Airborne spread of plant disease - Lagrangian stochastic model




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Published on Jan 16, 2015

How does a plant pathogen spread? The answer is blowing in the wind, you might say. And to get at that, we’ve used a combination of field experiments and mathematical models. First, we started with a known source, a field known to have a particular disease. Before the first spores released, we put down an array of sensors surrounding the source, going out to 1 km.

When the spores start to release, they will move together downwind, just like a smoke plume from a fire, but they are so small and far apart from one another, that they are effectively invisible to the naked eye.

Temporally varying strength
We put a spore sensor in the middle of our diseased field and monitored the number of spores getting released, which we call the source strength. It’s not uniform, instead we see this spiky pattern, which corresponds to nightly releases of spores which start out small and then build up, until all the spores in the field have been released. Using a mathematical model of how the spores are spread in the wind, we can predict where the spores will land on the ground.

Here we used a 'Lagrangian stochastic dispersal model'. We can also use a Gaussian plume model, which is shown in another video here:

Comparing that with the sensor measurements, we can see if we can predict the amount of disease spread over a given distance. This curve, called the dispersal kernel, is an important input for epidemic models of disease spread.

For the paper, see
'Experimental validation of a long-distance transport model for plant pathogens: application to Fusarium graminearum', Agricultural and Forest Meteorology 203, 118-130. doi:10.1016/j.agrformet.2014.12.009
(A.J. Prussin, L.C. Marr, D.G. Schmale, R. Stoll, S.D. Ross [2015])

The region shown is Kentland Farm, run by Virginia Tech in Blacksburg, Virginia


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