[Day 2] Center Presentation: ILRI

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Presented by An Notenbaert at the CGIAR-CSI Annual Meeting 2009: Mapping Our Future. March 31 - April 4, 2009, ILRI Campus, Nairobi, Kenya

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[Day 2] Center Presentation: ILRI

  1. 1. GIS @ILRI A quick overview and some examples
  2. 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. 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. 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. 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. 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. 7. SLP drivers of change FRAMEWORK
  8. 8. SLP drivers of change 4 Scenarios: Reference METHODS Bio-fuels Scenario Irrigation Expansion Low meat Demand
  9. 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. 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. 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. 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. 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. 14. Epidemiology Risk map for Avian Influence in Nigeria (Acho Okike ILRI Ibadan))
  15. 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. 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. 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. 18. Thank you
  19. 19. Crops: You and Wood Ag.Pot: LGP>180days or equipped for irrigation MA: less than 8 hours to >250K

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