Biological control of pathozone behaviour and disease dynamics of Rhizoctonia solani by Trichoderma viride

Authors


To whom correspondence should be addressed. E-mail: djb21@cus.com.ac.uk

SUMMARY

This paper presents and tests a method to scale up from the dynamics of infection and disease on single plants in order to predict the behaviour of epidemics in populations of plants, in the presence of a biological control agent. Specifically, we quantify the effect of the antagonistic fungus, Trichoderma viride, Pers ex. Gray, on the pathozone dynamics of the damping-off fungus, Rhizoctonia solani Kühn on radish. The results from these individual-based experiments are used to predict the progress of an epidemic and the results are compared with experimental epidemics in microcosms. The addition of T. viride close to a germinating radish plant reduced the extent of the pathozone influence from 22.6 to 13.8 mm. Trichoderma viride also inhibited the evolution of infection efficiency of R. solani. The evolution of infection efficiency over time is described by a simple non-linear model for the probability of infection with distance, in which certain of the parameters vary with time. By combining this with a model based on conditional probabilities for the location and infectivity of randomly dispersed propagules within the pathozone, we were able to scale up and predict the disease progress of R. solani in a population of radish plants, and the extent of control effected by T. viride. Disease progress rose progressively to a maximum of 42 % diseased plants in the unprotected crop compared with a sigmoidal approach to 13 % in the protected crop. We show that these properties are consistent with a monomolecular function for primary infection with a temporally-varying rate parameter. We also show that the central region of the pathozone, where the joint probabilities of occurrence of a randomly located propagule and its ability to infect are maximal, has a large influence on the sensitivity of epidemics to pathozone dynamics. This is an important example of interpreting population behaviour of an epidemic from the infection and disease of single plants.

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