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Ecology and Epidemiology

A Stochastic Model for Anthracnose Development in Stylosanthes scabra. G. K. Smyth, Senior lecturer, Department of Mathematics, University of Queensland, Queensland, Australia 4072; S. Chakraborty(2), R. G. Clark(3), and A. N. Pettitt(4). (2)Senior research scientist, Commonwealth Scientific and Industrial Research Organisation, Division of Tropical Crops and Pastures, 306 Carmody Road, St. Lucia, Queensland, Australia 4067; (3)Research assistant, Department of Mathematics, University of Queensland, Queensland, Australia 4072; (4)Professor, School of Mathematics, Queensland University of Technology, Gardens Point, Queensland, Australia 4001. Phytopathology 82:1267-1272. Accepted for publication 23 July 1992. Copyright 1992 The American Phytopathological Society. DOI: 10.1094/Phyto-82-1267.

Spatial and temporal progress of anthracnose caused by Colletotrichum gloeosporioides in quantitatively resistant accessions of the tropical pasture legume Stylosanthes scabra was studied in a field experiment at the Southedge Research Station, Queensland, Australia. An anthracnose epidemic was initiated by inoculating a group of susceptible seedlings planted at the center of each plot. The speed with which the disease spread from the infection focus to plants within a plot depended on their proximity to the focus and level of resistance of the accessions. A stochastic Markov chain model, in which the probability of a plant developing a given disease severity level depends on its current disease state and that of its neighbors, was used to describe disease progress. The probability of a disease-free plant with disease-free neighbors developing anthracnose within a 1-wk period was estimated to be 52% for the susceptible cultivar Fitzroy, 2.8% for the resistant accession 93116, and 6.523% for accessions with quantitative resistance. In all accessions, the probability of a plant becoming diseased or progressing to a higher state of severity increased with the severity level of its nearest neighbors. An accession effect parameter served as an estimate of the relative susceptibility of the accessions. Accession ranking based on this parameter was highly correlated with that based on the area under the disease progress curve. The model effectively described both spatial and temporal aspects of anthracnose progress.

Additional keywords: logistic regression, nearest-neighbor analysis, ordinal regression, probability model.