Modelling the spatio‐temporal deployment of resistant varieties to reduce the incidence of rice tungro disease in a dynamic cropping system

Abstract

A dynamic cropping system was represented by a square lattice of ®elds in which crops were successively harvested and replanted. A spectrum of crop ages existed at any one time and the virus disease persisted by spread of inoculum between crops. Such a situation is typical of many areas of tropical irrigated rice cultivation in which rice tungro virus disease (RTVD) occurs. Using a mathematical model of the epidemiology of RTVD in the cropping system, the deployment of ®elds of a genotype expressing some resistance to the disease was investigated. Previous studies on the effect of genotype mixtures on disease progress within a single crop have shown that both the rate of disease increase and the rate of focus expansion were proportional to the logarithm of the fraction of susceptible plants in the mixture. Here, looking at long-term disease incidence in a dynamic cropping system, it was found that this same`logarithmic rule' applied, provided that resistant crop deployment was spatially random. A relatively large proportion of ®elds had to be planted with resistant varieties in order to have suf®cient area-wide impact on inoculum to reduce disease incidence in ®elds of susceptible varieties. In many rice cropping systems there are two growing seasons per year and the modelling indicated that the best strategy was to concentrate deployment of resistant varieties in the season of greatest disease spread. Attempts to minimize inoculum carry-over to the`high spread' season by concentrating resistant varieties in the previous season had little effect over a range of simulated conditions. In considering recommendations for the management of RTVD, a con¯ict existed between the reduction of disease incidence strategically and in the individual ®elds of a newly deployed variety. To maximize area-wide strategic impact, small genotype units and random patterns were best, but to protect individual ®elds, large units and concentrated deployment were best.

Impact of major gene resistance management for sunflower on fitness ofPlasmopara halstedii(downy mildew) populations

“…-Alternation: This is based on the inclusion in the rotation of varieties with different sources or types of resistance to diversify selection pressure on the parasite (Holt and Chancelor, 1999). The present results showed that this strategy limited the number of plants attacked in most years, in spite of a high level of attack the first year.…”

Epidemiology of insect‐transmitted plant viruses: modelling disease dynamics and control interventions

“…Temporal and spatial patterns of virus spread were described and analysed in a notable series of reviews by Thresh (Thresh, 1974, 1976, 1983) and to some extent these patterns were related to the mobility characteristics of vectors. The largely descriptive approach to spatio‐temporal dynamics of plant virus epidemics has been used in devising disease management strategies for rice tungro virus disease (Holt & Chancellor, 1997, 1999). Madden et al .…”

A general model of plant‐virus disease infection incorporating vector aggregation

“…Both statistical analyses and simulation modelling indicate that aggregation of diseased plants decreases disease progress (Ferriss & Berger, 1993;McRoberts et al, 1996;Holt & Chancellor, 1999) and it is vector movement and host-plant preference that cause this aggregation (Kennedy, 1951;Baker, 1960;Colvin et al, 1999).…”