Assessment and phenotypic identification of millet germplasm ( L.) in Liaoning, China.

AIMS

This study evaluated millet germplasms in Liaoning Province to support the collection, preservation and innovation of millet germplasm resources.

METHODS

The study was conducted from 2018 to 2020, involved the selection of 105 millet germplasm resources from the Germplasm Bank of the Liaoning Academy of Agricultural Sciences (LAAS), the observation and recording of 31 traits, and the application of multivariate analysis methods to assess phenotypic diversity.

RESULTS

From the diversity analysis and correlation analysis, it was found that the tested traits had abundant diversity and complex correlations among them. Principal component analysis (PCA) comprehensively analyzed all quantitative traits and extracted seven principal components. Grey relational analysis (GRA) highlighted the varied contributions of different traits to yield. Through systematic cluster analysis (SCA), the resources were categorized into six groups at Euclidean distance of 17.09. K-mean cluster analysis determined the distribution interval and central value of each trait, then identified resources with desirable traits.

CONCLUSION

The results revealed resources that possess characteristics such as upthrow seedling leaves, more tillers and branches, larger and well-formed ears, and lodging resistance prefer to higher grain yield. It was also discovered that the subear internode length (SIL) could be an indicator for maturity selection. Four specific resources, namely, Dungu No. 1, Xiao-li-xiang, Basen Shengu, and Yuhuanggu No. 1, were identified for further breeding and practical applications.