Data collection and analysis tools for food security and nutrition

Particular fundamental areas of FSN where lack 
of sufficient data is especially relevant include, 
the impact of pests, natural calamities, conflicts 
or other shocks on food security and nutrition. 
(
SEE BOX 8
).
Another area where granularity of data is 
crucial is that of meteorological and soil fertility 
data. These data have important implications 
for drought monitoring and early warning 
regarding possible risks to local food availability. 
When meteorological data are available, those 
derived from ground-based stations may record 
variables such as rainfall, temperature and 
wind, but not more technical measures such 
as humidity and solar radiation. This may lead 
to significant data gaps and inaccuracy. Earth 
Observation (EO) climate data products, based 
on broad hydrometeorological monitoring tools 
such as Global Land Data Assimilation System 
(GLDAS), can fill gaps by substituting missing 
data and generating data sets (Colston 
et al., 
2018). However, these data disaggregated by 
agroecological zones are largely lacking.
Overall, this is an area where more granular, 
georeferenced data collected through different 
means and technologies (such as the 
Google 
earth engine
), holds great promise (see further 
discussion of technologies in Chapter 4).
FSN DATA AND INFORMATION AT THE 
MESO (PROXIMAL) LEVEL
The structure of the agriculture, food, health and 
other related sectors have strong implications 
for food security and nutrition. National food 
and agricultural data (on food supplies, prices 
and inflation, for instance) and the structure of 
national food supply chains, among other factors, 
shape the social and economic environment in 
which citizens live and operate and are vital to 
inform most dimensions of food security at the 
meso level, including availability, access, stability 
and sustainability. Ideally, such data should be 
included in national official statistics, regulated 
in most countries by national statistical laws and 
coordinated by national statistical offices (NSOs). 
Despite efforts, there are still gaps and notable 
differences among countries in this regard. The 
timeliness, completeness and quality of official 
statistics on agriculture, fisheries and other sectors 
of relevance to food security and nutrition, is still 
largely insufficient in many low- and middle-
income countries (LMICs). According to the latest 
FAO assessment for example, 92 countries have 
not conducted an agricultural census in the last 
ten years, 52 of which have not done so in the 
last 20 years (
ANNEX TABLE 3
). Ten of these countries 
(including Cuba, Ukraine and South Sudan) have 
no agricultural census on record at all. This is