aagw2010 june 09 an notenbaert gis rs at ilri
TRANSCRIPT
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GIS/RS @ILRI
An Notenbaert
African Agriculture GIS Week8-16 June 2010Nairobi, Kenya
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Attention! Attention!!!
1. Index-Based Livestock Insurance
2. Down-scaled climate projections
Different (spatial and temporal) scalesDifferent target audiencesDifferent position along research-development gradient
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Attention! Attention!!!
1. Index-Based Livestock Insurance
2. Down-scaled climate projections
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IBLI
Protecting Pastoralists from the Risk of Drought Related Livestock Mortality:
Piloting Index-Based Livestock Insurancein Northern Kenya
http://www.ilri.org/ibli/
http://www.ilri.org/ibli/http://www.ilri.org/ibli/ -
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Managing Risk in the ASALs
ASAL residents, particularly in Northern Kenya, confront harshand volatile environments.
High level of risk: Droughts, Diseases, Conflict
Low levels of capacity: Infrastructure deficient Few alternative livelihoodopportunities
= A high degree of vulnerability to risk
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Impact of Drought on Livelihoods The Marsabit Pilot
Livestock is both the principal assetand source of income for the vastmajority of ASAL residents
Drought is the single greatest causeof livestock mortality
Most drought related livestockmortality occurs under severeconditions
0
100
200
300
400
500
600
700
800
June 20 00 Sept. 200 0 Dec . 20 00 Ma rc h2001
June 20 01 Sept. 2001 Dec. 200 1 Ma rc h2002
June 2002
Other
Bad Water
Rain
Old Age
Killed to protect mother
Accident / injury
Predator
Disease
Pas ture / drought / starvation
44%
10%4%
15%
14%
6%
4% 2%
Milk
Livestock Sal e
Slaughter
Food aid
Salary/ wage
Cultivation
TradeGift
Proportion of total income by source
Livestock mortality by cause
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Insurance and Agricultural Development
Such risk imposes considerable economic and welfare costs Sustainable insurance can prevent this
But can insurance be sustainably offered in the ASAL? Conventional (individual) insurance unlikely to work, especially
in small scale agro-pastoral sector: Transactions costs Moral hazard/adverse selection
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Index Based Insurance
New innovation in insurance avoids problems that make traditionalinsurance unprofitable for small, remote clients:
Policy holders paid based on external index that triggers paymentsto all insured clients
Suited for risks affecting a large number of people simultaneouslyand for which a suitable index exists.
No transactions costs of measuring individual losses
Preserves effort incentives (no moral hazard) as no single individual can
influence index. Adverse selection does not matter as payouts do not depend on the
riskiness of those who buy the insurance
Problem of basis risk (imperfect correlation loss index)
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Need for a measure that is :
1. Highly correlated with livestock mortality2. Reliably and cheaply available for wide range of locations3. Historically available (pricing)
NDVI ~ vegetation available for livestock to consume
Predicted livestock mortality index
The index
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NASA NDVI Image Produced By: USGS-EROS Data Center. Source: Famine Early Warning System Network (FEWS-NET)
NDVI February 2009, Dekad 3 Deviation of NDVI from long-term averageFebruary 2009, Dekad 3
Laisamis Cluster
-3-2-1
012345
1 9 8 1
1 9 8 2
1 9 8 3
1 9 8 4
1 9 8 5
1 9 8 6
1 9 8 7
1 9 8 7
1 9 8 8
1 9 8 9
1 9 9 0
1 9 9 1
1 9 9 2
1 9 9 3
1 9 9 4
1 9 9 5
1 9 9 6
1 9 9 7
1 9 9 8
1 9 9 8
1 9 9 9
2 0 0 0
2 0 0 1
2 0 0 2
2 0 0 3
2 0 0 4
2 0 0 5
2 0 0 6
2 0 0 7
2 0 0 8
Karare
Logologo
Ngurunit
Korr
Laisamis Cluster, zndvi (1982-2008)
Historical droughts
NDVI Data
Real-timeavailable in 88km 2 resolution
27 yearsavailable sincelate 1981
Source Data
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Cumulative differential NDVI
Product Design
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Derivation of livestock mortality index
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Cumulative zNDVI & Temporal structure of IBLI contract
Product Design
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
Period of continuing observation of NDVIfor constructing LRLD mortality index
LRLD season coverage SRSD season coverage
1 year contract coverage
Sale periodFor SRSD
Predicted SRSD mortality is announced.Indemnity payment is made if triggered
Period of NDVI observationsfor constructing SRSDmortality index
Prior observation of ND VI sincelast rain for LRLD season
Sale period
For LRLD
Sale periodFor SRSD
Predicted LRLD mortality is announced.
Indemnity payment is made if triggered
Prior observation of NDVI since last rainfor SRSD season
Short Rain Short Dry Long Rain Long Dry Short Rain Short Dry
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
Period of continuing observation of NDVIfor constructing LRLD mortality index
LRLD season coverage SRSD season coverage
1 year contract coverage
Sale periodFor SRSD
Predicted SRSD mortality is announced.Indemnity payment is made if triggered
Period of NDVI observationsfor constructing SRSDmortality index
Prior observation of ND VI sincelast rain for LRLD season
Sale period
For LRLD
Sale periodFor SRSD
Predicted LRLD mortality is announced.
Indemnity payment is made if triggered
Prior observation of NDVI since last rainfor SRSD season
Short Rain Short Dry Long Rain Long Dry Short Rain Short Dry
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Attention! Attention!!!
1. Index-Based Livestock Insurance
2. Down-scaled climate projections
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Climate models (GCMs) information on future global climate inresponse to the forcing provided by greenhouse gas emissions. Verycoarse: 200-300 km grid cells
GCMs cannot possibly reproduce the details of local weather (impactsof smallish water bodies, variations in elevation, etc).
So:
How to generate climate information at a scale that is useful fordecision-making by policy makers, researchers, etc? How to generate data useful to assess possible impacts on, for example,crop and pasture production?
From global climate change models to local impacts
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AOGCMs used in the downscaling work
Randall et al. (2007)
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Scheme of the down-scaling analysis
MarkSimstochasticweather
generator
Observed climategrid at resolution
of choice
Generate daily datacharacteristic of a
chosen year (time -slice) from 2000-2099
Applications
WorldClimCRU etcWeather typing
Jones, Thornton, Heinke (2009). Generating characteristic daily weather data using downscaled climate model data from IPCCs Fourth Assessment
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Applications
Daily data that are characteristic (to some extent) of theclimatology of future time slices:
Rainfall Maximum temp Minimum temp
With these, can derive or estimate other variables:
Daily: Solar radiation (a function of Tmax, Tmin, lat, long)
Seasonal: Length of growing period, season start date,duration, ending date (simple water balance, soil data)
Drive vegetation, crop, livestock models
http://futureclim.info/
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Livestock Expertise
Hardly any agriculture without livestock
ILRI is truly & explicitly integrating: Livestock Crops Poor people NRM
Examples: our work on feedscollaboration with IWMI (WUE, etc)
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Targeting and Systems Classification Framework
Characteristics: Simple and map-able Differentiating: production systems, main agro-ecologies, key commodities, livelihood
strategies Distinguishing vulnerable and poor populations Easy to relate to in relation to different centres/MPs activities
Process: Step 1: mapping Step 2: identification development challenges and researchable issues
Aim: Articulate development challenges/system/MP Target activities and interventions in MPs Priority regions Differentiate MP1.1 and MP1.2
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Forward looking perspective
Experience from past & current projects, lots of up-coming projects
Avian influenza - transport model, risk assessment
Global futures comprehensive modelingenvironment CC Vulnerability, GHG inventories, adaptation, Healthy futures decision support for water-borne
diseases Animal change
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ILRIs offering
Livestock as an integral part of agriculturalproduction systems
Targeting Forward looking perspective
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Future beauties
More collaboration Wider application field More and more users
Bigger datasets
sharing of data, tools, methodologies more computing power skill/capacity building
Towards a BECA-like GeoScience Hub?
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Example services
CGIAR and beyond
Targeting and priority setting Earth Observation/GIS support to MPs EO for Impact Assessment Capacity Building Knowledge Management