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Modeling of Relationships Between Weather and Septoria tritici Epidemics on Winter Wheat: A Critical Approach

First and fourth authors: Rothamsted Research, Rothamsted, Harpenden, Hertfordshire AL5 2JQ, UK; second author: Department of Agricultural Botany, School of Plant Science, The University of Reading, Reading RG6 6AS, UK; and third author: ADAS High Mowthorpe, Duggleby, Malton, North Yorkshire YO17 8BP, UK

Two models for predicting Septoria tritici on winter wheat (cv. Riband) were developed using a program based on an iterative search of correlations between disease severity and weather. Data from four consecutive cropping seasons (1993/94 until 1996/97) at nine sites throughout England were used. A qualitative model predicted the presence or absence of Septoria tritici (at a 5% severity threshold within the top three leaf layers) using winter temperature (January/February) and wind speed to about the first node detectable growth stage. For sites above the disease threshold, a quantitative model predicted severity of Septoria tritici using rainfall during stem elongation. A test statistic was derived to test the validity of the iterative search used to obtain both models. This statistic was used in combination with bootstrap analyses in which the search program was rerun using weather data from previous years, therefore uncorrelated with the disease data, to investigate how likely correlations such as the ones found in our models would have been in the absence of genuine relationships.

Additional keywords: binary data, data mining, discriminant analysis, Window Pane.