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Earth observation applications in Index-based
Livestock Insurance (IBLI): challenges and
opportunities
Francesco Fava
OnePlanet Workshop, Nairobi, 14-15 March 2017
PROBLEM & RATIONALE
Pastoral populations of Sub-Saharan Africa are particularly
vulnerable to environmental shocks, particularly drought,
which contribute to livestock mortality and therefore losses
in both wealth and productive assets.
Can we develop a sustainable risk reduction approach using
innovative technologies?
 Index-based Livestock Insurance (IBLI) an innovation in
insurance design suitable for pastoral areas in Africa.
 EO-based forage availability/scarcity assessment to
design precise and cheap asset protection insurance
contracts.
 First launched in Marsabit in January 2010.
Now scaled to Ethiopia and across Northern Kenya with
increasing commitment of commercial entities,
government and development partners.
HOW IBLI WORKS
 Satellite based (MODIS) Rangeland mapping &
monitoring
 Early detection of forage availability
 Payouts in case of drought related forage scarcity
http://sensorweb.vancouver.wsu.edu/index.html
cimss.ssec.wisc.edu
Z-scoring to get seasonal index
Vrieling et al., 2014,2016
Standardization and deviation
from ‘historical’ mean
Temporal accumulating
Seasonal cumulated NDVI
NDVI spatially aggregated
1-10 May 2011
MODIS NDVI image (10 day)
Spatial
aggregation
Components of a sustainable Index Insurance Program
IBLI COMPONENTS
• About 20000 contracts sold (Kenya and Ethiopia, private and public)
• Positive impacts on household welfare and health indicators
• Demand for expansion from several countries
• Recent big payouts. Over 2M USD to 17000 households.
IBLI area of operation IBLI potential expansion
IBLI ACHIEVEMENTS
GAPS & OPPORTUNITIES
EARTH OBSERVATION AND RANGELANDS
 NDVI limitations as a forage availability index.
Can we use new/multiple indicators (e.g soil moisture, RFE,
Sentinel)?
 Poor characterization of rangeland systems (e.g. in relation to
palatability and land condition)
Multi-scale mapping (drones/very high res. satellites).
 Poor link between vegetation and livestock production.
Livestock detection, grazing patterns, water points mapping.
 Lack of long term data for “validation” of mapping products.
Crowdsourcing, ground networks (webcams), drones.
GAPS & OPPORTUNITIES
BEYOND REMOTE SENSING…
Information scarcity (collection/dissemination) is severely limiting
development and resource management in the pastoral areas of
Africa
How to close this gap in a sustainable way?
 Need timely, accurate and spatial explicit data collection and
information extraction approaches (but cost effective!).
 Needs to bundle multiple “services” (feed & forage, veterinary
information, market information, rangeland condition, etc).
 Need of efficient information dissemination and continuous
capacity/awareness support (m-Learning).
GAPS & OPPORTUNITIES
Multi-scale EO-Data in combination with Mobile technologies ,
crowdsourcing and big data analyses (computer vision, machine learning...)
PROTOTYPE MOBILE-BASED COLLECTION & DISSEMINATION SYSTEM
Thanks!
This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence.
better lives through livestock
ilri.org
ILRI thanks all donors and organizations which globally support its work through their contributions to
the CGIAR system
www.ilri.org/ibli
francesco.fava@cgiar.org

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Earth observation applications in Index-based Livestock Insurance (IBLI): Challenges and opportunities

  • 1. Earth observation applications in Index-based Livestock Insurance (IBLI): challenges and opportunities Francesco Fava OnePlanet Workshop, Nairobi, 14-15 March 2017
  • 2. PROBLEM & RATIONALE Pastoral populations of Sub-Saharan Africa are particularly vulnerable to environmental shocks, particularly drought, which contribute to livestock mortality and therefore losses in both wealth and productive assets. Can we develop a sustainable risk reduction approach using innovative technologies?  Index-based Livestock Insurance (IBLI) an innovation in insurance design suitable for pastoral areas in Africa.  EO-based forage availability/scarcity assessment to design precise and cheap asset protection insurance contracts.  First launched in Marsabit in January 2010. Now scaled to Ethiopia and across Northern Kenya with increasing commitment of commercial entities, government and development partners.
  • 3. HOW IBLI WORKS  Satellite based (MODIS) Rangeland mapping & monitoring  Early detection of forage availability  Payouts in case of drought related forage scarcity http://sensorweb.vancouver.wsu.edu/index.html cimss.ssec.wisc.edu Z-scoring to get seasonal index Vrieling et al., 2014,2016 Standardization and deviation from ‘historical’ mean Temporal accumulating Seasonal cumulated NDVI NDVI spatially aggregated 1-10 May 2011 MODIS NDVI image (10 day) Spatial aggregation
  • 4. Components of a sustainable Index Insurance Program IBLI COMPONENTS
  • 5. • About 20000 contracts sold (Kenya and Ethiopia, private and public) • Positive impacts on household welfare and health indicators • Demand for expansion from several countries • Recent big payouts. Over 2M USD to 17000 households. IBLI area of operation IBLI potential expansion IBLI ACHIEVEMENTS
  • 6. GAPS & OPPORTUNITIES EARTH OBSERVATION AND RANGELANDS  NDVI limitations as a forage availability index. Can we use new/multiple indicators (e.g soil moisture, RFE, Sentinel)?  Poor characterization of rangeland systems (e.g. in relation to palatability and land condition) Multi-scale mapping (drones/very high res. satellites).  Poor link between vegetation and livestock production. Livestock detection, grazing patterns, water points mapping.  Lack of long term data for “validation” of mapping products. Crowdsourcing, ground networks (webcams), drones.
  • 7. GAPS & OPPORTUNITIES BEYOND REMOTE SENSING… Information scarcity (collection/dissemination) is severely limiting development and resource management in the pastoral areas of Africa How to close this gap in a sustainable way?  Need timely, accurate and spatial explicit data collection and information extraction approaches (but cost effective!).  Needs to bundle multiple “services” (feed & forage, veterinary information, market information, rangeland condition, etc).  Need of efficient information dissemination and continuous capacity/awareness support (m-Learning).
  • 8. GAPS & OPPORTUNITIES Multi-scale EO-Data in combination with Mobile technologies , crowdsourcing and big data analyses (computer vision, machine learning...) PROTOTYPE MOBILE-BASED COLLECTION & DISSEMINATION SYSTEM
  • 9. Thanks! This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence. better lives through livestock ilri.org ILRI thanks all donors and organizations which globally support its work through their contributions to the CGIAR system www.ilri.org/ibli francesco.fava@cgiar.org