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Remote sensing based drought tolerant maize targeting in SSA

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Remote sensing –Beyond images
Mexico 14-15 December 2013

The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

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Remote sensing based drought tolerant maize targeting in SSA

  1. 1. Remote sensing based drought tolerant maize targeting in SSA Remote sensing – Beyond Images Kai Sonder, Jill Cairns, Olaf Erenstein, Girma Tesfahun, Kindie Tesfaye, Victor Hernandez, Dave Hodson Mexico City, 14-15.12.2013
  2. 2. DTMA project: Drought tolerant maize for Africa Mali Nigeria Uganda Ghana Benin Angola Zambia Zimbabwe Ethiopia • • • • • • • Funded by Bill & Melinda Gates Foundation 3rd phase currently CIMMYT and IITA and many partners 13 countries in West, East and Southern Africa Develop drought tolerant maize germplasm Strengthen small seed companies Make DT materials available to farmers Kenya Tanzania Mozambique Malawi
  3. 3. WHY do this? •Ex ante impact assessment •Recommendations on areas for upscaling •Fit breeding program to areas not currently covered HOW to target the areas where DT material would be most useful to farmers? • • • • Ideally use high res daily rainfall data but not really available in SSA Failed seasons probability Drought indices based on monthly data Remote sensed data
  4. 4. Probability of failed seasons Developed by Peter Jones Based on a climate generator (MARKSIM) No time series Doesn’t cover short or mild droughts Thornton et al. 2006 Mapping Climate Vulnerability and Poverty in Africa.
  5. 5. Standardized Precipitation Index (SPI) Based on rainfall only Can be calculated at different time scales McKee et al., 1993 •Calculated for one month and three month droughts for all of SSA •Mild, Moderate, Severe, Extreme droughts classified •Using monthly CRU 3.1 data 19502010 (in theory 1900-2012) •Doesn’t capture fully cover short droughts •WMO recommended •Widely used
  6. 6. DSI (Drought Severity Index) •Satellite image (MODIS) based (Mu et al., 2013) •Mix of MODIS based NDVI and MODIS based ET/PET •MOD16 ET/PET MOD13 NDVI •Annual and monthly and 8 day calculations possible •Time series available currently 2000 – 2011 •Time series can be expanded
  7. 7. DSI Classes Mu et al. 2013
  8. 8. DSI annual time series
  9. 9. Frequencies of moderate and milder droughts in Eastern Africa • • • • • • Applied to SPAM 2000 maize production areas Redo with SPAM 2005 Calculate maize areas exposed to different types of drought Estimation of number of rural population in those areas Estimation of poor in the areas
  10. 10. Next steps •Evaluate 8 day product for short term events •Validate with ground data from CIMMYT sites •Relate DTMA on farm trials to drought events •Compare performance of DT materials in different sites •Evaluate for climate risk exposure studies with socio economists
  11. 11. Future possibilities •Evaluate more remote sensing sources •TRMM (Tropical Rainfall Measuring Mission) •New generation GPM (Global precipitation Measurement) •Soil moisture (eg ERS/MetOp, Vienna) Soil Water Index (SWI) •Combine with crop models http://www.ipf.tuwien.ac.at/radar/index.php?go=home
  12. 12. THANK YOU

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