| Phenotypic diversity analysis and fuzzy clustering in barley germplasm resources introduced from abroad |
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| DOI：10.7606/j.issn.1000-7601.2010.05.02 |
| Key Words: barley germplasm agronomic characters principal component analysis fuzzy clustering comprehensive analysis |
| Author Name | Affiliation | | XIE Songfeng | College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
Ankang Institute of Agricultural Sciences, Ankang, Shaanxi 725021, China | | OU Xingqi | He'nan Institute of Science and Technology, Xinxiang, He'nan 453003, China | | ZHANG Bairen | Ankang Institute of Agricultural Sciences, Ankang, Shaanxi 725021, China | | NIE Xiaojun | College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China | | DU Xinghong | College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
Yangling Branch of China Wheat Improvement Center, Yangling, Shaanxi 712100, China | | ZHANG Baojun | College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China | | SONG Weining | College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
Yangling Branch of China Wheat Improvement Center, Yangling, Shaanxi 712100, China
Shaanxi Key Lab of Molecular Biology for Agriculture, Yangling, Shaanxi 712100, China |
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| Abstract: |
| In order to select fine barley germplasm resources for the development of poor soils and improvement of agriculture in arid and semiarid areas, principal component analysis (PCA) and fuzzy clustering analysis (FC) are used to study and evaluate the agronomic traits of 107 barley varieties (lines) introduced from abroad. Correlation analysis is also carried out among various traits to compare their growth characteristics and adaptability in Shaanxi Province, in a view to make rational utilization of these germplasm resources. The results show that the five integrated principal components can represent 91.0268 of original data information of 12 phenotypic variables of barley. The 107 materials of barley germplasm can be divided into 3 categories by using fuzzy membership function values WPGMA clustering metric D, and the clustering results can reflect soundly regional characteristics of breeding and distribution of these germplasm resources, of which the wild groups perform better and have a high value in use of cultivar selection. Through PCA, the multiple traits with strong correlation are re-converted into several new independent ones which have a strong representation of the integrated variables (traits). To make comprehensive evaluation of the phenotype of barley in combination with fuzzy clustering method can better reveal relationships among barley varieties (lines) and groups. |
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