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Remote Sensing of Wheat Rusts - A dream or reality?


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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 of Wheat Rusts - A dream or reality?

  1. 1. Remote Sensing of Wheat Rusts A dream or reality? Bale, Ethiopia Nov 2013 Dave Hodson CIMMYT-Ethiopia
  2. 2. Wheat Rusts (“Cereal Killers”) ● Major fungal diseases of wheat. All capable of serious economic losses (millions or billions of $) ● ** Stem Rust: Historically, most feared disease. 100% loss possible. Reduced threat for last 40 years. New virulent races now a concern ● *** Yellow (stripe) rust: Current major problem globally. Losses 60%+ ● * Leaf Rust: Most widespread. Losses usually around 10%.
  3. 3. Current Global Rust Monitoring ● “Eyes on the ground” • 14,000+ survey records • Network of 30+ countries • Large % of developing world wheat • Most comprehensive disease monitoring system for a major crop? • Added value: Hotspots Spore Deposition Modeling J.A. Cox. Cambridge Uni
  4. 4. Problem / Current Situation ● Can we do better / be more efficient? ● Increased area coverage ● $ cost of surveys ● Timely detection / reporting (Improved early warning) ● Improved targeting of control measures ● Reliable estimates of damage / size of epidemic
  5. 5. RS Approaches in literature (Stripe Rust) ● Canopy Spectra – PhRI (Physiological Reflectance Index) [more precision ag approach] ● Airborne Hyperspectral – PRI (photochemical reflectance Index) – disease progress with time ● Spectral K Base (SKB) – link hypersectral to mod resolution multi-spectral images – wider areas? ● RS land surface temp (LST) from MODIS – suitability zones – wider areas Crop Disease and Pest Monitoring by Remote Sensing Wenjiang Huang et al 2012, Beijing Research Center for Information Technology in Agriculture, Beijing, China Identifying and Mapping Stripe Rust in Winter Wheat using Multi-temporal Airborne Hyperspectral Images LIN-SHENG HUANG et al 2012
  6. 6. RS “Wish List” ● Options for Early warning? [functional platform, large areas?] ● Can we reliably detect rusts? (e.g., yellow rust vs yellowing due to nutrients / water issues) ● How low can we go? Min areas to detect (already too late for control once you can detect?) ● Conducive areas / suitability zones? ● Host monitoring options? e..g, crop distribution + phenology ● Epidemic extent + damage estimates? [Ex-post assessments]
  7. 7. Are we there yet? Sharp et al. 1985. Phytopathology