Spatial pattern analysis of strawberry leaf blight in perennial productionsystems

Spatial pattern of the incidence of strawberry leaf blight, caused by Phomo psis obscurans, was quantified in commercial strawberry fields in Ohio usin g statistics for heterogeneity and spatial correlation. For each strawberry planting, two transects were randomly chosen and the proportion of leaflet s (out of 15) and leaves (out of five) with leaf blight symptoms was determ ined from N = 49 to 106 (typically 75) evenly spaced sampling units, thus e stablishing a natural spatial hierarchy to compare patterns of disease. The beta-binomial distribution fitted the data better than the binomial in 92 and 26% of the 121 data sets over 2 years at the leaflet and leaf levels, r espectively, based on a likelihood ratio test. Heterogeneity in individual data sets was measured with the index of dispersion (variance ratio), C(alp ha) test, a standard normal-based test statistic, and estimated theta param eter of the beta-binomial. Using these indices, overdispersion was detected in approximately 94 and 36% of the data sets at the leaflet and leaf level s, respectively. Estimates of the slope from the binary power law were sign ificantly (P < 0.01) greater than I and estimates of the intercept were sig nificantly greater than 0 (P < 0.01) at both the leaflet and leaf levels fo r both years, indicating that degree of heterogeneity was a function of inc idence. A covariance analysis indicated that cultivar, time, and commercial farm location of sampling had little influence on the degree of heterogene ity. The measures of heterogeneity indicated that there was a positive corr elation of disease status of leaflets (or leaves) within sampling units. Me asures of spatial association in disease incidence among sampling units wer e determined based on autocorrelation coefficients, runs analysis, and a ne w class of tests known as spatial analysis by distance indices (SADIE). In general, from 9 to 22% of the data sets had a significant nonrandom spatial arrangement of disease incidence among sampling units, depending an which test was used. When significant associations existed, the magnitude of the association was small but was about the same for leaflets and leaves. Compa ring test results, SADIE analysis was found to be a viable alternative to s patial autocorrelation analysis and has the advantage of being an extension of heterogeneity analysis rather than a separate approach. Collectively, r esults showed that incidence of Phomopsis leaf blight was primarily charact erized by small, loosely aggregated clusters of diseased leaflets, typicall y confined within the borders of the sampling units.