Presentation on theme: "Agrometeorological Simulation Using PERO Model for Grape Vine Downy Mildew in Greece. Agrometeorological Simulation Using PERO Model for Grape Vine Downy."— Presentation transcript:
Agrometeorological Simulation Using PERO Model for Grape Vine Downy Mildew in Greece. Agrometeorological Simulation Using PERO Model for Grape Vine Downy Mildew in Greece. N.R. Dalezios, D. Bampzelis and G. Daoularis Department of Agriculture Animal Production and Aquatic Environment. University of Thessaly, Volos, Greece.
PERO Model was applied for Grape Vine Downy Mildew. Area of Study: Thessaly (Central Greece). Specific Areas: Tirnavos: Year N. Aghialos: Years 2000 and 2001.
Meteorological Data used in the Analysis: Daily Rain Data. Hourly Temperature Values. Hourly Relative Humidity Values. Hourly Leaf Wetness Values (measured by sensor). Starting and ending dates of infections Start infectionEnd infection 29/4/200116/5/ /5/200111/6/ /5/200111/6/ /5/200115/6/2001 Start infectionEnd infection 29/4/200111/5/2001 9/6/200121/6/ /6/200116/7/2001 N. Aghialos 2000 N. Aghialos 2001 Tyrnavos 2000 Start infectionEnd infection 29/4/200011/5/ /5/200025/5/ /5/200015/6/ /5/200015/6/2000
Results of Application of the PERO Model for both Areas. Numbers of new, latent, visible, and total oilspots per hectare for the ending dates of the infection for the N. Aghialos area (a), (b), and Tyrnavos area (c). End infection New oilspots Latent oilspots Visible oilspots Total oilspots 11/5/ /5/ /6/ /5/ /6/ /6/ /5/ /6/ /7/ (a) (b) (c)
Model results for the new, latent, visible, total oilspots.
Counted oilspots reduced in hectare.
Comparison of calculated / counted oilspots for N. Aghialos area dates(counted- simulated)/ hectare (counted- simulated)/ counted 28/5/ ,97 5/6/ ,97 15/6/ ,50 27/6/ ,28 10/7/ ,38 Results of the simulation were compared with actual measurements of the disease taken by agronomists in situ for the same periods
Variance of total error
Results of the Application (for both Areas) 1.The model made reliable forecasts of the infection. 2.The model forecasted correctly the variation and the intensity of the disease especially in its critical stage, when maximum infection occurred. 3.Variations in Relative Humidity and leaf wetness play an important role for spreading of the disease.