The document provides an overview of GIS activities at ILRI. It discusses that GIS is used for a wide variety of research projects within different themes at ILRI. It also describes GIS services such as data management, advice, capacity building, and data sharing. The document then highlights several past GIS outputs from 2008 and planned GIS activities for 2009 and beyond, covering topics such as livestock production systems, climate change impacts, vulnerability analysis, and disease mapping.

1. GIS @ILRI
A quick overview and some examples

2. GIS at ILRI
• Research:
 Wide variety of projects
 Within the different “themes”
• Services:
 Part of RMG (Research Methods Group)
 SW and data management
 Advice and services
 Capacity Building
 Data sourcing and sharing

3. Some exciting GIS outputs anno 2008
• Poor Livestock Keepers / Value of Production
 SSA and SA  An Notenbaert, Patrick Kariuki, Abisalom Omolo
• USLE Based Potential Erosion Map
 Nile Basin  Paulo van Breugel, A. Notenbaert, L. Claessens, J. VdSteeg
• Livestock water productivity and crop water use
 Nile Basin  Paulo van Breugel
• Simplified productions systems map (4 classes) + projection to 2030
 Global  An Notenbaert, R. Kruska, P. Thornton, M. Herrero
• Projections for crops, livestock, livestock products, water use, malnutrition
 Developing world  Mario Herrero, An Notenbaert
• Climate Change hotspots + VOPs
 ASARECA  Jeannette Van de Steeg, M. Herrero, P. Thornton
• Vulnerability indicators
 GHA  James Kinyangi, A. Notenbaert, M. Herrero
• Composite Risk maps
 COMESA  An Notenbaert, Stella Massawe
• GOBLET and the “development domains tool”
 Global  Carlos Quiros, An Notenbaert
• Avian Influenza Risk maps
 Africa, Asia, Indonesia  Wachira Theuri, Russ Kruska, Acho Okiko
• Innovation successes
 Ethiopia  Patrick Kariuki, R. Puskur
• Updated poverty maps
 Uganda  Patrick Kariuki
• M&E Site selection – chilling plants and hubs for small-holder dairy.
 East Africa  Pamela Ochungo
• Kitengela Atlas (Wildlife and livestock, fences)
 Kitengela  Shem Kifugo, Mohamed Said

4. What is planned for 2009 (and beyond)
• LS production systems toolbox (incl. standard classifications) and LS productivity
 An Notenbaert, M. Herrero, P. Thornton, R. Kruska
• Length Growing Period and Cereal production under different scenarios / GCMs
 Philip Thornton
• Global rangeland model + carbon sinks + responses to CC
 Stefano Disperati / Joseph Maitima, M. Herrero
• Dynamic vulnerability for SSA (+ Mali & Mozambique)
 An Notenbaert, M. Herrero, P. Thornton, N. Johnson
• Intensification thresholds and nutrient balances (global)
 Jeannette Van de Steeg, M. Herrero
• Ecosystem services in the pastoral areas (+ links with food/environmental security)
 Stefano Disperati, J. van de Steeg, M. Said, M. Herrero
• Methane emissions from livestock (global)
 Mario Herrero, P. Thornton, R. Kruska
• Feed supply (crops, forages, rangelands) & feed demand + impacts CC + Feed
markets (global)
 Mario Herrero, Michael Blummel, A. Notenbaert
• Integration of livestock in LU and economic models
 Mario Herrero, P Thornton
• Water poverty and vulnerability in the Nile Basin
 James Kinyangi, T. Ouma, A. Notenbaert
• Climate – Land use interactions in East-Africa
 Joseph Maitima, Jenny Olson
• Evaluation of Arid Lands Resource Management Program
 Abisalom Omolo, A. Notenbaert
• Landscape genomics
 Steve Kemp
• East Coast Fever (risk mapping, spatial targeting of delivery), RVF and bird flue
 Phil Toye, Frank Hansen, Jeff Mariner
• Value chains and market access (distance to markets and services; collection and
distribution of market information, risks and diseases)
 Steve Staal, Derek Baker

5. 1. SLP drivers of change
Drivers of change in crop-livestock systems and
their potential impacts on agro-ecosystem
services and human well-being to 2030
Herrero, M., Thornton, PK, Notenbaert, A., Msangi, S., Wood,
S., Kruska, R., Dixon, J., Bossio, D., van de Steeg, J.,
Freeman, H.A., Li, X. and Parthasarathy Rao, P.
CGIAR Systemwide Livestock Programme.

6. SLP drivers of change
PROBLEM Systems are changing:
Population increasing, Urbanisation, Increased demand for LS products,
Intensification, Climate change, Technology shifts, Globalisation, ….
…. can the poor benefit from these changes?
…. can we change without compromising food security,
ecosystems services and livelihoods?

7. SLP drivers of change
FRAMEWORK

8. SLP drivers of change
4 Scenarios:
Reference
METHODS
Bio-fuels Scenario
Irrigation Expansion
Low meat Demand

9. SLP drivers of change
1. Mixed intensive systems in the developing world are under significant
pressure
SOME KEY FINDINGS
From 2.5 to 3.4 billion people, from 150 to 200 million cattle
Sustaining most of the pigs and poultry and still increasing by 30-40%
Most of the crops yields as well as areas stagnating
Water and soil fertility problems
 Important productivity gains could be made in the more extensive systems
Rate of Change - Cereal Production
Annual changes in Cereal Production
2000 -- 2030
2000 2030
Rates lower than those of Rates of growth of mixed
6 population growth Catching up intensive similar to developed
countries
5
4
%
3
2
1
0
CSA EA SA SEA SSA WANA Total
AgroPastoral Mixed Extensive Mixed Intensive Other Developed countries

10. SLP drivers of change
2. Growth rates of cereal production are diminishing due to water and other
constraints
SOME KEY FINDINGS
… while LS production is growing at significantly faster rates
Annual rates of change - beef production 2000-2030 Annual rates of change - milk production 2000-2030
8 9
7 8
6 7
5 6
5
%
4
%
3 4
2 3
1 2
0
1
0
CSA EA SA SEA SSA WANA Total
CSA EA SA SEA SSA WANA Total
AgroPastoral Mixed Extensive Mixed Intensive Other Developed countries
AgroPastoral Mixed Extensive Mixed Intensive Other Developed countries
Annual rates of change - poultry production Annual rates of change - pork production
14 8
12
6
10
8 4
%
6
%
2
4
0
2
CSA EA SA SEA SSA WANA Total
0 -2
CSA EA SA SEA SSA WANA Total -4
AgroPastoral Mixed Extensive Mixed Intensive Other Developed countries AgroPastoral Mixed Extensive Mixed Intensive Other Developed countries
Increases in: Income, Demand, Pressure on resources, Demand for
grains

11. SLP drivers of change
3. “Moving megajoules” - fodder markets are likely to expand as demand for
meat and milk increases
SOME KEY FINDINGS
4. Expansion of bio-fuels will likely reduce household food consumption in
all systems
5. Some systems may need to de-intensify or stop growing to ensure
sustainability of agro-ecosystems services
Better understanding of intensification thresholds: regulatory framework
and M&E system
Incentives to protect environment / equitable “smart” schemes for
payment of eco-system services
We need significant efficiency gains (in crops, livestock and other sectors
alike)

12. 2. Epidemiology
spatial
Distribution of diseases in populations as well as factors
influencing their occurrence
Thrusfield, M. (1995): Veterinary Epidemiology. Blackwell Science
- epidemiology is the ecology of diseases
„Unter Oecologie verstehen wir die gesamte Wissenschaft von den
Beziehungen des Organismus zur umgebenden Außenwelt.“
“ecology is the science of the relationships of
the organism to the surrounding world”
Ernst Haeckel 1866
German ecologist
space
Epidemiology is a spatial discipline
yet study of spatial interactions is often neglected

13. Epidemiology
What’s happening in ILRI?
- disease risk mapping
- spatially explicit, agent based dynamic system modelling
- Bird flue in 5 countries in Africa and Indonesia
- East Coast Fever in East Africa
- Rift Valley Fever in Kenya

14. Epidemiology
Risk map for Avian Influence in Nigeria (Acho Okike ILRI Ibadan))

15. A transport model for the spread of Avian Influenza in Nigeria
- AI mainly spread by transport of infected chicken or equipment
- Model calculates how far infection can maximally spread based on time
to cross a grid cell

16. Distribution of Ripicephalus appendiculatus the vector of Theileria parva
the causative agent of East Coast Fever
- huge economic losses in cattle
- native breeds more resistant
- exotic and mixed breeds increase productivity
but are very susceptible to ECF www.nhc.ed.ac.uk
In planning:
- derive habitat model
- predict habitat under climate change
scenarios
- predict future distribution of vector
and disease
- targeting control measures
www.fao.org

17. Rift Valley Fever
- Mosquito-borne disease of cattle and humans
- periodic outbreaks can be predicted by weather conditions
- risk-based Decision Support Tool to plan intervention (vaccination, vector control..)
- in planning: revise Decision Support Tool and include economic measures
http://outreach.eos.nasa.gov

18. Thank you

19. Crops: You and Wood
Ag.Pot: LGP>180days or equipped for irrigation
MA: less than 8 hours to >250K