Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology

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.

Transgressive segregation, hopeful monsters, and phenotypic selection drove rapid genetic gains and breakthroughs in predictive breeding for quantitative resistance to Macrophomina in strawberry

“…1 ). Such non-linearities are common in genetic studies where large-scale visual phenotyping is necessary, large-plot analyses of disease incidence and severity are impractical and unnecessary, and high-throughput phenotyping alternatives are limited, untested, or unproven [ 5 , 57 – 61 ]. To increase the probability of selecting parents carrying novel favorable alleles and cope with the uncertain accuracy of C0 resistance phenotypes, we had to relax the truncation selection threshold among prospective selection cycle 1 parents ( Supplemental File S1 ).…”

Comparative Analysis of Phenotypic and Molecular Data on Response to Main Pear Diseases and Pest Attack in a Germplasm Collection

“…Therefore, even if the procedures were quite laborious, the recorded data allowed us to apply a reliable statistical calculation using percent of DA instead of ordinal scales in the case of scoring, credit rating scales, or descriptors. There are many studies and debates about disease evaluation terms and concepts and the correctness of the data regarding the assessment of plant diseases [ 63 , 64 , 65 ], but it is widely agreed that quantitative ordinal disease scales are inherently less accurate since they lack the clarity of a 0 to 100% scale [ 66 , 67 ]. Thus, the genotypes with proper response (which we preferred to call ‘tolerant’, avoiding the term ‘resistant’, which could appear much too subjective) were differentiated quite clearly for each disease analyzed and for the pests represented by psyllids.…”

Coinfection with a virus constrains within‐host infection load but increases transmission potential of a highly virulent fungal plant pathogen

“…Proportion of infected plants in a population reflects the realized transmission for pathogens that rely on horizontal transmission. We used a categorical scale in estimating the pathogen population size because it is the most feasible way of estimating disease in the field, and especially suitable in the analysis of low disease prevalence (Chiang & Bock, 2021). Host population size is expected to increase infection risk of populations (Parratt et al, 2016) and thus, we estimated the host population size as coverage of the host plant in square meters.…”