A Bayesian Approach for Extracting Turfgrass Seasonality from Turf Quality Ratings in the National Turfgrass Evaluation Program

A Bayesian Approach for Extracting Turfgrass Seasonality from Turf Quality Ratings in the National Turfgrass Evaluation Program | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 January 2026 V1 Latest version Share on A Bayesian Approach for Extracting Turfgrass Seasonality from Turf Quality Ratings in the National Turfgrass Evaluation Program Authors : Hok Nip Leung , Olena Boiko 0009-0000-2585-4238 , Len Kne 0000-0003-1932-3101 , Ambika Chandra , Kevin Morris 0000-0002-6902-3206 , and Yuanshuo Qu 0000-0003-2046-469X [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176903360.06892214/v1 63 views 59 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Turfgrass quality is commonly evaluated using a 1-9 visual rating scale in the National Turfgrass Evaluation Program (NTEP). To derive meaningful insights from these subjective, ordinal ratings, a structured analytical framework is essential. Building upon previous work, we introduce an improved probabilistic modeling framework that quantifies seasonal variation in turfgrass quality while accounting for within-trial spatial heterogeneity and rater-specific behavior. The model integrates Bayesian hierarchical modeling, Gaussian Processes (GP), and Item Response Theory (IRT) to extract latent quality scores from longitudinal visual ratings. We applied the model to the 2017 NTEP Kentucky Bluegrass Trial conducted in Adelphia, New Jersey, and validated its parameter recovery performance using synthetic data. The results revealed substantial variability in the behavior of the rater and allowed the extraction of seasonality curves at the cultivar level. To address the computational burden inherent in GP-based inference, we incorporated two approximation techniques: Hilbert basis expansions for modeling temporal effects and Fourier methods for spatial effects. These reduced the computational complexity from Ο(n^3) to Ο(nm^2) and Ο(nlogn) respectively. The enhanced model not only outperformed its predecessor in model performance, but also achieved significant gains in computational efficiency. Parameter recovery using synthetic data confirmed the model’s ability to accurately capture latent quality, spatial structure, and rater thresholds. This framework provides a robust and scalable approach for extracting seasonality from turfgrass trials and offers a foundation for future multi-location analyses. Supplementary Material File (ntep_rsm_manuscript.pdf) Download 612.82 KB Information & Authors Information Version history V1 Version 1 21 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords bayesian model cultivar evaluation gaussian process informatics irt visual ratings Authors Affiliations Hok Nip Leung Oxbridge Economics View all articles by this author Olena Boiko 0009-0000-2585-4238 University of Minnesota System View all articles by this author Len Kne 0000-0003-1932-3101 University of Minnesota System View all articles by this author Ambika Chandra Texas A&M University System View all articles by this author Kevin Morris 0000-0002-6902-3206 NTEP View all articles by this author Yuanshuo Qu 0000-0003-2046-469X [email protected] NTEP View all articles by this author Metrics & Citations Metrics Article Usage 63 views 59 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Hok Nip Leung, Olena Boiko, Len Kne, et al. A Bayesian Approach for Extracting Turfgrass Seasonality from Turf Quality Ratings in the National Turfgrass Evaluation Program. Authorea . 21 January 2026. DOI: https://doi.org/10.22541/au.176903360.06892214/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.176903360.06892214/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe4f182dc7458d3',t:'MTc3OTIxMzAyMA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();