Crop blight definition1/10/2024 ![]() The current definition is not applicable to diseases that manifest by a progression of symptoms that do not lend themselves to area estimations (for example, huanglongbing, zucchini yellow mosaic virus, and cassava mosaic disease Houngue et al. ![]() It is worth considering this definition of disease severity as it is limited to only the increase in the magnitude of disease intensity that can be measured or estimated based on a proportion or percentage of specimen area (for example, soybean rust, pecan scab, and rice brown spot ). Plant disease severity, the subject of this review, is currently defined as the “area of a sampling unit (plant surface) affected by disease expressed as a percentage or proportion of the total area” Nutter Jr et al. ![]() Thus, accurate disease intensity estimates would appear to be vital. ![]() Decisions based on such conclusions could result in wasted resources, increased disease, yield loss, and ultimately reduced profit. During the research process, incorrect quantification could result in a type II error (failure to reject the null hypothesis when it is false) when comparing treatments in any experiment situation, which will have ramifications for the conclusions drawn from those experiments. The term agreement can be considered synonymous with accuracy where actual values are concerned (Madden et al. Throughout all of these applications, visual estimates of disease are used to draw conclusions and/or take actions-and thus they should be as accurate as possible given available resource and purpose-where accuracy is operationally defined as the closeness of the visual estimate to the actual value Nutter Jr et al. 1991) is required for many different purposes including monitoring epidemics in experiments or surveys, understanding yield loss, comparing phenotypes for disease resistance, and evaluating effects of treatments (chemical, biological, agronomic, or environmental factors) on disease (James 1974 Kranz 1988 Cooke 2006 Madden et al. Quantification of plant disease intensity (amount of disease in a population, Nutter Jr et al. A list of best-operating practices in plant disease quantification and future research on the topic is presented based on the current knowledge. This review provides a historical perspective of visual severity assessment, accounting for concepts, tools, changing paradigms, and methods to maximize accuracy of estimates. Several rater-related (experience, inherent ability, training) and technology-related (instruction, scales, and SADs) characteristics have been shown to affect accuracy. Since that time, various approaches, some of them based on principles of psychophysics, have provided a foundation to understand sources of error during the estimation process as well as to develop different disease scales and disease-specific illustrations indicating the diseased area on specimens, similar to that developed by Cobb, and known as standard area diagrams (SADs). Awareness of the importance of accuracy of visual estimates of severity began in 1892, when Cobb developed a set of diagrams as an aid to guide estimates of rust severity in wheat. Although sensor technology has been available to measure disease severity using the visible spectrum or other spectral range imaging, it is visual sensing and perception that still dominates, especially in field research. These include evaluating treatment effect, monitoring epidemics, understanding yield loss, and phenotyping for host resistance. The pathogen can remain viable in infected debris for several years.Plant disease quantification, mainly the intensity of disease symptoms on individual units (severity), is the basis for a plethora of research and applied purposes in plant pathology and related disciplines. Once the disease is on a site, infections can develop and spread rapidly, especially when humidity is high. Spread to new areas is mainly through movement of infected plants, but not all infected plants show symptoms, which can make long-distance spread difficult to manage. Biologyīoxwood blight is caused by two closely related fungal pathogens that can infect all above-ground plant parts, resulting in leaf lesions, leaf drop, stem lesions and severe dieback. Neither species of boxwood blight have been identified in Minnesota. ![]() Calonectria henricotiae has only been identified in Europe. in 2011 (Connecticut) and is now known to occur in 25 states, in both landscape and nursery settings. The boxwood blight fungus, Calonectria pseudonaviculata, was first found in the U.S. Boxwood is a broad-leaved evergreen (leaves do not drop in winter) shrub and is sometimes used in decorative wreaths, which can be infected with the disease. Boxwood ( Buxus) is the primary host for boxwood blight, but also infects Pachysandra and sweet box ( Sarcococca). ![]()
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