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What Interval Characteristics Make a Good Categorical Disease Assessment Scale?

By Kuo-Szu Chiang, Shih-Chia Liu, Clive H. Bock and Tim R. Gottwald

Abstract

Plant pathologists most often obtain quantitative information on disease severity using visual assessments. Category scales have been used for assessing plant disease severity in field experiments, epidemiological studies, and for screening germplasm. The most widely used category scale is the Horsfall-Barratt (H-B) scale, but reports show that estimates of disease severity using the H-B scale are less precise compared with nearest percent estimates (NPEs) using the 0 to 100% ratio scale. Few studies have compared different category scales. The objective of this study was to compare NPEs, the H-B midpoint converted data, and four different linear category scales (5 and 10% increments, with and without additional grades at low severity [0.1, 0.5, 1.0, 2.0, 5.0, 10.0, 15.0, 20.0…100%, and 0.1, 0.5, 1.0, 2.0, 5.0, 10.0, 20.0, 30.0…100%, respectively]). Results of simulations based on known distributions of disease estimation using the type II error rate (the risk of failing to reject H0 when H0 is false) showed that at disease severity ≤ 5%, a 10% category scale had a greater probability of failing to reject H0 when H0 is false compared with all other methods, while the H-B scale performed least well at 20 to 50% severity. The 5% category scale performed as well as NPEs except when disease severity was ≤ 1%. Both the 5 and 10% category scales with the additional grades included performed as well as NPEs. These results were confirmed with a mixed model analysis and bootstrap analysis of the original rater assessment data. A better knowledge of the advantages and disadvantages of category scale types will provide a basis for plant pathologists and plant breeders seeking to maximize accuracy and reliability of assessments to make an informed decision when choosing a disease assessment method

Topics: Computer Simulation, Data Interpretation, Statistical, Logistic Models, Plant Diseases, Sample Size
Year: 2015
DOI identifier: 10.1094/PHYTO-10-13-0279-R
OAI identifier: oai:ir.lib.nchu.edu.tw:11455/86387
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