|Gent, David - Dave|
|FARNSWORTH, JOANNA - Oregon State University|
|JOHNSON, D - Washington State University|
Submitted to: Plant Pathology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/31/2010
Publication Date: 6/22/2011
Citation: Gent, D.H., Farnsworth, J.L., Johnson, D.A. 2011. Spatial analysis and incidence-density relationships for downy mildew on hop. Plant Pathology. DOI:10.1111/j.1365-3059.2011.02491.x.
Interpretive Summary: Disease sampling is an important aspect of making informed management decisions, but sampling can be time consuming and cost prohibitive. In this research we described the patterns of hop shoots affected by downy mildew and showed that the number of diseased shoots was related to the number of plants with the disease. The disease was highly aggregated on individual plants, although aggregation among plants was less common. Under conditions where sampling would be most valuable for disease management, the relationships between disease density and disease incidence described in this research will be helpful for developing appropriate sampling strategies for downy mildew.
Technical Abstract: Sampling often can be expedited by noting only the presence of disease symptoms without quantifying the level of disease in what is termed “binomial sampling”. To this end, the spatial pattern of downy mildew on hop (Pseudoperonospora humuli) was characterized over four years to aid in deriving an appropriate incidence-density relationship. From 472 disease assessments (data sets), various discrete distributions were fit to the data sets to determine aggregation of disease density. Where these distributions were able to be fit, the Poisson distribution fit 4% of the data sets and the negative binomial distribution fit 87% of the data sets. 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 indicating disease density was aggregated and related to mean disease density in all years. Disease incidence and density were related by saturation-type relationships based on the zero term of the negative binomial distribution or an empirical regression. Certain individual data sets were not described well by any incidence-density model, particularly when disease density was greater than about 0.8 diseased shoots per plant with the cultivar Cascade. When applied to 56 validation data sets, 88% of the variation in observed disease incidence was explained by the incidence-density models. Under conditions where sampling would be most valuable for disease management, the requisite conditions appear to be in place for development and use of a binomial sampling plan for downy mildew.