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Genomic selection (GS), combined with rapid generation cycles, offers a strategy to accelerate genetic improvement. We conducted whole\u2010genome resequencing of 610 accessions and present the first evaluation of genomic prediction in quinoa evaluated across six field trials in Australia and Pakistan for seven phenological and yield\u2010related traits. Using \u223c1.8\u00a0million single\u2010nucleotide polymorphisms, we compared four models\u2014genomic best linear unbiased prediction, reproducing kernel Hilbert space, BayesC, and light gradient boosting machine\u2014for genotype ranking under four cross\u2010validation schemes: predicting new genotypes (CV1), sparse testing (CV2), leave\u2010one\u2010location\u2013year\u2010out (CV0), and across locations. Model performance was evaluated using Pearson's correlation for overall accuracy and normalized discounted cumulative gain (NDCG@10) for ranking top performers. The four models were similar, with no method dominating across traits. NDCG@10 scores revealed that predictions remained useful for selecting superior genotypes even for difficult traits. Prediction accuracy was strongly associated with heritability and trait correlations across and within\u2010 location environments. Accuracy was highest for developmental traits and lowest for seed yield, while seed traits showed location\u2010specific responses with higher accuracy in Australia. These findings support GS as a promising tool for quinoa breeding and provide benchmarks for global implementation.\n                  <\/jats:p>","DOI":"10.1002\/tpg2.70277","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T06:25:17Z","timestamp":1787207117000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Genomic prediction in quinoa across contrasting environments using statistical and machine learning models"],"prefix":"10.1002","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3596-9108","authenticated-orcid":false,"given":"Clara S.","family":"Stanschewski","sequence":"first","affiliation":[{"name":"Plant Science Program, Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST)  Thuwal Saudi 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Division King Abdullah University of Science and Technology (KAUST)  Thuwal Saudi Arabia"},{"name":"Crop Genetics Team BASF Belgium Coordination Center CommV  Gent\u2010Zwijnaarde Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriele","family":"Fiene","sequence":"additional","affiliation":[{"name":"Plant Science Program, Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST)  Thuwal Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6252-8973","authenticated-orcid":false,"given":"Evan","family":"Craine","sequence":"additional","affiliation":[{"name":"Department of Crop and Soil Sciences Washington State University  Pullman Washington USA"},{"name":"The Land Institute  Salina Kansas 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Abdullah University of Science and Technology (KAUST)  Thuwal Saudi 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