Full TGIF Record # 324971
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Web URL(s):https://scisoc.confex.com/scisoc/2022am/meetingapp.cgi/Paper/141737
    Last checked: 01/26/2023
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Publication Type:
Content Type:Abstract or Summary only
Author(s):Qu, Henry; Kne, Len; Watkins, Eric; Graham, Steve; Morris, Kevin N.
Author Affiliation:Qu: Presenting Author and National Turfgrass Evaluation Program; Kne: University of Minnesota; Watkins: University of Minnesota-Twin Cities; Graham: University of Minnesota Duluth; Morris: National Turfgrass Evaluation Program
Title:A latent scale model to minimize subjectivity for visual rating data in the National Turfgrass Evaluation Program
Section:Turfgrass science oral II
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C05 turfgrass science
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Meeting Info.:Baltimore, Maryland: November 6-9, 2022
Source:ASA, CSSA, SSSA International Annual Meeting. 2022, p. 141737.
Publishing Information:[Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America]
# of Pages:1
Abstract/Contents:"The National Turfgrass Evaluation Program (NTEP) is an internationally renowned turfgrass research program. The traditional evaluation procedure in NTEP relies on the visual assessment of 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, and presented a model-based approach to minimize subjectivity that not only provides objective comparisons of varieties across different subjects and research groups but also allows better monitoring and analysis of the current evaluation procedure. We demonstrate how to fit the described model in a Bayesian framework with Stan, using datasets on overall turf quality ratings in the 2017 NTEP Kentucky bluegrass trials at seven testing locations."
This item is an abstract only!
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Qu, H., L. Kne, E. Watkins, S. Graham, and K. N. Morris. 2022. A latent scale model to minimize subjectivity for visual rating data in the National Turfgrass Evaluation Program. Agron. Abr. p. 141737.
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