Payments to promote biodiversity conservation and implications for poverty reduction among pastoral communities in East Africa
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Presented by Philip Osano, ILRI, Nairobi, 16 August 2011
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Payments to promote biodiversity conservation and implications for poverty reduction among pastoral communities in East Africa
- 1. Payments to promote biodiversity conservation and implications for poverty reduction among pastoral communities in East Africa Philip Osano (PhD Candidate) Geography Department, McGill University, Canada Graduate Fellow, ILRI (PLE) August 16, 2011
- 5. 2. Pastoralists are becoming more poorer in rangelands Homewood et al. 2009; Reid et al. 2008; Okwi et al., 2007; UNDP, 2006; WRI, 2007; Norton-Griffith, 2007; Ferraro & Kiss, 2002; Pagiola et al. 2005; Grieg-Gran et al., 2005; Zilbermann et al., 2006; Barret & Arcese 1995; Horan et al., 2008 ; Pastoralists are diversifying income sources due to increase in poverty Wildlife payments (PES) can potentially provide stable , predictable and reliable income to pastoral landholders â future of wildlife conservation in private land (mostly pastoral) Past approaches (e.g. ICDPs, CBC, CBWM etc) insufficient: only 5% of wildlife tourism revenue accrue to local landowners in Kenya yet they bear heaviest costs of conservation Direct payments or (PES) can contribute to wildlife conservation and poverty reduction among pastoral communities e.g. Maasai in Amboseli (Bulte et al., 2008)
- 6. Conceptual Framework of the study
- 8. Study Objectives, Hypothesis and Questions
- 11. Study Design and Preliminary Results
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- 15. Proportion Below Poverty Line among Members and Non-Members of Olare Orok Conservancy (OOC) Ref: Osano et al. (in prep.) Year 2008 2009 Proportion below Poverty Line (US$ 1/capita/day OOC+PWC 24/72 22/73 33 % 30% OOC-PWC 26/52 22/51 50% 43 % OOC+PWC (Ex-Providers) 2/6 2/6 33% 33%
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- 21. Thank You for Listening!
- 22. Add a trend line showing area of Nairobi National Park (ha) and area of Athi Kapiti Plains
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- 28. Study Design Data and Data Analysis Question Data Requirements Data Analysis 3. Influence of Institutions Membership in local resource associations (e.g. land, wildlife) Implementation of laws and policies (e.g. land, wildlife, environment, etc) Qualitative analysis of household survey responses, users, intermediaries and key informants PES program reports Policy and Institutional Analysis (e.g. review of published /grey literature on local/national policy development planning) Policy screening 4. PES risks mitigation and future scenarios PES financing (short and long term) Data on use of PES income (displacement/leakage effect) Data on withdrawals/reasons Local drivers of change (land sales) National drivers of change (policy) Qualitative analysis Scenario analysis
Hinweis der Redaktion
- Rangelands comprise Ÿ of SSA (made up of arid/semi-arid) lands and Ÿ of Kenyaâs terrestrial land area Production is limited by lack of plant-available water Cultivation, Offtake and livestock have increasing rates of production, per annum rates of production of wildlife is decreasing The high rate of losses in wildlife is of major concern because 70% of wildlife is found outside PA. Rates of wildlife loss is happening in both PA and outside PAs There are various reasons why this is happening (e.g. human population growth, expansion of domestic and international markets, improved transport networks and information networks (e.g. mobile phone coverage), improved access to financial services, increasing opportunities for off-farm jobs and investments). A potential cause in large wildlife declines could be because of habitat loss and fragmentation An important change, evident everywhere throughout rangelands is the rapid evolution of property rights from large parcels of land under communal ownership to small parcels of land under private ownership (this does not cover wildlife)
- According to Okwi et al., (2007), the highest prevalence of poverty in Kenya is in the rangelands The costs include human-wildlife conflict, livestock predation, wildlife disease transmission etc
- Need to define who the poor are; the following categories Supply side Poor environmental service sellers/providers) â this is the focus group of my study Demand side Poor environmental service (ES) buyers Poor environmental service (ES) free-riders
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- The ideal situation would be to compare program participants versus non-program participants (control group) to see if program participants are better off than non-participants. However, this is only possible if there is a baseline (at the start of the program) where participation is random (i.e. every household has a chance to participate in the program). In reality (for example in WLP case), there is self-selection bias because selection is not random Regression Analysis I intend to follow the method used by Pagiola et al. (2008) who assessed the intensity of participation of poor households in a PES program There is no control group, therefore the analysis focus entirely on participants (similarities difficult to establish as households are different in various aspects) Select a proxy for intensity of household participation (land allocated to PES or Proportion of land allocated to PES etc) and use as the dependent variable in regression analysis. The independent variables will include household and geographical factors
- The