Downy mildew of hop (Humulus lupulus), caused by Pseudoperonospora humuli, is managed in the Pacific Northwestern United States by regular application of fungicides. A degree-day model that forecasts the first emergence of shoots systemically infected with P. humuli (termed basal spikes) and a risk index for secondary spread of the disease were evaluated over four seasons in western Oregon. In surveys conducted in 34 hop yards, the predicted first spike emergence occurred on average 11.6 days (median 12 days) after spike emergence using a simple average degree-day model (base temperature 6.5°C) developed for Washington State. Predictions based on a single sine model (base temperature 6°C) provided on average 4.9 days (median –0.5 days) of advanced warning before the first spike emerged. Downy mildew severity in a previous season was negatively correlated with the degree-day emergence date of spikes the following year (r = –0.39). In experimental plots, disease severity was significantly greater where fungicide applications were timed using a risk index compared to routine fungicide applications in 2005 and 2007, but statistically similar between these treatments in 2006 and 2008. However, in 2006, 2007, and 2008, treatments initiated using a degree-day threshold resulted in an area under the disease progress curve similar to or smaller than in treatments with routine fungicide applications. Model-aided treatments required four fewer fungicide applications compared to routine fungicide applications. These studies indicate that downy mildew can be managed effectively with fewer fungicide applications than currently made by hop growers in this region if fungicide applications are timed to coincide with the predicted emergence of basal spikes and subsequent disease risk forecasts.

Downy mildew of hop, caused by Pseudoperonospora humuli, is an important disease in most regions of hop production and is managed largely with regular fungicide applications. A PCR assay specific to P. humuli and the related organism P. cubensis was developed and used to monitor airborne inoculum in hop yards to initiate fungicide applications. The PCR amplified as little as 1 fg of genomic DNA of P. humuli, and yielded an amplicon in 70% of reactions when DNA was extracted from single sporangia. In the presence of 25 mg of soil, an amplicon was amplified in 90% of reactions when DNA was extracted from 10 or more sporangia. During nine location-years of validation, PCR detection of the pathogen in air samples occurred no later than 8 days after the appearance of trace levels of disease signs and ⁄ or detection of airborne spores in a volumetric spore sampler. Inoculum was detected on average 4AE5 days before (range )8 to 14 days) the first appearance of basal spikes in six commercial yards, or 1AE3 days after (range )5 to 1 days) sporangia were detected in a volumetric spore sampler in experimental plots. In commercial yards, use of PCR to initiate the first fungicide application led to enhanced disease control or a reduction in fungicide use in four of six yards compared to growers' standard practices. These results indicate that the efficiency and efficacy of hop downy mildew management can be improved when control measures are timed according to first detection of inoculum.

The spatial pattern of downy mildew (Pseudoperonospora humuli) on hop (Humulus lupulus) was characterized over 4 years to aid in deriving an appropriate incidence-density relationship. From 472 disease assessments (datasets), discrete distributions were fitted to the datasets to determine aggregation of disease density. Where distributions were able to be fitted, the Poisson distribution fitted 4% of the datasets and the negative binomial distribution fitted 87% of the datasets. Larger-scale patterns of disease were assessed by autocorrelation and runs analysis; both indicated aggregation of diseased plants was less common than aggregation of disease within plants. Taylor's power law indicated disease density was aggregated and related to mean disease density in all years. Disease incidence and density were linked by saturation-type relationships based on the zero term of the negative binomial distribution or an empirical regression. Certain individual datasets were not described well by any incidence-density model, particularly when disease density was greater than about 0AE8 diseased shoots per plant with the cultivar Cascade. When applied to 56 validation datasets, 88% of the variation in observed disease incidence was explained by the incidence-density models. Under conditions where sampling would be implemented for disease management, the requisite conditions appear to be in place for a binomial sampling plan for downy mildew.

No abstract

No abstract