The Use of Mathematical Models to Guide Fungicide Resistance Management Decisions

Historically, models have had little influence on decision-making in fungicide resistance management. The reasons are found in the level of abstraction of these models making it difficult for stakeholders to interpret them and inadequate connection between modelling and experimentation. Recently, ho...

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Main Authors: van den Bosch, F., Fraaije, B., Oliver, Richard, van den Berg, F., Paveley, N.
Format: Book Chapter
Published: Springer 2015
Online Access:http://hdl.handle.net/20.500.11937/37531
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author van den Bosch, F.
Fraaije, B.
Oliver, Richard
van den Berg, F.
Paveley, N.
author_facet van den Bosch, F.
Fraaije, B.
Oliver, Richard
van den Berg, F.
Paveley, N.
author_sort van den Bosch, F.
building Curtin Institutional Repository
collection Online Access
description Historically, models have had little influence on decision-making in fungicide resistance management. The reasons are found in the level of abstraction of these models making it difficult for stakeholders to interpret them and inadequate connection between modelling and experimentation. Recently, however, the authors of this chapter have developed models in close collaboration with stakeholders and experimenters. These models range from simple functions representing governing principles of resistance evolution to complex models for quantitative studies. In this chapter we discuss the development and testing of these models. A governing principle is discussed predicting whether a change in a fungicide application programme will increase or decrease the rate of selection for fungicide resistance. Complex models are discussed that reflect sufficient biological detail to study specific plant-pathogen- fungicide combinations. Ultimately we describe the combined experimental and modelling work that is currently undertaken to informing resistance management methods.
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institution Curtin University Malaysia
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publishDate 2015
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spelling curtin-20.500.11937-375312018-12-14T00:54:26Z The Use of Mathematical Models to Guide Fungicide Resistance Management Decisions van den Bosch, F. Fraaije, B. Oliver, Richard van den Berg, F. Paveley, N. Historically, models have had little influence on decision-making in fungicide resistance management. The reasons are found in the level of abstraction of these models making it difficult for stakeholders to interpret them and inadequate connection between modelling and experimentation. Recently, however, the authors of this chapter have developed models in close collaboration with stakeholders and experimenters. These models range from simple functions representing governing principles of resistance evolution to complex models for quantitative studies. In this chapter we discuss the development and testing of these models. A governing principle is discussed predicting whether a change in a fungicide application programme will increase or decrease the rate of selection for fungicide resistance. Complex models are discussed that reflect sufficient biological detail to study specific plant-pathogen- fungicide combinations. Ultimately we describe the combined experimental and modelling work that is currently undertaken to informing resistance management methods. 2015 Book Chapter http://hdl.handle.net/20.500.11937/37531 10.1007/978-4-431-55642-8_4 Springer restricted
spellingShingle van den Bosch, F.
Fraaije, B.
Oliver, Richard
van den Berg, F.
Paveley, N.
The Use of Mathematical Models to Guide Fungicide Resistance Management Decisions
title The Use of Mathematical Models to Guide Fungicide Resistance Management Decisions
title_full The Use of Mathematical Models to Guide Fungicide Resistance Management Decisions
title_fullStr The Use of Mathematical Models to Guide Fungicide Resistance Management Decisions
title_full_unstemmed The Use of Mathematical Models to Guide Fungicide Resistance Management Decisions
title_short The Use of Mathematical Models to Guide Fungicide Resistance Management Decisions
title_sort use of mathematical models to guide fungicide resistance management decisions
url http://hdl.handle.net/20.500.11937/37531