Foxtail millet (Setaria italica) is an important cereal crop with rich nutritional value. Distinctness, Uniformity, and Stability (DUS) are the prerequisites for the application of new variety rights for foxtail millet. In this study, we investigated 32 DUS test characteristics of 183 foxtail millet resources, studied their artificial selection trends, and identified the varieties that conform to breeding trends. The results indicated significant differences in terms of the means, ranges, and coefficients of variation for each characteristic. A correlation analysis was performed to determine the correlations between various DUS characteristics. A principal component analysis was conducted on 31 test characteristics to determine their primary characteristics. By plotting PC1 and PC2, all the germplasm resources could be clearly distinguished. The trends in foxtail millet breeding were identified through a differential analysis of the DUS test characteristics between the landrace and cultivated varieties. Based on these breeding trends, the optimal solution types for multiple evaluation indicators were determined; the weight allocation was calculated; and a specific TOPSIS algorithm was designed to establish a comprehensive multi-criteria decision-making model. Using this model, the breeding potential of foxtail millet germplasm resources were ranked. These findings provided important reference for foxtail millet breeding in the future.

Foxtail millet (Setaria italica) is an important cereal crop with rich nutritional value. Distinctness, Uniformity, and Stability (DUS) are the prerequisites for the application of new variety rights for foxtail millet. In this study, we investigated 32 DUS test characteristics of 183 foxtail millet resources, studied their artificial selection trends, and identified varieties that conform to breeding trends. The results indicated significant differences in terms of means, ranges, and coefficients of variation for each characteristic. Correlation analysis was performed to determine the correlations between various DUS characteristics. Principal component analysis was conducted on 31 test characteristics to determine their primary characteristics. By plotting PC1 and PC2, all germplasm resources could be clearly distinguished. Trends in foxtail millet breeding were identified through differential analysis of DUS test characteristics between landrace varieties and cultivated varieties. Based on these breeding trends, optimal solution types for multiple evaluation indicators were determined; weight allocation was calculated, and a specific TOPSIS algorithm was designed to establish a comprehensive multi-criteria decision-making model. Using this model, the breeding potential of foxtail millet germplasm resources was ranked. These findings provided important reference for foxtail millet breeding in future.

The Distinctness, Uniformity, and Stability (DUS) testing guidelines for mung beans (Vigna radiata L.) offer a standardized framework for new variety assessment. Although these guidelines are essential for variety management, the actual efficiency and breeding value of the 31 specified DUS characteristics in improving yield potential remain largely underexplored and lack systematic validation. To address this critical research gap, 180 genetically diverse mung bean accessions were analyzed using principal component analysis (PCA) and correlation analysis. The results revealed intrinsic relationships among characteristics and identified key variation dimensions centered on “plant morphology”, “pod characteristics”, and “seed characteristics”. Cluster analysis classified the 180 accessions into four distinct clusters. Cluster 2, in particular, offers a clear selection reference for breeding materials targeting high-yield and quality. The DTOPSIS (Dynamic Technique for Order Preference by Similarity to Ideal Solution) multi-criteria decision-making model was applied, with index weights assigned using an objective weighting method. Following systematic evaluation, Yingge 2 was identified as an outstanding phenotype. Breeders may refer to its quantitative characteristics in subsequent breeding cycles. Linear regression analysis was employed to construct a yield prediction model, identifying leaf greenness, pod number per plant, and hundred-grain weight as three core DUS characteristics with statistically significant effects on final yield (p < 0.05). This study performed a systematic, multi-dimensional analysis and comprehensive evaluation of mung bean germplasm resources based on DUS characteristics, with the aim of identifying key yield-related DUS traits, screen elite germplasm for high-yield breeding, and providing a theoretical basis and practical reference for the efficient improvement and selective breeding of new mung bean varieties.

Distinctness, uniformity, and stability (DUS) testing is the statutory basis and core element for the protection and registration of new plant varieties. In this study, 116 genetically diverse maize accessions (inbred lines, CS hybrids, CH) were evaluated using phenotypic data from Xinzhou and Datong (2024–2025). The spatiotemporal stability of 39 DUS characteristics was systematically analyzed using coefficient of variation (CV), principal component analysis (PCA), and UPGMA clustering. The results showed the following: (1) characteristics were categorized into three stability groups based on CV quantitative analysis, with 20 characteristics identified as spatiotemporally stable to serve as a routine observation baseline, while C2, C25, and C30.2 were recognized as time-sensitive characteristic. (2) PCA indicated that the spatial environment primarily controlled phenotypic variation (cumulative variance contribution: 74.92%). An optimized evaluation system using seven core characteristics, including plant height and grain type, was established to simplify the DUS testing process. (3) Validation showed that the spatial repetition model achieved a cluster retrieval (CR) rate of 92.31%, with accuracies of 94.36% for distinctness and 96.23% for uniformity. These values significantly exceeded those of the traditional temporal model, effectively reducing the impact of environmental noise and annual climatic variation. This study established an efficient spatial repetition testing paradigm based on characteristic stability and demonstrated the scientific feasibility of reasonably shortening testing cycles, thereby providing a rigorous scientific foundation for optimizing Chinese maize DUS testing standards and enhancing the efficiency of substantive examinations.

Maize is a staple crop supporting global food security. Comprehensive evaluation of its phenotypic variation and environmental adaptability is urgently needed. In this study, three mutually independent maize germplasm panels with a total of 1197 hybrids were characterized using 39 standardized DUS traits: Panel A (783 hybrids, single location, 2023), Panel B (129 hybrids, two locations in the same year), and Panel C (285 hybrids, single location across two consecutive years). Tassel- and anthocyanin-related traits exhibited wide phenotypic variation and strong germplasm discrimination ability, while leaf margin anthocyanin (Char. 8) displayed rare genotypes with unique identification values. Hierarchical clustering divided all materials into eight distinct groups. PCA revealed weak overall phenotypic differentiation, which is likely driven by long-term directional selection in modern maize breeding. AMMI-based stability evaluation demonstrated that grain morphological traits (Char. 33, Char. 35) maintained stable performance across locations and years, whereas anthocyanin-related descriptors (Char. 8, Char. 14) showed severe environmental plasticity. Using the classic Multi-Trait Stability Index (MTSI), we screened top stable genotypes: 77, 69 and 52 for cross-location trials, and N90, N85 and N107 for multi-year trials. BLUP five-fold cross-validation revealed that grain morphological traits achieved high cross-location predictive correlation (r² > 0.80), whereas cross-year predictability was notably lower, reflecting greater influence of annual climatic fluctuations. Collectively, this study constructs a standardized analytical pipeline to mine routine multi-environment DUS records, identifies stable elite germplasm, and provides data-driven references for optimizing maize breeding and improving official DUS testing efficiency.