Degree Type

Dissertation

Date of Award

2005

Degree Name

Doctor of Philosophy

Department

Plant Pathology and Microbiology

First Advisor

Philip M. Dixon

Second Advisor

Forrest W. Nutter, Jr.

Abstract

Research was conducted from 2001 to 2003 in Iowa to determine ideal sampling methods, as well as the effect of planting date with/without seed insecticides to reduce corn flea beetle vector (CFB) (Chaetocnema pulicaria) feeding and Stewart's disease (Pantoea stewartii) of corn. Sampling for CFB's was conducted at Ames, Crawfordsville, and Sutherland in 2001 and Crawfordsville and Johnston in 2002. Yellow sticky cards were placed at 15 combinations (five replications) of height (0.15, 0.3, 0.45, 0.6, 0.9 m) and orientation (vertically, horizontally, or 30° angles) at each location. The 0.3 m and vertically facing cards significantly captured more CFB's (1.1 to 35 times) during 2003. To study the effects of planting date with/without seed insecticides, trials were conducted at Crawfordsville (2002 and 2003). Ten (2002) and eight (2003) planting dates were established in multifactorial combinations with seed insecticide (nontreated, PonchoRTM, CruiserRTM) in a randomized complete block design (four replications). Five (2002) and six (2003) planting dates were examined for incidence of CFB feeding scars and Stewart's disease. Analyses revealed a reduction in incidence of the early wilt phase with delayed plantings, however, increased numbers of CFB's and rates of Stewart's disease were observed for these delayed plantings. Yield was also significantly reduced in delayed plantings. Delayed planting as a viable management tactic for Stewart's disease was deemed unfeasible. Furthermore, we examined forecasting models for Stewart's disease. Our goal was to increase pre-plant prediction accuracy at the county-level. We used binary logistic regression for modeling. The Stevens and Stevens-Boewe models were found to greatly under predict Stewart's disease occurrence, while the Iowa State Model improved forecasting. Also, two-factor models using air temperature (Iowa State Model, frequency of days with minimum air temp ≤ -6.7°C, sum mean monthly temperatures (Dec., Jan., and Feb.)), plus previous history of Stewart's disease in a county increased accuracy to 75-80%. Using receiver operating characteristic curves and an economic cost function for false predictions (positive and negative), a probability of 40% was defined for forecasting Stewart's disease. Overall, this thesis provides relevant new information in order to improve forecasting and management of Stewart's disease in Iowa.

DOI

https://doi.org/10.31274/rtd-180813-15370

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/

Copyright Owner

Paul David Esker

Language

en

Proquest ID

AAI3200414

File Format

application/pdf

File Size

177 pages

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