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

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Karare

Logologo

Ngurunit

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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