Guohang Yang

Data imbalance is common in multi-environment trials (METs) and complicates genotype and environment evaluations. To address this, linear mixed models (LMMs) and factor-analytic linear mixed models (FA-LMMs) were employed to estimate best linear unbiased predictions (BLUPs) for genotype effects. The highly unbalanced wheat grain yield data from the 2021–2025 national regional trials in the Northern Winter Wheat Region (NWWR) served as our case study. These BLUP values were then integrated into the genotype plus genotype-by-environment interaction (GGE) biplot framework to construct BLUP-GGE biplots. Subsequently, these biplots were systematically compared in terms of genotype evaluation, environment evaluation, and mega-environment delineation. Based on Akaike Information Criterion (AIC) and cross-validation, FA2 was chosen as the representative FA-LMM for comparison with LMM. For genotype evaluation, the two biplots produced highly consistent rankings. Based on the FA-LMM output, the top five genotypes were BH5318, BH2605, BH7868, JD39, and ZM8729. For mega-environment delineation, both approaches consistently identified one major and one minor mega-environment, with eight and three environments, respectively. The FA-LMM-based biplot provided clearer separation between them. For environment evaluation, the two biplots showed highly significant correlations for discriminating ability, representativeness, and desirability index. Luannan ranked first, followed by Zunhua, Baodi, Gu’an, Changping, and Shunyi, Xushui and Wuqing were intermediate, while Taiyuan, Tunliu, and Qixian performed relatively poorly. In summary, both methods are effective and reliable for analyzing highly unbalanced MET data, and show high consistency in genotype and environment evaluation. However, the FA-LMM-based BLUP-GGE biplot provides superior goodness-of-fit and more precise mega-environment delineation.

Molecular characterization of wheat varieties is of high importance for wheat breeding. In the present study, 1883 Chinese wheat cultivars released between 1971 and 2025 were genotyped using a wheat 90K SNP array. Analysis of genetic diversity indicated a consistent A > B > D pattern, with the D subgenome showing the lowest genetic diversity and fastest LD decay (4.2 Mb), indicating diminished genetic diversity driven by breeding and selection. Population structure analysis revealed ten genetic clusters with clear ecological preferences, whereas genetic differentiation was weak (FST < 0.05) and gene flow extensive (Nem > 1), indicating the co-existence of strong ecological adaptation and frequent interregional germplasm exchange. Selective sweeps were identified on chromosome 5A (TaVRN1) and 2D (Rht8/RNHL-D1) regions controlling vernalization, plant height, and stress tolerance. Despite high gene flow, these loci for variety adaptiveness remained differentiated, highlighting the impact of artificial selection. Average pairwise genetic similarity was 0.56, but only a minority of cultivar pairs exceeded 0.98, especially in the Northeastern Spring Wheat and Middle-Lower Yangtze River Facultative Wheat regions, revealing severe genetic homogeneity from the excessive use of core parents. Nucleotide diversity increased to a peak in 2011–2015. These findings indicate that Chinese wheat cultivars still harbor significant potential for genetic improvement, highlighting the critical need to integrate exotic germplasm into core breeding parents to drive future advancements.

The accurate delineation of mega-environments (MEs) and the rigorous evaluation of test locations are critical for optimizing regional variety trial schemes, particularly when addressing unbalanced datasets from multi-year, multi-location wheat (Triticum aestivum L.) trials. This study aimed at refining the regional wheat trial framework in the Huanghuai Winter Wheat Region (HWWR) of China using an integrated BLUP-GGE biplot approach, which combines best linear unbiased prediction (BLUP) values with genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis to account for temporal variability and experimental error. We systematically evaluated the BLUP-GGE biplot approach, focusing on its goodness of fit and its ability to resolve inter-location relationships. We further assessed test location representativeness, discriminating ability, and overall desirability via the BLUP-GGE biplot, and contrasted ME delineation outcomes between the traditional “which-won-where” polygon method and the test location clustering-based approach. The BLUP-GGE biplot explained 72.9% of total phenotypic variation, with all location vectors displaying positive correlations (maximum angle = 88.8°), confirming the ecological homogeneity of the target region and yielding robust evaluation results. Based on the ideal tester view, Puyang was identified as the most desirable location, followed by Zhumadian, Shangqiu, and Huixian, while Lianyungang and Suqian exhibited relatively poor comprehensive performance. MEs delineated by the “which-won-where” method showed strong inter-ME correlations and insufficient differentiation, whereas the location clustering-based method markedly enhanced inter-ME discrimination (maximum vector angle > 60°), stably partitioning the HWWR into three distinct MEs with clear cultivar–ME interaction patterns: ME1 (Lianyungang, Suqian, Fuyang, Suzhou, Guoyang, Huixian, Huai’an, Xinmaqiao, Huayin, and Yangling), ME2 (Luoyang, Xinxiang, Zhumadian, Shangqiu, Puyang, and Luohe), and ME3 (Baoji, Xuzhou, Yuanyang, Sheyang, and Xingyang). This study confirms the superiority of the BLUP-GGE biplot for analyzing unbalanced multi-year multi-environment trial data and validates a robust clustering strategy for ME delineation. The findings provide a scientific basis for optimizing wheat regional trial systems and facilitating precise cultivar deployment in the HWWR, and offer a reference for analogous studies on other crops or ecological regions.

Accurate delineation of mega-environments (MEs) and rigorous evaluation of test lo-cations are critical for optimizing regional variety trial schemes, particularly when addressing unbalanced datasets from multi-year, multi-location wheat (Triticum aestivum L.) trials. This study aimed to refine the regional wheat trial framework in the Huanghuai winter wheat region (HWWR) of China using an integrated BLUP-GGE biplot approach, which combines best linear unbiased prediction (BLUP) values with genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis to account for temporal variability and experimental error. We systematically com-pared GGE biplots constructed from raw phenotypic data and BLUP values in terms of goodness of fit and their ability to resolve inter-location relationships. We further assessed test location representativeness, discriminating ability, and overall desirability via the BLUP-GGE biplot, and contrasted ME delineation outcomes between the traditional “which-won-where” polygon method and the test location clustering-based approach. The BLUP-GGE biplot explained 81.1% of total phenotypic variation, a substantial improvement over the conventional raw-data GGE biplot (62.4%). Raw-data GGE biplots exhibited highly complex inter-location correlations, distorted by unaccounted year effects and environmental noise, which hindered reliable location evaluation and ME classification. In contrast, all location vectors in the BLUP-GGE biplot displayed positive correlations (maximum angle = 83.9°), confirming the ecological homogeneity of the target region and yielding robust evaluation results. Based on the ideal tester view, ZMD was identified as the most desirable location, followed by SQU and PY, while LYG, BJ, SQ, and XY exhibited relatively poor comprehensive performance. MEs delineated by the “which-won-where” method showed strong inter-ME correlations and insufficient differentiation, whereas the location clustering-based method markedly enhanced inter-ME discrimination (maximum vector angle ≈70°), stably partitioning the HWWR into three distinct MEs with clear cultivar–ME interaction patterns: ME1 (HX, SZ, FY, SQ, LYG), ME2 (ZMD, SQU, GY, XX, HA, LH, XMQ, LY, HY, YL, PY, XZ), and ME3 (SY, YY, XY, BJ). This study confirms the superiority of the BLUP-GGE biplot for analyzing unbalanced multi-year multi-environment trial data and validates a robust clustering strategy for ME delineation. The findings provide a scientific basis for optimizing wheat regional trial systems and facilitating precise cultivar deployment in the HWWR, and offer a reference for analogous studies on other crops or ecological regions.