Sampling the vulnerability reduction sustainable intensification continuum

a West African paradigmfor site selection in Dryland Systems

TraoreLamien

AyantundeBayala

KalinganireBinamCarey

Emechebe

NamaraTondoh

Vodouhe& al.

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

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

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)

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), globalizationLand degradation: physical, chemical

How do these criteria vary over spaceand 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

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

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

• 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

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

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)

3. Two SRT strata with AI=0.35 as threshold

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.

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

The real map of West Africa

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.

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.

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.

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)

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

Another map of West Africa

Contact: p.s.traore@cgiar.org