development to alleviate poverty in rural Kenya Sally Brailsford - PowerPoint PPT Presentation

Modelling pastoral policy development to alleviate poverty in rural Kenya Sally Brailsford and Saidimu Leseeto UK System Dynamics Conference, London, February 2013

Kenya and Samburu district Population = 40 m 2

Kenya facts • Ranked 128 th out of 169 UN Development Programme countries in 2010, based on Human Development Index (measures development in terms of life expectancy, educational attainment and standard of living) • Poverty rate* increased from 47% to 53% between 1994-1997: now just under 50%, 80% of whom live in rural areas • Main economic activity is agriculture (half of which is livestock) employing ~80% of the total labour force but contributing only 21% of GDP • Over 80% of livestock are in Arid & Semi-Arid Lands (ASAL) where regular droughts cause loss, low productivity, market instability, malnutrition and food insecurity * % of working-age people who earn less than an international dollar a day 3

Population growth and poverty • Very rapid growth: Human Population Kenyan population has 40 240 tripled over past 30 38.6 223.90 35 210 Samburu Population (,000s) years, now 40 million 28.7 National Pop (Millions) 30 180 154.40 25 150 • Samburu district: 21.4 20 120 population 224,000, 15.3 108.80 15 90 8.6 10.9 annual growth rate 4.5% 79.90 10 60 5.4 2.5 5 30 • 82.3% of people live in 0 0 1897 1948 1962 1969 1979 1989 1999 2009 poverty; pastoralists Census Years form 63% of these District Pop. National Pop. 4

Environmental factors • Strong linkages between poverty and environmental degradation, particularly poor water management, soil erosion, declining soil fertility and land degradation • Effects of climate change are undermining an already fragile resource base and have contributed to declining agricultural yields over the past decades • Drought is a perennial problem in parts of Kenya 5

Droughts • Periodic feature… or impact of climate change? • Land degradation effects measured in NDVI (Normalized Difference Vegetation Index) using satellite data • People have to travel further (or move) for water; children stop attending school; cattle die; people become poorer; people die 6

Drought data 1979 - 2000 Year Impact Livestock mortality & Area of Study Inter-drought duration 1979-1980 Severe 4 (1974/6) 50-70%,Turkana district 63% Cattle, 45% camels & 55% sheep and goats 1984 Severe 4 years 50% in Baringo district 56%, Ethiopia (East African Country 69% Kenya 1987-1988 Mild 4 Years None established 1991-1992 Severe 4 years 50-60%,Garissa,Northern Kenya 86% Northern Kenya 1997/8 Mild 5 years 40% Samburu, 1999-2000 Severe 2 years 50% cattle & 20% goats, Samburu district 53%, Ethiopia (E.A Country) 7

Tourism industry • Major source of income: contributes 11% of GDP • Wildlife compete with agricultural livestock for rangeland 8

Livestock • Measured in Tropical Livestock Units (TLU): one TLU = 1 cow, 1 camel, 10 goats or 10 sheep • Source of nutrition: meat, milk and blood • Source of income: sale of live animals, carcass, skin and hides • Store of wealth: measure of wealth and reserves • Insurance against risks: droughts, diseases and raids, predation, and accidents • Symbol of status and respect • Instrument for building social relations e.g. marriages, penalties/fines and friendship 9

System Interaction; Rangeland, Livestock and Human Wellbeing

High-level influence diagram (R) + Livestock Human Population Population + + (B2) Drought (B1) (B4) Event - - - - + Productive Wildlife Rangeland Population (B3) - 11

Study area for data collection 2 1 3 4 Sample Area 5 12

Samburu district • A typical pastoral system: households depend on rangeland productivity totally influenced by climate variability • Socio-economic activities are agro-pastoral and pastoral, with over 90% of the total land mass being pure pastoral • Land is communally owned and is used for rearing cattle, sheep, goats, camels and donkeys in addition to using it for settlement. • Most households are entirely dependent on livestock and livestock products. The County is ranked one of the poorest in the country, with the district poverty index of 84%, and below 30% literacy rate 13

Model data • Rainfall, NDVI and other pastoral data from the Arid Lands Resource Management Project (ALRMP) • Market prices, livestock births and mortality etc from the International Livestock Research Institute (ILRI) in Nairobi • Demographic, socio-economic and educational data from Kenya National Bureau of Statistics • Plus primary data collected locally in Samburu District by Saidimu over a 3-month period (interviews with 30 households) 14

Outcome measures: the “Five Capitals” • Developed in the 1990’s by Forum for the Future • Human, Financial, Social, Manufactured and Natural • In this study the term Physical was used in place of Manufactured (the physical assets, i.e. livestock) 15

Model outputs • Human capital: prevalence of food insecurity, measured by the % of children below 5 years old with Middle-Upper Arm Circumference (MUAC) readings less than 135mm; educational attainment (size of skilled workforce) • Natural capital: annual biomass (grass) production in kg • Physical capital: # of livestock, measured in TLU • Social capital: poverty rates • Financial capital: TLU per household and household milk consumption 16

Stella (ithink ) model …. 17

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Physical asset (livestock) dynamics 20

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Model validation 40 Rate of malnutrition (%) 30 20 10 0 Time (Jan 2006- March 2010) SD model malnutrition rate (%) Actual malnutrition rate (%) 100 Rate of poor households (%) 90 80 70 60 50 40 Time (Jan 2006- March 2010) Actual poverty (%) SD model poverty (%) 23

Simulation experiments • 25-year run, dt = 1 month • Baseline plus various combinations of 8 basic strategies, derived following discussions with local decision-makers, plus three additional education strategies: – Increase school retention by feeding programmes for schoolchildren and thereby reduce drop-out rate by 50% – Increase school enrolment rate from 50% (current local level) to 74% (current national level) – Both the above • In total 21 different risk mitigation strategies were tested (and costed) 24

Basic strategies Broad strategy Implementation criteria Impact 1 Land reclamation Planting grass on degraded rangelands Reclaim 5% of degraded rangeland annually for every good rainfall year Replacing weed and other shrubs by grass , preventing soil erosion 2 Settlement Give priority to settlement at degraded Resettle 50% of all households planning rangeland Settle 100% of new households 3 Livestock feeding Purchase supplementary feeds for livestock Sell livestock to purchase 2/3 of the feeds required and whenever there is a shortage of pasture reduce drought mortality by 1/3 4 Veterinary services Treating livestock through vaccination Reduce drought mortality by 20%; reduce average against common diseases diseases caused deaths by 50%; sell SSU to finance 100% of veterinary costs. 5 Restocking Livestock insurance Sell cattle to finance 5.5% of livestock value as premiums; restock livestock lost through drought through compensation 6 Security Rule out inter-ethnic conflicts Reduce livestock losses arising from insecurity by 100% 7 Market Encourage voluntary livestock off-take Double sales rate during drought years and reduce infrastructure drought mortality rate proportionately. Repurchase 50% sold after the drought 8 Enhance Increase core conservation areas by 30% by Reduce productive rangeland by 30% of core conservation conservation the end of 2030 Compensate with reclamation by recovering Increase productive rangeland by 50% of degraded land 25 50% of degraded land