Graduate Research Assistant
Plant and Environmental Sciences Department, Edisto Research and Education Center
Ph.D. in progress Plant Pathology
M.S. Plant Pathology
Mississippi State Universtiy 2019
B.S. Plant and Soil Science
University of Tennessee at Martin 2016
I am a PhD candidate in Dr. Dan Anco’s peanut lab at Edisto REC in Blackville, SC. I am a member of the American Phytopathological Society (APS), APS – Southern Division, American Peanut Research and Education Society (APRES), PES Graduate Student Association (PES GSA), and Edisto Research Association (ERA). I have been awarded the Wade Stackhouse Fellowship for the 2022-2023 year.
My primary interests include epidemiology of fungal pathogens and fungicide resistance.
My current research focuses on the devastating fungal foliar disease, late leaf spot (LLS) of peanut, caused by Nothopassalora personata. My projects are diverse, but the overall theme of my dissertation is to improve the management of this disease. I am assessing the use of peanut residue decomposition treatments for reducing LLS inoculum, using spore traps coupled with qPCR and weather data to quantify the dispersal of N. personata and model the environmental variables that drive dispersal, and screening for fungicide resistant populations, specifically interested in SDHI cross-resistance, of N. personata in SC peanut fields. My final chapter is a little different: I am using image analysis and logistic regression to develop algorithms for diagnosing symptoms of foliar diseases and disorders.
Previous research efforts were on characterizing a novel species of Xylaria, now Xylaria necrophora, causing taproot decline (TRD) of soybean in the southeastern US. Since this was a relatively new disease, my research focused on: identifying the distribution of TRD in Mississippi, the optimal growth range of X. necrophora, in vivo pathogenicity and host range studies, and screening for fungicide resistance in vitro.
Renfroe-Becton, H., Kirk, K.R., and Anco, D.J. 2022. Using image analysis and regression modeling to develop a diagnostic tool for peanut foliar symptoms. Agronomy 12:2712. Doi: 10.3390/agronomy12112712.