The dynamics of infection by the take‐all fungus on seminal roots of wheat: sensitivity analysis of a stochastic simulation model

summary The effects and interactions of inoculum density ( Iρ ), rate of fungal growth ( kf ) and the maximum distances for primary ( w p ) and secondary (cross) ( w s ) infection on the temporal progress of infection of the take‐all fungus, Gaeumannomyces graminis , in small populations of contiguous wheat plants are examined in factorial combination using a stochastic simulation model. The effects are analyzed in relation to infection progress curves for the percentage infected roots, the total length of infected root, the density of separate infection and the mean length of separate infections as well as the densities of primary and cross infections. Linear models are used to analyze the effects of changing parameter values on components of the infection progress curves. Non‐linear, logistic models are used to summarize infection progress curves and to map the effects of changing simulation model parameters onto the parameters of the simpler, growth curve function. The proportion of infected roots was most influenced by Iρ and w p with a negligible effect due to w s . The density and length of infections on roots was principally controlled by kf. Changes in infection length were non‐monotonic. The average length of infections increased towards a temporary maximum, following the first wave of primary infections, dipped as infections overlapped and then increased rapidly as root colonization progressed. The first wave of cross infection occurred 7 d after the initiation of primary infection. The density of primary infections was controlled principally by Iρ and w p . The density of cross infections was controlled not only by kf and w s , which are directly involved in cross infection, but also by kf and w p which affect the amount of infected and susceptible tissue. The asymptotic parameter of the logistic model for the infection progress curves was the most frequently affected parameter, followed by the delay parameter, with relatively few changes affecting the rate parameter. Some problems in the use of complex, stochastic simulation models to simulate experimental conditions are discussed.