Disease Warning System Research Articles

Evaluation of leaf wetness duration models for operational use in strawberry disease-warning systems in four US states.

Leaf wetness duration (LWD) plays a key role in disease development and is often used as an input in disease-warning systems. LWD is often estimated using mathematical models, since measurement by sensors is rarely available and/or reliable. A strawberry disease-warning system called "Strawberry Advisory System" (SAS) is used by growers in Florida, USA, in deciding when to spray their strawberry fields to control anthracnose and Botrytis fruit rot. Currently, SAS is implemented at six locations, where reliable LWD sensors are deployed. A robust LWD model would facilitate SAS expansion from Florida to other regions where reliable LW sensors are not available. The objective of this study was to evaluate the use of mathematical models to estimate LWD and time of spray recommendations in comparison to on site LWD measurements. Specific objectives were to (i) compare model estimated and observed LWD and resulting differences in timing and number of fungicide spray recommendations, (ii) evaluate the effects of weather station sensors precision on LWD models performance, and (iii) compare LWD models performance across four states in the USA. The LWD models evaluated were the classification and regression tree (CART), dew point depression (DPD), number of hours with relative humidity equal or greater than 90% (NHRH ≥90%), and Penman-Monteith (P-M). P-M model was expected to have the lowest errors, since it is a physically based and thus portable model. Indeed, the P-M model estimated LWD most accurately (MAE <2h) at a weather station with high precision sensors but was the least accurate when lower precision sensors of relative humidity and estimated net radiation (based on solar radiation and temperature) were used (MAE = 3.7h). The CART model was the most robust for estimating LWD and for advising growers on fungicide-spray timing for anthracnose and Botrytis fruit rot control and is therefore the model we recommend for expanding the strawberry disease warning beyond Florida, to other locations where weather stations may be deployed with lower precision sensors, and net radiation observations are not available.

Use of an Empirical Model to Estimate Leaf Wetness Duration for Operation of a Disease Warning System Under a Shade in a Ginseng Field.

Ginseng foliar diseases are typically controlled by spray application using periodic schedules. Few disease warning systems have been used for effective control of ginseng foliar diseases because ginseng is grown under shade nettings, which makes it difficult to obtain weather data for operation of the disease warning system. Using weather data measured outside the shade as inputs to an empirical leaf wetness duration (LWD) model, LWD was estimated to examine if operation of a disease warning system would be feasible for control of ginseng foliar diseases. An empirical model based on a fuzzy logic system (fuzzy model) was used to estimate LWD at two commercial ginseng fields located in Gochang-gun and Jeongeup-si, Korea, in 2011 and 2012. Accuracy of LWD estimates was assessed in terms of mean error (ME) and mean absolute error (MAE). The fuzzy model tended to overestimate LWD during dew eligible days whereas it tended to underestimate LWD during rainfall eligible days. Still, daily disease risk ratings of the TOM-CAST disease warning system, which are derived from estimates of wetness duration and temperature, had a tendency to coincide with that derived from measurements of weather variables. As a result, spray advisory dates for the TOM-CAST disease warning system were predicted within ±3 days for about 78% of time windows during which the action threshold for spray application was reached. This result suggested that estimates of LWD using an empirical model would be helpful in control of a foliar disease in a ginseng field. It was also found that a spray application time model using meteorological observations may prove successful without the requirement of leaf wetness sensors within the field. Development of empirical correction schemes to the fuzzy model and a physical model for LWD estimation in a ginseng field could improve accuracy of LWD estimates and, as a result, spray advisory date prediction, which merits further studies.

Reconsidering Leaf Wetness Duration Determination for Plant Disease Management.

Relationships between leaf wetness and plant diseases have been studied for centuries. The progress and risk of many bacterial, fungal, and oomycete diseases on a variety of crops have been linked to the presence of free water on foliage and fruit under temperatures favorable to infection. Whereas the rate parameters for infection or epidemic models have frequently been linked with temperature during the wet periods, leaf wetness periods of specific time duration are necessary for the propagule germination of most phytopathogenic fungi and for their penetration of plant tissues. Using these types of relationships, disease-warning systems were developed and are now being used by grower communities for a variety of crops. As a component of Integrated Pest Management, disease-warning systems provide growers with information regarding the optimum timing for chemical or biological management practices based on weather variables most suitable for pathogen dispersal or host infection. Although these systems are robust enough to permit some errors in the estimates or measurements of leaf wetness duration, the need for highly accurate leaf wetness duration data remains a priority to achieve the most efficient disease management.

Effect of temperature, wetness duration and cultivar on the development of anthracnose in guava fruits

The development of a large number of postharvest diseases is closely associated with fruit ripeness. Environmental conditions may affect both the pathogen development and the fruit ripening rate. The aim of this study was to determine the most favorable temperature and wetness duration to the development of anthracnose in guava fruits. Cultivars 'Kumagai' (white pulp) and 'Pedro Sato' (red pulp) were inoculated with a conidial suspension of Colletotrichum gloeosporioides and C. acutatum and incubated at constant temperature ranging from 10 to 35ºC and wetness duration of 6 and 24 hours. Disease severity and incidence were evaluated at every two days during 12 days. No infection occurred at 10 and 35ºC, regardless of the wetness duration. The optimum conditions for fruit infection were 26 and 27ºC for 'Kumagai' and 25 and 26ºC for 'Pedro Sato', adopting 24 hours of wetness. In general, the disease development in 'Kumagai' cultivar was more affected by the wetness period, compared to 'Pedro Sato'. Disease severity for 'Kumagai' fruits was maximal between 25 and 30ºC , depending on the Colletotrichum species. Regarding 'Pedro Sato', the mean diameter of lesions was greater in fruits stored at 20, 25 and 30ºC , compared to 'Kumagai' cultivar, depending on the wetness period and the species. The incubation period (between 6 and 7 days) and the latent period (between 8 and 10 days) were minimal at 30ºC. The data generated in this study will be useful either for the development of a disease warning system or for the increase in the shelf life of guavas in the postharvest.

Sistema de alerta temprana para la roya en el café basado en códigos de salida de corrección de error: una propuesta

Colombian coffee producers have had to face the severe consequences of the coffee rust disease since it was first reported in the country in 1983. Recently, machine learning researchers have tried to predict infection through classifiers such as decision trees, regression Support Vector Ma­chines (SVM), non-deterministic classifiers and Bayesian Networks, but it has been theoretically and empirically demonstrated that combining multiple classifiers can substantially improve the classification perfor­mance of the constituent members. An Early Warning System (EWS) for coffee rust disease was therefore proposed based on Error Correcting Output Codes (ECOC) and SVM to compute the binary functions of Plant Density, Shadow Level, Soil Acidity, Last Nighttime Rainfall Intensity and Last Days Relative Humidity.

Development of a Disease Risk Prediction Model for Downy Mildew (Peronospora sparsa) in Boysenberry

Downy mildew caused by Peronospora sparsa has resulted in serious production losses in boysenberry (Rubus hybrid), blackberry (Rubus fruticosus), and rose (Rosa sp.) in New Zealand, Mexico, and the United States and the United Kingdom, respectively. Development of a model to predict downy mildew risk would facilitate development and implementation of a disease warning system for efficient fungicide spray application in the crops affected by this disease. Because detailed disease observation data were not available, a two-step approach was applied to develop an empirical risk prediction model for P. sparsa. To identify the weather patterns associated with a high incidence of downy mildew berry infections (dryberry disease) and derive parameters for the empirical model, classification and regression tree (CART) analysis was performed. Then, fuzzy sets were applied to develop a simple model to predict the disease risk based on the parameters derived from the CART analysis. High-risk seasons with a boysenberry downy mildew incidence >10% coincided with months when the number of hours per day with temperature of 15 to 20°C averaged >9.8 over the month and the number of days with rainfall in the month was >38.7%. The Fuzzy Peronospora Sparsa (FPS) model, developed using fuzzy sets, defined relationships among high-risk events, temperature, and rainfall conditions. In a validation study, the FPS model provided correct identification of both seasons with high downy mildew risk for boysenberry, blackberry, and rose and low risk in seasons when no disease was observed. As a result, the FPS model had a significant degree of agreement between predicted and observed risks of downy mildew for those crops (P = 0.002).

An early warning and response system for hand, foot and mouth Disease in China, 2011-2012

Background: A China Infectious Disease Automated-alert and Response System (CIDARS) has been increasingly utilized nationwide for early outbreak detection and rapid response for infectious diseases...

Effects of Temperature and Wetness Duration on the Sporulation Rate of Phomopsis viticola on Infected Grape Canes.

Controlled-environment studies were conducted to examine effects of temperature (T) and wetness duration (W) on the sporulation rate of Phomopsis viticola on infected grape canes and to determine effects of interrupted wetness duration (IWD) on sporulation. A split-plot design was used to determine T and W effects, with T (5, 12, 15, 18, 20, 22, 25, 28, and 35°C) as the whole-plot and W (11, 23, 35, 47, and 71 h) as the subplot. Linear and nonlinear mixed models were fitted to the data. Lower and upper limits of sporulation were estimated to be 4 and 36°C, respectively, based on the modeling results, optimum sporulation was near 21°C, and sporulation increased monotonically with increasing wetness duration. Of the examined models, a generalization of the Analytis Beta model fit the data best, based on a collection of goodness-of-fit statistical criteria. To determine effects of IWD, a split-plot was used, with T (12, 15, and 20°C) as the whole-plot and IWD (0, 2, 4, 8, 12, and 24 h) as the subplot. Generally, sporulation declined with increasing IWD. An IWD of 8 h or more resulted in significantly and substantially less sporulation compared to the control (0 h IWD) (P < 0.01). Temporal patterns of spore density in the field were determined using a repeated-measures design, in which spore density and environmental data were measured in the vineyard during and following individual rain events over 3 years. The developed model from the controlled-environment study, coupled with a time-of-season weight function and a dispersal index (based on total rain per rain episode), predicted the trend in spore density over time reasonably well, although the total magnitude of spore density could not be predicted because the density of lesions was not known. Results can be used for improving the accuracy of a disease warning system that currently only considers infection of grapes by P. viticola.

Development stage-dependent susceptibility of cocoa fruit to pod rot caused by Phytophthora megakarya

Pod rot causes up to 30 % losses in world cocoa production. In order to predict the risk evolution of disease, it is important to take into consideration the developmental stage of fruits. In fact, it has been shown that the risk of attack by pod rot depends amongst others on the developmental stage of fruits. We proposed here to estimate the susceptibility at different stages. Susceptibility of fruit to disease was investigated at three fruit developmental stages (cherelle, young pod and adult pod); disease severity was assessed in laboratory conditions, on detached, artificially inoculated fruits, while disease incidence was assessed in the field, under natural inoculum pressure. In both assessment fruits at the cherelle stage were the most susceptible whereas the young and adult fruits were equally susceptible. The vertical position of the fruits on the tree did not influence their susceptibility. Estimates of the fruit susceptibility and of the infectious potential were derived from the severity and incidence measurements, using a model assuming that the number of spores on a fruit follows a Poisson distribution with the mean, the density of spores per fruit as the parameter. The estimated parameter values allowed the evaluation of the probability of attack of a fruit by the disease, which could be implemented in a disease warning system.

A data-driven model simulating primary infection probabilities of cucumber downy mildew for use in early warning systems in solar greenhouses

Treatment during the primary infection phase is essential for controlling cucumber downy mildew in solar greenhouses. An early warning model applicable to this phase would represent a foundation for early warning systems for managing the disease and reducing pesticide usage. Based on the input parameters that were both readily available and appropriately limited in number, EWMPICDW (early warning model for primary infection of cucumber downy mildew in solar greenhouses) was developed based on monitoring data, early warning theory and plant disease epidemiology. The elaboration of this model included clarification of the meaning of warning, monitoring the warning indicators, forecasting the warning situation, tracing the warning sources and controlling the warning situation. The definition of warning included disease occurrence (yes or no) and probability. Because the leaf wetness duration (LWD) played an important role in disease warning systems for crops in solar greenhouses and was difficult to monitor, the leaf wetness sensor and RH threshold model were investigated and combined to form a practical estimation solution for LWD. Within the warning situation forecasting model system, the infection condition and incubation early warning submodels received the most attention. The infection condition early warning submodel was developed by using a threshold method based on the combination of LWD and mean temperature in LWD. The temperature was chosen as the warning indicator for incubation, and the incubation early warning submodel was defined using nonlinear regression methods. The warning sources traceability algorithm was developed in relation to expert knowledge and in terms of a mode of disaster mitigation that involved cutting the disaster chain from the headstream. The method for controlling the warning situation was based on good agricultural practices (GAP). The early warning model was implemented as a system and was evaluated using data for 4 years at two sites in Beijing, China. The warnings can be provided more than 2 d before symptoms appear. Using EWMPICDW, a positive early warning is associated with a change in the chance of disease occurrence from 0.68 to 0.96. Accordingly, the probability of disease occurrence calculated for the early warning model was 96%. These results demonstrate that the data-driven model will support the development of early warning systems for primary infection by cucumber downy mildew in solar greenhouses.

Haemophilus influenzae type b carriage in Indigenous children and children attending childcare centers in the Northern Territory, Australia, spanning pre- and post-vaccine eras

This paper investigates Haemophilus influenzae type b (Hib) carriage in Indigenous children and children attending childcare centres in the Northern Territory of Australia, 1992–2005. More than 10 years after the introduction of the Hib conjugate vaccine, Hib carriage persists in Indigenous children (3.4%, 2003–2005) and children attending childcare centres (0.2%, 2004). This is the first Australian study to examine Hib carriage spanning the pre- and post-vaccination eras. Increasing carriage rates contribute to Hib disease resurgence especially in those with higher disease burdens, such as remote Indigenous Australians, ongoing carriage surveillance provides a sentinel warning system for invasive disease. Background Following the introduction of H. influenzae type b (Hib) conjugate vaccine to Australia in 1993 as a three dose (2, 4, 12 month) schedule, the incidence of Hib disease decreased dramatically in children, especially in those aged under 5 years. We investigate Hib carriage in Indigenous children and children attending childcare centres from the Northern Territory (NT) of Australia between 1992 and 2005. We report Hib carriage rates in this well vaccinated population, with previously documented high rates of invasive disease. Methods Hib carriage was reviewed in nasopharyngeal or nasal swabs collected between 1992 and 2005; from over 2000 children (61% Indigenous) aged 0–6 years enrolled in 7 otitis media or carriage surveillance studies in the NT. Results More than 10 years after the introduction of the Hib conjugate vaccine, Hib carriage persists at low levels, but at a higher rate in Indigenous children (3.4%, 2003–2005) than children attending childcare centres (0.2%, 2004), in the NT of Australia. Conclusions This is the first Australian study to examine Hib carriage spanning the pre- and post-vaccination eras. Ongoing carriage surveillance provides a sentinel warning system for invasive disease. This notifies public health professionals of potential invasive disease, creating early warning systems for intervention if Australian Indigenous children or children attending childcare centres are to follow current international trends associated with high rates of carriage preceding invasive disease—despite high vaccination rates. Internationally there is growing concern that increasing carriage rates are the driving force behind Hib disease resurgence especially in those with higher disease burdens, such as remote Indigenous Australians. Changes to the vaccination schedule from PRP-OMPC (PedvaxHIB ®), to PRP-T (2, 4, 6, 12 months) from January 2010—may affect carriage and in time, invasive disease rates. This work is important for national and International comparisons as well as feeding back information to vaccine policy makers of the Hib carriage environment throughout this period.

Spectral Signatures of Leaf Fall Diseases in Hevea Brasiliensis Using a Handheld Spectroradiometer

Subtle sensitive changes in leaf canopy reflectance of a disease infected trees can be detected by a spectroradiometer. A typical method of detecting tree stress caused by diseases or pest infestations includes the analysis of spectroradiometry. Early detection of forest tree stress would be useful to minimize tree losses especially in a forest plantation area. The main purpose of this study is to develop the spectral library of individual rubber trees being attacked by diseases using a ground-based handheld field spectroradiometer. The specific objective is to identify the spectral signature characteristics of healthy (control) and “unhealthy” or stressed rubber trees due to leaf diseases as causal factors. The spectral reflectance of each infected rubber tree was separated according to the different wavelength and percent reflectance. The spectral signatures of rubber trees being attacked by diseases were characterized by a low reflectance probably due to the low chlorophyll content in the leaves leading to the tree under stress, thus easily separated from the healthy rubber. Results indicated that three groups of infected trees were well separated at the 530 - 650 nm (visible) wavelength averaging from 0 – 30 percent reflectance. The spectral reflectances of rubber trees with leaf disease in visible (VIS) wavelength were not consistently separable. However, the spectral reflectance of leaf diseases can be well separated at the near infrared range region covering from 700 - 850 nm wavelength with a 30 – 80 percent reflectance for leaf diseases, respectively. The study implies that leaf diseases for rubber trees can only be identified successfully at the NIR range of wavelength from 700 – 850 nm with a 20-80 percent reflectance. The development of such signature library profile of disease affecting rubber trees will certainly assists in the development of an early disease warning system using an airborne hyperspectral imaging system technology being currently developed in UPM’s Forest Geospatial Information & Survey Laboratory, at Lebuh Silikon, Universiti Putra Malaysia, Serdang.

Evaluation of Two Disease Warning Systems for Botryosphaeria Panicle and Shoot Blight of California Pistachio and Efficient Control Based on Early-Season Sprays.

Two empirical models to predict infection events were evaluated for control of Botryosphaeria panicle and shoot blight, caused by a Fusicoccum sp., as well as the effectiveness of early-season fungicide sprays on the control of this disease of pistachio. A model incorporating wetness duration was superior to one based solely on duration of rains ≥1 mm/h for ≥4 h and with temperature ≥11°C. The wetness duration threshold (W) for rain events ≥4 mm at a given temperature (T) for high-risk infection events was W = -7.8 + 397/T and the threshold for medium-risk events was W = -6.9 + 220/T. Wet periods interrupted by ≤12 h were added together to calculate W. In two orchards with high levels of inoculum, one high-risk event resulted in 20 to 23% blighted fruit at harvest and two or three high-risk events resulted in 31 to 80% blighted fruit. Latent infections were 0 to 1% in instances where only low-risk events (one to two events) occurred prior to collection of pistachio fruit for determination of latent infections and were 17 to 36% with one to three high-risk events. Early-season fungicide sprays in April to May effectively controlled panicle and shoot blight when applied up to 12 days before predicted infection events or 5 days after.

Relationships between cocoa Phytophthora pod rot disease and climatic variables in Cameroon

Phytophthora pod rot disease causes almost 30% loss in world cocoa production. Epidemiological observations showed that the disease appears if fruit at any growth stage is present on trees when the rainy season begins. The overall effects of environment on the epidemic are not well known in cocoa-producing zones. The aim of this study was to assess and quantify the influence of rainfall and temperature on the temporal development of the disease in the cocoa-producing areas of Cameroon. Disease incidence and fruit production by 100 trees from each of three regions (Mbankomo, Goura and Barombi-Kang) were recorded weekly. Rainfall and temperature data were collected daily. Field observations were made over a period of 3 years (1999–2001). Autocorrelations, cross correlations, and multiple regression analyses were used to establish relationships between disease incidence and the environmental variables. The study showed that pod rot incidence is associated with rainfall but that there is a lag in the relationship between the two variables: the disease symptoms occurred 1–2 weeks after rainfall events. This recognition of this lag has the potential to be used to create a disease warning system to better advise cocoa farmers about disease risk. Therefore, the present study is an important step towards a better understanding of quantitative relations between disease incidence and the environment.

Actual Pathogen Detection: Sensors and Algorithms - a Review

Pathogens feed on fruits and vegetables causing great food losses or at least reduction of their shelf life. These pathogens can cause losses of the final product or in the farms were the products are grown, attacking leaves, stems and trees. This review analyses disease detection sensors and algorithms for both the farm and postharvest management of fruit and vegetable quality. Mango, avocado, apple, tomato, potato, citrus and grapes were selected as the fruits and vegetables for study due to their world-wide consumption. Disease warning systems for predicting pathogens and insects on farms during fruit and vegetable production are commonly used for all the crops and are available where meteorological stations are present. It can be seen that these disease risk systems are being slowly replaced by remote sensing monitoring in developed countries. Satellite images have reduced their temporal resolution, but are expensive and must become cheaper for their use world-wide. In the last 30 years, a lot of research has been carried out in non-destructive sensors for food quality. Actually, non-destructive technology has been applied for sorting high quality fruit which is desired by the consumer. The sensors require algorithms to work properly; the most used being discriminant analysis and training neural networks. New algorithms will be required due to the high quantity of data acquired and its processing, and for disease warning strategies for disease detection.

Obtaining weather data for input to crop disease-warning systems: leaf wetness duration as a case study

Disease-warning systems are decision support tools designed to help growers determine when to apply control measures to suppress crop diseases. Weather data are nearly ubiquitous inputs to warning systems. This contribution reviews ways in which weather data are gathered for use as inputs to disease-warning systems, and the associated logistical challenges. Grower-operated weather monitoring is contrasted with obtaining data from networks of weather stations, and the advantages and disadvantages of measuring vs. estimating weather data are discussed. Special emphasis is given to leaf wetness duration (LWD), not only because LWD data are inputs to many disease-warning systems but also because accurate data are uniquely challenging to obtain. It is concluded that there is no single " best" method to acquire weather data for use in disease-warning systems; instead, local, regional, and national circumstances are likely to influence which strategy is most successful.

Spatial Heterogeneity of Leaf Wetness Duration in Apple Trees and Its Influence on Performance of a Warning System for Sooty Blotch and Flyspeck.

To determine the effect of sensor placement on the performance of a disease-warning system for sooty blotch and flyspeck (SBFS), we measured leaf wetness duration (LWD) at 12 canopy positions in apple trees, then simulated operation of the disease-warning system using LWD measurements from different parts of the canopy. LWD sensors were placed in four trees within one Iowa orchard during two growing seasons, and in one tree in each of four orchards during a single growing season. The LWD measurements revealed substantial heterogeneity among sensor locations. In all data sets, the upper, eastern portion of the canopy had the longest mean daily LWD, and was the first site to form dew and the last to dry. The lower, western portion of the canopy averaged about 3 h less LWD per day than the top of the canopy, and was the last zone where dew formed and the first to dry off. On about 25% of nights when dew occurred in the top of the canopy, no dew formed in the lower, western canopy. Intracanopy variability of LWD was more pronounced when dew was the sole source of wetness than on days when rainfall occurred. Daily LWD in the upper, eastern portion of the canopy was slightly less than reference measurements made at a 0.7-m height over turfgrass located near the orchard. When LWD measurements from several canopy positions were input to the SBFS warning system, timing of occurrence of a fungicide-spray threshold varied by as much as 30 days among canopy positions. Under Iowa conditions, placement of an LWD sensor at an unobstructed site over turfgrass was a fairly accurate surrogate for the wettest part of the canopy. Therefore, such an extra-canopy LWD sensor might be substituted for a within-canopy sensor to enhance operational reliability of the SBFS warning system.

Challenges in Adaptation of Plant Disease Warning Systems to New Locations: Re-Appraisal of Billing's Integrated System for Predicting Fire Blight in a Warm Dry Environment

ABSTRACT The purpose of this letter is to describe approaches and possible pitfalls when a fire blight model developed in one climatic area is evaluated in a new location where weather conditions are markedly different. A case is described where a modified form of Billing's integrated system, BIS95, which was developed in a cool moist climate, was tested in a country where weather is warmer and drier. Prior to this, some features of fire blight epidemiology, management, and risk assessment are outlined.

Evaluation of the curative and protectant activity of fungicides and fungicide–adjuvant mixtures on Phomopsis cane and leaf spot of grape: A controlled environment study

Phomopsis cane and leaf spot of grape—an economically important disease of grapes in many regions of the world—can be controlled in Ohio by applying fungicide mixed with adjuvant in response to infection periods predicted with the use of a disease-warning system. A series of experiments was conducted in this study to determine the possible curative activity of fungicides against Phomopsis viticola when used with certain adjuvants. Grape plants were sprayed with fungicide (with or without adjuvant) either before or after inoculation with the pathogen. Leaf and internode disease severity for all post-inoculation treatments were not different from that found for the inoculated (unsprayed) control, even when fungicides were used at 150% of labeled doses, or when fungicides were applied within 20 h of the start of the infection period or 4 h from the end of the wetness period. However, excellent disease control was obtained with pre-inoculation application of mancozeb or azoxystrobin, with experiments that varied in duration of the wetness periods for infection. Use of an adjuvant did not improve disease control. Thus, suppression of disease symptoms in the field based on the use of the disease-warning system was likely due to the purely protective activity of the fungicide.

Evaluations of New and Current Management Strategies to Control Phomopsis Cane and Leaf Spot of Grape

To extend our understanding of the epidemiology and control of Phomopsis cane and leaf spot of grape, caused by Phomopsis viticola, studies were conducted to: (i) determine efficacy of a dormant fungicide spray program for controlling the disease in its early stage of development (e.g., spring); (ii) evaluate a disease warning system by applying fungicides and fungicide-adjuvant combinations in response to predicted infection periods based on weather conditions; and (iii) correlate commercial control practices and environmental conditions with disease incidence based on a state-wide survey of commercial fields. With the warning system, control was equal to that obtained with a 7-day protectant fungicide program, but with fewer fungicide applications per season. A dormant application provided consistent, yet only moderate, control of the disease. Growers who applied a dormant-season application or early spring protectant fungicides tended to have lower disease incidence than those who did not. There was high variability in disease incidence within vineyards and farms, but no significant difference in incidence among regions of the state. Overall, early season application of protective fungicides was shown to be a key factor for successful management of P. viticola. Accepted for publication 29 March 2007. Published 26 July 2007.