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RURAL MARKETS, NATURAL CAPITAL AND DYNAMIC POVERTY TRAPS IN EAST AFRICA

RURAL MARKETS, NATURAL CAPITAL AND DYNAMIC POVERTY TRAPS IN EAST AFRICA. Principal Investigator C. BARRETT - CORNELL Co-Principal Investigators F. MURITHI - KARI F. PLACE - ICRAF J. RASAMBAINARIVO - FOFIFA. PRESENTATION OUTLINE. PROBLEM STATEMENT

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RURAL MARKETS, NATURAL CAPITAL AND DYNAMIC POVERTY TRAPS IN EAST AFRICA

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  1. RURAL MARKETS, NATURAL CAPITAL AND DYNAMIC POVERTY TRAPS IN EAST AFRICA Principal Investigator C. BARRETT - CORNELL Co-Principal Investigators F. MURITHI - KARI F. PLACE - ICRAF J. RASAMBAINARIVO - FOFIFA

  2. PRESENTATION OUTLINE • PROBLEM STATEMENT • RESEARCH GOAL &OBJECTIVES • RESEARCH METHODOLOGY • POLICY RELEVANCE • RESEARCH TIME LINE • EXPECTED OUTPUTS

  3. Problem Statement Agrarian poverty may create incentives to follow land and livestock management practices which further reduce agricultural labor productivity by depleting natural capital: resource degradation poverty traps (RDPTs). • Key Sources of RDPTs (threshold effects): -missing/imperfect factor, product and asset markets - biologically-induced non-convex technologies

  4. Resource Degradation Poverty Traps HUMAN BIOPHYSICAL Declining productivity(labor, capital) Extensification/ intensification Income & wealthinequalities Agriculture (declining output) Soils degradation Reduction of critical ecological functions Imperfect and missing asset/factor/product markets An integrated model of dynamic processes giving rise to poverty traps in rural Africa

  5. Research Goal Contribute towards an improved understanding of the interaction between economic and biologicalprocesses in poor rural communities and disseminate findings to lay decision makers through policy briefs and community meetings to improve local management practices.

  6. Study Objectives • Examine empirically how biological processes and market conditions interact to create or extend dynamic poverty traps • Simulate policy experiments that might sustainably reduce poverty and/or improve resource management • Build capacity with local partners to carry out such analysis and simulations locally

  7. Research Methodology • Field data collection • Econometric estimation of behavioral and biological response functions • Integrated simulation modeling (CLASSES)

  8. Field Data Collection Design Drier 1.Central highlands, Kenya (Embu) 2. Central highlands, Madagascar (Vakinankaratra) 1. North Central Kenya (Baringo) Better MARKET ACCESS 1. Northern Kenya(Marsabit) 1. Western Kenya (Siaya /Vihiga) 2. Southern highlands, Madagascar (Fianarantsoa) Worse Wetter AGRO-ECOLOGICAL CONDITIONS

  9. Research Sites Kenya Madagascar

  10. CLASSES Simulation Model • Crop, Livestock And Soils in Smallholder Economic Systems (CLASSES) • an integrated model of heterogeneous agents and landscapes with dynamic feedback loops linking field and tree crop and livestock productivity to soil conditions and to human choices and welfare in the presence of potentially imperfect or missing markets

  11. Policy Relevance • Use models to simulate policy experiments, allowing for differences according to market and agroecological conditions. For example - What are the consequences of improving market access on poverty and soils over time? - How might biological interventions (e.g., liming soils, extending improved fallows) change labor allocation and income trajectories? - What targeting mechanisms and transfer forms (e.g., livestock species) are likely to prove most effective in sustainably reducing agrarian poverty?

  12. RESEARCH TIME LINE

  13. Research Output • Direct dissemination of research findings to specific sites, users and to areas with similar conditions • Provide an empirical basis for policy recommendations and implementation • Publications (English, French, Malagasy, Swahili) • Capacity building (FOFIFA, KARI) • Generate relevant data bases for future use

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