FORECASTING MODELS OF GRAPEVINE DOWNY MILDEW.pptx

This ppt is about the epidemiology grapevine downy mildew and also about different types of model for the disease

1. FORECASTING MODELS OF GRAPEVINE DOWNY MILDEW

2. GRAPEVINE DOWNY MILDEW Pathogen : Plasmopara viticola Host : All cultivars of grapes in the species Vitis vinifera and many Vitis intraspecific hybrid cultivars. Spread : Through sporangia by wind, rain etc. Survival : As oospores present in the infected leaves, shoots and berries. Also asdormant mycelium in infected twigs. Optimum temperature : 20-22°C Relative humidity : 80-100 percent

3. SYMPTOMS LEAVES ROUGHLY CIRCULAR YELLOWISH DISCOLOURATIONS CALLED “OIL SPOTS” WHITE FLUFFY GROWTH PRIMARILY ON THE LOWER SURFACE LEAF SURFACE AS LESIONS AGE,THEY TURN BROWN FROM THE CENTER OUTWARD SEVERE INFECTED LEAVES MAY DROP

4. SHOOTS INFECTED SHOOT TIPS CURL AND COVERED WITH WHITE FLUFFY SPORULATION SYMPTOMS

5. SYMPTOMS BERRIES APPEAR GRAYISH WHEN INFECTED WHITE FLUFFY SPORULATION ON SMALL BERRIES MAY SHRIEVAL AND DROP OFF

6. 6 10-10-24 Rule (Empirical Model) Type: Rule-based, threshold model Description: This is one of the simplest and most widely used forecasting models. It predicts the risk of primary downy mildew infection based on three key factors: 10 mm of rainfall within a 24-hour period. 10°C or higher temperature during that period. 24 hours of continuous leaf wetness Usage: When these conditions are met, there is a high risk of primary infection, and farmers are advised to apply fungicides. Strengths: Easy to use, requires minimal data, and provides a quick indicator of infection risk.

7. 7 Type : Dynamic simulation model Description : VitiMeteo is a more advanced simulation model that predicts both primary and secondary infections based on weather data (temperature, rainfall, and humidity) and grapevine phenology. It simulates the pathogen's lifecycle and provides detailed disease risk levels. Usage: It is used primarily in Europe (Germany, Switzerland) and integrates with decision support systems to provide real-time recommendations for fungicide applications. Strengths : Highly accurate and provides daily infection risk forecasts VitiMeteo Plasmopara

8. 8 Type: Empirical model. Description: This model provides a set of conditions based on temperature and leaf wetness to predict downy mildew infections, and it is commonly used in combination with other systems to refine disease predictions. Strength: Easy to implement, providing quick risk assessments. Usage: Used globally alongside other models for more accurate infection prediction Goidanich’s Infection Model

9. 9 Type: Empirical model. Description: A classic model that relates the duration of leaf wetness and air temperature to the severity of downy mildew infections. It is often used to predict secondary infections. Strength: Simple to implement, requiring only temperature and wetness data. Usage: Still widely referenced in vineyards where secondary infection risk needs monitoring Mills Rule

10. THANK YOU