Abstract
The severity of plant diseases, traditionally defined as the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases but is prone to error. Plant pathologists face many situations in which the measurement by nearest percent estimates (NPEs) of disease severity is time-consuming or impractical. Moreover, rater NPEs of disease severity are notoriously variable. Therefore, NPEs of disease may be of questionable value if severity cannot be determined accurately and reliably. In such situations, researchers have often used a quantitative ordinal scale of measurement—often alleging the time saved, and the ease with which the scale can be learned. Because quantitative ordinal disease scales lack the resolution of the 0 to 100% scale, they are inherently less accurate. We contend that scale design and structure have ramifications for the resulting analysis of data from the ordinal scale data. To minimize inaccuracy and ensure that there is equivalent statistical power when using quantitative ordinal scale data, design of the scales can be optimized for use in the discipline of plant pathology. In this review, we focus on the nature of quantitative ordinal scales used in plant disease assessment. Subsequently, their application and effects will be discussed. Finally, we will review how to optimize quantitative ordinal scales design to allow sufficient accuracy of estimation while maximizing power for hypothesis testing.
Highlights
Disease severity data is widely used by plant pathologists for predicting yield loss, monitoring and forecasting epidemics, in disease surveys and for assessing crop germplasm for disease resistance, and for understanding fundamental biological processes including coevolution (Bock et al 2010b)
The results indicated that the HB category scale was never better than nearest percent estimates (NPEs) for comparing treatments, and the HB scale could result in less precise data, elevating the risk of a type II error
If an “optimal” scale design can be adopted to measure disease severity, it will be of considerable practical value in many arenas where disease severity data is required
Summary
Disease severity (the proportion of a plant unit diseased, Nutter Jr. et al 1991) data is widely used by plant pathologists for predicting yield loss, monitoring and forecasting epidemics, in disease surveys and for assessing crop germplasm for disease resistance, and for understanding fundamental biological processes including coevolution (Bock et al 2010b). Several scale types are used in visual plant disease assessment (Bock et al 2010b; Madden et al 2007), and these include nominal, ordinal, and ratio scales (Stevens 1946). Where disease area (the proportion of the specimen area showing symptoms) is visually estimated by raters, the continuous percentage ratio scale is commonly used (Yadav et al 2013; Schwanck and Del Ponte 2014). Using NPE estimates of disease severity may be of questionable value if severity cannot be determined accurately and reliably, or perhaps if there is insufficient time to apply the percentage scale. In such situations, researchers have often used an ordinal scale of measurement
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
