Brown Rot of Fruits and Associated Diseases in Australia

Harrison, Travis Henry John

doi:10.1080/00275514.1935.12017079

Cited by 2 publications

(2 citation statements)

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“…These and earlier studies (e.g. Bertram 1916;Harrison 1933) also emphasized that insects were significantly related to yield loss caused by M. fructigena.…”

Section: Introductionmentioning

confidence: 84%

“…Construction of a submodel for certain plant pathogens can directly contribute to practical spray application. In case of brown rot, caused by M. fructigena, several studies emphasized that insect and/or bird injuries were significantly related to yield loss caused by M. fructigena (Bertram 1916;Harrison 1933;Lack 1989;Van Leeuwen et al 2000;Xu and Robinson 2000), and the study of Holb and Scherm (2008) quantified the contribution of insect injury to brown rot development using correlation analyses and Jaccard indices. Autoregressive models for seasonal spore dispersal were developed, and the relationship between weather parameters and spore dispersal/ infection as well as between seasonal spore dispersal and corresponding disease incidence data were determined for M. fructigena (Xu and Robinson 2000;Holb 2008;Bannon et al 2009).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2010

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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.

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“…These and earlier studies (e.g. Bertram 1916;Harrison 1933) also emphasized that insects were significantly related to yield loss caused by M. fructigena.…”

Section: Introductionmentioning

confidence: 84%