Structural Characterization of Bean Root Rot Epidemics

Campbell, C. L.; Madden, L. V.; Pennypacker, S. P.

doi:10.1094/phyto-70-152

Cited by 30 publications

(13 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The estimated parameter values for those models can then be used to evaluate disease control treatments (39). 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.…”

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®

View full text Add to dashboard Cite

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.

show abstract

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®

View full text Add to dashboard Cite

show abstract

“…Variables tested from Georgetown and Newark, respectively, were from soil dilutions (1,7), root isolations (2,8), stand counts (3,9), internal root disease (4,10), external root disease index (5,11), and yield (6J2). The dependent variable was determined by dividing yield in inoculated plots (variables 6 and 12) by yield in uninoculated plots, averaging, and multiplying by 100%.…”

Section: Resultsmentioning

confidence: 99%

“…Ridge trace of coefficients from Georgetown and Newark. Parameters from Georgetown and Newark, respec tively, are from soil dilutions (1,7), root isolations (2,8), stand counts (3,9), internal root disease index (4,10), external root disease index (5,11), and yield (6,12). Here B* is a vector of estimated ridge coefficients and k is a bias factor where B* = (X'X + kir'X'Yk^O.…”

Section: Resultsmentioning

confidence: 99%

“…Ridge regression decreases these inflation factors by adding small positive quantities (k) to the diagonal of X'X after it has been standardized but before it is inverted. The ridge equation is, therefore: B*=(X'X + kI) r XTk^0 [3] where: I = an identity matrix; B* = the vector of estimated ridge coefficients (9). The ridge coefficients are biased but may have decreased variances; however variance can decrease more rapidly than the bias increases.…”

Section: Y=xb+ementioning

confidence: 99%

See 1 more Smart Citation

Use of ridge regression to predict yield reduction byFusariumsp. in selected soybean cultivars

Leath

Carroll

1985

Canadian Journal of Plant Pathology

View full text Add to dashboard Cite

“…Therefore, according to MoraAguilera et al (1996) and Holb et al (2005), three disease variables (β, AUDPC S and Y f ) are recommended to characterise fruit brown rot development in apple orchards under high disease pressure. Campbell et al (1980); Kranz (1968) and MoraAguilera et al (1996) demonstrated that the disease variables which characterize the entire pattern of a disease development can be used to improve the disease management and/or in breeding for disease resistance. Moreover, Zadoks and Schein (1979); Campbell and Madden (1990) and Berrie and Xu (2003) pointed out that basic information on the amount of disease and inoculum levels is necessary for producing an up-to-date disease forecast which then can be used successfully for improving disease management.…”

Section: Brown Rot Management Perspectivesmentioning

confidence: 99%

Development and evaluation of a model for management of brown rot in organic apple orchards

et al. 2010

View full text Add to dashboard Cite

Temporal development of brown rot (Monilinia fructigena) on fruits was analysed in two organic apple orchards on three apple cultivars in Eastern Hungary from 2002 to 2006. The threeparameter logistic function gave the best fit to brown rot over four non-linear growth functions in all cultivars, years and orchards. Depending on location, year and cultivar, disease increased continuously from 6 to 8 weeks before harvest up to harvest, reaching 19-37% of disease incidence. Disease variables of Y f , the final disease incidence; β, relative rate of disease progress; AUDPC S , standardized area under disease progress curve; T 1.5 , the time when disease incidence reaches 1.5% (day), and M, the inflection point were derived from the three-parameter logistic function. The disease variables of Y f , β, and AUDPC S were used in a computer simulation for predicting temporal brown rot development, and the disease variables of T 1.5 , M, and Y f were used to determine threshold values for epidemic intensity. Afterwards these were used to construct a fundamental model for developing a brown rot forecasting and management strategy (BRFMS). The fundamental model contained four parts: i) data insertion and analyses by computer simulation of pathogen submodels, ii) calculation of yield loss threshold levels based on disease incidence, iii) determination of epidemic intensity levels and iv) a decision module with suggestions for disease management practices for each epidemic intensity level. The fundamental model was supplemented with the prediction of occurrence of the first fruit rot symptoms and with the insect injury prediction related to brown rot development in order to complete a BRFMS for organic apple orchards. In a 3-year field evaluation from 2006 to 2008, season-long application of BRFMS treatments reduced the number of sprays against brown rot by 22-33% compared with the treatments of general spray schedules against brown rot.

show abstract

Structural Characterization of Bean Root Rot Epidemics

Cited by 30 publications

References 0 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

Use of ridge regression to predict yield reduction byFusariumsp. in selected soybean cultivars

Development and evaluation of a model for management of brown rot in organic apple orchards

Contact Info

Product

Resources

About