Remote Sensing of Wheat Rusts - A dream or reality?

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Remote Sensing of Wheat Rusts - A dream or reality? Dave Hodson CIMMYT-Ethiopia Bale, Ethiopia Nov 2013

description

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)

Transcript of Remote Sensing of Wheat Rusts - A dream or reality?

Page 1: Remote Sensing of Wheat Rusts - A dream or reality?

Remote Sensing of Wheat Rusts - A dream or reality?

Dave HodsonCIMMYT-Ethiopia

Bale, Ethiopia Nov 2013

Page 2: Remote Sensing of Wheat Rusts - A dream or reality?

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%.

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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?•www.rusttracker.cimmyt.org

Added value: Hotspots

Spore Deposition Modeling

J.A. Cox. Cambridge Uni

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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

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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 SensingWenjiang Huang et al 2012, Beijing Research Center for Information Technology in Agriculture, Beijing, China

Identifying and Mapping Stripe Rust in Winter Wheat usingMulti-temporal Airborne Hyperspectral ImagesLIN-SHENG HUANG et al 2012

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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]

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Are we there yet?

Sharp et al. 1985. Phytopathology