scholarly journals Spatial Heterogeneity of the Incidence of Powdery Mildew on Hop Cones

Plant Disease ◽  
2006 ◽  
Vol 90 (11) ◽  
pp. 1433-1440 ◽  
Author(s):  
David H. Gent ◽  
Walter F. Mahaffee ◽  
William W. Turechek
Keyword(s):  
Powdery Mildew ◽  
Spatial Heterogeneity ◽  
Power Law ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Disease Assessment ◽  
Cluster Sampling ◽  
Data Sets ◽  
Ratio Test ◽  
Frequency Distributions

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 loglikelihood 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. Similarly, the C(α) test indicated that 54% could be described by the beta-binomial distribution. The heterogeneity parameter of the beta-binomial distribution, θ, 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; however, 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; however, correlation was detected in only 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 disease intensities. Use of the sampling curves developed in this research should help to improve sampling methods for disease assessment and management decisions.

scholarly journals Sampling for Plant Disease Incidence

1999 ◽  
Vol 89 (11) ◽  
pp. 1088-1103 ◽  
Author(s):  
L. V. Madden ◽  
G. Hughes
Keyword(s):  
Power Law ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Cluster Sampling ◽  
Diseased Plant ◽  
Ratio Test ◽  
Wide Range ◽  
Sequential Probability ◽  
The Relationship

Knowledge of the distribution of diseased plant units (such as leaves, plants, or roots) or of the relationship between the variance and mean incidence is essential to efficiently sample for diseased plant units. Cluster sampling, consisting of N sampling units of n individuals each, is needed to determine whether the binomial or beta-binomial distribution describes the data or to estimate parameters of the binary power law for disease incidence. The precision of estimated disease incidence can then be evaluated under a wide range of settings including the hierarchical sampling of groups of individuals, the various levels of spatial heterogeneity of disease, and the situation when all individuals are disease free. Precision, quantified with the standard error or the width of the confidence interval for incidence, is directly related to N and inversely related to the degree of heterogeneity (characterized by the intracluster correlation, ρ). Based on direct estimates of ρ (determined from the θ parameter of the beta-binomial distribution or from the observed variance) or a model predicting ρ as a function of incidence (derived from the binary power law), one can calculate, before a sampling bout, the value of N needed to achieve a desired level of precision. The value of N can also be determined during a sampling bout using sequential sampling methods, either to estimate incidence with desired precision or to test a hypothesis about true disease incidence. In the latter case, the sequential probability ratio test is shown here to be useful for classifying incidence relative to a hypothesized threshold when the data follows the beta-binomial distribution with either a fixed ρ or a ρ that depends on incidence.

scholarly journals Spatial Pattern of Strawberry Powdery Mildew (Podosphaera aphanis) and Airborne Inoculum

Plant Disease ◽  
2014 ◽  
Vol 98 (1) ◽  
pp. 43-54 ◽  
Author(s):  
H. Van der Heyden ◽  
M. Lefebvre ◽  
L. Roberge ◽  
L. Brodeur ◽  
O. Carisse
Keyword(s):  
Powdery Mildew ◽  
Power Law ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Disease Incidence ◽  
Data Sets ◽  
Incidence Data ◽  
Airborne Inoculum ◽  
The Relationship

The relationship between strawberry powdery mildew and airborne conidium concentration (ACC) of Podosphaera aphanis was studied using data collected from 2006 to 2009 in 15 fields, and spatial pattern was described using 2 years of airborne inoculum and disease incidence data collected in fields planted with the June-bearing strawberry (Fragaria × ananassa) cultivar Jewel. Disease incidence, expressed as the proportion of diseased leaflets, and ACC were monitored in fields divided into 3 × 8 grids containing 24 100 m2 quadrats. Variance-to-mean ratio, index of dispersion, negative binomial distribution, Poisson distribution, and binomial and beta-binomial distributions were used to characterize the level of spatial heterogeneity. The relationship between percent leaf area diseased and daily ACC was linear, while the relationship between ACC and disease incidence followed an exponential growth curve. The V/M ratios were significantly greater than 1 for 100 and 96% of the sampling dates for ACC sampled at 0.35 m from the ground (ACC0.35m) and for ACC sampled at 1.0 m from the ground (ACC1.0m), respectively. For disease incidence, the index of dispersion D was significantly greater than 1 for 79% of the sampling dates. The negative binomial distribution fitted 86% of the data sets for both ACC1.0m and ACC0.35m. For disease incidence data, the beta-binomial distribution provided a good fit of 75% of the data sets. Taylor's power law indicated that, for ACC at both sampling heights, heterogeneity increased with increasing mean ACC, whereas the binary form of the power law suggested that heterogeneity was not dependent on the mean for disease incidence. When the spatial location of each sampling location was taken into account, Spatial Analysis by Distance Indices showed low aggregation indices for both ACCs and disease incidence, and weak association between ACC and disease incidence. Based on these analyses, it was found that the distribution of strawberry powdery mildew was weakly aggregated. Although a higher level of heterogeneity was observed for airborne inoculum, the heterogeneity was low with no distinct foci, suggesting that epidemics are induced by well-distributed inoculum. This low level of heterogeneity allows mean airborne inoculum concentration to be estimated using only one sampler per field with an overall accuracy of at least 0.841. The results obtained in this study could be used to develop a sampling scheme that will improve strawberry powdery mildew risk estimation.

scholarly journals Sequential Sampling for Estimation and Classification of the Incidence of Hop Powdery Mildew II: Cone Sampling

Plant Disease ◽  
2007 ◽  
Vol 91 (8) ◽  
pp. 1013-1020 ◽  
Author(s):  
David H. Gent ◽  
William W. Turechek ◽  
Walter F. Mahaffee
Keyword(s):  
Powdery Mildew ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Model Construction ◽  
Data Sets ◽  
Data Set ◽  
Sampling Plans ◽  
Simulated Sampling

Sequential sampling models for estimation and classification of the incidence of powdery mildew (caused by Podosphaera macularis) on hop (Humulus lupulus) cones were developed using parameter estimates of the binary power law derived from the analysis of 221 transect data sets (model construction data set) collected from 41 hop yards sampled in Oregon and Washington from 2000 to 2005. Stop lines, models that determine when sufficient information has been collected to estimate mean disease incidence and stop sampling, for sequential estimation were validated by bootstrap simulation using a subset of 21 model construction data sets and simulated sampling of an additional 13 model construction data sets. Achieved coefficient of variation (C) approached the prespecified C as the estimated disease incidence, [Formula: see text], increased, although achieving a C of 0.1 was not possible for data sets in which [Formula: see text] < 0.03 with the number of sampling units evaluated in this study. The 95% confidence interval of the median difference between [Formula: see text] of each yard (achieved by sequential sampling) and the true p of the original data set included 0 for all 21 data sets evaluated at levels of C of 0.1 and 0.2. For sequential classification, operating characteristic (OC) and average sample number (ASN) curves of the sequential sampling plans obtained by bootstrap analysis and simulated sampling were similar to the OC and ASN values determined by Monte Carlo simulation. Correct decisions of whether disease incidence was above or below prespecified thresholds (pt) were made for 84.6 or 100% of the data sets during simulated sampling when stop lines were determined assuming a binomial or beta-binomial distribution of disease incidence, respectively. However, the higher proportion of correct decisions obtained by assuming a beta-binomial distribution of disease incidence required, on average, sampling 3.9 more plants per sampling round to classify disease incidence compared with the binomial distribution. Use of these sequential sampling plans may aid growers in deciding the order in which to harvest hop yards to minimize the risk of a condition called “cone early maturity” caused by late-season infection of cones by P. macularis. Also, sequential sampling could aid in research efforts, such as efficacy trials, where many hop cones are assessed to determine disease incidence.

scholarly journals Sequential Sampling for Estimation and Classification of the Incidence of Hop Powdery Mildew I: Leaf Sampling

Plant Disease ◽  
2007 ◽  
Vol 91 (8) ◽  
pp. 1002-1012 ◽  
Author(s):  
David H. Gent ◽  
William W. Turechek ◽  
Walter F. Mahaffee
Keyword(s):  
Monte Carlo ◽  
Powdery Mildew ◽  
Monte Carlo Simulations ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Data Sets ◽  
Sampling Plans ◽  
Binomial Approximation

Hop powdery mildew (caused by Podosphaera macularis) is an important disease of hops (Humulus lupulus) in the Pacific Northwest. Sequential sampling models for estimation and classification of the incidence of powdery mildew on leaves of hop were developed based on the beta-binomial distribution, using parameter estimates of the binary power law determined in previous studies. Stop lines, models that indicate that enough information has been collected to estimate disease incidence and cease sampling, for sequential estimation were validated by bootstrap simulations of a select group of 18 data sets (out of a total of 198 data sets) from the model-construction data, and through simulated sampling of 104 data sets collected independently (i.e., validation data sets). The achieved coefficient of variation (C) approached prespecified C values as the achieved disease incidence ([Formula: see text]) increased. Achieving a C of 0.1 was not possible for data sets in which [Formula: see text] < 0.10. The 95% confidence interval of the median difference between the true p and [Formula: see text] included zero for 16 of 18 data sets evaluated at C = 0.2 and all data sets when C = 0.1. For sequential classification, Monte-Carlo simulations were used to determine the probability of classifying mean disease incidence as less than a threshold incidence, pt (operating characteristic [OC]), and average sample number (ASN) curves for 16 combinations of candidate stop lines and error levels (α and β). Four pairs of stop lines were selected for further evaluation based on the results of the Monte-Carlo simulations. Bootstrap simulations of the 18 selected data sets indicated that the OC and ASN curves of the sequential sampling plans for each of the four sets of stop lines were similar to OC and ASN values determined by Monte Carlo simulation. Correct classification of disease incidence as being above or below preselected thresholds was 2.0 to 7.7% higher when stop lines were determined by the beta-binomial approximation than when stop lines were calculated using the binomial distribution. Correct decision rates differed depending on the location where sampling was initiated in the hop yard; however, in all instances were greater than 86% when stop lines were determined using the beta-binomial approximation. The sequential sampling plans evaluated in this study should allow for rapid and accurate estimation and classification of the incidence of hop leaves with powdery mildew, and aid in sampling for pest management decision making.

scholarly journals Analysis of Fire Blight Shoot Infection Epidemics on Apple

Plant Disease ◽  
2008 ◽  
Vol 92 (9) ◽  
pp. 1349-1356 ◽  
Author(s):  
Alan R. Biggs ◽  
William W. Turechek ◽  
Tim R. Gottwald
Keyword(s):  
Power Law ◽  
Binomial Distribution ◽  
Fire Blight ◽  
Disease Incidence ◽  
High Rate ◽  
Parameter Estimates ◽  
Data Sets ◽  
Full Data ◽  
Data Set ◽  
Shoot Blight

Fire blight incidence and spread of the shoot blight phase of the disease was studied in four apple cultivars in replicated blocks over 4 years (1994 to 1997). Cv. 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 h 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 spatial positions (row and column) of all infected plants in each subplot were recorded on plot maps on each sampling date. The binomial and β-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 β-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 was 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.

Patterns of macroparasite aggregation in wildlife host populations

Parasitology ◽  
1998 ◽  
Vol 117 (6) ◽  
pp. 597-610 ◽  
Author(s):  
D. J. SHAW ◽  
B. T. GRENFELL ◽  
A. P. DOBSON
Keyword(s):  
Poisson Distribution ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Data Sets ◽  
Frequency Distributions ◽  
System A ◽  
Host Sex ◽  
Host Parasite

Frequency distributions from 49 published wildlife host–macroparasite systems were analysed by maximum likelihood for goodness of fit to the negative binomial distribution. In 45 of the 49 (90%) data-sets, the negative binomial distribution provided a statistically satisfactory fit. In the other 4 data-sets the negative binomial distribution still provided a better fit than the Poisson distribution, and only 1 of the data-sets fitted the Poisson distribution. The degree of aggregation was large, with 43 of the 49 data-sets having an estimated k of less than 1. From these 49 data-sets, 22 subsets of host data were available (i.e. host data could be divided by either host sex, age, where or when hosts were sampled). In 11 of these 22 subsets there was significant variation in the degree of aggregation between host subsets of the same host–parasite system. A common k estimate was always larger than that obtained with all the host data considered together. These results indicate that lumping host data can hide important variations in aggregation between hosts and can exaggerate the true degree of aggregation. Wherever possible common k estimates should be used to estimate the degree of aggregation. In addition, significant differences in the degree of aggregation between subgroups of host data, were generally associated with significant differences in both mean parasite burdens and the prevalence of infection.

Spatial and Temporal Dynamics of the Weed Community in a Seashore Paspalum Turf

Weed Science ◽  
2009 ◽  
Vol 57 (3) ◽  
pp. 248-255 ◽  
Author(s):  
Xin-Ming Xie ◽  
You-Zhi Jian ◽  
Xiao-Na Wen
Keyword(s):  
Spatial Heterogeneity ◽  
Power Law ◽  
Temporal Dynamics ◽  
Line Transect ◽  
Frequency Of Occurrence ◽  
Power Law Model ◽  
Seashore Paspalum ◽  
Frequency Distributions ◽  
Weed Communities ◽  
Spatial And Temporal Dynamics

The temporal dynamics of spatial heterogeneity was studied for the weed communities in a seashore paspalum turf with the use of a power-law model. Surveys were conducted in January, March, May, July, September, and November in 2007. In every survey, we set 100 quadrats (50 by 50 cm) referred to as L quadrats on a 50-m line transect at the same position in the turf. Each L quadrat was then divided into four S quadrats (25 by 25 cm) and all plant species occurring in each of these S quadrats were identified and recorded. These data were summarized into frequency distributions and the percentage of S quadrats containing a given species, and the variance of each species was estimated. The power law was used to evaluate the spatial heterogeneity (δ) and frequency of occurrence (p) for each species in the weed communities in six survey months. The results showed that weeds emerged more frequently in the summer–spring season than in winter–autumn, and the spatial heterogeneity was much higher in summer–spring than winter–autumn, especially in summer. The Shannon–Wiener diversity indexes (H') from large to small were July (5.9202) > May (5.6775) > September (5.6631) > March (5.5727) > January (5.1742) > November (4.9668). Likewise, the spatial heterogeneity index (δc) of the whole community was also different in different months. The biggest δc (0.2790) was in July, and the smallest (0.1811) in November. Meanwhile, manilagrass had a high p (= 1.0), indicating that it occurred in all S quadrats in every weed community of every month. However, the turfgrass, seashore paspalum, only emerged in March, May, July, and November, and possessed a low p, indicating the seashore paspalum turf has been naturally replaced by manilagrass.

scholarly journals Quantitative and Molecular Epidemiology of Bacterial Blight of Onion in Seed Production Fields

2006 ◽  
Vol 96 (12) ◽  
pp. 1345-1354 ◽  
Author(s):  
L. Humeau ◽  
P. Roumagnac ◽  
Y. Picard ◽  
I. Robène-Soustrade ◽  
F. Chiroleu ◽  
...  
Keyword(s):  
Seed Production ◽  
Binomial Distribution ◽  
Bacterial Blight ◽  
Disease Incidence ◽  
Aflp Analysis ◽  
Data Sets ◽  
Spatial Dependency ◽  
Nested Polymerase Chain Reaction ◽  
Incidence Data ◽  
Polymerase Chain

Onion, a biennial plant species, is threatened by the emerging, seed-borne, and seed-transmitted Xanthomonas axonopodis pv. allii. Bacterial blight epidemics were monitored in seed production fields over two seasons. Temporal disease progress was different between the two seasons, with final incidence ranging from 0.04 to 0.06 in 2003 and from 0.44 to 0.61 in 2004. The number of hours with temperatures above 24°C was the best descriptor for predicting the number of days after inoculation for bacterial blight development on inoculated plants. Fitting the β-binomial distribution and binary power law analysis indicated aggregated patterns of disease incidence data. The β-binomial distribution was superior to the binomial distribution for 97% of the examined data sets. Spatial dependency ranged from 5.9 to 15.2 m, as determined by semivariance analysis. Based on amplified fragment length polymorphism (AFLP) analysis, it was concluded that plots predominantly were infected by the inoculated haplotype. A single other haplotype was identified by AFLP in all plots over the 2 years, and its detection in the field always followed wind-driven rains. X. axonopodis pv. allii-contaminated seed were detected by semiselective isolation and a nested polymerase chain reaction assay at levels up to 0.05% when final disease incidence was 0.61. Contaminated seed originated from both diseased and asymptomatic plants.

scholarly journals Relationships Between Incidence and Severity of Fusarium Head Blight on Winter Wheat in Ohio

2005 ◽  
Vol 95 (9) ◽  
pp. 1049-1060 ◽  
Author(s):  
P. A. Paul ◽  
S. M. El-Allaf ◽  
P. E. Lipps ◽  
L. V. Madden
Keyword(s):  
Fusarium Head Blight ◽  
Prediction Interval ◽  
Disease Incidence ◽  
Disease Assessment ◽  
Data Sets ◽  
Head Blight ◽  
Incidence Data ◽  
Treatment Comparisons ◽  
Time Required ◽  
The Relationship

To determine the relationship between incidence (I; proportion of diseased spikes) and severity (S; mean proportion of diseased spikelets per spike) for Fusarium head blight of wheat and to determine if severity could be predicted reliably from incidence data, disease assessments were made visually at multiple sample sites in artificially and naturally inoculated research and production fields between 1999 and 2002. Ten distinct data sets were collected. Mean disease intensity ranged from 0.023 to 0.975 for incidence and from 0.0003 to 0.808 for severity. A model based on complementary log-log transformation of incidence and severity performed well for all data sets, based on calculated coefficients of determination and random residual plots. The I-S relationship was consistent among years and locations, with similar slopes for all data sets. For 7 of the 10 data sets and for the pooled data from all locations and years, the estimated slope from the fit of the model ranged from 1.03 to 1.26. Time of disease assessment affected the relationship between incidence and severity; however, the estimated slopes from each assessment time were also close to 1. Based on the width of the 95% prediction interval, severity was estimated more precisely at lower incidence values than at higher values. The number of sampling units and the index of dispersion of disease incidence had only minor effects on the precision with which S was predicted from I. The estimation of mean S from I would substantially reduce the time required to assess Fusarium head blight in field surveys and treatment comparisons, and the observed relationship between I and S could be used to identify genotypes with some types of disease resistance.

scholarly journals Incidence and Density Relationships of Powdery Mildew on Apple

2002 ◽  
Vol 92 (9) ◽  
pp. 1005-1014 ◽  
Author(s):  
Xiangming Xu ◽  
Laurence V. Madden
Keyword(s):  
Powdery Mildew ◽  
Power Law ◽  
Disease Incidence ◽  
Type A ◽  
Incidence Density ◽  
Random Pattern ◽  
Simple Relationship ◽  
Taylor’S Power Law ◽  
Colony Density ◽  
Taylor's Power Law

The relationships between disease incidence and colony density and between leaf and shoot disease incidences for apple powdery mildew were investigated over four seasons in order to derive a simple relationship for predicting density using incidence. The Neyman type A distribution generally provided a good fit to the observed number of colonies per leaf and shoot, and provided a significantly better fit than the Poisson distribution, indicating a degree of aggregation of mildew colonies. In general, Taylor's power-law satisfactorily described the observed variance-mean relationship for colony density; however, Taylor's power-law broke down at very high levels of mean density. Incidence of leaf infection could be determined based on average number of colonies per leaf assuming a fixed variance-mean relationship and a Neyman type A distribution for colony density. Regression models using the complemen- tary log-log transformation of incidence also provided accurate predictions of leaf (or shoot) disease incidence from colonies per leaf (or per shoot). Similar accuracies of these incidence-density models suggested that variance-mean ratio of colony density was more or less constant over time. Unlike the case with colony density, the number of mildewed leaves per shoot generally had a random pattern, as indicated by the good fit of the binomial distribution. Thus, it was possible to estimate the leaf incidence of the youngest unrolled leaves on a shoot using the shoot incidence. It is argued that the leaf incidence-density relationships developed in the present study may be used in making practical disease management decisions.

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