Transcript Dia 1 - Hoefsloot

Monitoring rangelands and
pastoralists' trekking routes
in the Afar, Ethiopia
Ben Sonneveld - Centre for World Food Studies of the VU
University Amsterdam (SOW-VU)
Kidane Georgis - GEOSAS, Ethiopia
Fekadu Beyene - Institute of Pastoral and Agropastoral
Studies, Haramaya University (IPAS)
Sponsors: OPEC Fund for International Development
Dutch Ministry of Foreign Affairs/Dev. Coop.
Haramaya University
Project
 Objectives
– Improve wellfare of pastoralist community
– With special attention to the role of policy interventions:
 Optimal geographical stratification of water pumps
 Price-weather insurance between pastoralists and traders
 Approach
– Create consolidated data base (biophysical/socio-economic)
– Spatial welfare model
 Node link network/monthly time steps
 Accounts for existing institutions on NRM
– Dialogue with stakeholders
– Capacity building
Overview of presentation
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Afar: institutions under pressure
Intended contributions of the project
Monitoring rangeland
Monitoring trekking routes
Further research
Afar: institutions under pressure

Institutional Characteristics
–
–
–
–
–

Open access of dryland resources
For about a 100 clans
No supervision
Regulations on land and water use
Jurisdiction by clans (Madaa)
A long time neglected area
‘… many of these pastoarlists are
politically marginalized by national
authorities….
Davis, 2006

An under-researched region
‘… drylands require more attention of
scientists and researchers
Georgis, 2006
Pastoralism in the Afar
Livestock
Cattle
Nr of heads
(in million)
2
Perc. national
7
Sheep
2.3
11
Goats
4
37
Camels
0.8
80*
0.003
*
Mules/Horses
Source: CSA, 2003
Afar: institutions under pressure (2)
Shifting paradigms “Hardin vs Ostrom”
 Economic theory on ‘open access resources’ indicates the
absence of price structure for its use and lack of incentives
for its custody. In short, the “Tragedy of the commons”
(Hardin, 1964)
Yet,
 Reality proofs that these open access resources are well
managed without a clear and expensive supervision and
that arrangements between its members has been the
guarantee of a centuries-old sustainable livestock
production system under harsh climatic conditions.
(Ostrom, 1993)
Afar: institutions under pressure (3)
Yet,
– ‘.. they [institutions] often fail when rapid change
occurs or problems at a larger scale.’
(Dietz, Ostrom and Stern, 2003. ‘The struggle to govern the
commons’, Science)
And,
– ‘…traditional institutions in pastoralist societies
are increasingly challenged by new constraints;
and do not always find appropriate answers,...
(The Red Cross, 2009)
Afar: institutions under pressure (4)
This also holds for the Afar where several drivers are reducing
access to water and land resources.
Like,
 Fast population growth
 Increasing encroachment of sedentary agriculture;
 Border regulations
And results in:



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Poverty
Land degradation
Water pollution
Conflicts
Intended contributions of the project
Yet,
‘..promising new strategies emerge that address these problems by
facilitating: dialogue, experimentation, learning, and change.’
Dietz, Ostrom and Stern (2003) ‘The struggle to govern the
commons’,Science.
 Our project aims to enhance institutions’ initiatives that address the
new challenges that are faced by Afar pastoralists
Moreover,
 Project wants to address the rising need of the donor community to
provide a coherent information system for investments in drylands
Two monitoring systems
 Monitoring
– Rangeland quality in nomadic pastoralist
systems
– Monitoring Tekking Routes of Nomadic
Pastoralists
Monitoring Rangeland Quality in
Nomadic Pastoralist Systems
Approach:
 Confront spatial patterns of:
– Supply-Demand forage ratio
(driver: overgrazing)
with
– Rainfall Use Efficiency trends
(impact: land degradation)
under
– various Accessibility scenarios
(response: migration).
Rainfall, livestock density
and forage demand
Grazing demand based on
Boudet and Riviere (1968)
and Minson and McDonald
(1987): assuming livestock
needs 2.5% of its body
weight for a sustained
growth.
Consumption of 6.25 kg of
forage dry matter daily for
each TLU.
Figure 3. TLU density per woreda.
Spatial forage production function
y  max  0, 0  1x1  2 x2 
β0
-573.32
β1
-545.22
β2
9.26
*All
parameters significant at
95% CL
**R-sq = 0.46
***Regression using annual
rainfall for scenario APC
Supply demand ratio for forage
by woreda, zone and region
Supply demand ratio for forage by a) woreda, b) zone, c) state.
RUE analysis: linear regression
RUE
0.035
0.03
0.025
0.02
0.015
0.01
0.005
0
0
5
10
Years
Y=-0.00035X+0.0269 r2=0. 65 t=0.15
15
20
Monitoring Livestock Production and Land
Degradation in Nomadic Pastoralist Systems
 Conclusion
– Supply/demand improves at higher spatial aggregation
levels
– Supply/demand ratio at Afar state level is more or less 1.
– Degradation absent except for some pockets near
mountains and in Northern part
– Much of the findings rely on accessibility scenarios
 Further research
– Need for more detailed information about trekking routes
under various climatic conditions
Monitoring Tekking Routes of
Nomadic Pastoralists
Problem
 Absence of detailed information on nomadic
trekking routes and decision making aboute
migration patterns
 Personal following of herds
– Difficult, dangerous and expensive
– herder will select routes that guarantee safety of
observer
Pilot studie
Objective
 Testing of ‘remote tracking’ systeem
– Analysis of herd movements without the
presence of external observer
– Correlation between migration patterns and
available satellite information: NDVI
Remote tracking: how does it work
• ‘Beacon’ transmits GPS signal to satellite
• Satellite transmits coordinates to ground rada
• From radar to central unit
• From central unit to client
Visualisation of migration patterns in e
‘Google Earth’
The herder
 Selection by local counterpart
 District Ayssaita, Stad Mamulei
 Herd: 5 camels, 35 cattle,
25 goats, 10 sheep
 Monitoring: 30 October-10 December, 2007
 Dry spell
NDVI data
 Normalized Difference Vegetation Index:
‘Greenings Index’
 10-day averages
 1 X 1 km
 VITO Belgium
RESULTS
 Trekking routes
 Relation visited pixels and NDVI
Figure: Phase diagram
dynamic herd
movements
Thickness line
segments: time
between observations
Crossing lines:
homestead
• During mornings slow movements to rangeland and watering
pints; afternoons rapid return to home stead
average
max
min
Speed (km/hr)
1.2
4.6
0.01
Distance (km)
9.3
38.9
<1
Figure: Frequency NDVI
classes study area.
X-as: NDVI classes
Blue-grey: all pixels
Brown: pixels visited by
herder
• Distribution visited pixels middle-high NDVI values:
rangeland
• Highest NDVI values avoided; perennial vegetation
• Lowest NDVI values avoided: bare land
Hypotheses (Scanlon et al., 2005)
 Three archetypes visible from NDVI response on
rainfall
– Rangeland: high variation in NDVI values
– Perennial vegetaion: high NDVI values low variation
– Bare land: low NDVI values without variation
Figure: NDVI values and
grazing intensies over time.
Grazing intensities
(number of visits):
Blauw: Intensive (>20)
Groen: High (5-20)
Rood: Moderate (1-4)
Zwart: Low (0)
Start
rainfall
Dry spell
• High variation of NDVI values: rangeland
• High NDVI data low variation: perennial vegetation
• Low NDVI data no variation: bare land
Conclusions
 Pilot with ‘remote tracking’ successful
 Dedicated software for processing NDVI data and migration patterns
 improvements:
– Smaller beacons
– Battery with solar energy as used by bird ‘tracking’
 Herd has a potential range of 40 km per day
 Water most important reason for migration
 Morning slow movements for grazing; afternoon rapid return to
homestead
 NDVI-variation as indicator for vegetation composition
Further research
 Expansion of pilot to obtain regional information on nomadic trekking
routes
 Remote sensing information combining with interviews.
 Vegetation compositioin per pixel: rainfall response
 ‘Groundtruthing’ NDVI information
 Rangeland production assessment from satelite data and vegetation
samples
 Opportunities
–
–
–
–
Determine corridors
Management of water pumps
Rangeland improvement
Possibilities for export markets