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Influence of Assessment Time and Modeling Approach on the Relationship Between Temperature-Leaf Wetness Periods and Disease Parameters of Septoria glycines on Soybeans. W. Schuh, Assistant Professor, Department of Plant Pathology, Pennsylvania State University, University Park 16802; A. Adamowicz, Research Assistant, Department of Plant Pathology, Pennsylvania State University, University Park 16802. Phytopathology 83:941-948. Accepted for publication 11 May 1993. Copyright 1993 The American Phytopathological Society. DOI: 10.1094/Phyto-83-941.

The disease severity and number of lesions of Septoria glycines were significantly influenced by temperature and length of leaf wetness period. In general, disease severity increased with increasing leaf wetness periods from 6 to 36 h at all assessment dates. The optimum temperature was 25 C, but disease symptoms were also observed at 15, 20, and 30 C. Similar trends were found for the number of lesions per square centimeter. Infection of soybean leaves by S. glycines led to premature senescence even at low disease severity. No disease threshold for early senescence could be determined, because leaf senescence was influenced by the location of the lesions on the leaf in addition to disease severity. Results from this study should be useful in determining favorable infection conditions in the field. The interpretation of the data sets for disease management or prediction was strongly influenced by the assessment date (7, 14, and 21 days after inoculation), and to a lesser degree by the choice of modeling approach (linear and nonlinear regression analysis). The number of lesions per square centimeter could only be modeled for data obtained on day 7. The inability to model the relationship at the later assessment dates was due to a decrease in lesion numbers caused by lesion merger at optimal temperature–dew period conditions and a further increase in lesion numbers at nonoptimal conditions. This was independent of the modeling approach. Disease severity was modeled successfully at days 14 and 21 by linear regression analysis, and at day 14 by nonlinear regression analysis. In general, both models described the data satisfactorily as determined through R2 values (>0.85). Implications for disease management are discussed.