A latent scale model to minimize subjectivity in the analysis of visual rating data for the National Turfgrass Evaluation Program

Frontiers in Plant Science

Plant Science

Eric Watkins¹  Yuanshuo Qu²  Kevin Morris²  Steve Graham³  Len Kne⁴ 

[1] Department of Horticultural Science, University of Minnesota, St. Paul, MN, United States;National Turfgrass Evaluation Program, Beltsville, MD, United States;U-Spatial, University of Minnesota, Duluth, MN, United States;U-Spatial, University of Minnesota, Minneapolis, MN, United States;

关键词: NTEP;    visual ratings;    cultivar evaluation;    subjectivity minimization;    Bayesian model;   

DOI  :  10.3389/fpls.2023.1135918

 received in 2023-01-02, accepted in 2023-05-31,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionTraditional evaluation procedure in National Turfgrass Evaluation Program (NTEP) relies on visually assessing replicated turf plots at multiple testing locations. This process yields ordinal data; however, statistical models that falsely assume these to be interval or ratio data have almost exclusively been applied in the subsequent analysis. This practice raises concerns about procedural subjectivity, preventing objective comparisons of cultivars across different test locations. It may also lead to serious errors, such as increased false alarms, failures to detect effects, and even inversions of differences among groups.MethodsWe reviewed this problem, identified sources of subjectivity, and presented a model-based approach to minimize subjectivity, allowing objective comparisons of cultivars across different locations and better monitoring of the evaluation procedure. We demonstrate how to fit the described model in a Bayesian framework with Stan, using datasets on overall turf quality ratings from the 2017 NTEP Kentucky bluegrass trials at seven testing locations.ResultsCompared with the existing method, ours allows the estimation of additional parameters, i.e., category thresholds, rating severity, and within-field spatial variations, and provides better separation of cultivar means and more realistic standard deviations.DiscussionTo implement the proposed model, additional information on rater identification, trial layout, rating date is needed. Given the model assumptions, we recommend small trials to reduce rater fatigue. For large trials, ratings can be conducted for each replication on multiple occasions instead of all at once. To minimize subjectivity, multiple raters are required. We also proposed new ideas on temporal analysis, incorporating existing knowledge of turfgrass.

【 授权许可】

Unknown   
Copyright © 2023 Qu, Kne, Graham, Watkins and Morris