Aagw2010 June 09 an Notenbaert Gis Rs at Ilri

8/9/2019 Aagw2010 June 09 an Notenbaert Gis Rs at Ilri

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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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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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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/
http://futureclim.info/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

Documents

Aagw2010 June 09 an Notenbaert Gis Rs at Ilri