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A foliar disease model for use in wheat disease management decision support systems
Author(s) -
Audsley E.,
Milne A.,
Paveley N.
Publication year - 2005
Publication title -
annals of applied biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.677
H-Index - 80
eISSN - 1744-7348
pISSN - 0003-4746
DOI - 10.1111/j.1744-7348.2005.00023.x
Subject(s) - septoria , powdery mildew , biology , fungicide , rust (programming language) , disease management , canopy , wheat leaf rust , agronomy , puccinia , disease , mildew , growing season , horticulture , botany , computer science , medicine , biochemistry , systematic review , medline , pathology , virulence , gene , programming language
Abstract A model of winter wheat foliar disease is described, parameterised and tested for Septoria tritici (leaf blotch), Puccinia striiformis (yellow rust), Erysiphe graminis (powdery mildew) and Puccinia triticina (brown rust). The model estimates disease‐induced green area loss, and can be coupled with a wheat canopy model, in order to estimate remaining light‐intercepting green tissue and hence the capacity for resource capture. The model differs from those reported by other workers in three respects. First, variables (such as weather, host resistance and inoculum pressure) that affect disease risk are integrated in their effect on disease progress. The agronomic and meteorological data called for are restricted to those commonly available to growers by their own observations and from meteorological service networks. Second, field observations during the growing season can be used both to correct current estimates of disease severity and to modify parameters that determine predicted severity. Third, pathogen growth and symptom expression are modelled to allow the effects of fungicides to be accounted for as protectant activity (reducing infections that occur postapplication) and eradicant activity (reducing growth of presymptomatic infections). The model was tested against data from a wide range of sites and varieties and was shown to predict the expected level of disease sufficiently accurately to support fungicide treatment decisions.