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Landscape Model of a Weather-Driven Process: the case of wind dispersed Conyza canadensis seed

Landscape Model of a Weather-Driven Process: the case of wind dispersed Conyza canadensis seed. Ed Luschei University of Wisconsin – Madison Department of Agronomy. Outline. Preaching to converted: Importance Example: GR Conyza canadensis Population movement via aerial seed dispersal

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Landscape Model of a Weather-Driven Process: the case of wind dispersed Conyza canadensis seed

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  1. Landscape Model of a Weather-Driven Process: the case of wind dispersed Conyzacanadensisseed Ed Luschei University of Wisconsin – Madison Department of Agronomy

  2. Outline • Preaching to converted: Importance • Example: GR Conyzacanadensis • Population movement via aerial seed dispersal • Biology and weather linked/correlated in several ways • Implicit treatment of weather • Extrapolating phenology • Future priorities - Cooperative development • SBML standards and WeedML • Why don’t standards already exist? • Why doesn’t the weather “back end” exist (in the public sphere)? • Is it worth trying to fix?

  3. Horseweed (C. canadensis) Project • Glyphosate resistant Conyza canadensis, problem for no-till soybean producers • Spatial modeling at several scales • Field scale & LDD via boundary layer escapees • Regional -- with fields as “units” • Landscape w/ counties as units • More details elsewhere… • Dauer, J., E. Luschei, and D. Mortensen. 2009. Effects of landscape composition on spread of an herbicide-resistant weed. Landscape Ecology 24:735-747. • Dauer, J.T., D.A. Mortensen, E.C. Luschei, S. Isaard, E. Shields, M. VanGessel. 2009. Release, Escape and Transport of Conyzacanadensis seed in the Lower Atmosphere. Agricultural and Forest Meteorology. 149(3/4): 526-534.

  4. Elson Shields - Cornell Aerial Sampling Tower of Dauer ~6m Horseweed

  5. Conyza canadensisVertical Seed Distribution

  6. 5 Year Simulation 76 98 34 0 22

  7. Spatial Movement (First Detection) • Data: Presence/absence by county by year • Distribution of “high quality” habitat • Pct of county area in no-till GR soybean • Map to follow [Data from CTIC] • Probability of Absent  Present is logistic function of distance and habitat quality • Use scenario-based Monte-carlo

  8. HR No-Till Soybean Acreage

  9. Where is the Weather Part? • Implicit • Observed patterns and then modeled as diffusive process with major assumptions • Perhaps best described as a “thought experiment” on how heterogeneous land-use affects rate of spread • Why can’t we do better than that?

  10. Phenology & Forecasting • Weedometer (weedometer.net) – including a rough spatial extrapolation via Hopkin’s “Law”

  11. u

  12. Multispecies “Gantt Charts”

  13. Claim • Data-driven Bio-Clim modeling needs two things in order to work • 1. Modeling standards, transparent, modifiable, that can interface with… • 2. Publically supported data “archiving” and a delivery system

  14. Example of Model Standards • Symbolic-Biology Markup Language (SBML) • http://sbml.org • WeedML (paper forthcoming from Niels Holst, Denmark, in “Weed Science” journal) • http://weedml.org

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