Role of Forecasting system in plant disease management

Role of Forecasting system in plant disease management and new technology in plant disease management

1. Presented by- DINESH CHAND (Reg. No. 22DPPC0101) Ph.D. Scholar Pl. Patho . Department of Plant Pathology (COLLEGE OF AGRICULTURE, JODHPUR) Chairperson Dr. R. K. SHARMA Associate Professor Dept. of Plant Pathology Doctoral Seminar on ROLE OF FORECASTING SYSTEM IN PLANT DISEASE MANAGEMENT 2

2. CONTENTS Introduction History Components of plant disease. Pre-requesting for disease forecasting system. Method of disease forecasting system. Examples of Disease Forecasting system. GIS (Geographic Information system). Successful plant disease forecasting system. Uses of forecasting system. Conclusion References

3. DISEASE FORECASTING Forecasting of plant diseases means predicting for the occurrence of plant disease in a specified area ahead of time, so that suitable control measures can be undertaken in advance to avoid losses. It involves well organized team work and expenditure of time, energy and money. It is used as an aid to the timely application of chemicals before infection. Among the first spray warning services to be established for growers, were the grapevine downy mildew forecasting schemes in France, Germany and Italy in the 1920s. Cont...

4. HISTORY In1911- One of the first attempts at predicting forecasting was made by 'Lutman' who concluded that epidemics were favoured in wet and cold conditions. In1926- 'Van Everdingen ' in Holland proposed the first model based on four climatic conditions necessary for disease development . In 1933- In England, Beaumont and Stanilund emphasized the importance of late blight occurrence. In 1956- Burke described the "Irish Rule " that describes the conditions favourable for disease forecasting .

5. In1959- Simcast is derived from a simulation model describing the effects of climate, fungicide and host resistance on Phytophthora infestans. In1978- a computerized forecasting system called FAST was developed for Alternaria solani , a fungus that infects tomato, to identify periods when environmental conditions are favorable for early blight development (Madden, et al, 1978). In1985- a modified FAST program called Tom- Cast was developed to aid in the management of anthracnose, Septoria leaf spot and early blight on tomatoes ( Pitblado , 1992).

6. Components for successful Disease Development The most three important components of plant Disease:- Susceptible Host Virulent pathogen Favorable environment For disease to occur all three of these must be present. No Disease Severe Disease

7. Host Factors Prevalence of susceptible varieties in the given locality. Response of host at different stages of the growth to the activity of host. Density and distribution of the host in a given locality.

8. Pathogen factors Amount of primary initial, inoculum in the air, soil or planting material. Dispersal of inoculum. Spore germination. Infection. Incubation period. Sporulation on the infected host. Re-dispersal / Dissemination of spores. Inoculum potential and density in the seed, soil and air.

9. Environmental factors Humidity Temperature Wind Velocity Light Intensity Soil pH Toxic chemical Nutrient deficiency or excess Other non living agents.

10. Pre-requisites for disease forecasting system or Developing a forecasting system These factors are most necessary: The crop must be a cash crop or economically valuable. The disease must have potential to cause losses (yield losses) which is economically significant. Control measures should be available at a economically acceptable cost(reasonable cost). The disease must vary each season in the timing of the first infections and its subsequent rate of progress i.e. the disease should not be regular. Cont …

11. The model used in making a prediction must be based on sound investigational work carried out in the laboratory and in the field and tested over a number of years to establish its accuracy and applicability in all the locations where its use is envisaged Growers must have sufficient man power and equipments to follow the management measures when disease warning is given. Long-term warnings or predictions are more useful than short-term warning or predictions.

12. Methods of disease forecasting system Forecasting based on primary inoculums: Forecasting based on weather conditions: Forecasting based on correlative information: Use of computer for disease forecasting: EPIDEM BLITECAST FAST TOMCAST

13. 1. Forecasting based on primary inoculums Presence of primary inoculum, its density and viability are determined in the air, soil or planting material. Occurrence of viable spores or propagules in the air can be assessed by using different air trapping devices. In the case of soil-borne diseases the primary inoculum in the soil can be determined by monoculture method. E.g. Loose smut of wheat, ergot of pearl millet and viral diseases of potato.

14. 2. Forecasting based on weather conditions Weather parameters like temperature, relative humidity, rainfall, light, wind velocity etc., during the crop season and during the inter crop season are measured. Weather conditions above the crop and at the soil surface are also recorded. Forecasting based on weather conditions given by late blight of potato(Dutch rules).

15. Dutch rules (In Holland) Van Everdingen (1926): After evaluating the weather parameters that are associated with the development and spred of late blight of potato caused by phytophthora infestans. He had proposed a set of essential conditions called ‘Four Dutch Rules’ which combined the following for temperature, leaf wetness, cloudiness and rainfall. Night temperature below dew point at least for 4 hours. A minimum temp. of 10°C or below. A mean cloudiness on the next day of 80% of the sky covered by cloud. During periods at least 0.1mm rainfall during the next 24 hours.

16. 3. Forecasting based on correlative information Weather data of several years are collected and correlated with the intensity of the diseases. The data are compared and then the forecasting of the disease is done. Forecasting criteria developed from comparisons of disease observation with meteorological data have been provided for diseases like leaf blotch of wheat, fire blight of apple and barley powdery mildew.

17. 4. Use of computer for disease forecasting In some advanced countries forecasting of disease is made by the use of computers which gives the quick result. One such computer-based programs in the USA is known as ‘ Blitecast ‘ for potato late blight. Many computers simulations models have been developed to predict plant disaeses . Waggoner and Horsfall (1969) Developed the first computer simulation model (EPIDEM) against Alternaria solani causing early blight disease of tomato and potato.

18. Some Computer Disease Forecasting Models SIMCAST - It is derived from a simulation model describing the effect of climate, fungicide and host resistance on Phytophthora infestans development PLASMO - Downy mildew of grapevine caused by Plasmopara viticola . FAST/TOMCAST -Early blight of potato and tomato caused by Alternaria solani . WISDOM(BLITECAST)- Late blight on tomatoes & potatoes caused by Phytophthora infestans. EPICORN -Southern corn leaf blight Helminthosporium maydis .

19. EPIPRE - Diseases of winter wheat especially yellow rust caused by Puccinia striformis . CERCOS - Cercospora blight of celery. Mary blight - Fire blight of apples caused by Erwinia amylovora MYCOS - Mycosphaerella blight of chrysanthemums. EPIVEN - Apple scab caused by Venturia inaequalis . EPIGRAM -Barley powdery mildew caused by Blumeria graminis BARSIM-I - Barley leaf rust caused by Puccinia hordei . EPIDEMIC - Wheat stripe rust caused by Puccinia striformis .

20. Examples of Disease Forecasting models Sclerotinia Stem Rot forecasting Rice blast forecasting Early and late leaf spots of groundnut Potato late blight forecasting model in India TOMCAST(Tomato disease forecasting)

21. Sclerotinia Stem Rot forecasting Sclerotinia stem rot ( Sclerotinia sclerotiorum ) is one of the most important diseases on spring sown oilseed rape. forecasting method of Sclerotinia stem rot has been developed in Sweden in the year 1998. The method is mainly based upon a number of risk factors, such as crop density, crop rotation. Formation of sclerotia rainfall during , early summer and during flowering and weather forecast.

22. Rice blast forecasting In India, forecasting rice blast ( Pyricularia oryzae ) is done by correlative information method. • The disease is predicted on the basis of minimum night temperature 20-26°C in association with high relative humidity of 90% or above. • Computer based forecasting system has also been developed for rice blast in India.

23.   Early and late leaf spots of groundnut Early and late leaf spots of groundnut  caused respectively by   Cercospora   arachidicola and C. personata . A technique has been developed for forecasting early and late leaf spots of groundnut in the U.S.A. When the groundnut foliage remains wet for a period greater than or equal to 10 h and the minimum temperature is 21°C or higher for two consecutive days or nights, the disease development is forecasted.

24. Potato late blight forecasting model in India Development of late blight mainly depends on moisture, temperature and cloudiness. In India, the rains are heavy and the weather is cool and cloudy during summer in hills but in the plains the weather is generally clear with scanty rains. There are several models for late blight forecasting developed in India by various Scientists but the most successful models are: JHULSACAST . Singh et al. (2000) developed JHULSACAST , a computerized forecast of potato late blight in Western Uttar Pradesh for rainy and non rainy year. Cont ….

25. The weather parameters used in this programme are daily rainfall in millimeter, hourly temperature and relative humidity. Arora et al.,(2012) modified JHULSACAST and developed a modified model for late blight forecasting at Punjab. The model specifies that 7 day moving sum of RH less than 85% for at least 90 hr coupled with a 7 day moving sum of temperature between 7.2 to 26.6°C for at least 115 hrs. would predict appearance of late blight within 10 days of satisfying the conditions. One such computer based programmes in the USA is known as “ Blistcast ” for potato late blight. ( Singh et al., Arora et al., HARDWICK, N . Disease Forecasting . In The epidemiology of plant Diseases. Springer.)

26. BLITECAST Disease Spectrum Late blight of Potato and Tomato

27. TOMCAST TOMato disease foreCASTing Purpose: Assist processing tomato growers with fungicide application timing based on early blight development, using a “protectant” fungicide program. Use local weather to guide fungicide schedule. Alternative to 7-14 day calendar spray programs. Only a PART of the Disease management component of an overall IPM Program for tomatoes.

28. PREDICTED Early Blight Anthracnose NOT PREDICTED Bacterial Canker Tomato spotted Late Blight Septoria leaf spot TOMCAST Disease Spectrum

29. Geographic information system Geographic information systems  (GIS) and related technologies like remote sensing are increasingly used to analyze the geography of disease, specifically the relationships between pathological factors (causative agents, vectors and hosts, people) and their geographical environments. A GIS is a computer system designed to capture, store, manipulate, analyze, manage and present all types of spatial or geographical data. GIS provide important tools that can be applied in predicting, monitoring and controlling diseases. Use of GIS tools on data collected to identify critical intervention areas to combat the spread of Banana Xanthomonas wilt (BXW).   ( Dlamini S.N.)

30. Successful plant disease forecasting system Reliability - use of sound biological and environmental data. Simplicity - The simpler system and more likely it will be applied and used by producers. Importance -The disease is of economic importance to the crop, but sporadic enough that the need for treatment is not a given. Usefulness -The forecasting model should be applied when the disease or pathogen can be detected reliably.

31. ❖ Availability - necessary information about the components of the disease triangle should be available ❖ Multipurpose applicability - monitoring and decision-making tools for several diseases and pests should be available. ❖ Cost effectiveness - forecasting system should be cost affordable relative to available disease management tactics.

32. USES OF FORECASTING SYSTEM Forecasting and assessment of disease is important for crops production management For timely plant protection measures : Information whether the disease status is expected to be below or above the threshold level. For loss assessment : Forecasting actual intensity of loss and yield reduction can be predicted. Plant pathologists and meteorologists have often collaborated to develop disease forecasting or warning systems that attempt to help growers make economic decisions for managing diseases. Cont …

33. These types of warning systems may consist of supporting a producer’s decision-making process for determining cost and benefits for applying pesticides, selecting seed or propagation materials, or whether to plant a crop in a particular area. This system is boon to growers as it encourages use of pesticide economically and as required. This not only saves the money and energy of the farmers without risking the crop health, but also avoids the environmental pollution.

34. CONCLUSION A successful plant disease forecasting system is attributed to its reliability, simplicity, economic importance of crop, usefulness, availability of necessary information about the components of the disease triangle, multipurpose applicability and cost effectiveness. Plant disease forecasting systems have been developed to help growers to make important economic decisions about disease management. Although there are various problems in successful application of forecasting system but accuracy in prediction system with proper time management, it could create a revaluation in future farming system. Cont …

35. REFERENCE Agrios , G.N. Introduction to plant pathology. In Plant Pathology, 3rd ed.; Academic Press: San Diego, CA, USA, 1988; pp. 3–40. Arora , R.K., Ahmad, I., & Singh, BP. (2012). Forecasting late blight of potato in Punjab using Jhulacast model. Potato J . 39 (2), 173-176. David H Gent, W. F. (2013). The use and role of predictive systems in disease management. Annual Review of Phytopathology , 51 , 267-289. Francl , L. The Disease Triangle: A plant pathological paradigm revisited. Plant Health Instr. 2001. HARDWICK, N. (2006). Disease Forecasting . In The epidemiology of plant Diseases. Springer , Dordrecht , 239-267.

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