Full TGIF Record # 333464
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Web URL(s):https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/148936
    Last checked: 12/04/2023
Publication Type:
i
Report
Content Type:Abstract or Summary only
Author(s):Qu, Henry Yuanshuo; Morris, Kevin N.
Author Affiliation:Qu: Presenting Author and National Turfgrass Evalutation Program, Wakefield, MA; Morris: National Turfgrass Evaluation Program, Beltsville, MD
Title:On the data analysis of small-plot field trials in turfgrass science
Section:Turfgrass science oral I (includes student competition)
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C05 turfgrass science
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Meeting Info.:St. Louis, Missouri: October 29-November 1, 2023
Source:ASA, CSSA, SSSA International Annual Meeting. 2023, p. 148936.
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 small-plot field trial is widely used in turfgrass research for cultivar evaluation, testing of practices, and crop protection products. Historically, the classical analysis of variance (ANOVA) was developed for the analysis of data from small plot field trials. An important underlying assumption for ANOVA is that model errors are independent and identically distributed with constant variance. This assumption requires an efficient experimental design with blocking and randomization. Even with appropriate randomized block designs, spatial variation in field trials can only be partly accounted for. The introduction of the mixed model and Bayesian approach led to new methodologies that better addressed the spatial correlation among plots. Small plot field trials often last for several years in turfgrass research. Analysis of the data from a temporal aspect is important but oftentimes overlooked. For example, breeders are interested in which cultivar greens up first and which cultivar turns dormancy last. In this study, we use data from multiple small plot field trials across multiple years to illustrate different modeling strategies and graphical tools to analyze data both spatially and temporally."
Language:English
References:0
Note:"225-13"
This item is an abstract only!
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Qu, H. Y., and K. N. Morris. 2023. On the data analysis of small-plot field trials in turfgrass science. Agron. Abr. p. 148936.
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https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/148936
    Last checked: 12/04/2023
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