Monitoring rangelands and pastoralists' trekking routes

in the Afar, EthiopiaBen Sonneveld - Centre for World Food Studies of the VU

University Amsterdam (SOW-VU)Kidane Georgis - GEOSAS, EthiopiaFekadu 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

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 researchersGeorgis, 2006

Pastoralism in the Afar

Livestock Nr of heads(in million)

Perc. national

Cattle 2 7

Sheep 2.3 11

Goats 4 37

Camels 0.8 80*

Mules/Horses 0.003 *

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: 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

Figure 3. TLU density per woreda.

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.

Spatial forage production function 0 1 1 2 2max 0,y x x

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.Supply demand ratio for forage by a) woreda, b) zone, c) state.

RUE analysis: linear regressionRUE

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0 5 10 15 20

Years

Y=-0.00035X+0.0269 r2=0. 65 t=0.15

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 radar• 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 minSpeed (km/hr) 1.2 4.6 0.01

Distance (km) 9.3 38.9 <1

Figure: Frequency NDVI classes study area. X-as: NDVI classesBlue-grey: all pixelsBrown: 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)

• High variation of NDVI values: rangeland• High NDVI data low variation: perennial vegetation• Low NDVI data no variation: bare land

Startrainfall

Dry spell

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