ICARDA annual report 2004

ICARDA Annual Report 2004
56
If users want to represent the
results obtained as resource
endowments, rather than resource
poverty, an agricultural-resourceendowment
index (AREI) can easily
be calculated as 100-ARPI. This
method was used to develop an
AREI map of CWANA (Fig. 24),
which was validated using various
case studies. These were undertaken
at different scales, ranging from
the regional to the local, and
showed that relationships exist
between the AREI and various
indicators of poverty—such as a
country’s agricultural GDP, malnutrition,
village income, and population
density. They also showed that
the method is scalable and can be
used outside CWANA.
This simple tool for integrating
complex natural-resource data into
a single indicator of resource
poverty or endowment has already
been used to map the distribution
of agricultural income in Syria.
Mapping the agricultural
regions of Syria
‘Agricultural regions’ are integrated
spatial units in which water
resources, climate, terrain, and soil
conditions combine to create
unique environments associated
with distinct farming systems and
land-use and settlement patterns.
ICARDA researchers have developed
a technique to map these
regions in order to produce a single
synthesis map that represents several
issues in agriculture such as
land degradation and identification
of areas with agricultural potential
or constraints, planning rural land
use, developing input and subsidy
policies, and targeting efforts to
alleviate rural poverty.
Mapping agricultural regions
requires a good multi-thematic
database, expert knowledge, and
validation of the map produced
Fig. 24. Distribution of the Agricultural Resource Endowment Index (AREI) in CWANA.
through fieldwork and remote
sensing. When mapping the agricultural
regions of Syria,
researchers delineated the boundaries
between mapping units using
recent satellite imagery and secondary
information, including geological,
soil, landform and climate
maps. The boundaries were drawn
in vector format, based on a visual
interpretation of Landsat imagery
for the spring of 2003 and the summer
of 2002, using the 15-m
Landsat panchromatic band
Fig. 25. Provisional agricultural regions of Syria.
merged with three multi-spectral
bands 541 in RGB. This produced a
provisional map based on 27 mapping
units (Fig. 25). The map’s legend
consists of ‘labels’ to which
large attribute tables can be
attached; the map can easily be
modified by regrouping some of
the units. The limited number of
spatial units used means that this
type of map can easily be understood
by decision-makers and used
to target different policies and
interventions.