AUTHOR=Qu Yuanshuo , Kne Len , Graham Steve , Watkins Eric , Morris Kevin 

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

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1135918

DOI=10.3389/fpls.2023.1135918

ISSN=1664-462X

ABSTRACT=The National Turfgrass Evaluation Program (NTEP) is an internationally renowned turfgrass research program. Traditional evaluation procedure in 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. In this project, we briefly reviewed this problem, identified sources of subjectivity, \hl{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. Compared with the existing method, our model 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.