Adam H. Sparks
PhD Candidate,
KSU Department of Plant Pathology
asparks@ksu.edu ·
785.532.1341
I am a Ph.D. candidate in the Department of Plant Pathology at Kansas State University in Manhattan, Kansas. I work in Dr. Garrett's Plant Disease Ecology Lab. My interests and expertise include epidemiology and the ecology of plant diseases; GIS applications in plant pathology; agricultural extension outreach and teaching. My Ph.D. studies have focused on major plant pathogen groups, ecology and epidemiology, modeling, GIS, and remote sensing. My full CV is available for download as a pdf file, Adam Sparks's Curriculum Vitae.
My research involves scaling plot-level models up to larger spatial and temporal scales. Many plant disease forecasting models are created using fine-scale temporal resolution, hourly or less, weather data to predict disease severity or the need for control measures to be enacted. Large amounts of coarse temporal resolution data, e.g. daily or monthly weather summaries, exist in ready to use datasets. In certain situations rather than backcasting to create hourly weather data it may be desireable to create a quick estimation of disease severity using these coarser datasets.
System
Pathosystem: late
blight of potato caused by Phytophthora infestans
Initial Model Used for Development: SimCast
using hourly time-step data, written in R
Coarse Weather Resolution Models: Generalized
Additive Models (GAM), written in R based on SimCast outputs
which are capable of using daily, weekly or monthly time step,
weather data.
Research Objectives
- Create late blight prediction models capable of utilizing daily, weekly, or monthly resolution weather data.
- Compare models for different temporal resolutions.
- Evaluate the effects of possible climate change scenarios on late blight severity.
- Compare effects of the use of late blight resistant cultivars with susceptible cultivars.
Web-based teaching modules for plant pathology applications in the R programming environment
These teaching modules are housed permanently on the APSNet Education Center website, Web-based teaching modules for plant pathology applications in the R programming environment