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Temporal and Spatial Analysis of Maize Dwarf Mosaic Epidemics. L. V. Madden, Associate professor, Department of Plant Pathology, The Ohio State University (OSU), Ohio Agricultural Research and Development Center (OARDC); Raymond Louie, and J. K. Knoke. Research plant pathologist and research entomologist, respectively, Agricultural Research Service (ARS), U. S. Department of Agriculture (USDA), Wooster, OH 44691. Phytopathology 77:148-156. Accepted for publication 1 July 1986. This article is in the public domain and not copyrightable. It may be freely reprinted with customary crediting of the source. The American Phytopathological Society, 1987.. DOI: 10.1094/Phyto-77-148.

Epidemics of maize dwarf mosaic (MDM) were monitored in experimental field plots of susceptible maize at three locations in Ohio (Wooster, Marietta, and Portsmouth). Based on regression analysis, initial disease incidence (yo) was less than 0.003 in all plots. The weighted mean rate of disease increase (p) ranged from 0.001 to 0.062 per day. The lowest rates, and the lowest final disease incidences (yr) were in the northern Ohio plots; these three plots were best described by the monomolecular model and the flexible Weibull model with a shape parameter (c) equal to 1. At the other locations, disease progression was best described by the logistic or Gompertz models, and by the Weibull model with c > 1. Disease incidence reached a level of yr ≥0.995 in nine of 12 plots at Marietta and Portsmouth. MDM incidence also was accurately described by a nonlinear function of the cumulative number of aphids trapped in all plots. Spatial patterns of diseased plants changed in most plots during the season. Ordinary runs declined in 10 of 13 plots; a random pattern usually was detected during the early part of the epidemic and a clustered pattern later. Patchiness also declined in nine of 13 plots. Most plots exhibited random and clustered patterns of MDM, depending on the time. Six of the plots had an overall clustered pattern as determined by Taylorís power relation; this did not always agree with the individual patchiness calculations. Additionally, analysis based on ordinary runs and patchiness (or the variance-to-mean ratio) often led to different conclusions about randomness or clustering. There was a significant (P ≤ 0.05) negative correlation between the final level of patchiness in the plots and both yo and p. Initial level of aggregation was not correlated with the temporal characteristics of the MDM epidemics.

Additional keywords: comparative epidemiology, disease progress curves, dispersion, quantitative epidemiology, spatial distribution, vectors, Zea mays.