Agriculture Reference
In-Depth Information
Fig. 5.15.
A comparison of observed sporulation by onion downy mildew,
Peronospora destructor
, and predictions of sporulation made by the computer
model MILIONCAST2 using measurements of temperature and relative humidity
logged every 10 min from sensors close to the onion leaves (from Gilles
et al
.,
2004. Courtesy of
Plant Disease
).
the ground. This combination of models is being tested with forecast weather to
give a prediction of sporulation/infection events a day or two in advance, rather
than an estimate of their likelihood following observed microclimatic conditions.
A disease forecasting system has also been developed for purple blotch caused
by
Alternaria porri
based on weather-based predictions for the production of spores
and on daily hours of leaf wetness and the age-dependent susceptibility for
infection of onion leaves (Lorbeer
et al.
, 2002).
Current disease risk prediction models are regarded as aids to crop
management, mainly indicating when scouting for signs of disease should
be intensified (Lorbeer
et al.
, 2002; Friedrich
et al.
, 2003) or when spray
programmes should commence. Since most models are based on current
microclimate data their sporulation infection estimates are retrospective, and
protectant fungicides would be ineffective in preventing infection. However,
prompt application of systemic fungicides can check downy mildew infection,
and application of these following high risk predictions could ensure maximum
efficacy for the limited number of such sprays that are allowed in a growing
season (Gilles
et al.
, 2004).
Although microclimate-based models predict when conditions are suitable
for sporulation infection, they give no indication of whether the inoculum to
initiate disease is present in the vicinity of the crop. The number of leaf lesions
due to
B. squamosa
was found to be linearly related to the concentration of
spores in the air around the crop (see Fig. 5.16).
