Modelling plant disease epidemics for crop protection

Research output: Contribution to journalConference articlepeer-review

Abstract

Initially plant disease models were developed as simple rules, graphs, or tables, and later as descriptive tools. Advances in environmental monitoring, automatic data processing, and botanical epidemiology enabled the development of a new class of mechanistic dynamic models, which are more accurate and robust. They explain mathematically the relations within a pathosystem (including both pathogens and host plants) by means of linked differential equations, and describe the way in which the system changes over time and space as an effect of external variables. Thus, the equation parameters do not have fixed values but vary according to the influencing weather conditions. These models require input data, particularly meteorological data, to be collected over time and space. Scales of time and space for inputs may differ according to the application of the model: from warning services, which use models to produce crop protection information at the collective level on a territorial scale, to precision agriculture which uses models at a within-plot scale. While the use of mechanistic dynamic models in warning services for crop protection is well established, their use in precision agriculture has yet to be developed. These models could be used to draw dynamic maps of current and future spatial distribution of both visible and latent infections within a plot, so that timing, active ingredients and rates of fungicides may be defined accordingly. The main challenge that needs to be overcome before this can be accomplished is the lack of meteorological inputs at the within-plot level.
Original languageEnglish
Pages (from-to)259-259
Number of pages1
JournalJournal of Plant Pathology
Publication statusPublished - 2008
Event9th International Congress of Plant Pathology - Torino
Duration: 24 Aug 200829 Aug 2008

Keywords

  • crop protection
  • machanistic dynamic model
  • plant disease model

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