Submitted to: International Congress of Plant Pathology Abstracts and Proceedings
Publication Type: Other
Publication Acceptance Date: 7/20/2002
Publication Date: 2/20/2003
Citation: PAULITZ, T.C., COOK, R.J., ZHANG, H. SPATIAL ANALYSIS OF RHIZOCTONIA ORYZAE IN DIRECT-SEEDED CEREALS. INTERNATIONAL CONGRESS OF PLANT PATHOLOGY ABSTRACTS AND PROCEEDINGS. 2003. p 118. Interpretive Summary: Rhizoctonia oryzae causes root rot of barley and wheat. In this study, we look at the spatial distribution of the disease and the pathogen across a 36 ha direct-seeded farm north of Pullman, WA. We sampled 100 GPS located sites over two seasons, and measured root rot and incidence of root colonization by the pathogen. Disease and pathogen showed an aggregated distribution, with most of the disease in the southwest corner of the site. We adapted a spatial generalized linear mixed model to account for the skewed distribution of the data, and used geostatistical methods to interpolate points and derive maps of the disease and pathogen across the landscape. This is the first attempt to look at the distribution of a soilborne pathogen at such a large scale, with sampling points 30 to 100 m apart.
Technical Abstract: Rhizoctonia oryzae causes root rot and stunting of wheat, barley, and other small grains, and is widely distributed in eastern Washington. The spatial distribution of both the pathogen and disease were studied over two seasons in a 36-ha field north of Pullman, WA. The field was direct-seeded with spring barley in 2000 and divided and planted to winter wheat and spring wheat in 2001. The incidence of crown-root rot and root colonization by R. oryzae were measured on plants taken from 95 GPS-located sites 30-100 m apart on a nonaligned grid. The incidence of crown-root rot was low, averaging 18%, 16%, and 13% on the spring barley, spring wheat, and winter wheat, respectively. The incidence of root colonization by R. oryzae was even lower, averaging 4%, <1%, and 2% on the spring barley, spring wheat, and winter wheat, respectively. The frequency distributions were highly skewed, with a high frequency of zero or low values. The distributions were fit to a beta binomial distribution, indicating an aggregated or overdispersed distribution. Because of the highly skewed data, traditional geostatistical approaches did not show spatial correlation. However, a spatial generalized linear mixed model (GLMM) did show spatial correlation and was used to interpolate values to produce pathogen and disease maps. This large-scale mapping of soilborne pathogens may have applications in precision agriculture.