Maximizing rice yield potential has always been the focus of high-yield rice cultivation research. For high-yield rice cultivation and breeding, more research into the link between yield and yield components is essential. In this experiment, 38 rice varieties with different yield types and 185 rice varieties as materials were chosen. The relationships between yield and yield components were studied. The regulation effects of total nitrogen application rate (TNAR) on yield and yield components were observed. The results showed that (1) the grain yield of high-yield varieties was 189.3−195.6%, 76.1−77.7%, and 27.0−28.7% higher than that of super-low-yield, low-yield, and medium-yield varieties, respectively. Compared with rice varieties with other yield types, rice varieties with high-yield type have a higher total number of spikelets. (2) The spikelet number per panicle and total number of spikelets were significantly positively linked with grain yield, but significantly negatively correlated with filled grains and grain weight. (3) With an increase in TNAR (0−340 kg ha⁻¹), the panicles, spikelet number per panicle, and total spikelets of rice varieties with different yield types increased gradually, and the filled grains and grain weight decreased gradually. The higher the TNAR, the more obvious the decrease in filled grains and grain weight. The grain yield of rice varieties with different yield types was the highest under the TNAR at 250 kg ha⁻¹. The main factor contributing to its high yield was the substantial increase in total spikelets. The above results showed that increasing the spikelet number per panicle and total spikelets played a material role in improving rice yield.

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.

Pepper breeding in China faces challenges including narrow genetic backgrounds, variety homogenization, and authenticity issues in the seed market. The original SSR-based variety identification system (NY/T 2475-2013) has limitations such as low polymorphism and uneven marker distribution, failing to meet current efficiency requirements. In this study, 240 pepper varieties were used. Fluorescent capillary electrophoresis was employed to analyze issues in the original system. A total of 150 SSR primer pairs was screened using eight phenotypically distinct varieties, followed by rescreening with 96 varieties from different origins. After marker optimization, a new identification system was established, validated using 144 varieties, and a DNA fingerprint database was constructed. Phenotypic experiments were conducted to evaluate applicability. The new system comprises 26 core primer pairs: 12 newly selected and 14 retained from the original standard. These primers are evenly distributed across chromosomes, with annealing temperatures optimized to 57 °C and grouped into seven multiplex panels. Across 240 pepper varieties, the 26 primer pairs detected 153 allelic variants and 293 genotypes. Average polymorphism information content (PIC) increased from 0.43 to 0.49 compared to the original system, and the discrimination efficiency reached 99.975% among 28,680 pairwise variety combinations. Phylogenetic and population structure analyses divided the 240 pepper varieties into six groups. Varieties with molecular genetic distance approaching zero showed high phenotypic similarity. The newly developed SSR-based system demonstrated excellent performance and strong applicability. It can be used for authenticity and purity identification, parentage testing, and auxiliary selection in DUS testing, supporting new variety rights protection and providing technical support for standardized management of the pepper seed industry.

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.