Modeling of relationships between weather and Septoria tritici epidemics on winter wheat: a critical approach

• Published in: Phytopathology Vol. 93; no. 10; pp. 1329 - 1339
• Main Authors: Pietravalle, S, Shaw, M.W, Parker, S.R, Bosch, F. van den
• Format: Journal Article
• Language: English
• Published: St. Paul, MN American Phytopathological Society 01.10.2003
• Subjects: Biological and medical sciences; climatic factors; discriminant analysis; disease outbreaks; equations; Fundamental and applied biological sciences. Psychology; Fungal plant pathogens; mathematical models; pathogenicity; Phytopathology. Animal pests. Plant and forest protection; plant pathogenic fungi; prediction; rain; Septoria; Septoria tritici; temperature; Triticum aestivum; wind speed; winter wheat; England; Epidemic; Monocotyledones; Plant pathology; Microbiology; Plant pathogen; Modeling; Fungi; Gramineae; Angiospermae; Spermatophyta; Septoria tritici; Fungi Imperfecti; Triticum aestivum; Thallophyta
• ISSN: 0031-949X; 1943-7684
• DOI: 10.1094/PHYTO.2003.93.10.1329

ABSTRACT 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.