ZHOU Xian-ming, XU Chun, YANG Han-bo, CHEN Zhi, GUO Hong-ying, HUANG Zhen, WANG Zeliang. Plus Tree Selection of Alnus cremastogyne by Principal Component Analysis[J]. Journal of Sichuan Forestry Science and Technology, 2018, 39(3): 98-102. doi: 10.16779/j.cnki.1003-5508.2018.03.020
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ZHOU Xian-ming, XU Chun, YANG Han-bo, CHEN Zhi, GUO Hong-ying, HUANG Zhen, WANG Zeliang. Plus Tree Selection of Alnus cremastogyne by Principal Component Analysis[J]. Journal of Sichuan Forestry Science and Technology, 2018, 39(3): 98-102. doi: 10.16779/j.cnki.1003-5508.2018.03.020
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Plus Tree Selection of Alnus cremastogyne by Principal Component Analysis
More Information
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Forestry Technology Extension Station of Zhongjiang Province, Deyang Zhongjiang 618100, China;Forestry Bureau of Dongxing District in Neijiang Province, Neijiang 641003, China;Sichuan Academy of Forestry, Sichuan Chengdu 610081, China
- Received Date: 2018-04-20
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Abstract
A principal component analysis (PCA) method was established for plus tree selection of Alnus cremastogyne Burk.and the selection criteria were determined, which should provide a theoretical basis for the early seedling selection and selective breeding of A. cremastogyne. 157 plus trees were used as the research object. The growths and wood properties traits were analyzed by variance analysis and PCA method. The result showed that the stem fullness (SF) and stem straightness (ST) were no significant difference among forest stands, while the difference of other traits reached an extremely siginificant. Genetic variation of SF, ST and air density were small, but other traits showed rich genetic differences. The rich variability of A. cremastogyne provided the prerequisite condition for plus tree selection. By PCA analysis, height (H), diameter at breast height (DBH) and volume of timber (V) were used as the optimal criteria of selection. Eight plants with integrated superior growth were selected, including GZLD-5, GZLD-6, MYPW-8, GZTJ-1, GZLD-8, MYPW-7, MYPW-10 and GZLD-12. The results conformed to the phenotypic expression, which indicated that the optimization methods were scientific and feasible.
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