However, calculating true economic costs is not straightforward because of the difficulty in quantifying the benefits of induced resistance (i.e. disease reduction) and establishing whether they outweigh the associated yield losses. An example of this is the use of ASM+ imidacloprid (IMD) to control Tomato spotted wilt virus (TSWV) in flue-cured tobacco. The combination treatment, though highly efficacious, can cause significant growth loss in transplants and is more economic, compared with IMD alone, only when TSWV incidence in the field is greater than 30% (Cherry and Mila, 2011). The problem for growers is being able to predict which fields might have sufficiently high TSWV incidence to justify the use of ASM+IMD. It has been proposed that the development of accurate TSW risk prediction models would be of value as an additional decision support tool to assist with ASM implementation in this system. Various disease risk prediction models have been developed as decision support tools to facilitate more efficient use of management options; these are generally based on the rationale that pest and disease development follow predictable life cycles and that by monitoring key epidemiological parameters it is possible to target more accurately events critical for management (Gent et al., 2011). Further research on the use of disease risk prediction models to coordinate activator application in other crop production systems would be of value because of the importance of early intervention when relying on induced resistance for disease control. The development of new mathematical models to predict the temporal dynamics of induced resistance may assist growers further with the timing and frequency of activator applications.
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