Submitted to: Plant Disease
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 27, 2008
Publication Date: June 20, 2008
Citation: Biggs, A., Turechek, W., Gottwald, T.R. 2008. Analysis of fire blight shott infection epidemics on apple. Plant Disease. 92:1349-1356. Interpretive Summary: Fire blight is one of the most important diseases of commercial apple and pear production. Fire blight incidence and spread of the shoot blight phase of the disease was studied in four apple cultivars ('Fuji', 'Golden Delicious', 'Liberty' and 'York') in replicated orchard plots in West Virginia over four years (1994-1997). The study showed that the cultivar ‘York’ was highly susceptible, followed by ‘Fuji’ and ‘Golden Delicious’ which were moderately susceptible, and ‘Liberty’ which was least susceptible. Trees infected in the past-season were not the first trees to exhibit disease in the following year. The rate of disease spread can be described by a logistic model with apparent infection rates of up to 0.2/day for the susceptible cultivar York and 0.16/day for ‘Fuji’. Shoot blight epidemics exhibit aggregation that can be described by the beta-binomial distribution, and the degree of aggregation changes systematically with disease incidence and is affected by temporal and location factors. Results reported in this paper will be useful to growers and cooperative extension agents interested in managing fire bight.
Technical Abstract: Fire blight incidence and spread of the shoot blight phase of the disease was studied in four apple cultivars in replicated blocks over four years (1994 - 1997). The cultivar ‘York’ was highly susceptible, followed by ‘Fuji’ and ‘Golden Delicious’ which were moderately susceptible, and ‘Liberty’ which was least susceptible. On ‘York’, the first appearance of shoot blight was within 48 hours of its predicted appearance according to the Maryblyt model in 3 of the 4 years studied. Shoot blight epidemics in ‘York’ in 1995 and 1996, and ‘Fuji’ in 1995, were best described with a logistic model that showed apparent infection rates ranging from 0.05 to 0.20, indicating a low to moderately high rate of disease increase. The association between diseased plants at the end of the 1994 growing season and the first diseased plants in the 1995 epidemic year was not significant. The mean disease incidence in 1995 of plants that were diseased in 1994 was 60.3% and the mean time of disease observation was during the third week. The spatial positions (row, column) of all infected plants in each subplot were recorded on plot maps on each sampling date. The binomial and beta-binomial distributions were fit to the data to test for spatial aggregation of disease incidence for each cultivar plot. Maximum likelihood estimation was possible for 92 (43.6%) of the 211 data sets subjected to this analysis. Of these, 35 data sets were better fit by the beta-binomial distribution than the binomial distribution. The binary power law was used to characterize the relationship between the variance among quadrats within each plot to the variance expected for that plot given the observed level of disease incidence. The binary power law provided an excellent fit to the full data set and to nearly all of the subsets, and with b>1 indicated that heterogeneity changed systematically with disease incidence. A covariance analysis, conducted to determine the effect of the factors ‘year’, ‘cultivar’, ‘orchard plot’, and ‘observation date’ on the intercept and slope parameters of the binary power law. In general, ‘plot’ followed by ‘year’ had the greatest impact on parameter estimates and is an indication that location and seasonal factors impact heterogeneity of disease, although the specifics could not be ascertained from this study. Ordinary runs analysis was used to analyze the pattern of diseased trees within rows and detected significant nonrandom patterns of disease incidence in 63.5% of the orchard plots over the 4 year study. From these data sets, 68.7% had significantly fewer runs, particularly at disease incidences greater than 0.1. The fewer than expected runs at incidences greater than 0.10 provides strong evidence of localized spread.