Disease measurement in a study of apple scab epidemics

Jeger, Michael

doi:10.1111/j.1744-7348.1981.tb05128.x

Cited by 25 publications

(26 citation statements)

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“…For severity, the estimated relative rate parameter (β) was also important and of the same sign as M (Table 7). These relationships indicate that incidence and severity develop in different ways but are depend- (4,36,37), and recently, Holb et al (30) demonstrated good incidence-severity relationships for summer development of apple scab in integrated and organic production regimes. They proposed nonlinear functions for such relationships that may differ from those of epidemics in the primary infection season.…”

Section: Discussionmentioning

confidence: 91%

“…Moreover, interactions between host, pathogen, and weather as represented by disease progress curves have been tested by multivariate techniques, such as factor analysis or principal component analysis (19,45,53). Disease progress curves describing the seasonal development of apple scab have been constructed in a few studies (2,3,36,37) but no attempt has been made to analyze the relationships between the estimated parameters for the corresponding growth model or the effect of disease control treatments.…”

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confidence: 99%

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Analysis of Summer Epidemic Progress of Apple Scab at Different Apple Production Systems in the Netherlands and Hungary

Holb¹,

Heijne²,

Withagen³

et al. 2005

Phytopathology®

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Holb, I. J., Heijne, B., Withagen, J. C. M., Gáll, J. M., and Jeger, M. J. 2005. Analysis of summer epidemic progress of apple scab at different apple production systems in the Netherlands and Hungary. Phytopathology 95:1001-1020. Two, 4-year studies on summer epidemic progress of apple scab were conducted at Randwijk, the Netherlands, from 1998 until 2001 and at Eperjeske, Hungary, from 2000 until 2003. Disease assessments were made on scab-susceptible cv. Jonagold. A range of nonlinear growth functions were fitted to a total of 96 disease progress curves (3 treatment classes × 2 plant parts × 2 disease measures × 4 years × 2 locations) of apple scab incidence and severity. The three-parameter logistic model gave the most consistent fit across three treatment classes in the experiment (integrated, organic-sprayed, and organic-unsprayed). Parameters estimated or calculated from the three-parameter logistic function were used to analyze disease progress. These were disease incidence and severity on the day of the first assessment (Y s ); final disease incidence or upper asymptote for incidence (Y if ) or severity (Y sf ); fruit incidence and severity on day 40, after which no new lesions on fruits appeared (Y 40 ); leaf incidence and severity on day 75, at which shoot growth stopped (Y 75 ); relative (β) and "absolute" (θ) rates of disease progress; inflection point (M); and area under the disease progress curve (AUDPC S ) standardized by the duration of the total epidemic. Comparisons among disease progress curves were made by correlation and factor analysis followed by Varimax rotation. There were large differences but high positive correlations among the parameters Y s , Y f , θ, and AUDPC S across the three treatment classes. In the factor analysis, two factors accounted for more than 85% of the total variance for both incidence and severity. Factor 1 gave an overall description of epidemic progress of both scab incidence and severity and included the parameters Y f , Y 40 , Y 75 , θ, and AUDPC S . Factor 2 identified a relationship between the relative rate parameter (β) and the inflection point (M) for severity and a relationship between disease incidence and severity. For an integrated or an organic orchard, θ, AUDPC S , and one of Y f or Y 75 (because of the link with host phenology) can characterize apple scab epidemics during summer. Based on these findings, improved scab management approaches were provided for integrated and organic apple production systems.Additional keywords: Akaike information criterion, disease management, disease warning model, epidemiology, fixed effect, nonlinear mixedeffect modeling, random effect, R language.

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Section: Discussionmentioning

confidence: 91%

mentioning

confidence: 99%

Analysis of Summer Epidemic Progress of Apple Scab at Different Apple Production Systems in the Netherlands and Hungary

Holb¹,

Heijne²,

Withagen³

et al. 2005

Phytopathology®

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“…However, incidence is generally easier and faster to measure than severity or lesion density. For apple scab, lesion density is thought to be better related to yield loss and to provide a better estimation of disease progress (Jeger 1981), but the exact number of lesions is often difficult to count because of coalescence or of the presence of atypical lesions (Holb et al 2005).…”

Section: Introductionmentioning

confidence: 99%

“…It is possible to define the relationship between disease incidence and lesion density such that measurements of disease incidence can be used to predict lesion density (Jeger 1981;Seem 1984). These types of relationships often rely on measuring disease on a plant unit or at a scale higher than that of direct interest to predict disease at the unit or scale of interest.…”

Section: Introductionmentioning

confidence: 99%

“…Disease intensity can be assessed as either incidence (defined as the proportion of plant units diseased), severity (defined as the area of plant tissue diseased), or lesion density (defined as the number of lesions per plant unit). Various parameters have been proposed to study apple scab epidemics, cultivar susceptibility, and fungicide efficacy including disease incidence, the number of lesions or conidia per shoot, the number of conidia per lesion, and the number of lesions or conidia per unit area of leaf tissue (Dewdney et al 2003;Jeger 1981). In these studies both incidence and severity were considered as good estimators of apple scab epidemics.…”

Section: Introductionmentioning

confidence: 99%

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Spatial heterogeneity, incidence-incidence and incidence-lesion density relationship of apple scab (Venturia inaequalis) in managed orchards

2011

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The spatial pattern of apple scab was characterized using 10 years of disease incidence and lesion density data collected in managed orchards located in Quebec, Canada. Distributional analyses indicated that scab incidence was better characterized by the betabinomial than the binomial distribution in 53 and 65% of the data sets at the leaf and shoot scales, respectively. Median values of the beta-binomial parameter θ, a measure of small-scale aggregation, were near 0 (0.003 and 0.028) at both sampling scales, indicating that disease incidence was close to being randomly distributed (low degree of aggregation). For lesion density, the negative binomial distribution fitted the data better than the Poisson distribution in 86% of the data sets at the leaf scale. The median value of the index of dispersion k was 0.068, indicating that aggregation was present. For all apple scab measurements, the power law models provided a good fit to the data. The estimated slope and intercept parameters were significantly greater than 1 and 0, respectively, suggesting that spatial heterogeneity changed systematically with disease incidence. Results of a covariance analysis showed that spatial heterogeneity of scab incidence at both scales and lesion density was not dependent upon shoot type but that spatial heterogeneity of scab incidence and lesion density at the leaf scale was influenced by the sampling period. A hierarchical analysis showed that scab incidence at the tree scale increased as a saturationtype curve with respect to incidence at the leaf or shoot scales. A similar relationship was observed for incidences at the shoot and leaf scales. An effective sample size model based on the binary power law parameters (Madden and Hughes, Phytopathology 89:770-781, 1999) gave the best fit to the leaf and shoot data, respectively. The incidence-lesion density relationship at both scales was well described by a complementary log-log (CLL) and log transformation model R 2 adj ¼ 0:97 and R 2 adj ¼ 0:94 , however, the models tended to underestimate lesion density. The information of the spatial relations of apple scab within and between hierarchical scales acquired from this study can be used in developing and evaluating practical disease management strategies and to improve apple scab assessments for fungicide or cultivar susceptibility trials.

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Disease assessment and yield loss

Cooke

The Epidemiology of Plant Diseases

100

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Disease measurement in a study of apple scab epidemics

Cited by 25 publications

References 14 publications

Analysis of Summer Epidemic Progress of Apple Scab at Different Apple Production Systems in the Netherlands and Hungary

Analysis of Summer Epidemic Progress of Apple Scab at Different Apple Production Systems in the Netherlands and Hungary

Spatial heterogeneity, incidence-incidence and incidence-lesion density relationship of apple scab (Venturia inaequalis) in managed orchards

Disease assessment and yield loss

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