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With the right selection criteria, genotypes with yield stability across variable environmental conditions can be selected. Methods such as Finlay\u2013Wilkinson random regression (FWRR) may allow us to use sparse datasets\u2014common in plant breeding pipelines\u2014and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability. Our objective was to examine how the number of environments and the variance among those environments affect stability predictions. We also integrate FWRR as a genomic prediction tool for characterizing yield stability, comparing it to the traditional genomic prediction models as a reference. We used three datasets: one highly unbalanced dataset for oats (\n                    <jats:italic>Avena sativa<\/jats:italic>\n                    L.) and two completely balanced datasets with different numbers of environments for barley (\n                    <jats:italic>Hordeum vulgare<\/jats:italic>\n                    L.) and wheat (\n                    <jats:italic>Triticum aestivum<\/jats:italic>\n                    L.). We fit standard Finlay\u2013Wilkinson (FW) and FWRR models to estimate grain yield and stability under various scenarios. We found that the estimated stability values obtained were similar using balanced datasets for FW or FWRR. FWRR also achieved moderate predictive ability for stability using unbalanced datasets under 10\u2010fold cross\u2010validation (CV1) with new genotypes. In terms of environmental representation, selecting the right set of environments for inclusion in the model was more important than adding more environments. Our results suggest the possibility of using FWRR to select stable genotypes earlier in line development, as well as to design resource\u2010efficient stability\u2010testing schemes.\n                  <\/jats:p>","DOI":"10.1002\/tpg2.70269","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:57:16Z","timestamp":1788951436000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Finlay\u2013Wilkinson random regression for yield and yield stability prediction in cereals"],"prefix":"10.1002","volume":"19","author":[{"given":"Pablo","family":"Sandro","sequence":"first","affiliation":[{"name":"Department of Plant and Agroecosystem Sciences University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Blancon","sequence":"additional","affiliation":[{"name":"UMR GDEC, INRAE, Universit\u00e9 Clermont Auvergne  Clermont\u2010Ferrand France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1991-5310","authenticated-orcid":false,"given":"Jeffrey","family":"Neyhart","sequence":"additional","affiliation":[{"name":"USDA\u2010ARS, Genetic Improvement for Fruits &amp; Vegetables Laboratory  Chatsworth New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julie","family":"Dawson","sequence":"additional","affiliation":[{"name":"Department of Plant and Agroecosystem Sciences University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2957-3086","authenticated-orcid":false,"given":"Lucia","family":"Guti\u00e9rrez","sequence":"additional","affiliation":[{"name":"Department of Plant Breeding Swedish University of Agricultural Sciences  Alnarp Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.15835\/nbha51113020"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010025\u201055872\u20104"},{"key":"e_1_2_11_4_1","unstructured":"Annicchiarico P.(2002).Genotype x environment interactions: Challenges and opportunities for plant breeding and cultivar recommendations.Food and Agriculture Organization of the United Nations.https:\/\/books.google.com\/books?hl=en&lr=&id=so4UJSzWNbIC&oi=fnd&pg=PR8&dq=Genotype+x+environment+interactions:+Challenges+and+opportunities+for+plant+breeding+and+cultivar+recommendations&ots=dD_pjzx8S5&sig=URiP9ZDJN3XK_V_cNWIG929YYJI"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.14232\/analecta.2019.1.11\u201020"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1439\u20100523.1988.tb00261.x"},{"key":"e_1_2_11_7_1","doi-asserted-by":"crossref","unstructured":"Bekele W. 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A.(2024).Global genomic population structure of wild and cultivated oat reveals signatures of chromosome rearrangements.Research Square.https:\/\/doi.org\/10.21203\/rs.3.rs\u20105726397\/v1","DOI":"10.21203\/rs.3.rs-5726397\/v1"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.2135\/cropsci1994.0011183X003400010003x"},{"key":"e_1_2_11_9_1","volume-title":"Breeding for quantitative traits in plants","author":"Bernardo R.","year":"2010"},{"key":"e_1_2_11_10_1","volume-title":"Essentials of plant breeding","author":"Bernardo R.","year":"2014"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.3835\/plantgenome2019.04.0028"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pbi.2020.101986"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eja.2020.126156"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.2135\/cropsci2011.06.0299"},{"key":"e_1_2_11_15_1","volume-title":"ASReml\u2010R reference manual","author":"Butler D. 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Plant and Animal Genome Conference\/PAG 31 San Diego CA."},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1071\/AR9630742"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.4141\/cjps78\u2010157"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.2135\/cropsci1996.0011183\u00d7003600040002x"},{"key":"e_1_2_11_24_1","unstructured":"Gauch J.(1992).Statistical analysis of regional yield trials: AMMI analysis of factorial designs.https:\/\/www.cabidigitallibrary.org\/doi\/full\/10.5555\/19931643324"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/csc2.20269"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.2135\/cropsci2018.11.0692"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy15102275"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.2135\/cropsci2014.03.0249"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13253\u2010020\u201000406\u20102"},{"key":"e_1_2_11_30_1","doi-asserted-by":"publisher","DOI":"10.1017\/9781009157896"},{"key":"e_1_2_11_31_1","volume-title":"Applied linear statistical models","author":"Kutner M. 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