Agriculture Reference
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spore dispersal gradients observed in inoculated plots during flowering, although the
spore dispersal gradients had become much flatter by harvest (Johnson and
Powelson, 1983). This suggested that the primary inoculum during flowering was
the main cause of the pod rot. Gradients have helped to overcome the problem of
inter-plot interference in small plot experiments with wind-borne pathogens.
Paysour and Fry (1983) were able to use an exponential spore dispersal gradient
model to identify plot sizes and spacings which would decrease interference to
acceptable levels in their experiments with potato late blight. Knowledge of
gradients can also be used to formulate recommendations for growers about
distances away from sources of inoculum (e.g. infected stubble) at which a new
susceptible crop may safely be planted.
The effectiveness of mixtures of susceptible and resistant cultivars in decreasing
the rate of spread of epidemics can be influenced by disease gradients. Simulation
models suggested that such mixtures would be most effective against pathogens with
shallow spore dispersal gradients (Fitt and McCartney, 1986); these predictions were
confirmed in experiments with the wind-borne barley powdery mildew pathogen. By
contrast, such mixtures were relatively ineffective in decreasing spread of the
splash-dispersed wheat glume blotch pathogen ( S. nodorum ), which has steep spore
(conidia) dispersal gradients (Jeger, 1983; see also Chapter 10).
As many crop diseases occur as patches, especially early in the epidemic, it has
been suggested that spraying patches to control disease would be less
environmentally damaging than spraying whole fields (West et al., 2003). Recent
technological advances in optical sensors may make the detection of disease patches
feasible (Bravo et al., 2003). However, knowledge of disease gradients is required to
make the best use of such measurements as areas of latent (symptomless) infection
at the edges of the patches would also need to be sprayed for disease control to be
effective (West et al., 2003).
6.4 DISEASE SPREAD: MODELLING DEVELOPMENT OF FOCI
Epidemics may be considered as the consequence of the development of many
individual disease foci. Therefore, understanding disease spread from an initial focus
of infection is fundamental to understanding the spatial and temporal dynamics of
epidemics. It is thus not surprising that much effort has been expended to develop
models that describe disease focus development. This work has largely concentrated
on the development of mathematically-based models that simulate focal expansion
in time and space (Minogue, 1986; Jeger, 1990; Ferrandino, 1993; Shaw, 1994,
1995; Zadoks and van den Bosch, 1994; Mundt, 1995; Maddison et al., 1996; Xu
and Ridout, 1996, 1998)). Simulation models of disease spread vary in their
complexity, formulation and methods of solution, but often have a similar
conceptual basis. The concepts behind disease spread simulation can be illustrated
by considering a simple one-dimensional model of disease spread within a row of
identical plants (Minogue and Fry, 1983a; Fitt and McCartney, 1986). Assumptions
are that the disease occurs as discrete identical lesions which produce spores for a
fixed length of time ( T p , the infectious period) and after a latent period ( T l ), each
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