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Mln presesntation very final


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Mln presesntation very final

  1. 1. Maize Lethal Necrosis Disease Threat in Africa: Current and Future Risk Analysis Using Ecological Nichie Models Rwomushana I1, Isabirye B.E1*, Masiga C. W1., Zziwa E1, Opio F1 1Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA), P.O.Box 765, Entebbe, Uganda *
  2. 2. Introduction •  Most important cereal crop: Staple food (>1.2 billion) & 34% global cereal production •  Africa grows 29 M ha & consumes 30% global maize produce. E.A average per capita consumption is 100 kg/ year •  Due to several constraints, Africa does not exploit her over 80M Ha potential land for maize production, hence imports 28% to fill the consumption deficit. •  Maize Lethal Necrosis Disease (MLND), caused by synergistic effect of maize chlorotic mottle virus (MCMV) and any potyvirus: MDMV, SCMV, WSMV has been causing maize losses lately! •  Maize production to drop by >15% by 2020 in much of sub-Saharan Africa. Estimated loss to Africa at $2 billion a year. Prices increase by 35% for maize
  3. 3. MLND Prevalence •  Incidence dates back to 1973 in Peru, and in Kansas, 1976 (Castillo & Hebert, 1974; Niblett & Claflin, 1978). •  China in 2010, and by 2011 in Argentina & Mexico (Nelson et al., 2011) •  In Africa: in Kenya in 2011 Bomet County (Wangai, 2012), Tanzania (Miano et al. 2013), and Uganda (CABI, 2013), Rwanda (RAB, 2013). •  Losses range btn 30-100% (FAO, 2012; CABI, 2013). Over 15,732 ha of maize infected, affecting over 300,000 farmers in Kenya by mid 2012 (FSNWG, 2012).
  4. 4. Climate Change Impact on Agriculture •  Variable and uncertain weather the greatest challenges to smallscale farmers whose livelihoods we aim to transform in ECA •  New technologies and knowledge - hardier crops and better ways to manage resources •  Need to address both mitigation of and adaptation to climate change. 4
  5. 5. The Study: Current and future Risk Analysis •  The spatiotemporal variation of MLN suitability and emergence remains poorly understood, making it difficult to design responsive mitigation and adaptation measures. §  Describe the geographic distribution and ecological niche (Grinnell, 1917) of MLN in Africa to identify potential risk areas using a landscape epidemiology approach. •  Important first attempt in identifying the geographic areas in Africa having the ecological conditions suitable for MLND in the environment. •  Premise: Knowing the suitable environmental conditions for specific vectors, hosts and pathogens in nature, one can use the landscape to identify the spatial and temporal distribution of disease risk (Meade & Earickson 2000; NASA 2006).
  6. 6. Landscape epidemiology with ENM •  In many cases, the details of ecologic parameters associated with occurrences of diseases may be unclear because of small sample sizes, biased reporting, or simply lack of detailed geographic or ecologic analysis. •  ENM has a suite of tools that relate known occurrences of these phenomena to raster geographic information system layers that summarize variation in several environmental dimensions. •  The result is an objective, quantitative picture of how what is known about a species or phenomenon relates to environmental variation across a landscape.
  7. 7. Disease ENM: Proof of Concept Characterization of ecologic features of outbreaks of hemorrhagic fever caused by Ebola and Marburg viruses
  8. 8. Methods for ENM of MLND Geographic Space Ecological Space occurrence points on current distribution Current Current range prediction Future range prediction Model of niche in ecological dimensions precipitation ecological niche modeling Projection back onto geography Variable Bio1 Bio2 Bio3 Bio4 Bio5 Bio6 Bio7 Bio8 Bio9 Bio10 Bio11 Bio12 Bio13 Bio14 Bio15 Bio16 Bio17 Bio18 Bio19 ! temperature Variable type Annual mean temperature Mean diurnal range: mean of monthly) Isothermality: (P2/P7) × 100 Temperature seasonality (SD × 100) Maximum temperature of warmest month Minimum temperature of coldest month Temperature annual range (P5 – P6) Mean temperature of wettest quarter Mean temperature of driest quarter Mean temperature of warmest quarter Mean temperature of coldest quarter Annual precipitation Precipitation of wettest month Precipitation of driest month Precipitation seasonality Precipitation of wettest quarter Precipitation of driest quarter Precipitation of warmest quarter Precipitation of coldest quarter
  9. 9. Methods for ENM of MLND… 1. Extensive Survey by Several Partners and laboratory confirmation with PCR, FERA-UK 2. Literature review for reported detections in region 3. GARP: Genetic algorithm that uses a set of phenomena point localities and a set of geographic layers representing the limiting environmental parameters. 4. Both current (1950-2000) and future (2020 and 2050) Scenarios were used (IPCC).
  10. 10. Results 1. Current Risk: Potential distribution and Hotspots MLN Records in Africa Current Potential Risk Areas Hot Spots distribution Eastern and Central Africa, and Southern and Mid-West Africa show suitability of MLN, with majority of hotspots located in the humid and sub-humid central and eastern Africa
  11. 11. Results… 2. Future Risk: 2020 Potential distribution & Hotspots 2020 Period Potential Risk Areas 2020 Hotspots distribution Shrinkages in MLN suitability predicted, with much of West Africa, Madagascar and Southern Africa becoming less suitable, but Eastern Africa will remain hotspots.
  12. 12. Results… 3. Future Risk: 2050 Potential distribution & Hotspots 2050 Period Potential Risk Areas 2050 Hotspots distribution Shrinkages in MLN suitability predicted, with much of West Africa, Madagascar and Southern Africa becoming less suitable, but Eastern Africa will remain hotspots.
  13. 13. Results…Limiting Factors Temperature (isothermality, annual range and mean temperature of coldest quarter) and precipitation (precipitation of the wettest month and quarter) had the greatest effect on the models
  14. 14. Conclusions •  MLND Risk in Africa is high! Predictive tests based on independent distributional data indicate that model predictions are robust (ROC and Kappa values ranging between 85 and 99%), while field observations confirm relationships between incidence and model predictions. •  There is need for better allocation of resources in the management of MLN, with special emphasis in the Eastern and Central African region which will remain a hotspot up to 2050. •  Landscape based epidemiology can resolve spatial resolution of geographic risk for current and emerging diseases. Propose inclusion in regional and national Early Warning Initiatives.