Submitted to: Plant Disease
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 27, 2006
Publication Date: November 30, 2006
Citation: Gent, D.H., Mahaffee, W.F., Turechek, W. 2006. Spatial Heterogeneity of the Incidence of Powdery Mildew on Hop Cones. Plant Disease. Interpretive Summary: Powdery mildew is an important disease of hop that can reduce marketable yields of the crop by 100% during certain years. Little is known about basic elements of disease development and epidemiology as related to infection of cones, the most economically important phase of the disease. In this study, the spatial patterns of disease incidence on cones were described using methods that quantify patterns at multiple scales. The analyses suggest that the incidence of powdery mildew on cones was slightly aggregated among plants, but patterns of aggregation larger than the sampling unit were rare. Geographic location of the yards, and the type of hop cultivar had little effect on aggregation of disease, but the year of sampling significantly influenced aggregation of diseased cones. Knowledge of the spatial patterns of disease was used to construct fixed sampling curves to precisely estimate the incidence of powdery mildew on cones at varying levels of disease. Use of the sampling curves developed in this research should help to improve sampling methods for disease assessment and aid growers in making sound management decisions.
Technical Abstract: The spatial heterogeneity of the incidence of hop cones with powdery mildew (Podosphaera macularis) was characterized from transect surveys of 41 commercial hop yards in Oregon and Washington from 2000 to 2005. The proportion of sampled cones with powdery mildew (p) was recorded for each of 221 transects, where N = 60 sampling units of n = 25 cones assessed in each transect according to a cluster sampling strategy. Disease incidence ranged from 0 to 0.92 among all yards and dates. The binomial and beta-binomial frequency distributions were fit to the N sampling units in a transect using maximum likelihood. The estimation procedure converged for 74% of the data sets where p> 0, and a log likelihood ratio test indicated that the beta-binomial distribution provided a better fit to the data than the binomial distribution for 46% of the data sets, indicating an aggregated pattern of disease in these data sets. Similarly, the C (a) test indicated that 54% could be described by the beta-binomial distribution. The heterogeneity parameter of the beta-binomial distribution, 0, a measure of variation among sampling units, ranged from 0.01 to 0.20 with a mean of 0.037 and a median of 0.015. Estimates of the index of dispersion ranged from 0.79 to 7.78 with a mean of 1.81 and a median of 1.37, and were significantly greater than 1 for 54% of the data sets. The binary power law provided an excellent fit to the data, with slope and intercept parameters significantly greater than 1, which indicated that heterogeneity varied systematically with the incidence of infected cones. A covariance analysis indicated that the geographic location (region) of the yards, and the type of hop cultivar had little effect on heterogeneity, but the year of sampling significantly influenced the intercept and slope parameters of the binary power law. Significant spatial autocorrelation was detected in only 11% of the data sets, with estimates of first-order autocorrelation, r1, ranging from -0.30 to 0.70, with a mean of 0.06 and a median of 0.04, but correlation only was detected in 20 and 16% of the data sets by median and ordinary runs analysis, respectively. Together, these analyses suggest that the incidence of powdery mildew on cones was slightly aggregated among plants, but patterns of aggregation larger than the sampling unit were rare (20% or less of data sets). Knowledge of the heterogeneity of diseased cones was used to construct fixed sampling curves to precisely estimate the incidence of powdery mildew on cones at varying levels of disease. Use of the sampling curves developed in this research should help to improve sampling methods for disease assessment and management decisions.