Fungicide models are key components of multiple modelling approaches for decision-making in crop protection

Abstract

Decision-making for integrated pest management (IPM) in crops requires the assessment of multiple risk factors. Plant disease models have been used to predict disease risk and support decisions about whether and when to protect crops based on environmental conditions. In addition to requiring information about disease risks, correct decision-making also requires answers to several questions. These include: Is the plant susceptible to infection? Is the plant protected by a previous fungicide application? Which fungicide should be used for the specific application? Which dose of the product should be used, and when should it be applied? Obtaining answers to these questions requires a multiple-modelling approach in which models for diseases are combined with those for plant growth and for the effects of fungicides. This review discusses models that predict fungicide efficacy dynamics based on fungicide physical mode of action, fungicide localisation on/within host plants, fungicide effects on the pathogen, and the dynamics of fungicide residues. Empirical and mechanistic models are considered. Empirical models have been mainly developed by fitting equations to field data. Mechanistic models consider the processes that determine fungicide dynamics and effects, and these are generally considered to be superior to empirical models, but parameterisation of mechanistic models is challenging. A new modelling approach is described that combines the main processes of mechanistic models with simple parameterisation based on laboratory experiments, practical knowledge, and technical information. Examples are also provided of systems that calculate fungicide dose and application time. Decision support systems are described as tools that provide farmers with all of the information required for correct decision-making in IPM.