Quantitative Ordinal Scale Estimates of Plant Disease Severity: Comparing Treatments Using a Proportional Odds Model

Studies in plant pathology, agronomy and plant breeding requiring disease severity assessment often use a special type of ordinal scale based on defined numeric ranges (a quantitative ordinal scale). This form of the ordinal scale is generally based on the percent area with symptoms [e.g. the Horsfall-Barratt (HB) scale]. Parametric proportional odds models (POMs) may be used to analyze the ratings obtained from disease scales directly, without converting ratings to percentages using range midpoints of quantitative ordinal scales (currently a standard procedure). Our aim was to evaluate the performance of the POM for the purpose of comparing treatments (e.g. varieties, fungicides, etc.) using ordinal estimates of disease severity to midpoint conversions (MCs) and nearest percent estimates (NPEs) using a t-test. A simulation method was implemented and the parameters of the simulation estimated using actual disease severity data from the field. The criterion for comparison 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 midpoint of the severity range at severity <40%. Especially at low disease severity (≤10%), the POM is superior to using the midpoint conversion method. Thus, for early onset of disease, or for comparing treatments with severities <40%, the POM is preferable for analyzing disease severity data based on quantitative ordinal scales when comparing treatments, and at severities >40% is equivalent to other methods.