The use and role of predictive systems in disease management

Modeling the Airborne Inoculum of Polystigma amygdalinum to Optimize Fungicide Programs Against Almond Red Leaf Blotch

Red leaf blotch (RLB) of almond, caused by the ascomycete Polystigma amygdalinum, is a severe foliar disease endemic in the Mediterranean Basin and Middle East. Airborne ascospores of P. amygdalinum were monitored from 2019 to 2021 in two almond orchards in Lleida, Spain, and a Bayesian beta regression was used to model its seasonal dynamics. The selected model incorporated accumulated degree-days (ADD), ADD considering both vapor pressure deficit and rainfall as fixed effects, and a random effect for the year and location. The performance of the model was evaluated in 2022 to optimize RLB fungicide programs by comparing the use of model predictions and action thresholds with the standard program. Two variants were additionally considered in each program to set the frequency between applications, based on (i) a fixed frequency of 21 days or (ii) specific meteorological criteria (spraying within 7 days after rainfalls greater than 10 mm, with daily mean temperatures between 10 and 20°C, and with a minimum frequency of 21 days between applications). Programs were evaluated in terms of RLB incidence and number of applications. The program based on the model with periodic fungicide applications was similarly effective as the standard program, resulting only in a 2.6% higher RLB incidence but with fewer applications (three to four, compared with seven in the standard program). When setting the frequency between applications by using the meteorological criteria, a higher reduction in the number of applications (two to three) was observed, while RLB incidence increased by roughly 16% in both programs. Therefore, the model developed in this study may represent a valuable tool toward a more sustainable fungicide schedule for the control of almond RLB in northeast Spain.

Effectiveness of potato late blight (Phytophthora infestans) forecast by meteorological estimation in mountainous terrain based on CARAH rules

Potato late blight (PLB) caused by Phytophthora infestans (Mont.) de Bary, its incidence and development are highly dependent on meteorological conditions. To solve the problem of PLB in mountainous terrain under the condition of limited meteorological monitoring capability, the air temperature and humidity was estimated based on the basic meteorological datasets, the forecast effect of the onset period and infection cycle of PLB based on CARAH rules was evaluated. The average MAE, RMSE and CI of the estimated air temperature and observations were 1.17 °C, 1.52 °C and 0.95, respectively. The average MAE, RMSE and CI of the estimated relative humidity and observations were 8.0 %, 10.7 % and 0.53, respectively. The curve of the infection cycle of PLB at different locations were estimated from the basic meteorological datasets based on the CARAH rules, and the false alarm and missing ratios were 8.8 % and 4.6 % respectively. It may be delayed by 1 or 2 fungal generations compared to the observations, and then the protective fungicide should be adjusted to a systemic fungicide. The false alarm of the infection cycle of PLB may increase in dry air conditions, and the missing report may occur in humid condition.

Fifty Years of Fungicide Development, Deployment, and Future Use

Plant disease management has not significantly changed significantly in the past 50 years, even as great strides have been made in the understanding of fungal biology and the etiology of plant disease. Issues of climate change, supply chain failures, war, political instability, and exotic invasives have created even more serious implications for world food and fiber security, and the stability of managed ecosystems, underscoring the urgency for reducing plant disease-related losses. Fungicides serve as the primary example of successful, widespread technology transfer, playing a central role in crop protection, reducing losses to both yield and postharvest spoilage. The crop protection industry has continued to improve upon previous fungicide chemistries, replacing active ingredients lost to resistance and newly understood environmental and human health risks, under an increasingly stricter regulatory environment. Despite decades of advances, plant disease management continues to be a constant challenge that will require an integrated approach, and fungicides will continue to be an essential part of this effort.

Refining Management of Apple Powdery Mildew in New York State with Weather-Based Fungicide Application Timing Programs

In the absence of durable host resistance among commercial cultivars, chemical management continues to be an essential component of disease control in apple production. Apple powdery mildew, caused by the ascomycete Podosphaera leucotricha, is largely managed with regular fungicide applications from the host phenological stages of tight cluster to terminal bud growth set, with applications typically being made in a prophylactic manner irrespective of existing disease pressure. Here we evaluated two management programs that aligned fungicide applications to specific weather thresholds conducive to powdery mildew development using a rotation of single-site fungicides and sulfur. In three separate orchards among four cultivars, we compared powdery mildew disease progression over the growing season for each of the weather factor-based programs and a typical calendar-based application program. In each year of the trial, we found that management programs with weather-based fungicide applications provided levels of disease control similar to the calendar program but required 50 to 83.3% fewer mildew-specific fungicide applications throughout the growing season. Our results provide a framework with which to evaluate future weather-based management programs for apple powdery mildew management. This knowledge could be implemented in the creation of a powdery mildew disease management decision support system to better inform and aid fungicide application programs for continued sustainable apple production in the northeast United States.

Evaluation of a Model for Predicting Onset of Septoria nodorum Blotch in Winter Wheat

Prediction models that aid growers in making decisions on timing of fungicide application are important components of integrated management programs for several foliar diseases of wheat. The risk of Septoria nodorum blotch (caused by Parastagonospora nodorum) onset in winter wheat has been reported to be influenced by location, amount of wheat residue in the field, and cumulative daily infection values 2 weeks prior to day of year (DOY) 102. A model previously developed based on these predictor variables was evaluated for its ability to predict disease onset under field conditions. An experiment was conducted at three locations in North Carolina in 2018, 2019, and 2020, where plots were either treated with >20% wheat residue or received no residue treatment. Plots were monitored for disease symptoms, and disease onset was defined to have occurred when mean disease incidence in a plot was 50%. Of the 298 disease cases recorded, disease onset occurred early (i.e., prior to DOY 102) in 257 cases, while onset was late (i.e., on or after DOY 102) in 41 cases. Model accuracy based on correct classification ranged from 0.67 to 0.95, with a mean of 0.87 across the study period. Similarly, sensitivity rates of the model ranged from 0.88 to 1.0 with a mean of 0.98 across all years. However, the model had low specificity, with a mean rate of 0.15 across the study period. Overall, there was no significant difference in the frequency of observed and predicted cases in the study (χ² = 0.50, P = 0.7788, df = 2). Time to disease onset was significantly correlated with grain yield and explained 26% of variation in yield (P < 0.0001). Results indicated that the disease onset model performs well in predicting early disease onset but requires further evaluation and improvement, particularly in the Piedmont, where it over-predicted early onset in 2 successive years.

Catching Spores: Linking Epidemiology, Pathogen Biology, and Physics to Ground-Based Airborne Inoculum Monitoring

Monitoring airborne inoculum is gaining interest as a potential means of giving growers an earlier warning of disease risk in a management unit or region. This information is sought by growers to aid in adapting to changes in the management tools at their disposal and the market-driven need to reduce the use of fungicides and cost of production. To effectively use inoculum monitoring as a decision aid, there is an increasing need to understand the physics of particle transport in managed and natural plant canopies to effectively deploy and use near-ground aerial inoculum data. This understanding, combined with the nuances of pathogen-specific biology and disease epidemiology, can serve as a guide to designing improved monitoring approaches. The complexity of any pathosystem and local environment are such that there is not a generalized approach to near-ground air sampler placement, but there is a conceptual framework to arrive at a "semi-optimal" solution based on available resources. This review is intended as a brief synopsis of the linkages among pathogen biology, disease epidemiology, and the physics of the aerial dispersion of pathogen inoculum and what to consider when deciding where to locate ground-based air samplers. We leverage prior work in developing airborne monitoring tools for hops, grapes, spinach, and turf, and research into the fluid mechanics governing particle transport in sparse canopies and urban and forest environments. We present simulation studies to demonstrate how particles move in the complex environments of agricultural fields and to illustrate the limited sampling area of common air samplers.

Current Trends and Perspectives on Predictive Models for Mildew Diseases in Vineyards

Environmental and economic costs demand a rapid transition to more sustainable farming systems, which are still heavily dependent on chemicals for crop protection. Despite their widespread application, powdery mildew (PM) and downy mildew (DM) continue to generate serious economic penalties for grape and wine production. To reduce these losses and minimize environmental impacts, it is important to predict infections with high confidence and accuracy, allowing timely and efficient intervention. This review provides an appraisal of the predictive tools for PM and DM in a vineyard, a specialized farming system characterized by high crop protection cost and increasing adoption of precision agriculture techniques. Different methodological approaches, from traditional mechanistic or statistic models to machine and deep learning, are outlined with their main features, potential, and constraints. Our analysis indicated that strategies are being continuously developed to achieve the required goals of ease of monitoring and timely prediction of diseases. We also discuss that scientific and technological advances (e.g., in weather data, omics, digital solutions, sensing devices, data science) still need to be fully harnessed, not only for modelling plant-pathogen interaction but also to develop novel, integrated, and robust predictive systems and related applied technologies. We conclude by identifying key challenges and perspectives for predictive modelling of phytopathogenic disease in vineyards.

Temperature Influences on Powdery Mildew Susceptibility and Development in the Hop Cultivar Cascade

The hop cultivar 'Cascade' possesses partial resistance to powdery mildew (Podosphaera macularis) that can be overcome by recently emerged, virulent isolates of the fungus. Given that hop is a long-lived perennial and that brewers still demand Cascade, there is a need to better understand factors that influence the development of powdery mildew on this cultivar. Growth chamber experiments were conducted to quantify the effect of constant, transient, and fluctuating temperature on Cascade before, concurrent to, and after inoculation as contrasted with another powdery mildew-susceptible cultivar, 'Symphony'. Exposure of plants to supraoptimal temperature (26 and 32°C) before inoculation led to more rapid onset of ontogenic resistance in intermediately aged leaves in Cascade as compared with Symphony. Cascade was overall less susceptible to powdery mildew when exposed to constant temperature ranging from 18 to 32°C directly after inoculation. However, cultivar also interacted with temperature such that proportionately fewer and smaller colonies developed on Cascade than Symphony at supraoptimal yet permissive temperatures for disease. When plants were inoculated and then exposed to high temperature, colonies became progressively more tolerant to temperatures of 26 to 30°C with increasing time from inoculation to exposure, as moderated by cultivar, the specific temperature, and their interaction. Subjecting plants to simulated diurnal temperature regimes at the time of inoculation or 24 h later indicated Cascade and Symphony responded proportionately similarly on days predicted to be marginally unfavorable or marginally favorable for powdery mildew, although Cascade was quantitatively less susceptible than Symphony. In sum, this research indicates that Cascade is overall less susceptible to powdery mildew than Symphony, and supraoptimal temperature before, concurrent to, or after infection may interact differentially to moderate disease risk in Cascade. Therefore, cultivar-specific risk assessments for powdery mildew appear warranted.

A Systematic Map of the Research on Disease Modelling for Agricultural Crops Worldwide

In this work, we developed a systematic map to identify and catalogue the literature pertaining to disease modelling for agricultural crops worldwide. Searches were performed in 2021 in the Web of Science and Scopus for papers reporting any type of disease model for 103 crops. In total, 768 papers were retrieved, and their descriptive metadata were extracted. The number of papers found increased from the mid-1900s to 2020, and most of the studies were from North America and Europe. More disease models were retrieved for wheat, potatoes, grapes, and apples than for other crops; the number of papers was more affected by the crop’s economic value than by its cultivated area. The systematic map revealed an underrepresentation of disease models for maize and rice, which is not justified by either the crop economic value or by disease impact. Most of the models were developed to understand the pathosystem, and fewer were developed for tactical disease management, strategic planning, or scenario analysis. The systematic map highlights a variety of knowledge gaps and suggests questions that warrant further research.

Application of the Maryblyt Model for the Infection of Fire Blight on Apple Trees at Chungju, Jecheon, and Eumsung during 2015-2020

To preventively control fire blight in apple trees and determine policies regarding field monitoring, the Maryblyt ver. 7.1 model (MARYBLYT) was evaluated in the cities of Chungju, Jecheon, and Eumseong in Korea from 2015 to 2020. The number of blossom infection alerts was the highest in 2020 and the lowest in 2017 and 2018. And the common feature of MARYBLYT blossom infection risks during the flowering period was that the time of BIR-High or BIR-Infection alerts was the same regardless of location. The flowering periods of the trees required to operate the model varied according to the year and geographic location. The model predicts the risk of "Infection" during the flowering periods, and recommends the appropriate times to control blossom infection. In 2020, when flower blight was severe, the difference between the expected date of blossom blight symptoms presented by MARYBLYT and the date of actual symptom detection was only 1-3 days, implying that MARYBLYT is highly accurate. As the model was originally developed based on data obtained from the eastern region of the United States, which has a climate similar to that of Korea, this model can be used in Korea. To improve field utilization, however, the entire flowering period of multiple apple varieties needs to be considered when the model is applied. MARYBLYT is believed to be a useful tool for determining when to control and monitor apple cultivation areas that suffer from serious fire blight problems.

Logistic Models Derived via LASSO Methods for Quantifying the Risk of Natural Contamination of Maize Grain with Deoxynivalenol

Models were developed to quantify the risk of deoxynivalenol (DON) contamination of maize grain based on weather, cultural practices, hybrid resistance, and Gibberella ear rot (GER) intensity. Data on natural DON contamination of 15 to 16 hybrids and weather were collected from 10 Ohio locations over 4 years. Logistic regression with 10-fold cross-validation was used to develop models to predict the risk of DON ≥1 ppm. The presence and severity of GER predicted DON risk with an accuracy of 0.81 and 0.87, respectively. Temperature, relative humidity, surface wetness, and rainfall were used to generate 37 weather-based predictor variables summarized over each of six 15-day windows relative to maize silking (R1). With these variables, least absolute shrinkage and selection operator (LASSO) followed by all-subsets variable selection and logistic regression with 10-fold cross-validation were used to build single-window weather-based models, from which 11 with one or two predictors were selected based on performance metrics and simplicity. LASSO logistic regression was also used to build more complex multiwindow models with up to 22 predictors. The performance of the best single-window models was comparable to that of the best multiwindow models, with accuracy ranging from 0.81 to 0.83 for the former and 0.83 to 0.87 for the latter group of models. These results indicated that the risk of DON ≥1 ppm can be accurately predicted with simple models built using temperature- and moisture-based predictors from a single window. These models will be the foundation for developing tools to predict the risk of DON contamination of maize grain.

Exploring Grower-sourced Data to Understand Spatiotemporal Trends in the Occurrence of a Vector, Pseudococcus maritimus (Hemiptera: Pseudococcidae) and Improve Grapevine Leafroll Disease Management

Grapevine leafroll disease is a significant concern in the wine grape industry, as it spreads rapidly and contributes to economically significant reductions in yield and grape quality. Our objective was to utilize 5 yr of grower-sourced data from Napa (California, USA) to improve local and regional disease management efforts. Specifically, we applied a spatially integrated multivariate clustering technique to improve understanding of spatiotemporal trends in Pseudococcus maritimus (Ehrhorn) male populations-the primary vector in the region. We also implemented generalized linear mixed models to evaluate the effects of two key practices, insecticide sprays and roguing, on disease incidence. Results show P. maritimus has a biannual flight pattern in the study area, with the first flight peaking in early May and the second between early August and early September. Clusters of P. maritimus flight data fall largely within the vineyard footprints of individual growers, but also showed clear neighborhood effects. We found that when disease incidence within a block is <1%, consistent monitoring and removal of diseased vines is required to contain within-block spread. As within-block disease incidence grows to 1-20%, both insecticide applications and roguing are effective practices to reduce spread. At incidence levels >20%, roguing is a critical practice. Our results emphasize the importance of individual management efforts, but also the value of programs that engage the wider neighboring community and highlight the power of community data collection to guide decision-making.

Validation of a Decision Support System for Blueberry Anthracnose and Fungicide Sensitivity of Colletotrichum gloeosporioides Isolates

Blueberry is an increasingly important crop in Florida. Anthracnose fruit rot (AFR), caused mostly by Colletotrichum gloeosporioides, is favored by long wetness periods and temperatures from 15 to 27°C. Currently, the model in the Strawberry Advisory System (StAS) guides fungicide applications targeting strawberry AFR. Given the similarity between blueberry and strawberry AFR, we hypothesized that the model used in StAS could be used in a decision support system (DSS) built for blueberry AFR. There is no information on inhibition posed by fungicides on C. gloeosporioides isolates from blueberry. Our objectives were to demonstrate that the model used in the StAS could be used for blueberry AFR management in Florida and to assess the sensitivity of isolates to fungicides. Four trials were undertaken in blueberry fields in Florida during two seasons to compare the effectiveness of fungicide applications according to the model with that of the growers' standard calendar. Sensitivity of blueberry C. gloeosporioides isolates to azoxystrobin, benzovindiflupyr, penthiopyrad, pydiflumetofen, boscalid, thiophanate-methyl, fluazinam, and fludioxonil was evaluated. AFR incidence and yield were compared between treatments. Following recommendations from the model resulted in disease control as effective as the standard program and in some cases with fewer applications. All isolates were sensitive to benzovindiflupyr, penthiopyrad, fluazinam, and fludioxonil. Low frequency of in vitro inhibition of isolates by azoxystrobin, pydiflumetofen, boscalid, and thiophanate-methyl should raise concern about fungicide resistance. Our results indicate that the model used in StAS could be used in a DSS to help Florida growers to manage AFR in blueberry.

Performance and Profitability of Rain-Based Thresholds for Timing Fungicide Applications in Soybean Rust Control

Soybean rust (SBR), caused by the fungus Phakopsora pachyrhizi, is the most damaging disease of soybean in Brazil. Effective management is achieved by means of calendar-timed sprays of fungicide mixtures, which do not explicitly consider weather-associated disease risk. Two rain-based action thresholds of disease severity values (DSV50 and DSV80) were proposed and compared with two leaf wetness duration-temperature thresholds of daily values of infection probability (DVIP6 and DVIP9) and with a calendar program, with regard to performance and profitability. An unsprayed check treatment plot was included for calculating relative control. Disease severity and yield data were obtained from 29 experiments conducted at six sites across four states in Brazil during the 2012-13, 2014-15, and 2015-16 growing seasons, which represented different growing regions and climatic conditions. The less conservative rainfall action threshold (DSV80) resulted in fewer fungicide sprays compared with the other treatments, and the more conservative one (DSV50) resulted in fewer sprays than the DVIP thresholds. Yield was generally higher with the increase in spray number, but the economic analysis showed no significant differences in the risk of not offsetting the costs of fungicide sprays regardless of the system. Therefore, based on the simplicity and the profitability of the rain-based model, the system is a good candidate for incorporating into the management of SBR in soybean production fields in Brazil.

Optimizing Cercospora Leaf Spot Control in Table Beet Using Action Thresholds and Disease Forecasting

Cercospora leaf spot (CLS), caused by the fungus Cercospora beticola, is the dominant foliar disease affecting table-beet production in New York. CLS epidemics occur annually and, if uncontrolled, will rapidly lead to defoliation. In broad-acre production, season-long maintenance of healthy leaves is important to facilitate harvest by top-pulling. Fungicides are the dominant means of CLS control and applications are initiated at an action threshold of 1 CLS lesion/leaf. Regular fungicide application occurs thereafter without regard for scheduling based on weather-based risk. The current action threshold was evaluated with selected fungicides in two replicated field trials. Copper oxychloride + copper hydroxide and propiconazole significantly improved CLS control if initiated prior to infection. Pydiflumetofen + difenoconazole significantly reduced area under the disease progress stairs compared with other fungicides tested and was most efficacious when applications began at 1 CLS lesion/leaf. Six replicated field trials also evaluated the utility of scheduling fungicides on weather-based risk rather than a calendar approach. Two risk thresholds (moderate and high) integrating the accumulation of daily infection values based on temperature and relative humidity from a forecaster for CLS in sugar beet were evaluated. Applications of pydiflumetofen + difenoconazole were reduced from three to two by using the forecaster at either risk threshold compared with calendar applications without affecting CLS control. For propiconazole, the moderate risk threshold provided CLS control equivalent to calendar applications and saved one spray per season. Thus, there was substantial scope to reduce spray frequency by scheduling based on weather-based risk rather than calendar applications. The optimal risk thresholds for pydiflumetofen + difenoconazole and propiconazole were high and moderate, respectively. In these trials, periods of high risk occurred less frequently than moderate risk, increasing the reapplication intervals and, hence, represented a less conservative approach to disease management.

Biocontrol of Botrytis cinerea on Grape Berries as Influenced by Temperature and Humidity

Six commercial biocontrol agents (BCAs, containing Aureobasidium pullulans, Bacillus amyloliquefaciens, Bacillus amyloliquefaciens plantarum, Bacillus subtilis, Pythium oligandrum, or Trichoderma atroviride) were applied to ripening berries that were then incubated at one of four temperatures (T, 15, 20, 25, and 30°C) and one of four relative humidity levels (RH, 60, 80, 90, and 100%). After 1 to 13 days of incubation (BCA colonization period), the berries were inoculated with conidia of Botrytis cinerea and kept at 25°C and 100% RH for 7 days, at which time Botrytis bunch rot (BBR) was assessed. The response of BBR control to T/RH conditions and BCA colonization period differed among BCAs; the coefficients of variation among the BCAs ranged from 44.7 to 72.4%. An equation was developed that accounted for the combined effects of T, RH, and BCA colonization period on BBR control. The equation, which had an R²>0.94, could help farmers select the BCA to be used for a specific application based on weather conditions at the time of treatment and in the following days.

Cooperation and Coordination in Plant Disease Management

Scaling of management efforts beyond the boundaries of individual farms may require that individuals act collectively. Such approaches have been suggested several times in plant pathology contexts but rarely have been implemented, in part because the institutional structures that enable successful collective action are poorly understood. In this research, we conducted in-depth interviews with hop producers in Oregon and Washington State to identify their motivations for and barriers to collective action regarding communication of disease levels, coordination of management practices, and sharing of best management practices and other data for powdery mildew (caused by Podosphaera macularis). Growers were generally open to and engaged in communication with neighbors and others on disease status in their hop yards and some evidence of higher levels of information sharing on management practices was found. However, growers who had developed extensive knowledge and databases were reluctant to share information viewed as proprietary. Relationships, trust, and reciprocity were facilitating factors for communication and information sharing, whereas lack of these factors and social norms of independence and pride in portions of the grower community were identified as impediments. Given the heterogeneity of trust, lack of confidence in reciprocity, and weak shared norms, communication of disease risk and coordinated management may be most successful if directed at a smaller scale as a series of neighborhood-based partnerships of growers and their immediate neighbors. Developing a disease reporting system and coordinated disease management efforts with more producers and at larger spatial extents would require formalized structures and rules that would provide assurance that there is consistency in disease data collection and reporting, reciprocation, and sanctions for those who use the information for marketing purposes against other growers. Given the analyses presented here, we believe there is potential for collective action in disease management but with limitations on the scope and nature of the actions.

A Threshold-Based Decision-Support System for Fungicide Applications Provides Cost-Effective Control of Citrus Postbloom Fruit Drop

Postbloom fruit drop (PFD) of citrus, caused by Colletotrichum acutatum sensu lato and C. gloeosporioides sensu lato, is an important disease in the humid tropics of the American continent. PFD mainly affects flowers, on which typical symptoms are characterized by orange-brown lesions with presence of acervuli. The disease has a sporadic occurrence, but preventative fungicide sprays are applied every season. The objective of this study was to evaluate the effectiveness of a fungicide spray strategy for PFD based on a predictive model of C. acutatum conidium germination linked to weather conditions. Fungicide sprays were performed when the model predicted pre-established thresholds of 10, 15, 20, and 25% of germinated spores (T10, T15, T20, and T25, respectively). Five experiments were conducted in two different seasons in the state of São Paulo, Brazil. PFD control efficacy of the threshold-based treatments was compared with a nontreated control and to a calendar-based spray system. Additionally, an economic analysis was performed to assess the gross income revenues of the fungicide spraying strategies. Disease control in plots treated at T10, T15, and T20 was as effective as the calendar-based strategy. The number of fungicide applications was reduced by 33 to 71% when sprays were applied at T15 and T20, and gross income increased or was comparable to that of the other treatments. Therefore, using a conidium germination model with a threshold of 15 or 20% is recommended as a spraying strategy for PFD management in Brazil.

Prediction of Spread and Regional Development of Hop Powdery Mildew: A Network Analysis

Dispersal is a fundamental aspect of epidemic development at multiple spatial scales, including those that extend beyond the borders of individual fields and to the landscape level. In this research, we used the powdery mildew of the hop pathosystem (caused by Podosphaera macularis) to formulate a model of pathogen dispersal during spring (May to June) and early summer (June to July) at the intermediate scale between synoptic weather systems and microclimate (mesoscale) based on a census of commercial hop yards during 2014 to 2017 in a production region in western Oregon. This pathosystem is characterized by a low level of overwintering of the pathogen as a result of absence of the ascigerious stage of the fungus and consequent annual cycles of localized survival via bud perennation and pathogen spread by windborne dispersal. An individual hop yard was considered a node in the model, whose disease status in a given month was expressed as a nonlinear function of disease incidence in the preceding month, susceptibility to two races of the fungus, and disease spread from other nodes as influenced by their disease incidence, area, distance away, and wind run and direction in the preceding month. Parameters were estimated by maximum likelihood over all 4 years but were allowed to vary for time transition periods from May to June and from June to July. The model accounted for 34 to 90% of the observed variation in disease incidence at the field level, depending on the year and season. Network graphs and analyses suggest that dispersal was dominated by relatively localized dispersal events (<2 km) among the network of fields, being mostly restricted to the same or adjacent farms. When formed, predicted disease attributable to dispersal from other hop yards (edges) associated with longer distance dispersal was more frequent in the June to July time transition. Edges with a high probability of disease transmission were formed in instances where yards were in close proximity or where disease incidence was relatively high in large hop yards, as moderated by wind run. The modeling approach provides a flexible and generalizable framework for understanding and predicting pathogen dispersal at the regional level as well as the implications of network connectivity on epidemic development.

Spinach Downy Mildew: Advances in Our Understanding of the Disease Cycle and Prospects for Disease Management

Downy mildew on spinach is caused by Peronospora effusa, an oomycete pathogen that poses a challenge to spinach production worldwide, especially in organic production. Following infection, P. effusa produces abundant amounts of asexual sporangia. Sporangia become windborne and initiate new infections locally or distantly, leading to widespread epidemics. Oospores produced from the union of opposite mating types have been observed within infected leaves and seeds and may remain viable for many years. Sexual reproduction increases the genetic diversity of P. effusa through sexual recombination, and thus, the movement of oospores on seed has likely fueled the rapid explosion of new pathotypes in different regions of the world over the past 20 years. This review summarizes recent advances in spinach downy mildew research, especially in light of the findings of oospores in contemporary commercial spinach seed lots as well as their germination. Knowledge of the role of the oospores and other aspects of the disease cycle can directly translate into new and effective disease management strategies.

Pesticides and pollinators: A socioecological synthesis

The relationship between pesticides and pollinators, while attracting no shortage of attention from scientists, regulators, and the public, has proven resistant to scientific synthesis and fractious in matters of policy and public opinion. This is in part because the issue has been approached in a compartmentalized and intradisciplinary way, such that evaluations of organismal pesticide effects remain largely disjoint from their upstream drivers and downstream consequences. Here, we present a socioecological framework designed to synthesize the pesticide-pollinator system and inform future scholarship and action. Our framework consists of three interlocking domains-pesticide use, pesticide exposure, and pesticide effects-each consisting of causally linked patterns, processes, and states. We elaborate each of these domains and their linkages, reviewing relevant literature and providing empirical case studies. We then propose guidelines for future pesticide-pollinator scholarship and action agenda aimed at strengthening knowledge in neglected domains and integrating knowledge across domains to provide decision support for stakeholders and policymakers. Specifically, we emphasize (1) stakeholder engagement, (2) mechanistic study of pesticide exposure, (3) understanding the propagation of pesticide effects across levels of organization, and (4) full-cost accounting of the externalities of pesticide use and regulation. Addressing these items will require transdisciplinary collaborations within and beyond the scientific community, including the expertise of farmers, agrochemical developers, and policymakers in an extended peer community.

Automated Analysis of Aggregated Datasets to Identify Climatic Predictors of Botrytis Bunch Rot in Wine Grapes

Botrytis bunch rot (BBR), caused by Botrytis cinerea, results in serious losses to wine-grape production in some seasons during the preharvest period. In order to predict seasons that are at risk from BBR, datasets consisting of 25 disease, weather and vine phenology variables were aggregated from 101 SiteYears across seven regions and nine growing seasons. Automated analyses were used to compare a range of statistical methods for their ability to predict BBR epidemics, including the Kruskal-Wallis test, logistic regression, receiver operating characteristic analysis, and skill-scores. Variables based on relative humidity and surface-wetness duration were significant and consistent predictors of BBR epidemics across the range of analyses applied. Variables integrating temperature and wetness duration, including the Bacchus and Broome models, also demonstrated high predictive ability; however, they did not outperform their constituent components in all analyses. Automation of data analyses was an effective way to compare a wide range of statistical methods and a large number of variables with minimal user input, following initial code development. Significant time was needed to check input data and software code, but a greater return on investment would occur should the analytical process be applied to new datasets, including those from other pathosystems.

Risk Factors for Bud Perennation of Podosphaera macularis on Hop

The hop powdery mildew fungus Podosphaera macularis persists from season to season in the Pacific Northwestern United States through infection of crown buds because only one of the mating types needed to produce the ascigerous stage is presently found in this region. Bud infection and successful overwintering of the fungus leads to the emergence of heavily infected shoots in early spring (termed flag shoots). Historical data of flag shoot occurrence and incidence in Oregon and Washington State during 2000 to 2017 were analyzed to identify their association with the incidence of powdery mildew, growers' use of fungicides, autumn and winter temperature, and other production factors. During this period, flag shoots were found on 0.05% of plants evaluated in Oregon and 0.57% in Washington. In Oregon, the incidence of powdery mildew on leaves was most severe and the number of fungicide applications made by growers greatest in yards where flag shoots were found in spring. Similarly, the incidence of plants with powdery mildew in Washington was significantly associated with the number of flag shoots present in early spring, although the number of fungicide applications made was independent of flag shoot occurrence. The occurrence of flag shoots was associated with prior occurrence of flag shoots in a yard, the incidence of foliar powdery mildew in the previous year, grower pruning method, and, in Washington, winter temperature. A census of hop yards in the eastern extent of the Oregon production region during 2014 to 2017 found flag shoots in 27 of 489 yards evaluated. In yards without flag shoots, 338 yards (73.2%) were chemically pruning or not pruned, whereas the remaining 124 (26.8%) were mechanically pruned. Of the 27 yards with flag shoots, 22 were either chemically pruned or not pruned and 4 were mechanically pruned in mid-April, well after the initial emergence of flag shoots. The prevalence of yards with flag shoots also was related to thoroughness of pruning in spring (8.1% of yards with incomplete pruning versus 1.9% of yards with thorough pruning). A Bayesian logistic regression model was fit to the data from the intensively assessed yards in Oregon, with binary risk factors for occurrence of a flag shoot in the previous year, occurrence of foliar mildew in the previous year, and thoroughness of pruning in spring. The model indicated that the median and 95% highest posterior density interval of the probability of flag shoot occurrence was 0.0008 (0.0000 to 0.0053) when a yard had no risk factors but risk increased to 0.0065 (0.0000 to 0.0283) to 0.43 (0.175 to 0.709) when one to all three of the risk factors were present. The entirety of this research indicates that P. macularis appears to persist in a subset of chronically affected hop yards, particularly yards where spring pruning is conducted poorly. Targeted management of the disease in a subset of fields most at risk for producing flag shoots could potentially influence powdery mildew development regionwide.

Functional Data Analysis of Weather Variables Linked to Fusarium Head Blight Epidemics in the United States

In past efforts, input weather variables for Fusarium head blight (FHB) prediction models in the United States were identified after following some version of the window-pane algorithm, which discretizes a continuous weather time series into fixed-length windows before searching for summary variables associated with FHB risk. Functional data analysis, on the other hand, reconstructs the assumed continuous process (represented by a series of recorded weather data) by using smoothing functions, and is an alternative way of working with time series data with respect to FHB risk. Our objective was to functionally model weather-based time series data linked to 865 observations of FHB (covering 16 states and 31 years in total), classified as epidemics (FHB disease index ≥ 10%) and nonepidemics (FHB disease index < 10%). Altogether, 94 different time series variables were modeled by penalized cubic B-splines for the smoothing function, from 120 days pre-anthesis to 20 days post-anthesis. Functional mean curves, standard deviations, and first derivatives were plotted for FHB epidemics relative to nonepidemics. Function-on-scalar regressions assessed the temporal trends of the magnitude and significance of the mean difference between functionally represented weather time series associated with FHB epidemics and nonepidemics. The mean functional weather-variable curve for epidemics started to deviate, in general, from that for nonepidemics as early as 40 days pre-anthesis for several weather variables. The greatest deviations were often near anthesis, the period of maximum susceptibility of wheat to FHB-causing fungi. The most consistent separations between the mean functional curves were seen with the daily averages of moisture-related variables (such as average relative humidity) and with variables summarizing the daily variation in temperature (as opposed to the daily mean). Functional data analysis was useful for extending our knowledge of relationships between weather variables and FHB epidemics.

Using Epidemiological Principles to Explain Fungicide Resistance Management Tactics: Why do Mixtures Outperform Alternations?

Whether fungicide resistance management is optimized by spraying chemicals with different modes of action as a mixture (i.e., simultaneously) or in alternation (i.e., sequentially) has been studied by experimenters and modelers for decades. However, results have been inconclusive. We use previously parameterized and validated mathematical models of wheat Septoria leaf blotch and grapevine powdery mildew to test which tactic provides better resistance management, using the total yield before resistance causes disease control to become economically ineffective ("lifetime yield") to measure effectiveness. We focus on tactics involving the combination of a low-risk and a high-risk fungicide, and the case in which resistance to the high-risk chemical is complete (i.e., in which there is no partial resistance). Lifetime yield is then optimized by spraying as much low-risk fungicide as is permitted, combined with slightly more high-risk fungicide than needed for acceptable initial disease control, applying these fungicides as a mixture. That mixture rather than alternation gives better performance is invariant to model parameterization and structure, as well as the pathosystem in question. However, if comparison focuses on other metrics, e.g., lifetime yield at full label dose, either mixture or alternation can be optimal. Our work shows how epidemiological principles can explain the evolution of fungicide resistance, and also highlights a theoretical framework to address the question of whether mixture or alternation provides better resistance management. It also demonstrates that precisely how spray tactics are compared must be given careful consideration. [Formula: see text] Copyright © 2018 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Evaluation of a Model for Predicting the Infection Risk of Squash and Cantaloupe by Pseudoperonospora cubensis

Infection risk models of downy mildew of cucumber caused by Pseudoperonospora cubensis were evaluated for their performance in predicting the infection risk of squash and cantaloupe plants under field conditions. Experiments were conducted from 2012 to 2014 in Clayton, NC and Charleston, SC, where disease-free potted plants were exposed to weather conditions during a 24- and 48-h period (hereafter 24- and 48-h models, respectively) within a plot with naturally occurring inoculum. Exposed plants were subsequently placed in a growth chamber where they were monitored for disease symptoms, which was indicative of a successful infection. Disease severity was assessed after 7 days as the proportion of leaf area with disease symptoms. Two predictor variables, day temperature and hours of relative humidity >80% during each exposure were used as inputs to generate model predictions that were compared with observed data. The threshold probability on the receiver operating characteristic (ROC) curve that minimized the overall error rate for the 24-h model was 0.85 for both squash and cantaloupe. The 24-h model was consistently more accurate than the 48-h model in predicting the infection risk for the two hosts. The accuracy of the 24-h model as estimated using area under ROC curve ranged from 0.75 to 0.81, with a correct classification rate ranging from 0.69 to 0.74 across the two hosts. Specificity rates for the model ranged from 0.81 to 0.84, while the sensitivity rates ranged from 0.58 to 0.67. Optimal decisions thresholds (P_OT) developed based on estimates of economic damage and costs of management showed that P_OT was dependent on the probability of disease occurrence, with the benefit of using the 24-h model for making management decisions being greatest at low levels of probability of disease occurrence. This 24-h model, previously developed using cucumber as the host, resulted in accurate estimates of the daily infection risk of squash and cantaloupe and could potentially be useful when incorporated into a decision support tool to guide fungicide applications to manage downy mildew in these other cucurbit host types.

Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model

Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States.

Meta-Analysis of a Web-Based Disease Forecast System for Control of Anthracnose and Botrytis Fruit Rots of Strawberry in Southeastern United States

Strawberry production in Florida and South Carolina is affected by two major diseases, anthracnose fruit rot (AFR) and Botrytis fruit rot (BFR), caused by Colletotrichum acutatum and Botrytis cinerea, respectively. The effective management of both diseases traditionally relied on weekly fungicide applications. However, to improve timing and reduce the number of fungicide sprays, many growers follow the Strawberry Advisory System (StAS), a decision support system for forecasting fungicide applications based on environmental conditions and previously developed models. The objective of this study was to perform a meta-analysis to determine the effectiveness of the StAS for AFR and BFR management compared with a calendar-based spray program. Thirty-nine trials were conducted from 2009 to 2014 in Florida and South Carolina commercial strawberry fields. Meta-analysis was conducted to quantify the treatment effects on four effect sizes, all based on the difference in response variables for StAS and the calendar-based treatments in each trial. The mean difference in BFR incidence, AFR incidence, yield, and number of marketable fruit between the two treatments was not significantly different from 0 (P < 0.05). However, the number of fungicide applications per season was reduced by a median of seven when using the StAS, a 50% reduction in sprays compared with the calendar-based approach. Effect sizes were not influenced by location or the favorability of the environment for disease development. These findings indicate that use of StAS in commercial fields is effective in controlling fruit rot diseases with no reduction in yield while substantially reducing fungicide applications.

Evaluation of Probabilistic Disease Forecasts

The statistical evaluation of probabilistic disease forecasts often involves calculation of metrics defined conditionally on disease status, such as sensitivity and specificity. However, for the purpose of disease management decision making, metrics defined conditionally on the result of the forecast-predictive values-are also important, although less frequently reported. In this context, the application of scoring rules in the evaluation of probabilistic disease forecasts is discussed. An index of separation with application in the evaluation of probabilistic disease forecasts, described in the clinical literature, is also considered and its relation to scoring rules illustrated. Scoring rules provide a principled basis for the evaluation of probabilistic forecasts used in plant disease management. In particular, the decomposition of scoring rules into interpretable components is an advantageous feature of their application in the evaluation of disease forecasts.

The Evidential Basis of Decision Making in Plant Disease Management

The evidential basis for disease management decision making is provided by data relating to risk factors. The decision process involves an assessment of the evidence leading to taking (or refraining from) action on the basis of a prediction. The primary objective of the decision process is to identify-at the time the decision is made-the control action that provides the best predicted end-of-season outcome, calculated in terms of revenue or another appropriate metric. Data relating to disease risk factors may take a variety of forms (e.g., continuous, discrete, categorical) on measurement scales in a variety of units. Log₁₀-likelihood ratios provide a principled basis for the accumulation of evidence based on such data and allow predictions to be made via Bayesian updating of prior probabilities.

Ecology and Genomic Insights into Plant-Pathogenic and Plant-Nonpathogenic Endophytes

Plants are colonized on their surfaces and in the rhizosphere and phyllosphere by a multitude of different microorganisms and are inhabited internally by endophytes. Most endophytes act as commensals without any known effect on their plant host, but multiple bacteria and fungi establish a mutualistic relationship with plants, and some act as pathogens. The outcome of these plant-microbe interactions depends on biotic and abiotic environmental factors and on the genotype of the host and the interacting microorganism. In addition, endophytic microbiota and the manifold interactions between members, including pathogens, have a profound influence on the function of the system plant and the development of pathobiomes. In this review, we elaborate on the differences and similarities between nonpathogenic and pathogenic endophytes in terms of host plant response, colonization strategy, and genome content. We furthermore discuss environmental effects and biotic interactions within plant microbiota that influence pathogenesis and the pathobiome.

Preventative Disease Management and Grower Decision Making: A Case Study of California Wine-Grape Growers

Preventative disease management is challenging to farmers because it requires paying immediate costs in the hopes of returning uncertain future benefits. Understanding farmer decision making about prevention has the potential to reduce disease incidence and minimize the need for more costly postinfection practices. For example, the grapevine trunk-disease complex (esca, Botryosphaeria dieback, Eutypa dieback, and Phomopsis dieback) significantly affects vineyard productivity and longevity. Given the chronic nature of the infections and inability to eradicate the fungal pathogens, the preventative practices of delayed pruning, applications of pruning-wound protectants, and double pruning (also known as prepruning) are the most effective means of management. We surveyed wine-grape growers in six regions of California on their use of these three practices. In spite of acknowledging the yield impacts of trunk diseases, a substantial number of respondents either choose not to use preventative practices or incorrectly adopted them in mature vineyards, too late in the disease cycle to be effective. Growers with more negative perceptions of cost efficacy were less likely to adopt preventative practices or were more likely to time adoption incorrectly in mature vineyards. In general, preventative management may require strong intervention in the form of policy or extension to motivate behavioral change.

Resolution of Probabilistic Weather Forecasts with Application in Disease Management

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

Season-Long Dynamics of Spinach Downy Mildew Determined by Spore Trapping and Disease Incidence

Peronospora effusa is an obligate oomycete that causes downy mildew of spinach. Downy mildew threatens sustainable production of fresh market organic spinach in California, and routine fungicide sprays are often necessary for conventional production. In this study, airborne P. effusa spores were collected using rotating arm impaction spore trap samplers at four sites in the Salinas Valley between late January and early June in 2013 and 2014. Levels of P. effusa DNA were determined by a species-specific quantitative polymerase chain reaction assay. Peronospora effusa was detected prior to and during the growing season in both years. Nonlinear time series analyses on the data suggested that the within-season dynamics of P. effusa airborne inoculum are characterized by a mixture of chaotic, deterministic, and stochastic features, with successive data points somewhat predictable from the previous values in the series. Analyses of concentrations of airborne P. effusa suggest both an exponential increase in concentration over the course of the season and oscillations around the increasing average value that had season-specific periodicity around 30, 45, and 75 days, values that are close to whole multiples of the combined pathogen latent and infectious periods. Each unit increase in temperature was correlated with 1.7 to 6% increased odds of an increase in DNA copy numbers, while each unit decrease in wind speed was correlated with 4 to 12.7% increased odds of an increase in DNA copy numbers. Disease incidence was correlated with airborne P. effusa levels and weather variables, and a receiver operating characteristic curve analysis suggested that P. effusa DNA copy numbers determined from the spore traps nine days prior to disease rating could predict disease incidence.

Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models

Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB), caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum). The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR) and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF), in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors) collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early assessment of the risk of SNB, facilitating sound disease management decisions prior to planting of wheat.

A Mechanistic Model of Botrytis cinerea on Grapevines That Includes Weather, Vine Growth Stage, and the Main Infection Pathways

A mechanistic model for Botrytis cinerea on grapevine was developed. The model, which accounts for conidia production on various inoculum sources and for multiple infection pathways, considers two infection periods. During the first period ("inflorescences clearly visible" to "berries groat-sized"), the model calculates: i) infection severity on inflorescences and young clusters caused by conidia (SEV1). During the second period ("majority of berries touching" to "berries ripe for harvest"), the model calculates: ii) infection severity of ripening berries by conidia (SEV2); and iii) severity of berry-to-berry infection caused by mycelium (SEV3). The model was validated in 21 epidemics (vineyard × year combinations) between 2009 and 2014 in Italy and France. A discriminant function analysis (DFA) was used to: i) evaluate the ability of the model to predict mild, intermediate, and severe epidemics; and ii) assess how SEV1, SEV2, and SEV3 contribute to epidemics. The model correctly classified the severity of 17 of 21 epidemics. Results from DFA were also used to calculate the daily probabilities that an ongoing epidemic would be mild, intermediate, or severe. SEV1 was the most influential variable in discriminating between mild and intermediate epidemics, whereas SEV2 and SEV3 were relevant for discriminating between intermediate and severe epidemics. The model represents an improvement of previous B. cinerea models in viticulture and could be useful for making decisions about Botrytis bunch rot control.

Integrating Experience, Evidence and Expertise in the Crop Protection Decision Process

Generically, farm-scale crop protection decision making may be characterized as a process beginning with an initial assessment of disease risk followed by the accumulation of evidence related to current risk factors, leading to a risk prediction. What action is then taken depends on the response of the decision owner, taking into account previous experience, advice from trusted sources, alongside policy or legislative constraints on crop protection practice that are intended to mitigate any impacts that may transcend the farm scale. This process has commonalities with decision-making in the strategy of preventive medicine. This article delves into the clinical literature in order to provide a perspective on some recent discussions of shared decision making presented there, discussions that relate to issues also faced in sustainable crop protection.

Pre- and Postinfection Activity of Fungicides in Control of Hop Downy Mildew

Optimum timing and use of fungicides for disease control are improved by an understanding of the characteristics of fungicide physical mode of action. Greenhouse and field experiments were conducted to quantify and model the duration of pre- and postinfection activity of fungicides most commonly used for control of hop downy mildew (caused by Pseudoperonospora humuli). In greenhouse experiments, control of downy mildew on leaves was similar among fungicides tested when applied preventatively but varied depending on both the fungicide and the timing of application postinfection. Disease control decreased as applications of copper were made later after inoculation. In contrast, cymoxanil, trifloxystrobin, and dimethomorph reduced disease with similar efficacy when applied 48 h after inoculation compared with preventative applications of these fungicides. When fungicides were applied 72 h after inoculation, only dimethomorph reduced the sporulating leaf area similarly to preinoculation application timing. Adaxial chlorosis, necrosis, and water soaking of inoculated leaves, indicative of infection by P. humuli, were more severe when plants were treated with cymoxanil, trifloxystrobin, and dimethomorph 48 to 72 h after inoculation, even though sporulation was suppressed. Trifloxystrobin and dimethomorph applied 72 h after inoculation suppressed formation of sporangia on sporangiophores as compared with all other treatments. In field studies, dimethomorph, fosetyl-Al, and trifloxystrobin suppressed development of shoots with systemic downy mildew to the greatest extent when applied near the timing of inoculation, although the duration of preventative and postinfection activity varied among the fungicides. There was a small reduction in efficacy of disease control when fosetyl-Al was applied 6 to 7 days after inoculation as compared with protective applications. Trifloxystrobin had 4 to 5 days of preinfection activity and limited postinfection activity. Dimethomorph had the longest duration of protective activity. Percent disease control was reduced progressively with increasing time between inoculation and application of dimethomorph. These findings provide guidance to the use of fungicides when applications are timed with forecasted or post hoc disease hazard warnings, as well as guidance on tank-mixes of fungicides that may be suitable both for resistance management considerations and extending intervals between applications.

Epidemiology and population biology of Pseudoperonospora cubensis: a model system for management of downy mildews

The resurgence of cucurbit downy mildew has dramatically influenced production of cucurbits and disease management systems at multiple scales. Long-distance dispersal is a fundamental aspect of epidemic development that influences the timing and extent of outbreaks of cucurbit downy mildew. The dispersal potential of Pseudoperonospora cubensis appears to be limited primarily by sporangia production in source fields and availability of susceptible hosts and less by sporangia survival during transport. Uncertainty remains regarding the role of locally produced inoculum in disease outbreaks, but evidence suggests multiple sources of primary inoculum could be important. Understanding pathogen diversity and population differentiation is a critical aspect of disease management and an active research area. Underpinning advances in our understanding of pathogen biology and disease management has been the research capacity and coordination of stakeholders, scientists, and extension personnel. Concepts and approaches developed in this pathosystem can guide future efforts when responding to incursions of new or reemerging downy mildew pathogens.

Playing on a pathogen's weakness: using evolution to guide sustainable plant disease control strategies

Wild plants and their associated pathogens are involved in ongoing interactions over millennia that have been modified by coevolutionary processes to limit the spatial extent and temporal duration of disease epidemics. These interactions are disrupted by modern agricultural practices and social activities, such as intensified monoculture using superior varieties and international trading of agricultural commodities. These activities, when supplemented with high resource inputs and the broad application of agrochemicals, create conditions uniquely conducive to widespread plant disease epidemics and rapid pathogen evolution. To be effective and durable, sustainable disease management requires a significant shift in emphasis to overtly include ecoevolutionary principles in the design of adaptive management programs aimed at minimizing the evolutionary potential of plant pathogens by reducing their genetic variation, stabilizing their evolutionary dynamics, and preventing dissemination of pathogen variants carrying new infectivity or resistance to agrochemicals.

A simulation model for epidemics of stem rust in ryegrass seed crops

A simulation model (STEMRUST_G, named for stem rust of grasses) was created for stem rust (caused by Puccinia graminis subsp. graminicola) in perennial ryegrass grown to maturity as a seed crop. The model has a daily time step and is driven by weather data and an initial input of disease severity from field observation. Key aspects of plant growth are modeled. Disease severity is modeled as rust population growth, where individuals are pathogen colonies (pustules) grouped in cohorts defined by date of initiation and plant part infected. Infections due to either aerial spread or within-plant contact spread are modeled. Pathogen cohorts progress through life stages that are modeled as disease cycle components (colony establishment, latent period, infectious period, and sporulation) affected by daily weather variables, plant growth, and fungicide application. Fungicide effects on disease cycle components are modeled for two commonly used active ingredients, applied preinfection or postinfection. Previously validated submodels for certain disease cycle components formed the framework for integrating additional processes, and the complete model was calibrated with field data from 10 stem rust epidemics. Discrepancies between modeled outcomes and the calibration data (log10[modeled]-log10[observed]) had a mean near zero but considerable variance, with 1 standard deviation=0.5 log10 units (3.2-fold). It appears that a large proportion of the modeling error variance may be due to variability in field observations of disease severity. An action threshold for fungicide application was derived empirically, using a constructed weather input file favorable for disease development. The action threshold is a negative threshold, representing a level of disease (latent plus visible) below which damaging levels of disease are unable to develop before the yield-critical crop stage. The model is in the public domain and available on the Internet.

Evaluation of the Ryegrass Stem Rust Model STEMRUST_G and Its Implementation as a Decision Aid

STEMRUST_G, a simulation model for epidemics of stem rust in perennial ryegrass grown to maturity as a seed crop, was validated for use as a heuristic tool and as a decision aid for disease management with fungicides. Multistage validation had been used in model creation by incorporating previously validated submodels for infection, latent period duration, sporulation, fungicide effects, and plant growth. Validation of the complete model was by comparison of model output with observed disease severities in 35 epidemics at nine location-years in the Pacific Northwest of the United States. We judge the model acceptable for its purposes, based on several tests. Graphs of modeled disease progress were generally congruent with plotted disease severity observations. There was negligible average bias in the 570 modeled-versus-observed comparisons across all data, although there was large variance in size of the deviances. Modeled severities were accurate in >80% of the comparisons, where accuracy is defined as the modeled value being within twice the 95% confidence interval of the observed value, within ±1 day of the observation date. An interactive website was created to produce disease estimates by running STEMRUST_G with user-supplied disease scouting information and automated daily weather data inputs from field sites. The model and decision aid supplement disease managers' information by estimating the level of latent (invisible) and expressed disease since the last scouting observation, given season-long weather conditions up to the present, and it estimates effects of fungicides on epidemic development. In additional large-plot experiments conducted in grower fields, the decision aid produced disease management outcomes (management cost and seed yield) as good as or better than the growers' standard practice. In future, STEMRUST_G could be modified to create similar models and decision aids for stem rust of wheat and barley, after additional experiments to determine appropriate parameters for the disease in these small-grain hosts.

Coupling Spore Traps and Quantitative PCR Assays for Detection of the Downy Mildew Pathogens of Spinach (Peronospora effusa) and Beet (P. schachtii)

Downy mildew of spinach (Spinacia oleracea), caused by Peronospora effusa, is a production constraint on production worldwide, including in California, where the majority of U.S. spinach is grown. The aim of this study was to develop a real-time quantitative polymerase chain reaction (qPCR) assay for detection of airborne inoculum of P. effusa in California. Among oomycete ribosomal DNA (rDNA) sequences examined for assay development, the highest nucleotide sequence identity was observed between rDNA sequences of P. effusa and P. schachtii, the cause of downy mildew on sugar beet and Swiss chard in the leaf beet group (Beta vulgaris subsp. vulgaris). Single-nucleotide polymorphisms were detected between P. effusa and P. schachtii in the 18S rDNA regions for design of P. effusa- and P. schachtii-specific TaqMan probes and reverse primers. An allele-specific probe and primer amplification method was applied to determine the frequency of both P. effusa and P. schachtii rDNA target sequences in pooled DNA samples, enabling quantification of rDNA of P. effusa from impaction spore trap samples collected from spinach production fields. The rDNA copy numbers of P. effusa were, on average, ≈3,300-fold higher from trap samples collected near an infected field compared with those levels recorded at a site without a nearby spinach field. In combination with disease-conducive weather forecasting, application of the assays may be helpful to time fungicide applications for disease management.

Development and validation of a weather-based model for predicting infection of loquat fruit by Fusicladium eriobotryae

A mechanistic, dynamic model was developed to predict infection of loquat fruit by conidia of Fusicladium eriobotryae, the causal agent of loquat scab. The model simulates scab infection periods and their severity through the sub-processes of spore dispersal, infection, and latency (i.e., the state variables); change from one state to the following one depends on environmental conditions and on processes described by mathematical equations. Equations were developed using published data on F. eriobotryae mycelium growth, conidial germination, infection, and conidial dispersion pattern. The model was then validated by comparing model output with three independent data sets. The model accurately predicts the occurrence and severity of infection periods as well as the progress of loquat scab incidence on fruit (with concordance correlation coefficients >0.95). Model output agreed with expert assessment of the disease severity in seven loquat-growing seasons. Use of the model for scheduling fungicide applications in loquat orchards may help optimise scab management and reduce fungicide applications.

Concepts of Sustainability, Motivations for Pest Management Approaches, and Implications for Communicating Change

Impact and relevance are valued by both plant pathologists and the supporters of research and extension. Impact has been characterized as the "So what?" of research results, and in applied research in agriculture typically involves some change in human behavior. This might involve, for instance, avoidance of broad spectrum pesticides, use of economic thresholds, or adoption of a new cultural practice in disease management. Changes in human behavior often are slow and difficult, even when the potential benefits of change seem clear. Research and extension personnel working with farmers have discussed for decades the apparent slow pace of adoption of integrated pest management (IPM) and other less-pesticide-intensive management practices. The reasons why change is slow are numerous, but one aspect that warrants consideration is how changes in farm practices are communicated to farmers. Effectively communicating changes in pest management practices at the farm level requires a system of research and extension management that differs from that to which most biological scientists are accustomed. What is the motivation for farmers to deviate from historical practices? How persuasive are concepts of environmental sustainability, integrated pest management, risk management, and economic gain in communicating the needs for change? In addressing these questions, it is useful to understand some of the basic determinants of farmers' decision processes and motivations to adopt practices. This article discusses these issues.

Fifty years since Silent Spring

Rachel Carson's 1962 Silent Spring exposed both observed and potential environmental and health externalities of the increasing organochlorine and organophosphate insecticide use in the United States post-World War II. Silent Spring was a critical component in a popular movement that resulted in increased regulation and the development of safer pesticides. Most changes in pesticide use in the global north have involved pesticide substitutions, although riskier pesticides remain in use. Many ideas in Silent Spring are compatible with the theory of integrated pest management (IPM), and IPM has been broadly embraced in the United States and internationally as a strategy for achieving least-use and/or least-risk pesticide use in agriculture. IPM is a politically feasible policy that purports to reduce pesticide use and/or risk in agriculture but often does not, except in extreme cases of pesticide overuse that result in negative agricultural/economic consequences for growers.