The Role of Risk Management in Pastoral Policy Evaluation and Poverty Reduction

Presented byLeseeto Saidimu

16 August 2011

SupervisorsProf. Sally Brailsford (School of Management)

Prof. Terry Dawson (Dundee University)

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24% of national milk production.

Over 90% Livelihood source for 1/3 for

national population.

Red meat comprise of 80% meat consumption

out of which 67% produced in ASAL

Supports tourism sector which

contributes 11% of the GDP

Role

of

Past

ora

l R

an

gela

nd

s

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Agriculture forms 21% of GDP in Kenya and supports 75% of national population.

Livestock contributes 50% to Agriculture

Indigenous livestock comprise of 75%

Over 80% Of indigenous are

Located in ASAL

National Economy

Arid and Semi-Arid Land (ASAL) accounts for 80% of Kenyan land mass.

It is home to approximately:

•A third of human population,

•70% of national livestock,

•75% of Wildlife population.ASAL is exposed to

high frequency, high impact climate

variability

Poverty

Trap

ASAL

Primary Pastoral Economy

The Role of Pastoral Economy

The Role of Risk Management

Vulnerability Measurements

3

Vulnerability Framework

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Drought as Asset driver in Pastoral SystemYear Impact Inter-drought

durationLivestock mortality & Area of

StudySource

1979-1980 Severe 4 (1974/6) 50-70%,Turkana district63% Cattle, 45% camels & 55% sheep and goats

Ellis and Swift (1988)McCabe (1978;2004)

1984 Severe 4 years 50% in Baringo district56%, Ethiopia (East African Country69% Kenya

Homewood & Lewis (1987);Angassa & Oba (2007); Oba (2001).

1987-1988 Mild 4 Years None established

1991-1992 Severe 4 years 50-60%,Garissa,Northern Kenya86% Northern Kenya

Angassa & Oba (2007);De Waal (1997);Oba(2001).

1997/8 Mild 5 years 40% Samburu, ILRI data, 2009.

1999-2000 Severe 2 years 50% cattle & 20% goats, Samburu district53%, Ethiopia (E.A Country)

Angassa & Oba (2007);McPeak & Little (2005).

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Sources of Pastoral RisksSources of Pastoral Risks Proactive StrategiesProactive Strategies

Risk EventRisk

EventReactive

StrategiesReactive

StrategiesConsequences of

Pastoral RisksConsequences of

Pastoral Risks

Climate VariabilityClimate Variability

Human PopulationHuman Population

Land DegradationLand Degradation

Market VariabilityMarket Variability

Biodiversity ConservationBiodiversity

Conservation

Pastoral Risks (Threats, Opportunities

and Uncertainties)

Pastoral Risks (Threats, Opportunities

and Uncertainties)

1. Livestock losses2. Food insecurity3. School drop-outs4. Human-wildlife conflicts5. Increased poverty6. Malnutrition7. Reduced land productivity

1. Livestock losses2. Food insecurity3. School drop-outs4. Human-wildlife conflicts5. Increased poverty6. Malnutrition7. Reduced land productivity

Risk Management Framework

Maximize on opportunities and minimize frequency of

threats

Maximize on opportunities and minimize impact of

threats

The framework is adopted from Crerand (2005)

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Literature Review Map-Summary

Drivers of market and livestock prices

1.Lybbert et al. (2004)2.Barrett & Luseno (2004)3.Fafchamps & Gavian (1996)4.Turner & Williams (2002)5.Oba,G.(2001)

Pastoral social setting and infrastructure

1.Aklilu and Wekesa (2002)2.Campbell D. (1999)3.Fratkin,E. (2004)4.Lesorogol,C. (2009)5.Carter,M. & Barrett C.(2006)

Drivers of rangeland productivity

1.Mude,A. et al (2009)2.Tucker, C. et al. (2005)3.Wittemyer G.B. et al. (2007)4.Abule, E. et al. (2007)5.Snelder,D Bryan (1995)

Research GapResearch Gap

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Data

Research Dimension

Early Warning System (EWS) indicator Variables

Natural capital Environmental indicators Rainfall and NDVIPhysical capital Economic indicators Food and livestock prices

Financial capital

Livelihoods indicators

Livestock ownership, Lactation rates, Mortality rates

Human capital Human nutritional status & Livelihood sources

Social capitalWealth status

Mitigation strategies Relief supply, copying strategies & migration

• Source: Arid Lands Resource Management Project (ALRMP-II) under the office of the prime minister.

• Period: Jan 2006-March 2010 • n=7,650 households

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Results: Pastoral Condition (Droughts)

Observations:

1. Three major droughts in the past 10 years,

2. 1999-2001 recorded prolonged negative pasture conditions,

3. The drought years 2006 and 2009 arose from deficiency in short rains (December rain) of the years 2005 and 2008 subsequently.

3-yr drought 2-yr drought 2-yr drought

3-year interval 2-year interval

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Wellbeing Measure Comparable Target

Non-Drought Year Averages

Drought Year Averages

Financial Capital (Total Livestock Unit) ASAL TLU 10-16 TLU=9.52<Min TLU=8.04<Min

Human Capital (Malnutrition rate) WHO 16.5% MUAC=16.9%>Max MUAC=24%>Max

Physical Capital (Meat-Cereal Price Ratio) Equilibrium 100% MCPR=128%>E.C MCPR=88.9%<E.C

Natural Capital (Rainfall mm) Equilibrium 33 pm Rain=38.8mm>E.C Rain=19.3mm<E.C

Social Capital (Poverty Percentage) National Level 50% Poor=67.9%>Nat. Avg. Poor=73%>Nat. Avg.

Comparison of wellbeing risk indicators between normal and drought conditions.

Observation: There is 10-50% change on the indicators from normal condition during droughts.

Vulnerability Risk Indicators

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Model Estimation

k

itiitkit xy 0

ity

itx

ij

i

Where Represents dependent variable for regions i, and time t.Is observed variables (independent variables)

unobserved error term

Is the subject specific residual and represents unmeasured individual factors which affects y (unknown intercept for each entity.

NB: “If unobserved variable does not change over time, then any changes in the dependent variable must be due to influences other than these fixed characteristics” (Stock and Watson, 2003, p.289-290).

Is the coefficient for the independent variables (slopes)Intercept (Model constant)0

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    Forms of Pastoral Capitals

  Financial (H2) Human (H3) Physical (H4) Social (H1) Natural

Dependent VariablesTotal Livestock

Units (TLU)

Malnutrition

(MUAC %age)Market Volatility

(MCP Ratio)Poor Households

(POOR)Pasture Condition

(NDVI)

Birth Rates-Cattle 0.004 (+) 0.267***(-) 0.869*** (+)    

Small Stock 0.142***(+) 0.136 (-) 0.551 (+)    

Mortality Rates- Cattle 0.062* (-) 0.297***(+) 2.057*** (-)    

Small Stock 0.010 (-)        

Camels     0.433 (-)    

Sales Rates-Cattle 0.323 (-) 2.375*** (+) 12.035*** (-)    

Small Stock 0.477***(-) 1.327*** (-) 2.919 (-)    

Pasture Condition-NDVI Lag 0 5.265* (-) 36.678***(-) 133.561***(+) 9.349 (+)  

NDVI-Lag 3 4.598 (+) 43.446***(-) 164.248***(+) 7.802 (-)  

NDVI-Lag 6 9.262***(+) 18.470** (-) 115.326***(+) 35.677*** (-)  

Livestock Asset Ownership-TLU   1.454***(-) 2.972** (+) 2.233*** (-)  

Market Condition-MCP Ratio   0.108***(+)   0.014 (+)  

Food Insecurity-MUAC %age     2.141*** (+) 0.194* (+)  

Mitigation Strategies-Relief Supply   0.053***(+) 0.295*** (-)    

Rainfall Variability- Rain Lag 0         0.0005*** (+)

Rain Lag 1         0.0007*** (+)

Rain Lag 2         0.0002** (+)

Rain Lag 3         0.0001 (-)

Constant 7.898 (+) 56.452 (+) 52.639 (-) 96.644 (+) 0.301 (+)

R-Squared 0.671 0.785 0.821 0.712 0.645

Pastoral Risks Decomposition

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Impact

Status

Change

Climate Wellbeing

Forms of Pastoral Capital

Financial Human Physical Natural Social

Indicator Variables

Integrated Impact Response strategies by

private and public sectors

Modelling and Management Mindset

ALRMP Data Analysis

System Dynamics Modelling

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Baseline Simulation Results

Percentage of children at risk of malnutrition

Test for data representationThe actual data Versus simulation runs

Scenario Wellbeing

Livestock mortalities Financial-TLU

Stable market prices Physical-MCP

Destocking/Restocking Financial-TLU

Food supplements Human-MUAC

Education Human/Financial

Reclamations/irrigation Natural-Rangeland

Disease control Financial-TLU

Possible Mitigation Strategies

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Conclusion• Drought is a threat magnifier and source of pastoral poverty.

• Pastoral condition is the most significant covariate in ASAL system but highly driven by climate.

• Livestock asset ownership (TLU) is declining and is likely to increase poverty and malnutrition.

• Government is expected to spend more funds in supporting the poor and malnourished.

• Risk management therefore bridges the gap between ASAL resource management and poverty reduction. This is achieved through SD model development and scenario runs.