Advances in Science and Research
www.adv-sci-res.net
1992-0628
1992-0636
2
7th EMS Annual Meeting and 8th European Conference on Applications of Meteorology 2007
2008
10.5194/asr-2-57-2008
http://www.adv-sci-res.net/2/57/2008/
http://www.adv-sci-res.net/2/57/2008/asr-2-57-2008.html
http://www.adv-sci-res.net/2/57/2008/asr-2-57-2008.pdf
57
60
2008-05-22
Operational application and improvements of the disease risk forecast model PROCULTURE to optimize fungicides spray for the septoria leaf blotch disease in winter wheat in Luxembourg
J. Junk
junk@lippmann.lu
K. Görgen
M. El Jarroudi
P. Delfosse
L. Pfister
L. Hoffmann
Centre de Recherche Public - Gabriel Lippmann, 41 rue du Brill, 4422 Belvaux, Luxembourg
Université de Liège-Campus d'Arlon, 185 Avenue de Longwy, 6700 Arlon, Belgium
The model PROCULTURE has been developed by the Université Catholique de
Louvain – UCL (Belgium) to simulate the progress of the septoria leaf
blotch disease on winter wheat during the cropping season. The model has
been validated in Luxembourg for four years at four distinct representative
sites. It is able to identify infection periods due to the causal agent
<i>Mycosphaerella graminicola</i> on the last five leaf layers by combining meteorological data with
phenological data from PROCULTURE's crop growth model component. The
meteorological forcing consists of hourly time-series of air temperature,
relative humidity and cumulative rainfall since the time of sowing,
retrieved from automatic weather stations for hindcast and numerical weather
prediction model outputs for the forecast periods. In order to improve the
model, leaf wetness – which is one of the most important drivers for the
spread of the disease – shall be added as an additional predictor.
Therefore leaf wetness sensors were set up at four test sites during the
2007 growing season. To get a continuous spatial coverage of the country, it
is planned to couple the PROCULTURE model offline to 12-hourly operational
weather forecasts from an implementation of the Weather Research and
Forecasting (WRF) model for Luxembourg at 1 km resolution. Because the WRF
model does not provide leaf wetness directly, an artificial neural network
(ANN) is used to model this parameter.
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