12/17/2014. Overview. PLP 6404 Epidemiology of Plant Diseases Spring 2015 Lecture 28: Disease forecasting. Introduction to disease forecasting

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1 PLP 6404 Epidemiology of Plant Diseases Spring 2015 Lecture 28: Disease forecasting Prof. Dr. Ariena van Bruggen Emerging Pathogens Institute and Plant Pathology Department, IFAS University of Florida at Gainesville Overview definition, why, when, how, constraints Approaches to disease forecasting Empirical models efficacy of initial inoculum Integral models secondary inoculum (direct and indirect) Integral models initial and secondary inoculum Mechanistic models based on weather forecasts Forecasting errors Summary Disease forecasting versus disease prediction Disease forecasting is a quantal educated guess; either some disease will occur (a positive forecast), or disease will not occur (a negative forecast). Disease prediction is a quantitative estimate of HOW MUCH NEW DISEASE will appear. Why would we want to develop a forecast system? Generally for three reasons: Avoid unnecessary costs to grower, consumer and environment to save on the number of sprays applied; to avoid disastrous attacks of disease; to plan for the future. That is, we may wish to choose a more resistant variety or to use other strategies of control. A forecast may be useful if: the crop is important; the disease is sporadic; the disease is potentially destructive; experimental data are available; there is a means to alert growers; control measures are available There is no need to develop a forecast system for: an uneconomic crop, a disease that rarely occurs in the area, a disease that occurs every season (control should always be used), when the crop is frequently attacked by several pathogens and pests (so that the growers use a tank mix of fungicides and insecticides) 1

2 Attributes of a successful forecaster reliability simplicity importance of the disease usefulness with respect to available management options availability multipurpose applicability cost effectiveness Potential constraints on forecasting plant diseases Many forecasters have been developed but only a few have been widely accepted by growers. Why? grower attitude (skepticism, risk averse) availability and dissemination of information equipment and labor requirements costs inconvenience real-time implementation (is there enough time to get the spray equipment ready?) presence of minor diseases/pests (which need to be controlled anyhow) Approaches to disease forecasting Disease forecasters predict an outbreak or increase in disease intensity based on: Host susceptibility Pathogen level (initial inoculum, secondary inoculum) or disease increase Sometimes: vector activity Pre-planting or post-planting environmental conditions Past, current or future weather Approaches to disease forecasting Empirical method (based on experience or regression) For example, a celery grower has observed outbreaks of pink rot (Sclerotinia sclerotiorum) after a frost For example, correlation of disease with past weather, host development, or prior observations. Fundamental, mechanistic method (based on experimental results). For example, spores germinate at specific temperatures, number of hours of high RH, or sporulation and infection occur with a certain number of hours with high RH Most forecasting systems use a bit of both empirical and fundamental approaches. Empirical models initial inoculum Forecasts based primarily on initial inoculum or initial disease most appropriate for diseases that are: Monocyclic Polycyclic with relatively few cycles Polycyclic with many cycles, but initial inoculum is still important Methods based on initial inoculum: Heald (1921) predicted wheat bunt a year in advance based on the amount of spores on the grain. Wilhelm (1950) grew a few tomatoes in a sample of field soil (as a trap crop for Verticillium albo-atrum) to determine the threat of wilt in cotton Stevens (1934) predicted Stewart s wilt on corn based on the sum of mean air temperatures (F) for Dec, Jan, Feb as Pantoea stewartii bacteria are vectored by corn flea beetle 2

3 Stewart s wilt vectored by flea beetle: still a severe disease in the Eastern part of the corn belt Plant parasitic nematodes: estimates of nematode populations -> potential damage and loss for a specific crop on a given soil type - action threshold Winter Temp index Seedling wilt phase Leaf blight phase (temp. sum, F) 100 or more Destructive Severe 90 to 100 Light to severe Severe 85 to 90 Nearly absent Moderate 80 to 85 Nearly absent Light Below 80 Nearly absent Trace Pataky, 2004 Empirical models efficacy of initial inoculum Example: Apple scab (Venturia inaequalis) based on identification of "infection periods" when environmental conditions favor infections free water is required for germination of ascospores and penetration of host tissue germination and penetration also influenced by temperature Mill s charts: relationships between expected severity, hours of leaf wetness and temperature Example: BLITECAST for potato late blight (Phytophthora infestans) BLITECAST (Krause, et al., 1975), is a combined computerized version of two forecasting systems: Hyre (1964, 1954): Disease symptoms expected 7-14 days after 10 consecutive blight-favorable days 5-day mean temp <=25.5C (78F) and 10-day total rainfall >=3 cm. Unfavorable if min temp falls below 7.2C (45F) Wallin (1950, 1951, 1962): First occurrence of blight predicted 7-14 days after the accumulation of severity values from the time of plant emergence Blight severity values (0-4) are accumulated based on average temp and periods of RH >=90% over the past 8 days. Blitecast for potato late blight Integral models secondary inoculum (direct) Early blight of celery (Cercospora apii) by Berger Weather is almost always favorable for disease development. Forecasting system based on spore counts in air Originally, growers in Florida applied up to fungicide applications over a 6-month growing period! 3

4 Early blight of celery (Cercospora apii) modification of Berger model by Reid (2008) based on relation between weather and spore counts A fungicide spray is triggered if the answer is yes to all of the following questions: 7 or more days since a protectant fungicide was applied? at least 12 h with RH >= 90% during previous day? mean temp >=15 O C but <27 O C during previous day? no temp 12 O C from 3 days ago to day before yesterday? Were night temp 15 O C and RH 95% in 2 previous nights? If yes, go to step 5. If all answers to above questions were yes, spray is advised. If any question was answered no, spray is not advised Empirical model - initial inoculum and secondary cycles Example: Forecasting infection periods of cherry leaf spot, caused by Blumeriella jaapii formerly Coccomyces hiemalis (Eisensmith and Jones,1981) Initial inoculum (ascospores released in spring) important Secondary cycles also important Calculation of an environmental favorability index (EFI) EFI = ( W T W T WT) 2 W=hrs of continuous leaf wetness, and T=mean air temp during wet period EFI of >14, >28, >42 correspond to low, moderate, or high infection periods Empirical model initial inoculum and secondary cycles Example: MELCAST (Melon Disease Forecaster) Alternaria leaf blight, gummy stem blight and anthracnose of cantaloupe and watermelon (Latin and Egel 2001) EFI calculated from hourly temperature and leaf wetness duration data Mechanistic models based on weather forecasts Example: Late blight (Raposo, et al., 1993) Based on simulated epidemics and simulated weather forecasts. Incorporation of weather forecasts information improved the efficiency of BLITECAST (same number of sprays, but reduced AUDPC) The more accurate the weather predictions, the greater the reduction in AUDPC. Mechanistic models based on weather forecasts Example: AUPNUT to predict infection periods for early and late leaf spot of peanut (Davis, et al., 1993) Recommends timing of first and subsequent fungicide applications based on number of "rain events", where a rain event=0.1 inches of rain (or irrigation) within a 24-h period and the average probability of rain over the next 5-days (5-day POP). Treatment # Fungicide applications Leaf spot severity (1-10 scale) Yield (lb/a) Nontreated a 1755 a 10-day schedule c 2356 b 14-day schedule bc 2364 b Correct and incorrect forecasts For disease forecasting, the estimates are relatively easy at the extremes of environment. For example, There will be no snow mold on wheat in Minnesota in July, Or we can expect to see disease next week when the disease is already present and the environmental conditions are favorable. The forecasts are most difficult at the borderline-favorable conditions. AUPNUT b 2277 b 4

5 Correct and incorrect forecasts Risks associated with making forecasts If a disease forecast is made, and it turns out to be wrong, the forecaster looks foolish (Type II error) If you would forecast that disease would occur, but it didn t occur, the grower could have wasted money on needless control (Type II error) If you would forecast that no disease would occur, but it did occur, the grower could suffer disastrous losses in crop yields (Type I error). So, the forecasts need to be as accurate as possible. Observed Predicted No disease Disease No disease correct Type I error Disease Type II error correct Summary definition, why, when, how, constraints Approaches to disease forecasting Empirical models efficacy of initial inoculum Integral models secondary inoculum (direct and indirect) Integral models initial and secondary inoculum Mechanistic models based on weather forecasts Forecasting errors 5