Disease forecasting
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This document discusses disease forecasting models that use weather data to predict outbreaks. It provides examples of models for rice blast, potato late blight, wheat yellow rust, and more. The potato late blight model for India, JHULSACAST, is specifically discussed. Disease forecasting is useful for giving advance warning to apply protective chemicals before infection starts and help control economically important crop diseases. Both empirical and fundamental forecasting systems are covered, along with their components and requirements for developing useful forecasting.
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INOCULUM DYNAMICS, POPULATION BIOLOGY OF PATHOGEN
**Inoculum Dynamics and Population Biology of Plant Pathogens:**
The study of inoculum dynamics and the population biology of plant pathogens is integral to understanding the patterns of disease spread, severity, and persistence in agricultural ecosystems. Here's a closer look at these concepts:
---
**1. Inoculum Dynamics:**
- **Definition:** Inoculum refers to the source of pathogenic organisms that initiate disease. This can include spores, mycelium, seeds, or any other form of the pathogen that can infect a susceptible host.
- **Sources:** Inoculum can come from various sources, including infected plant debris, soil, seeds, insects, and other infected plant material. Understanding the sources and availability of inoculum is crucial for predicting disease outbreaks.
- **Seasonal Fluctuations:** Inoculum levels often fluctuate seasonally due to changes in environmental conditions. For instance, certain pathogens may produce more spores during periods of high humidity or temperature.
- **Survival and Dispersal:** Pathogens have evolved various strategies for survival and dispersal. Some pathogens can survive for extended periods in soil or on plant debris, while others rely on wind, water, insects, or human activity for dispersal to new host plants.
- **Quantification:** Methods for quantifying inoculum levels include spore trapping, soil sampling, and molecular techniques such as PCR (Polymerase Chain Reaction) assays.
---
**2. Population Biology of Plant Pathogens:**
- **Population Growth:** Pathogens exhibit characteristic population growth patterns influenced by factors such as host availability, environmental conditions, and pathogen biology. The growth rate of a pathogen population depends on the rate of reproduction, dispersal, and host infection.
- **Epidemiological Patterns:** Pathogen populations often follow classic epidemiological patterns, including exponential growth, peak incidence, and decline. This is influenced by factors such as host susceptibility, pathogen virulence, and environmental suitability.
- **Host-Pathogen Interactions:** The dynamics of pathogen populations are shaped by interactions with host plants. Host resistance mechanisms, such as genetic resistance or induced systemic resistance, can reduce pathogen populations, while susceptible hosts can fuel pathogen growth.
- **Genetic Diversity:** Pathogen populations can exhibit genetic diversity, leading to differences in virulence, pathogenicity, and the ability to overcome host resistance. This genetic variability influences disease dynamics and the effectiveness of control measures.
- **Adaptation and Evolution:** Pathogens have the ability to adapt to changing environmental conditions and host defenses through natural selection. This can lead to the emergence of new strains or races with increased virulence or the ability to overcome resistant plant varieties.
---
**Significance and Applications:**
- **Disease Prediction:**
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Disease forecasting
- 2. Forecasting It is an advance warning/ forewarning to prevent the outbreak of disease and to cover the crop with a protective chemical before infection starts.
- 3. Forecasting models • Blast L (Japan – Haslumoto et al. 1984) • Pyricularia (Japan – 1986) • LEAF BLAST (Choi, Korea – 1987) • EPIBLAST (Choi, Korea – 1987) • EPIMAY (Southern corn leaf blight) • EPIPRE (Westend, wheat yellow rust, 1978) • BLITECAST- Potato late blight • SIMCAST (effect of climate, fungicide and host resistance) • PROGEB, PHYTOPRE, NEGFRY, PROPHY AND SIMPHYT (pathogen lifecycle, weather conditions, fungicides and host resistance)
- 5. POTATO LATE BLIGHT FORECASTING MODEL IN INDIA • JHULSACAST: Singh et al, (2000) developed JHULSACAST, a computerized forecast of potato late blight in Western Uttar Pradesh for rainy and non rainy year. • Weather data included temperature, relative humidity and rainfall on hourly basis. The current version of JHULSACAST was developed in Fox Pro under DOS environment and can be run on Pentium computer MS DOS/Window 9x operating system. • The main menu of the programme is late blight forecasting model(s), data entry, modification and model execution.
- 6. GEOGRAPHIC INFORMATION SYSTEM (GIS) • GIS – computer based system. Capture, store, manipulate, analyze, manage and present all geographical data. (Used for predicting, monitoring and controling the diseases)
- 7. Forecasting period (based on critical period concept) • An identifiable life span (weather is conducive for sporulation, dispersal and infection) 1. Empirical forecasting system 2. Fundamental forecasting system inductive/ logical/ derived
- 8. System • Each ends with a model 1. Biological model 2. Climatological model 3. Synoptic model
- 9. Biological model • Leads to a formalized statement of environmental condition. • Needed for induction of sporulation, dispersal and penetration.
- 10. Climatological model • The translation of biological methods into real meteorological situations.
- 12. Synoptic model • Comparison between Climatological model and real weather situation using synoptic weather maps.
- 13. Empirical forecasting system • Developed by studying and comparing historical records of disease occurrence and concurrent weather conditions in a same locality • Formulation of rules - Forecasting based on synoptic charts developed
- 14. Fundamental forecasting system • Based on data obtained from lab/ field. • Regarding relationship of biological and environmental conditions.
- 15. After developing empirical forcasting…… - The system is tested and modified to fit various geographical and environmental conditions. - Some questions arise during the conduct of experiments in controlled condition. - The relationships are defined mathematically. Ex. Potato late blight. (based on stepwise multiple regression using five meteorological parameters)
- 16. Ex. • Meteorological influence on sporangium production • Meteorological influence on sporangium germination • Meteorological influence on colonization • Magnitude and duration of high humidity periods • Effect of dry period on disease development
- 17. • 1 to 150 ratings- no forecasting alert • After 150- an alert is given that the infection will be occurring within 15- 45 days • After 270- an alert is given that the infection will be occurring within 15 days • Control measures to be taken
- 18. Requirements for developing useful and successful forecasting systems 1. The disease must cause economically significant damage in terms of quantity and quality. 2. The onset, speed of spread and destructiveness of disease are variable, mostly due to dependence which is variable. 3. Control measures must be available at an economically acceptable cost.
- 19. Usefulness of forecasting • By predicting dreadful diseases in advance, the costly plant protections measures could be saved. • Forecasting of disease in advance is useful for farmers to have enough time to go for disease resistant crop in the place of susceptible one. • Forecasting a disease is useful to estimate the loss of crop in different locations to be expected and useful to estimate the yield.
- 20. • The information on weather and disease relations should be known fully for a particular disease. • Forecasting should be developed only for the diseases which are not controlled by genetic resistance and which can completely destroy a major crop.