Abstract

ABSTRACT Epidemiological modeling combined with parameter estimation of experimental data was used to examine differences in the contribution of disease-induced root production to the spread of take-all on plants of two representative yet contrasting cultivars of winter wheat, Ghengis and Savannah. A mechanistic model, including terms for primary infection, secondary infection, inoculum decay, and intrinsic and disease-induced root growth, was fitted to data describing changes in the numbers of infected and susceptible roots over time at a low or high density of inoculum. Disease progress curves were characterized by consecutive phases of primary and secondary infection. No differences in root growth were detected between cultivars in the absence of disease and root production continued for the duration of the experiment. However, significant differences in disease-induced root production were detected between Savannah and Genghis. In the presence of disease, root production for both cultivars was characterized by stimulation when few roots were infected and inhibition when many roots were infected. At low inoculum density, the transition from stimulation to inhibition occurred when an average of 5.0 and 9.0 roots were infected for Genghis and Savannah, respectively. At high inoculum density, the transition from stimulation to inhibition occurred when an average of 4.5 and 6.7 roots were infected for Genghis and Savannah, respectively. Differences in the rates of primary and secondary infection between Savannah and Genghis also were detected. At a low inoculum density, Genghis was marginally more resistant to secondary infection whereas, at a high density of inoculum, Savannah was marginally more resistant to primary infection. The combined effects of differences in disease-induced root growth and differences in the rates of primary and secondary infection meant that the period of stimulated root production was extended by 7 and 15 days for Savannah at a low and high inoculum density, respectively. The contribution of this form of epidemiological modeling to the better management of take-all is discussed.

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