight and 
conclusions
Disseminate, share, 
review, discuss results, 
refine insights and 
conclusions
Use findings to make 
decisions
Macro
-Vegetable yield and 
Losses of vegetables and 
fruits (data system: Food 
Systems Dashboard; 
Databases: FAO; Ministry 
of Agriculture Databases)
-Rank vegetable yield 
and Losses of vegetables 
and fruits by type
-Incorporate data 
on FV availability, 
infrastructures, and 
access into a policy brief 
(e.g. the FAO Policy Brief 
on Promoting Fruit and 
Vegetable Consumption 
(available here: https://
www.fao.org/documents/
card/es/c/cb7956en)
-Engage key 
stakeholders: food 
system, trade and 
industry, social 
protection, health sector 
to design political actions 
(i.e. policies and/or 
programmes) to promote 
FV consumption
-FV innovation 
opportunities based on 
culturally appropriate 
and sustainable recipes
-Adaptations to 
procurement programme 
efforts/ new policies 
related to school feeding 
programmes
-School-based diet and 
health campaigns (e.g. 
to shift preferences to 
FV consumption, such as 
“Let’s Move!” or Jamie 
Oliver’s Learn Your Fruit 
and Veg Programme 
and Jamie Oliver Food 
Revolution Campaign) (*)
-Databases on FV 
infrastructure (e.g., 
transport/trip duration-
talk to country-level 
experts)
-Determine if sufficient 
FV infrastructure for 
local supply chains
-More interventions 
should be implemented 
to promote FV 
consumption, especially 
in early life
-Average distance 
of homes to farmers 
markets
-Determine average 
distance of homes 
to farmers’ markets 
(nationally)
-X% of municipalities 
do not have multiple 
farmers’ markets 
Meso
-Per capita supply of 
FV (data system: food 
systems dashboard)
-Determine regional per 
capita supply of FV
-FV production varies 
regionally within a given 
country
-Engage key 
stakeholders: food 
system, food industry, 
health sector, actors 
who can identify regional 
FV access to be able 
to refine insights and 
conclusions
-Supply chain 
adaptations (e.g., cold 
storage)
-Industry incentives and 
penalties
-Health sector to 
reinforce messaging
-Revise food composition 
databases
-Revise and adapt food 
safety guidelines 
-Prices and trends 
(data system: https://
ourworldindata.org/food-
prices)
-Regional FV prices and 
trends 
-Global conflicts and 
pandemics affect stability 
of global supply chains, 
which support the need 
to leverage local FV 
production
-Existence of food-
based dietary guidelines 
(database: Nourishing 
database)
-Analyse regional means 
of dissemination of food-
based dietary guidelines 
(if applicable)
-Are all fresh FV safe to 
eat?
-Average distance of 
homes to farmers’ 
markets
-Determine average 
distance of homes 
to farmers’ markets 
(regionally)
Micro
-Number of farmers’ 
markets per municipality
-Rank municipalities 
by number of farmers’ 
markets
-School feeding 
programmes should 
incorporate more locally/
regionally or nationally 
procured FV 
-Engage key 
stakeholders at regional 
and local level: regional 
and/or municipal 
governments, regional 
and/or municipal school 
programmes
-Local health sector to 
reinforce messaging
-Messaging at schools
FV incentives 
-Community groups 
that support community 
gardens and local FV 
distribution
-Analyse gaps in existing 
community groups
Individual 
food 
security and 
nutrition 
outcomes
-Individual FV intake 
(National Nutrition and 
Health Surveys)
-Analyse individual FV 
intake in terms of % FV 
portions per capita per 
day
-Individual FV intake 
should increase by at 
least one serving per 
capita per day based on 
new interventions and/or 
policy programmes
-Population 
disaggregated data 
essential to understand 
issues and propose 
solutions, such as the 
EU school fruit and 
vegetables programme
-Data used for advocacy, 
and to raise awareness 
of issues and relation to 
dietary intake of FVs
-The user-centred design 
process in community 
garden initiatives
(*) 
https://letsmove.obamawhitehouse.archives.gov/
https://www.thegoodfoundation.com.au/courses/jamie-olivers-learn-your-fruit-and-veg-online/
https://www.jamieoliver.com/campaigns/
Legend colour-coded six dimensions of food security:
Agency (orange) 
Short-term stability (green) 
Long-term sustainability (asparagus green)
Access (dark blue)
Availability (periwinkle)
Utilization (light grey)

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24 
]
Chapter 2
A REVIEW OF EXISTING FSN 
DATA COLLECTION AND 
ANALYSIS INITIATIVES
PHILIPPINES, 05 July 2018, Development of an Enhanced Production and Risk Management in Agriculture Integrated 
Decision Support System (EPRiMA).
© FAO/Veejay Villafranca

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[
25
2 
A REVIEW OF EXISTING FSN DATA COLLECTION AND ANALYSIS INITIATIVES
T
he conceptual framework for this report 
(
FIGURE 1
) highlights the multiple levels and 
systems relevant for tracking progress 
and informing actions for better FSN decision-
making. The myriad types of data that may be 
relevant at all levels for the different decision-
makers is an important challenge for FSN. A 
comprehensive review of all existing data would 
be a monumental task indeed and beyond the 
scope of this report. Here we focus instead 
on an illustrative review of some of the main 
frameworks, data systems and repositories 
that hold data relevant for FSN (summarized in 
ANNEX
TABLE 1
). Each of the listed data repositories 
and initiatives has been designed for a specific 
purpose or set of purposes. Considering the 
multiple sectors and topics implicated in the 
conceptual framework, It is no surprise that no 
single system currently exists that contains all 
types of FSN-relevant data, as it is difficult to 
imagine how such a system could ever exist.
In this chapter we have two main objectives. 
The first one is to provide an overview of FSN-
relevant data, discussed with reference to the 
conceptual framework (
FIGURE 1
) and linking 
them to the relevant FSN dimension. The 
illustrative overview of data sources will provide 
a sense of the wealth of FSN-relevant data that 
are available and will also help to highlight 
remaining gaps in data generation. The second 
objective is to draw from this overview a list of 
gaps and challenges at each step of the data 
cycle and highlight good practice examples 
of how they have been addressed in on-going 
efforts.
ILLUSTRATIVE OVERVIEW OF 
EXISTING FSN DATA
As reminder, the macro level factors relevant 
to FSN are those that are far removed from the 
sphere of influence and control of the individual. 
They may be global in perspective (e.g. the extent 
of integration and functioning of international 
food commodity markets), regional (e.g. regional 
food trade arrangements), but also national 
(e.g., political structures) or even local (e.g. 
sociocultural norms related to gender and land 
tenure). They influence factors at the meso 
level (e.g. food systems), which in turn influence 
factors that are more immediately in the sphere 
of inference and control of the individual (e.g. 
community markets, and household behaviours).
FSN DATA AND INFORMATION 
SYSTEMS RELEVANT AT THE MACRO 
(DISTAL) LEVEL
The top row of Annex Table 1 lists data on 
elements that are distal influences on FSN. 
These include the 
natural resource base
used 
for agriculture and food production, 
climate 
and the global environment
, the extent of 
integration and functioning of 
international 

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DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION
food commodity
markets
. As noted, several of 
these elements cover ground that goes beyond 
individual countries. For such data, therefore, 
strong international coordination is critical to 
ensure comparability, transparency and similar 
considerations. International organizations play 
a crucial role in supporting and enabling such 
efforts.
BOX 1:
FAO STATISTICAL SYSTEM
Coordinated by the Office of the Chief Statistician and operated by various departments, FAO regularly compiles 
data reported by Member Nations (e.g. official national statistics, agricultural or population censuses and surveys, 
etc.) and from other sources (such as international commodity market reports) related to all aspects of agriculture 
and food. Statistical activities fulfil the objective to provide open access data and information, reflecting one of the 
foundational components of the Organization’s mandate.
9
FAO statistical activities involve the compilation, curation 
and dissemination of data; the development and promotion of rigorous statistical methods and standards; and 
statistical capacity development for member countries (FAO, 2020a).
The FAO Statistics Division manages the Organization’s system of 
food and agriculture statistics
, compiling from 
different sources, with the explicit aim of covering almost all countries and territories in the world. Data are used for 
global purposes (including the development of statistical yearbooks covering food security and nutrition, crop and 
livestock, and economic, social and environmental statistics), but also at national and regional levels. Statistics are 
disseminated through two major dissemination platforms: FAOSTAT and the Rural Livelihoods Information System 
(RuLIS).
FAOSTAT food and agriculture data
is the largest and oldest repository of data and information on food and 
agriculture in the world. In its latest configuration, it includes eight different domains covering (a) statistics on crop, 
livestock and food production and trade; (b) agricultural and rural economic statistics; (c) environmental statistics; 
(d) food security and nutrition; (e) social statistics; (f) data and statistics derived from censuses of agriculture; (g) 
data and statistics derived from agricultural surveys; and (h) statistical methodological innovations.
FAOSTAT aims provide harmonized data and statistics that are comparable across time and space. The system is 
designed to facilitate extraction of time-series of variables for the 245 countries and territories, but also for groups 
of countries, entire regions, and at the global level.
FISHSTAT
, managed by the Fisheries and Aquaculture Division, provides open access to fisheries and aquaculture 
data covering 245 countries and territories. It comprises 9 different data domains covering all aspects of the 
fish products value chain: from aquaculture production and fisheries’ capture to information on the global trade, 
processing and distribution of fish and fish products. It also publishes statistics on employment in fisheries and fish 
farms and the size and characteristics of the fishing fleets.
AQUASTAT
, another global FAO database, contains information on water use in agriculture throughout the world, 
which is fundamental for food availability and sustainability considerations. This database also provides data and 
information for individual countries, regions or at the global level.
Data and information on distal factors that may 
influence 
food availability include natural resource 
use (mainly land, water and fisheries), input use, 
agricultural production and food trade. Large 
amounts of data covering these aspects are 
available, mostly housed in the statistical system 
of the Food and Agriculture Organization of the 
United Nations (FAO) (
SEE BOX 1
).
9 In defining the functions of the organization, Article 1,1 of the FAO Constitution includes the following: “The Organization shall collect, 
analyze, interpret and disseminate information relating to nutrition, food and agriculture” (FAO, 1945).
26 
]

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2 
A REVIEW OF EXISTING FSN DATA COLLECTION AND ANALYSIS INITIATIVES
BOX 2:
THE AGRICULTURAL MARKET INFORMATION SYSTEM (AMIS)
The 
Agricultural Market Information System
(AMIS) is an interagency platform to enhance food market transparency 
and policy response for food security. It was launched in 2011 by the G20 Ministers of Agriculture following the global 
food price hikes in 2007/08 and 2010. Bringing together the principal trading countries of agricultural commodities, 
AMIS assesses global food supplies (focusing on wheat, maize, rice and soybeans) and provides a platform to 
coordinate policy action in times of market uncertainty.
By enhancing transparency and policy coordination in international food markets, AMIS aims to provide timely 
information on the state of the main food commodities traded on international markets. This is useful to anticipate 
and prevent possible tensions, reduce price uncertainty and, thus, strengthen global food security.
AMIS comprises G20 members, Spain and seven additional major agricultural commodity importing/exporting 
countries.
To carry out its functions, AMIS consists of:
•
the Global Food Market Information Group, which assembles technical representatives from AMIS participants to 
provide reliable, accurate, timely and comparable market and policy information;
•
the Rapid Response Forum, comprising senior officials from AMIS participants, which promotes early discussion 
about critical market conditions and ways to address them; and
•
the Secretariat, involving ten international organizations and entities, which produces short-term market outlooks, 
assessments and analyses and supports all functions of the Information Group and the Forum.
Data at this level may also be important for 
considerations related to 
food access, given the 
importance of international trade and the links 
between international and domestic prices,