Agrometeorological Simulation Using PERO Model for Grape Vine Downy Mildew in Greece. Agrometeorological Simulation Using PERO Model for Grape Vine Downy.

Slides:

Advertisements

Similar presentations

Case Study 6: Agricultural Research – use and needs of climate services National Consultation on a Framework for Climate Services in Belize By Anil Sinha,

Advertisements

Model based estimation of nitrogen fertilization recommendations using agrometeorological data K. Christian Kersebaum WMO Expert meeting Geneva 11/2004.

The NAM Model. Evaporation Overland flow The excess rainfall is divided between overland flow and infiltration.

Water in the Atmosphere. Humidity Humidity is a measure of the amount of water vapor in the air. Air’s ability to hold water vapor depends on its temperature.

Dennis P. Lettenmaier Alan F. Hamlet JISAO Center for Science in the Earth System Climate Impacts Group and Department of Civil and Environmental Engineering.

MOS Developed by and Run at the NWS Meteorological Development Lab (MDL) Full range of products available at:

All Sensors Note: Be sure you have already selected your station and time interval before choosing this product.

CS April 2007 Models in Potato Pest Management (LateBlight) Client: Professor William Fry Sharmin Azam Christopher Brickley Nathan Cormier Ledet.

Forecast and Virtual Weather Driven Plant Disease Risk Modeling System L. Coop 1, A. Fox 2, W. Mahaffee 3, D. Gent 3, W. Pfender 3, C. Daly 4, C. Thomas.

1 A Computer-based Decision Support System for Septoriosis Control in Winter Wheat: implementation in Belgium and Grand-Duchy of Luxemburg. El JARROUDI.

Louisiana Yards and Neighborhoods Common Landscape Diseases Common Landscape Diseases

WMO / COST 718 Expert Meeting on Weather, Climate and Farmers November 2004 Geneva, Switzerland.

Radar Animation 9:30 AM – 7:00 PM CST November 10, 2006 …Excerpt from Meteorological Overview of the November 10, 2006 Winter Storm… Illustrate value of.

COSMO General Meeting Zurich, 2005 Institute of Meteorology and Water Management Warsaw, Poland- 1 - Verification of the LM at IMGW Katarzyna Starosta,

Savanna Grassland- Climate and Weather Claire S..

limited number of sensors, limited number of monitored wells (data travels) centralized sample collection, large number of monitored wells (samples travel)

1.5 Prediction of disease outbreaks

European Metrology Research Program (EMRP) MeteoMet Project (October 2011) WP3. Traceable measurements methods and protocols for ground based meteorological.

An Overview of Environmental Hazards Related to Crops in Greece By: Nicolas R. Dalezios U NIVERSITY O F T HESSALY L ABORATORY O F A GROMETEOROLOGY.

1 Department of Agriculture Animal Production and Aquatic Environment 2 Department of Management of Environment and Natural Resources University of Thessaly.

Section 12.3 Gathering Weather Data

MICHELLE M. MOYER CORNELL UNIVERSITY NYSAES-GENEVA Use of pan evaporation and temperature data in Powdery Mildew forecasting.

Site-Specific Weather Data for Disease Forecasting: Reality or Pipe Dream? Bob Seem Cornell University New York State Agricultural Experiment Station Geneva,

IDENTIFICATION OF COTTON PHENOLOGICAL FEATURES BASED ON THE VEGETATION CONDITION INDEX (VCI) C. Domenikiotis (1), E. Tsiros (2), M. Spiliotopoulos (2),

Seite 1 Snow-canopy-interaction demanding tasks in my PhD-project Elisabeth Mair, Research Center Global Change and Sustainability.

HortPlus NZ Ltd HortPlus team Mike Barley, Andrew Hodson, Lesley Hodson-Kersey HortPlus team Mike Barley, Andrew Hodson, Lesley Hodson-Kersey Acknowledge.

© The Critical Thinking Consortium Ancient Greece – Daily life # 1.

Latest results in verification over Poland Katarzyna Starosta, Joanna Linkowska Institute of Meteorology and Water Management, Warsaw 9th COSMO General.

NEWA – weather app’s for IPM NYS IPM Program’s Network for Environment & Weather Applications In collaboration with the Northeast.

Ping Zhu, AHC5 234, Office Hours: M/W/F 10AM - 12 PM, or by appointment M/W/F,

KU Koech – IAIA-27 THE 27 TH IAIA CONFERENCE- 2007, SEOUL, KOREA IMPACT ASSESSMENT OF KENYA’S BIODIVERSITY AND AGRICULTURE: CONTRIBUTIONS OF METEOROLOGICAL.

Data, Data, and More Data Kyle Roskoski. Data This is a summary of what environmental data we have access to for the year (Some data through.

11th EMS / 10th ECAM - Berlin 2011 / 181 Micrometeorological models applied to growth and production of Grapevine Claudio Cassardo 1, Caterina Francone.

Course on Crop-Weather Relationships

Our School Weather Station By Luke Davison Noah Morton Wyatt Hall and Hayden Current.

MWWG Presentation Use of Precipitation Duration Data from Weather Radar in Leaf Wetness Duration Estimates for Plant Disease Management MSc. Thesis, University.

After successful completion of this Module, you have learned to: Recognize the importance of downy mildew of pearl millet. Describe the symptoms of downy.

1.How weather forecasting is useful? 2.What is the aim of weather forecasting? 3.What is weather forecasting? 4.How weather is predicted? 5.How reliable.

CKSFERTILIZERS. ABN pH Modifiers Internationally there are many acidifying agents or pH modifiers that act to bind or inhibit ammonia.

AZ Space Grant Symposium 11 April 2014

SmartGrids and M2M Lew Roth VP Business Development Partners 1993.

1 Understanding Sources of Error and Uncertainty NOAA’S COLORADO BASIN RIVER FORECAST CENTER.

Presented by Bradley G. Illston 1, Steven Piltz 2, and Steve Amburn 2 1 Oklahoma Mesonet; 2 National Weather Service Forecast Office, Tulsa, OK Utilizing.

Crop Damage Assessment using Remote Sensing & Agrometeorological data Mahalanobis National Crop Forecast Centre, DACFW, New Delhi Space Applications Centre,

Quality Control: Analysis Of Data Pawan Angra MS Division of Laboratory Systems Public Health Practice Program Office Centers for Disease Control and.

Table 2.1 Monthly and annual total numbers of products issued by the RSMC Tokyo - Typhoon Center in 2011.

Diseases Management in Grape Nursery Grape seedlings are susceptible to diseases like Anthracnose, downy mildew, powdery mildew, leaf spot, leaf blight,

 Average – mean, median and mode are three “kinds” of average (commonly used to describe the mean).  Mean temperature – average temperature of the air.

University of Thessaly 1 "Severity-Duration-Frequency relationships of heatwaves in Greece" Nicolas R. Dalezios Professor of Agrometeorology University.

How Much Water Will I Need

Impact of climate change on agriculture An overview!

UEE Seminar Series Coping with Drought in a Changing Climate Abstract

Validation of a Tomato Crop Simulator for Mediterranean Greenhouses

Overview of Downscaling

Warm Up What is the mean, median, mode and outlier of the following data: 16, 19, 21, 18, 18, 54, 20, 22, 23, 17 Mean: 22.8 Median: 19.5 Mode: 18 Outlier:

Vintage 2017 The Fungal Trifecta

SOMEWHERE OVER THE RAINBOW

MOS Developed by and Run at the NWS Meteorological Development Lab (MDL) Full range of products available at:

Damon L. Smith and Andrea F. Payne

Welcome Scouts.

(PI: Peter Ojiambo) NCSU, Department of Plant Pathology August 8, 2014

Project - Execution stage

DAILY LIFE IN ANCIENT GREECE

Verification of Tropical Cyclone Forecasts

Environment, Natural Resources Conservation and Tourism

An Analysis of Possibilities of Forecasting the AVISO Agrometeorological Model Input Münster P. Chuchma F.

C. Domenikiotis (1), E. Tsiros (2), M. Spiliotopoulos (2),

MILDEW DISEASES OF GRAPES

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.