Use of the proportional odds model to analyze disease severity estimation data for comparing treatments

• Main Authors: Chien-Shin Liu, 劉建鑫
• Other Authors: Kuo-Szu Chiang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/sef5yz

碩士 === 國立中興大學 === 農藝學系所 === 106 === In the field of plant epidemiology, plant diseases severity generally needs to be assessed. Severity may be defined as the area of plant tissue affected by disease and may be expressed as quantitative data such data can be used to do research to predict yield loss, to compare treatments, and for monitoring and forecasting of plant diseases. The purpose of this study is to evaluate the performance of the proportional odds model in estimating disease severity for the purpose of comparing treatments (e.g., varieties, fungicides, etc.). A simulation method was employed to execute the study. The parameters of the simulation were estimated using the original data from the field based on a sample of citrus canker (Xanthomonas citri subsp. Citri)-infected grapefruit leaves. The proportional odds model was compared with the method using midpoint conversions of ordinal intervals. Here, the criteria for comparison is the power of hypothesis testing. The results of this study show that, at low disease severity (≤30%), the performance of the proportional odds model is superior to that of the midpoint conversion of the interval. However, as disease severity increases, the power of hypothesis testing by using the proportional odds model decreases. As the actual disease severity approaches 30%, the advantage of the proportional odds model will gradually disappear although it is still slightly better than using the categorical type of amended 10% scale, but is not better than the Horsfall-Barratt (H-B) scale. We hope that the results of this study will be helpful in improving the accuracy of disease severity assessment in plant epidemiology.