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.

Powdery mildew of hop (Humulus lupus L.), which is caused by Podosphaera macularis (formerly Sphaerotheca macularis) was found in the Yakima Valley, WA in 1996 and subsequently spread to the growing regions in Oregon and northern and southern Idaho. To rapidly assist growers in reducing the cost associated with the preventive fungicide program, the Gubler/Thomas grape powdery mildew risk infection model was adapted for hops. In addition, field surveys were utilized to identify other management practices that impacted disease development. Weather networks were established and utilized to deliver daily regional maps indicating the risk index. These maps were posted to the web for daily access. Lessons learned from this experience will be useful in addressing future pathogen introductions. Accepted for publication 28 March 2003. Published 13 November 2003.

Many disease management decision support systems (DSSs) rely, exclusively or in part, on weather inputs to calculate an indicator for disease hazard. Error in the weather inputs, typically due to forecasting, interpolation, or estimation from off-site sources, may affect model calculations and management decision recommendations. The extent to which errors in weather inputs affect the quality of the final management outcome depends on a number of aspects of the disease management context, including whether management consists of a single dichotomous decision, or of a multi-decision process extending over the cropping season(s). Decision aids for multi-decision disease management typically are based on simple or complex algorithms of weather data which may be accumulated over several days or weeks. It is difficult to quantify accuracy of multi-decision DSSs due to temporally overlapping disease events, existence of more than one solution to optimizing the outcome, opportunities to take later recourse to modify earlier decisions, and the ongoing, complex decision process in which the DSS is only one component. One approach to assessing importance of weather input errors is to conduct an error analysis in which the DSS outcome from high-quality weather data is compared with that from weather data with various levels of bias and/or variance from the original data. We illustrate this analytical approach for two types of DSS, an infection risk index for hop powdery mildew and a simulation model for grass stem rust. Further exploration of analysis methods is needed to address problems associated with assessing uncertainty in multi-decision DSSs.

Downy mildew is the most devastating disease threatening sustainable spinach production, particularly in the organic sector. The disease is caused by the biotrophic oomycete pathogen Peronospora effusa, and the disease results in yellow lesions that render the crop unmarketable. In this study, the levels of DNA from airborne spores of P. effusa were assessed near a field of susceptible plants in Salinas, CA during the winter months of 2013-14 and 2014/15 using rotating-arm impaction spore-trap samplers that were assessed with a species-specific quantitative polymerase chain reaction (qPCR) assay. Low levels of P. effusa DNA were detectable from December through February in both winters but increased during January in both years, in correlation with observed disease incidence; sharp peaks in P. effusa DNA detection were associated with the onset of disease incidence. The incidence of downy mildew in the susceptible field displayed logistic-like dynamics but with considerable interseason variation. Analysis of the area under the disease progress curves suggested that the 2013-14 epidemic was significantly more severe than the 2014-15 epidemic. Spatial analyses indicated that disease incidence was dependent within an average range of 5.6 m, approximately equivalent to the width of three planted beds in a typical production field. The spatial distribution of spores captured during an active epidemic most closely fit a power-law distribution but could also be fit with an exponential distribution. These studies revealed two important results in the epidemiology of spinach downy mildew in California. First, they demonstrated the potential of impaction spore-trap samplers linked with a qPCR assay for indicating periods of high disease risk, as well as the detection of long-distance dispersal of P. effusa spores. Second, at the scale of individual crops, a high degree of spatial aggregation in disease incidence was revealed.