Day 1 Session 2 TRIPS Meeting in WASDS Site Selection - This presentation sets out the criteria for the action sites selected for the CGIAR Research Program on Dryland Agricultural Systems

1. Traore
Lamien
Ayantunde
Bayala
Kalinganire
Binam
Carey
Emechebe
Namara
Tondoh
Vodouhe
& al.

2. The Dryland Systems CRP
• is targeted at the poor and highly vulnerable
populations of the dry areas, and aims to develop
technology, policy and institutional innovations to
improve food security and livelihoods using an
integrated systems approach
• takes ‘systems thinking’ to a new level, by
delivering interventions within context: it relies
on holistic approaches that aim to understand
complex smallholder systems, drivers of change,
and key factors for productivity growth

3. Targeted categories of dryland
production systems
• systems with the deepest endemic poverty and
most vulnerable people (SRT2: Strategic Research
Theme 2)
– emphasis: increasing resilience and mitigating risk
from biophysical and socioeconomic shocks despite
marginal conditions
• systems with the greatest potential to contribute
to food security and grow out of poverty in the
short to medium term (SRT3: Strategic Research
Theme 3)
– emphasis: sustainable intensification of production
systems to improve livelihoods

4. WAS&DS: SRTs?
• SRT2 / SRT3 not only function of aridity index,
because the strategic research framework
(SRF) target regions are “*…+ systems
characterized by major constraints, such as
drought or other agro-climatic challenges,
poor infrastructure and underdeveloped
markets, or weak institutions and governance
*…+” (ICARDA, 2012)

5. Selection criteria for identifying Dryland
Systems CRP target areas
Biophysical (n=25) Socioeconomic (n=16)
Accessibility: closeness to partners
headquarters, proximity to research facilities
Demography: population, poverty, employment
(e.g. women/men differential aspects), nutrition
status
Climate: rainfall patterns, temperature profile,
drought and heat indices, length of growing
period, elevation
Access to markets: distance, size,
competitiveness
Soils: nutrient-supply capacity, water-holding
capacity, morphology, soil erodability,
degradation / desertification
Access to water and land: communal/private
ownership, pricing, access
Biotic stresses: diseases, pests, weeds (e.g.
Striga spp.)
Gender and disadvantaged groups’
responsiveness: differential aspects, absolute
aspects
Farming systems: crops, vegetables, livestock,
trees, mixed systems, gap between actual
economic and potential yields
Governance, institutions, and policy:
inclusiveness of stakeholders, equity,
accountability, transparency
Sensitivity to global change: climate (variation
and change parameters), globalization
Land degradation: physical, chemical

6. How do these criteria vary over space
and time?
• SRT2 and SRT3 not mutually exclusive: many dryland
agricultural systems will contain areas or elements of both
• socio-economic criteria vary on shorter distances than
biophysical (over space AND time), therefore site selection is
scale-dependent, could follow a nested design:
– global to continental: mostly biophysical (e.g. AI)
– regional to district: increasingly socio-economic (e.g.
population density, market access)
– district to community: essentially infrastructural (e.g.
partnerships, accessibility)
• as we aim to influence processes of systems change, the time
dimension should receive particular attention

7. Additionally, which practical constraints
do we face for site selection in WAS&DS?
• Maximum 2 action sites with 2 satellites each
• Accommodate main representative production
systems (& policies) of the West African Drylands – if
possible 5 countries
• Keep the dimension of action sites logistically
tractable & operationally efficient
• Take into account existing / past research
investments & infrastructure
• Security issues

8. Approach to site selection
• Study domain: West African drylands defined by [0.03-0.65[ aridity
index range
• GIS data: aridity index (Zomer & al. 2008), population density
(ORNL, 2001), poverty levels (Wood & al., 2010)
• 1: Initial country ranking by area & population
• 2: Explore relationship between poverty, aridity and population
density
• 3: Identify & map natural break points in aridity, population density
distributions
• 4: Map high spatial rates of change in aridity, population density
gradients (assumed proxies for temporal change along the SRT2-
SRT3 continuum)
• 5: Choose action transects and satellites along and across gradients
and assess regional representativeness

9. • Drylands: half of West Africa’s landmass, half of its population
• “ share by area: 1. Mali, 2. Chad, 3. Niger, 4. Nigeria, 5. Burkina Faso
• “ share by population: 1. Nigeria, 2. Burkina Faso, 3. Mali, 4. Niger, 5. Senegal

10. 1. poor populations concentrate in the drylands (e.g. drylands host 35.7% of Ghana’s total
population, but 46.2% of Ghana’s poor). 2. spatial distribution of poverty independent from
latitude (& hence from aridity index), but also from population density

11. 1. Option 1: SRTs based on mostly latitudinal, static, monotonic aridity gradient; somewhat
arbitrary AI threshold. 2. numerous counter-examples of SRT2 conditions within SRT3 zone &
vice-versa (e.g. poorer nutritional status in Sikasso region; Maradi region net agric. exporter)

12. 3. Two SRT strata with AI=0.35 as threshold

13. 1. Option 2: SRTs based on mostly longitudinal, dynamic, non-monotonic population
gradient; documented intensification thresholds exist for PD breakpoint of ca. 70 hab.km2. 2.
dynamic gradients > opportunities to trade space for time.

14. 3. Two SRT strata with PD=70 hab.km2 as threshold

15. The real map of West Africa

16. 1. Combination of options 1 and 2 yields 4 strata. 2. Of these the low-low case is agro-
pastoral, often extensive, and was earlier deemed lower priority. 3. The vertical (horizontal)
KKM (WBS) action transect samples compressed AI (PD) gradients in high PD (AI) conditions.

17. 4. Average conditions orthogonally contrasted: mean AI (KKM lo, WBS hi), mean PD (KKM hi,
WBS lo) but parallel variabilities: var AI (KKM hi, WBS lo), var PD (KKM hi, WBS lo). 5. Area
representativeness KKM = 2 WBS, but (agri)-cultural coverage WBS = 10 KKM.

18. 6. Four satellite sites expand action transect ranges biophysically (WBS) and socio-
economically (KKM). Of these, three are CCAFS sites allowing for CRP cross-fertilization
without system saturation. Likewise for FTA CRP: overlapping transects but distinct districts.

19. Learnings
• Sub-national BAD on poverty is neither correlated with (higher
granularity of) AI nor PD – a priori there is no more (less)
justification for use of AI than PD for SRT2/SRT3 identification and
mapping
• Over space AI, PD follow power law distributions and uncorrelated
– no strong statistical backing for stratification of SRT2 vs SRT3,
rather a ‘systemic’ justification. Both drivers useful to understand
geographical expression of proposed SRT2-SRT3 continuum
• Action transects along compressed AI, PD gradients (with high
spatial rate of change) advantageous to sample proposed
continuum, representative of wider regional domain than any
‘homogeneous’ compact shapes of equal area (aridity-wise or
other)

20. Learnings (contd)
• They may express biophysically and do intertwine with biophysical
factors, but drivers of change are mostly socio-economic (with
steep longitudinal gradients in the region): e.g. rainfall perceptions
& myths, rural/urban ecotones, hydrological basins
• Site selection will impact R4D outcomes: with socio-economic
drivers at the core of Dryland Systems (research) design… need to
target our (research) investments accordingly
• 5 countries selected: Burkina Faso, Mali, Niger, Nigeria, Ghana
based on share of total region, national importance of drylands
area, population and poverty-wise
• 2 complementary action transects selected: KKM (along aridity
gradient), WBS (along population gradient) with satellites that trade
space for time. Not only trans-boundary at the political level, also
hydrological

21. Another map of West Africa

22. Contact: p.s.traore@cgiar.org