10. ilri 9june2010
description
Transcript of 10. ilri 9june2010
GIS/RS @ILRI
An Notenbaert
African Agriculture GIS Week8-16 June 2010Nairobi, Kenya
Attention! Attention!!!
1. Index-Based Livestock Insurance
2. Down-scaled climate projections
Different (spatial and temporal) scalesDifferent target audiencesDifferent position along research-development gradient
Attention! Attention!!!
1. Index-Based Livestock Insurance
2. Down-scaled climate projections
IBLI
Protecting Pastoralists from the Risk of Drought Related Livestock Mortality:Piloting Index-Based Livestock Insurance
in Northern Kenya
http://www.ilri.org/ibli/
Managing Risk in the ASALs
• ASAL residents, particularly in Northern Kenya, confront harsh and volatile environments.
• High level of risk:– Droughts, Diseases, Conflict
• Low levels of capacity:– Infrastructure deficient– Few alternative livelihood opportunities
= A high degree of vulnerability to risk
Impact of Drought on Livelihoods The Marsabit Pilot
• Livestock is both the principal asset and source of income for the vast majority of ASAL residents
• Drought is the single greatest cause of livestock mortality
• Most drought related livestock mortality occurs under severe conditions
Proportion of total income by source
Livestock mortality by cause
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
Index Based Insurance
• New innovation in insurance avoids problems that make traditional insurance unprofitable for small, remote clients:
• Policy holders paid based on external “index” that triggers payments to all insured clients
• Suited for risks affecting a large number of people simultaneously and 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)
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
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 average February 2009, Dekad 3
Laisamis Cluster
-3-2-1012345
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
Karare
Logologo
Ngurunit
Korr
Laisamis Cluster, zndvi (1982-2008)
Historical droughts
NDVI Data
Real-time available in 8×8 km2 resolution
27 years available since late 1981
Cumulative differential NDVI
Derivation of livestock mortality index
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 NDVI sincelast rain for LRLD season
Sale periodFor 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 NDVI sincelast rain for LRLD season
Sale periodFor 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
Attention! Attention!!!
1. Index-Based Livestock Insurance
2. Down-scaled climate projections
• Climate models (GCMs) information on future global climate in response to the forcing provided by greenhouse gas emissions. Very coarse: 200-300 km grid cells
• GCMs cannot possibly reproduce the details of local weather (impacts of smallish water bodies, variations in elevation, etc).
So:
• How to generate climate information at a scale that is useful for decision-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
AOGCMs used in the downscaling work
Randall et al. (2007)
Scheme of the down-scaling analysis
MarkSim stochastic weather
generator
Observed climate grid at resolution
of choice
Generate daily data characteristic 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 IPCC’s Fourth Assessment
Applications
Daily data that are characteristic (to some extent) of the climatology 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/
ILRI’s offering
• Livestock expertise• Targeting• Forward looking perspective
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)
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
Forward looking perspective
• Experience from past & current projects, lots of up-coming projects– Avian influenza - transport model, risk assessment– Global futures – comprehensive modeling
environment– CC – Vulnerability, GHG inventories, adaptation, …– Healthy futures – decision support for water-borne
diseases– Animal change– …
ILRI’s offering
• Livestock as an integral part of agricultural production systems
• Targeting• Forward looking perspective
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?
Example services
• CGIAR and beyond
• Targeting and priority setting• Earth Observation/GIS support to MPs• EO for Impact Assessment• Capacity Building• Knowledge Management