MODELING OF EARLY INFECTION OF CEREAL ROOTS BY THE TAKE-ALL FUNGUS - A DETAILED MECHANISTIC SIMULATOR

A stochastic model is constructed to simulate the spatial and temporal spread of infection of the take-all fungus, Gaeumannomyces graminis v ar. tritici (Sacc.) Arx and Olivier var. tritici Walker on seminal roo ts of wheat. The model is designed to synthesize information on the dy namics and spatial orientation of the growth of main seminal root axes of wheat and the dynamics of primary and secondary infection of the p athogen. Primary infection is initiated by the soil inoculum. Three ty pes of secondary infection by runner hyphal growth are distinguished; re-infection of the same root, infection of another root on the same p lant via the crown, and cross-infection between roots on different pla nts. There are nine pathogen parameters, 14 host parameters, as well a s four system parameters in addition to the location and orientation o f seedlings. The pathogen parameters comprise estimates for the size a nd density of inoculum, the rate of growth of the fungus on roots, and the mean distances and probability of occurrence for primary and seco ndary infection. The host parameters concern orientation, density, eme rgence, rates of growth and size of roots. The principal output variab les are total and infected root length, numbers of infections, proport ion of infected roots and the numbers of primary and cross infections. Results of sensitivity analysis of the output variables to selected i nput parameters are presented. The model is tested against independent data-sets from inoculum-density experiments for different soil temper atures and ranges of inoculum density Statistical methods of response curve analysis are used to compare the behaviour of inoculum density-d isease response curves for the simulated and experimental data. The mo del fitted the data satisfactorily for the majority of host and infect ion variables. Inclusion of secondary infection in the model improved the goodness-of-fit but the density of secondary infections was small relative to primary infections. Practical and conceptual problems in t he validation of complex simulation models for fungal infection are di scussed. The advantages and limitations of this and related models are critically assessed.