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Monitoring rangelands and pastoralists' trekking routes in the Afar, Ethiopia

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

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Monitoring rangelands and pastoralists' trekking routes in the Afar, Ethiopia

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  1. 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

  2. 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

  3. Overview of presentation • Afar: institutions under pressure • Intended contributions of the project • Monitoring rangeland • Monitoring trekking routes • Further research

  4. 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

  5. Pastoralism in the Afar Source: CSA, 2003

  6. 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)

  7. 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)

  8. 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

  9. 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

  10. Two monitoring systems • Monitoring • Rangeland quality in nomadic pastoralist systems • Monitoring Tekking Routes of Nomadic Pastoralists

  11. 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).

  12. Figure 3. TLU density per woreda. 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.

  13. Spatial forage production function *All parameters significant at 95% CL **R-sq = 0.46 ***Regression using annual rainfall for scenario APC

  14. 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.

  15. RUE analysis: linear regression Y=-0.00035X+0.0269 r2=0. 65 t=0.15

  16. 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

  17. 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

  18. 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

  19. Remote tracking: how does it work • ‘Beacon’ transmits GPS signal to satellite • Satellitetransmits coordinates to ground radar • From radar to central unit • From central unit to client

  20. Visualisation of migration patterns in e‘Google Earth’

  21. 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

  22. NDVI data • Normalized Difference Vegetation Index: ‘Greenings Index’ • 10-day averages • 1 X 1 km • VITO Belgium

  23. RESULTS • Trekking routes • Relation visited pixels and NDVI

  24. 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

  25. 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

  26. 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

  27. 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) Dry spell Startrainfall • High variation of NDVI values: rangeland • High NDVI data low variation: perennial vegetation • Low NDVI data no variation: bare land

  28. 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

  29. 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

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