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APPLICATIONS OF SYSTEM DYNAMICS TO APPLICATIONS OF SYSTEM DYNAMICS TO MODELING POVERTY TRAPS AND LAND MODELING POVERTY TRAPS AND LAND DEGRADATION IN EAST AFRICA DEGRADATION IN EAST AFRICA Investigators Investigators C. BARRETT C. BARRETT - CORNELL - CORNELL A. PELL A. PELL - CORNELL - CORNELL B. OKUMU B. OKUMU - CORNELL - CORNELL F. MURITHI F. MURITHI - - KARI KARI F. PLACE F. PLACE - - ICRAF ICRAF J. RASAMBAINARIVO J. RASAMBAINARIVO - FOFIFA - FOFIFA

APPLICATIONS OF SYSTEM DYNAMICS TO MODELING POVERTY TRAPS AND LAND DEGRADATION IN EAST AFRICA

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APPLICATIONS OF SYSTEM DYNAMICS TO MODELING POVERTY TRAPS AND LAND DEGRADATION IN EAST AFRICA. Investigators C. BARRETT - CORNELL A. PELL- CORNELL B. OKUMU- CORNELL F. MURITHI - KARI F. PLACE - ICRAF - PowerPoint PPT Presentation

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APPLICATIONS OF SYSTEM DYNAMICS TO APPLICATIONS OF SYSTEM DYNAMICS TO MODELING POVERTY TRAPS AND LAND MODELING POVERTY TRAPS AND LAND

DEGRADATION IN EAST AFRICADEGRADATION IN EAST AFRICA

InvestigatorsInvestigatorsC. BARRETT C. BARRETT - CORNELL- CORNELL

A. PELLA. PELL - CORNELL- CORNELL

B. OKUMUB. OKUMU - CORNELL- CORNELL

F. MURITHI F. MURITHI - KARI - KARI

F. PLACE F. PLACE - ICRAF- ICRAF

J. RASAMBAINARIVO J. RASAMBAINARIVO - FOFIFA- FOFIFA

Problem StatementProblem StatementAgrarian 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

Study ObjectivesStudy 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

Research DesignResearch DesignM

AR

KE

T A

CC

ES

S

Drier

Wor

se B

ette

r

Wetter

1. North Central Kenya (Baringo)

AGRO-ECOLOGICAL CONDITIONS

1.Central highlands, Kenya (Embu)

2. Central highlands, Madagascar (Vakinankaratra)

1. Northern Kenya(Marsabit)

1. Western Kenya (Siaya /Vihiga)

2. Southern highlands, Madagascar (Fianarantsoa)

Research SitesResearch Sites

Kenya Madagascar

SOIL

-Biology (microbes, micro-fauna and flora)-Chemistry (N, P, K) - Physics (structure, texture, moisture content)

HUMAN

LIVESTOCK

PLANT BIOMASS- Natural vegetation- crops

MilkmeattractionSavingsManure

Herd size + specieshusbandry,feedingpractices

Foragefeed

crops, green manure

cropproductionpracticesland usepatterns

MODEL FRAMEWORKMODEL FRAMEWORK

Soil/waterconservation, fertilizer, brown & green manure application

ENVIRONMENTAL & POLICY FACTORS - rainfall, - temperature - slope- prices - land tenure - land use restrictions

State or decision variables

Excreta,litter,

- Soil cover- Soil organic matter (SOM)

-Soil nutrients,

- moisture

Geographicaleffects

Study MethodologyStudy Methodology SD approach is chosen because it is consistent with

traditional economic approaches towards modeling dynamic systems i.e. use of ordinary differential or difference equations

It employs a very simplified structure of feedback and causal loops that are either balancing (stable equilibrium) or reinforcing (unstable equilibrium)

It is possible to integrate or nest micro-economic models in the SD framework

Study Methodology... Cont’dStudy Methodology... Cont’d SD yields numerical estimates of the paths taken by

key policy variables over time and space as well as any equilibrium to which they might converge (diverge)

Simulate policy experiments that might sustainably reduce poverty and/or improve resource management

Uses both quantitative and qualitative information

Graph for rainfall ratio

2

1.5

1

0.5

0

1977 1981 1985 1989 1993 1997

Time (Year)

Rainfall ratio : Normalized rainfall for Embu 1977-1999

Mean: 1270 Max: 1885 Min: 499

Simulation of ICRAF/KARI technologies on Soil depth levels

100

95

90

85

801977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999

Time (Year)

Soil depth : without technology interventions cm

Soil depth : with technology interventions cm

Sensitivity analysis of soil depth Sensitivity analysis of soil depth declinedecline

50% 75% 95% 100%

Soil depth100

95

90

85

801977 1983 1988 1994 1999

Time (Year)

Policy RelevancePolicy Relevance Models such as this one could be used 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?