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 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 Screening of Durum Wheat (Triticum turgidum L. subsp. durum (Desf.) Husn.) Italian Cultivars for Susceptibility to Fusarium Head Blight Incited by Fusarium graminearum

Plants ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 68
Author(s):  
Gaetano Bentivenga ◽  
Alfio Spina ◽  
Karim Ammar ◽  
Maria Allegra ◽  
Santa Olga Cacciola
Keyword(s):  
Durum Wheat ◽  
Fusarium Head Blight ◽  
Fusarium Graminearum ◽  
Triticum Turgidum ◽  
Disease Incidence ◽  
Morphological Characteristics ◽  
Pcr Amplification ◽  
Conidial Suspension ◽  
Head Blight ◽  
Italian Origin

In 2009, a set of 35 cultivars of durum wheat (Triticum turgidum L. subsp. durum (Desf.) Husn.) of Italian origin was screened for fusarium head blight (FHB) susceptibility at CIMMYT (Mexico) and in the 2019–20 cropping season, 16 of these cultivars, which had been included in the Italian National Plant Variety Register, were tested again in southern and northern Italy. Wheat cultivars were artificially inoculated during anthesis with a conidial suspension of Fusarium graminearum sensu lato using a standard spray inoculation method. Inoculum was a mixture of mono-conidial isolates sourced in the same areas where the trials were performed. Isolates had been characterized on the basis of morphological characteristics and by DNA PCR amplification using a specific primer set and then selected for their virulence and ability to produce mycotoxins. The susceptibility to FHB was rated on the basis of the disease severity, disease incidence and FHB index. Almost all of the tested cultivars were susceptible or very susceptible to FHB with the only exception of “Duprì”, “Tiziana” and “Dylan” which proved to be moderately susceptible. The susceptibility to FHB was inversely correlated with the plant height and flowering biology, the tall and the late heading cultivars being less susceptible.

A Comparison of Disease Assessment Methods for Southern Stem Rot of Peanut

2002 ◽  
Vol 29 (1) ◽  
pp. 66-71 ◽  
Author(s):  
S. L. Rideout ◽  
T. B. Brenneman ◽  
K. L. Stevenson
Keyword(s):  
Alternative Assessment ◽  
Disease Incidence ◽  
Sclerotium Rolfsii ◽  
Correlation Coefficients ◽  
Assessment Method ◽  
Assessment Methods ◽  
Alternative Methods ◽  
Stem Rot ◽  
Disease Assessment ◽  
Time Required

Abstract Southern stem rot (caused by the soilborne fungus Sclerotium rolfsii Sacc.) of peanut (Arachis hypogaea L.) traditionally has been assessed based on the percentage of infected 30.5-cm row segments, commonly referred to as disease incidence. Several alternative disease assessment methods were evaluated in four fungicide trials during the growing season (aboveground ratings) and immediately after peanut inversion (belowground ratings). Pearson's correlation coefficients compared disease assessments and yields for all trials. Across all disease assessment methods, belowground assessments at inversion showed a stronger correlation with yield than in-season aboveground assessments. Several of the alternative assessment methods showed a stronger negative correlation with yield than did the traditional disease incidence rating. However, none of the alternative methods were consistently more precise across all assessment dates and trials. There was a significant positive correlation between many of the alternative methods and the traditional disease incidence method. Furthermore, none of the alternative methods was better than the traditional method for detecting differences among fungicide treatments when subjected to ANOVA and subsequent Waller-Duncan mean separation tests (k-ratio = 100). Based on comparisons of the time required to assess disease intensity, the traditional disease assessment method was found to be the most time efficient method of those tested in this study.

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.

Quantification of the relationship between the environment and Fusarium head blight, Fusarium pathogen density, and mycotoxins in winter wheat in Europe

2012 ◽  
Vol 133 (4) ◽  
pp. 975-993 ◽  
Author(s):  
Alissa B. Kriss ◽  
Pierce A. Paul ◽  
Xiangming Xu ◽  
Paul Nicholson ◽  
Fiona M. Doohan ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Head Blight ◽  
The Relationship

scholarly journals Disease Risk, Spatial Patterns, and Incidence-Severity Relationships of Fusarium Head Blight in No-till Spring Wheat Following Maize or Soybean

Plant Disease ◽  
2015 ◽  
Vol 99 (10) ◽  
pp. 1360-1366 ◽  
Author(s):  
Pierri Spolti ◽  
Denis A. Shah ◽  
José Maurício C. Fernandes ◽  
Gary C. Bergstrom ◽  
Emerson M. Del Ponte
Keyword(s):  
Fusarium Head Blight ◽  
Large Scale ◽  
Disease Risk ◽  
Random Pattern ◽  
Southern Brazil ◽  
Head Blight ◽  
Previous Crop ◽  
Regional Sources ◽  
Epidemiological Surveys ◽  
The Relationship

The first large-scale survey of Fusarium head blight (FHB) in commercial wheat fields in southern Brazil was conducted over three years (2009 to 2011). The objectives were to: (i) evaluate whether increased FHB risk is associated with within-field maize residue; (ii) determine the spatial pattern of FHB incidence; and (iii) quantify the relationship between FHB incidence and severity. FHB was assessed in a total of 160 fields between early milk and dough. Incidence ranged from 1.0 to 89.9% (median = 25%) and severity from 0.02 to 18.6% (median = 1.3%). FHB risk was neither lower nor higher in wheat following maize than in wheat following soybean. Only 18% of fields were classified as having aggregated patterns of FHB-symptomatic spikes. A binary power law description of the variances was consistent with an overall random pattern of the disease. These results conform with the hypothesis that FHB epidemics in southern Brazil are driven by sufficient atmospherically-transported inoculum from regional sources. The incidence-severity relationship was coherent across growing season, growth stage, and previous crop; one common fitted curve described the relationship across all observations. Estimating severity from incidence may be useful in reducing the workload in epidemiological surveys.

scholarly journals Anthesis, the infectious process and disease progress curves for fusarium head blight in wheat

2016 ◽  
Vol 42 (2) ◽  
pp. 134-139 ◽  
Author(s):  
Erlei Melo Reis ◽  
Cristina Boareto ◽  
Anderson Luiz Durante Danelli ◽  
Sandra Maria Zoldan
Keyword(s):  
Fusarium Head Blight ◽  
Disease Incidence ◽  
Weather Conditions ◽  
Gibberella Zeae ◽  
Head Blight ◽  
Infectious Process ◽  
Disease Progress ◽  
Winter Cereals ◽  
Aggregation Pattern ◽  
Adverse Weather

ABSTRACT Fusarium head blight of wheat (Triticum aestivum), caused by the fungus Gibberella zeae, is a floral infecting disease that causes quantitative and qualitative losses to winter cereals. In Brazil, the sanitary situation of wheat has led to research in order to develop strategies for sustainable production, even under adverse weather conditions. To increase the knowledge of the relationship among the presence of anthesis, the infectious process, the disease progress and the saprophytic fungi present in wheat anthers, studies were conducted in the experimental field of University of Passo Fundo (UPF), using the cultivar Marfim, in the 2011 growing season. The disease incidence in spikes and spikelets was evaluated. The presence of exserted anthers increased the spike exposure time to the inoculum. The final incidence of fusarium head blight, in the field, was dependent on the presence of exserted anthers. The disease followed an aggregation pattern and its evolution increased with time, apparently showing growth according to secondary cycles. The fungi isolated from exserted anthers (Alternaria sp., Fusarium sp., Drechslera spp. and Epicoccum sp.) did not compete for the infection site of fusarium head blight in wheat, not interfering with the incidence of F. graminearum.

scholarly journals Characterizing Heterogeneity of Disease Incidence in a Spatial Hierarchy: A Case Study from a Decade of Observations of Fusarium Head Blight of Wheat

2012 ◽  
Vol 102 (9) ◽  
pp. 867-877 ◽  
Author(s):  
A. B. Kriss ◽  
P. A. Paul ◽  
L. V. Madden
Keyword(s):  
Spatial Heterogeneity ◽  
Fusarium Head Blight ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Disease Incidence ◽  
Geographical Location ◽  
Disease Status ◽  
Link Function ◽  
Head Blight ◽  
Growing Seasons

A multilevel analysis of heterogeneity of disease incidence was conducted based on observations of Fusarium head blight (caused by Fusarium graminearum) in Ohio during the 2002–11 growing seasons. Sampling consisted of counting the number of diseased and healthy wheat spikes per 0.3 m of row at 10 sites (about 30 m apart) in a total of 67 to 159 sampled fields in 12 to 32 sampled counties per year. Incidence was then determined as the proportion of diseased spikes at each site. Spatial heterogeneity of incidence among counties, fields within counties, and sites within fields and counties was characterized by fitting a generalized linear mixed model to the data, using a complementary log-log link function, with the assumption that the disease status of spikes was binomially distributed conditional on the effects of county, field, and site. Based on the estimated variance terms, there was highly significant spatial heterogeneity among counties and among fields within counties each year; magnitude of the estimated variances was similar for counties and fields. The lowest level of heterogeneity was among sites within fields, and the site variance was either 0 or not significantly greater than 0 in 3 of the 10 years. Based on the variances, the intracluster correlation of disease status of spikes within sites indicated that spikes from the same site were somewhat more likely to share the same disease status relative to spikes from other sites, fields, or counties. The estimated best linear unbiased predictor (EBLUP) for each county was determined, showing large differences across the state in disease incidence (as represented by the link function of the estimated probability that a spike was diseased) but no consistency between years for the different counties. The effects of geographical location, corn and wheat acreage per county, and environmental conditions on the EBLUP for each county were not significant in the majority of years.

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