For his research to Develop a Simulation Environment for Pathogen and Pest Spread in Vineyards, mechanical engineering associate professor Rob Stoll received a one-year $27,000 grant from the Agricultural Research Foundation Oregon State.

Each growing season a viticulturist makes a set of interconnected management decisions that
guide a vineyard from bud break to harvest. Each decision becomes increasingly dependent on
how the crop and associated diseases and pests are responding to previous decisions and the
environment. Growers use their expertise and knowledge to make these decisions in reaction to
their perceptions of the current conditions and predictions of what will develop. Since growers
have only limited forecast data and generally limited or incomplete information on which to base
these decisions, their ability to manage crop risk is impaired by the ambiguity of the data they have
and becomes highly dependent on their expertise.
Conventional wisdom and scientific research (Guest et al. 2001) indicate that expertise in any
endeavor requires considerable repetition before it is achieved. Viticultural expertise is extremely
expensive and difficult to obtain due to the limited scenarios an individual can experience in each
growing season. Gaining experience is also hindered by the high cost of any mistakes or
experimentation. These limitations result in a slow adoption of new technology or approaches.
Only a few early adopting growers will first experiment with a few vineyard rows, then expand
their experiment as they gain knowledge and confidence. The majority wait for others to
experiment. This cautious approach is due to the difficulty in managing the inherent uncertainty
Simulation environment for pathogen and pest spread associated with any new technology or approach.

The overall goal of this research is to develop a simulation platform that serves as a research
and training tool to help producers develop improved intuitive skill in making disease and pest
management decisions from incomplete data. We will begin accomplishing this goal by
integrating disease risk forecasters with models for air turbulence to predict pathogen dispersal
and spatially explicit disease risk. The specific objectives are:
1. Develop an epidemiological model based on realistic biological mechanisms and physics
driven by the integration of canopy-resolving models describing local micrometeorology
and microclimate along with a mesoscale climate model.
2. Model validation using laboratory and field experimentation.