Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

Quinoa ( Chenopodium quinoa Willd.) is gaining global importance for its nutritional value and adaptability; however, breeding progress remains limited. Genomic selection (GS), combined with rapid generation cycles, offers a strategy to accelerate genetic improvement. We conducted whole‐genome 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‐related traits. Using ∼1.8 million single‐nucleotide polymorphisms, we compared four models—genomic best linear unbiased prediction, reproducing kernel Hilbert space, BayesC, and light gradient boosting machine—for genotype ranking under four cross‐validation schemes: predicting new genotypes (CV1), sparse testing (CV2), leave‐one‐location–year‐out (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‐ location environments. Accuracy was highest for developmental traits and lowest for seed yield, while seed traits showed location‐specific responses with higher accuracy in Australia. These findings support GS as a promising tool for quinoa breeding and provide benchmarks for global implementation.

Quinoa ( Chenopodium quinoa ) is an important crop for the future challenges of food and nutrient security. Deep characterization of quinoa diversity is needed to support the agronomic improvement and adaptation of quinoa as its worldwide cultivation expands. In this study, we report the construction of chromosome-scale genome assemblies of eight quinoa accessions covering the range of phenotypic and genetic diversity of both lowland and highland quinoas. The assemblies were produced from a combination of PacBio HiFi reads and Bionano Saphyr optical maps, with total assembly sizes averaging 1.28 Gb with a mean N50 of 71.1 Mb. Between 43,733 and 48,564 gene models were predicted for the eight new quinoa genomes, and on average, 66% of each quinoa genome was classified as repetitive sequences. Alignment between the eight genome assemblies allowed the identification of structural rearrangements including inversions, translocations, and duplications. These eight novel quinoa genome assemblies provide a resource for association genetics, comparative genomics, and pan-genome analyses for the discovery of genetic components and variations underlying agriculturally important traits.