Studies in plant pathology, agronomy, and plant breeding requiring disease severity assessment often use quantitative ordinal scales (i.e., a special type of ordinal scale that uses defined numeric ranges); a frequently used example of such a scale is the Horsfall-Barratt scale. Parametric proportional odds models (POMs) may be used to analyze the ratings obtained from quantitative ordinal scales directly, without converting ratings to percent area affected using range midpoints of such scales (currently a standard procedure). Our aim was to evaluate the performance of the POM for comparing treatments using ordinal estimates of disease severity relative to two alternatives, the midpoint conversions (MCs) and nearest percent estimates (NPEs). A simulation method was implemented and the parameters of the simulation estimated using actual disease severity data from the field. The criterion for comparison of the three approaches was the power of the hypothesis test (the probability to reject the null hypothesis when it is false). Most often, NPEs had superior performance. The performance of the POM was never inferior to using the MC at severity <40%. Especially at low disease severity (≤10%), the POM was superior to using the MC method. Thus, for early onset of disease or for comparing treatments with severities 40% is equivalent to other methods.

Estimates of plant disease severity are crucial to various practical and research-related needs in agriculture. Ordinal scales are used for categorizing severity into ordered classes. Certain characteristics of quantitative ordinal scale design may affect the accuracy of the specimen estimates and, consequently, affect the accuracy of the resulting mean disease severity for the sample. The aim of this study was to compare mean estimates based on various quantitative ordinal scale designs to the nearest percent estimates, and to investigate the effect of the number of classes in an ordinal scale on the accuracy of that mean. A simulation method was employed. The criterion for comparison was the mean squared error of the mean disease severity for each of the different scale designs used. The results indicate that scales with seven or more classes are preferable when actual mean disease severities of 50% or less are involved. Moreover, use of an amended 10% quantitative ordinal scale with additional classes at low severities resulted in a more accurate mean severity compared to most other scale designs at most mean disease severities. To further verify the simulation results, estimates of mean severity of pear scab on samples of leaves from orchards in Taiwan demonstrated similar results. These observations contribute to the development of plant disease assessment scales to improve the accuracy of estimates of mean disease severities.