{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T07:30:29Z","timestamp":1793777429544,"version":"4.6.1"},"reference-count":95,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000980","name":"Grains Research & Development Corporation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000980","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100024049","name":"GRDC","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100024049","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovations in Variety Testing in Australia","award":["UOQ2003-011RTX"],"award-info":[{"award-number":["UOQ2003-011RTX"]}]},{"name":"Analytics for the Australian Grain Industry","award":["UOQ2301-010OPX"],"award-info":[{"award-number":["UOQ2301-010OPX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Predicting complex traits like yield in new environments is challenging due to genotype-by-environment interactions (GEI). Environmental covariates (ECs) can improve prediction by describing environmental variation relevant to GEI. However, approaches that model genotype-specific responses to 1 or multiple ECs can become highly parameterized and difficult to fit because of the need to estimate complex covariance structures. In addition, EC-based prediction methods typically assume that future environmental conditions are known. Building on recent work, we extended a reduced-rank factor-analytic random regression (RR) framework to enable simultaneous fitting of multiple ECs and higher-order polynomial responses. We further incorporated uncertainty in estimated reaction norms together with historical multivariate variation in ECs, addressing a realistic breeding scenario in which future EC values are unavailable. We applied the models to 8 yr of grain-yield data from the Australian National Variety Trials network (935 trials), spanning a wide range of environmental conditions. Using leave-one-year-out cross-validation, multi-EC RR achieved median predictive ability &amp;gt;20% higher than standard compound symmetry (CS) models and up to 30% higher than single-EC RR. Extending the models to borrow information across regions further increased median predictive ability by up to 4 percentage points. Moreover, standard errors of prediction relative to mean yield were reduced by up to 4-fold relative to models without ECs. Thus, our extended RR framework provides an effective approach for modeling GEI, delivering accurate predictions and explicit uncertainty quantification using a small set of biologically informative ECs and offering an interpretable alternative or complement to large-scale enviromic and machine learning approaches.<\/jats:p>","DOI":"10.1093\/g3journal\/jkag272","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T07:57:11Z","timestamp":1790323031000},"source":"Crossref","is-referenced-by-count":1,"title":["Reduced-rank random regression for improved yield prediction across the Australian wheatbelt"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0617-930X","authenticated-orcid":false,"given":"Jip J C","family":"Ramakers","sequence":"first","affiliation":[{"name":"Mathematical & Statistical Methods Group\u2014Biometris, Wageningen University & Research , 6700AA Wageningen ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1879-4588","authenticated-orcid":false,"given":"Martin P","family":"Boer","sequence":"additional","affiliation":[{"name":"Mathematical & Statistical Methods Group\u2014Biometris, Wageningen University & Research , 6700AA Wageningen ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9811-1336","authenticated-orcid":false,"given":"Jesse","family":"Hemerik","sequence":"additional","affiliation":[{"name":"Mathematical & Statistical Methods Group\u2014Biometris, Wageningen University & Research , 6700AA Wageningen ,","place":["The Netherlands"]},{"name":"Econometric Institute, Erasmus University , 3000DR Rotterdam ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3827-6726","authenticated-orcid":false,"given":"Daniela","family":"Bustos-Korts","sequence":"additional","affiliation":[{"name":"Mathematical & Statistical Methods Group\u2014Biometris, Wageningen University & Research , 6700AA Wageningen ,","place":["The Netherlands"]},{"name":"Facultad de Ciencias Agrarias y Alimentarias, Universidad Austral de Chile , Valdivia 5090000 ,","place":["Chile"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5025-2658","authenticated-orcid":false,"given":"Salvador A","family":"Gezan","sequence":"additional","affiliation":[{"name":"VSN International Ltd, Hemel Hempstead HP2 4TP ,","place":["United 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