Path and Ridge Regression Analysis of Seed Yield and Seed Yield Components of Russian Wildrye (Psathyrostachys juncea Nevski) under Field Conditions

文献类型： 外文期刊

第一作者： Wang, Quanzhen

作者： Wang, Quanzhen;Cui, Jian;Wang, Quanzhen;Wang, Xianguo;Zhou, He;Han, Jianguo;Zhang, Tiejun;Gislum, Rene

作者机构：

期刊名称：PLOS ONE （ 影响因子：3.24； 五年影响因子：3.788 ）

ISSN： 1932-6203

年卷期： 2011 年 6 卷 4 期

页码：

收录情况： SCI

摘要： The correlations among seed yield components, and their direct and indirect effects on the seed yield (Z) of Russina wildrye (Psathyrostachys juncea Nevski) were investigated. The seed yield components: fertile tillers m(-2) (Y-1), spikelets per fertile tillers (Y-2), florets per spikelet(Y-3), seed numbers per spikelet (Y-4) and seed weight (Y-5) were counted and the Z were determined in field experiments from 2003 to 2006 via big sample size. Y-1 was the most important seed yield component describing the Z and Y-2 was the least. The total direct effects of the Y-1, Y-3 and Y-5 to the Z were positive while Y-4 and Y-2 were weakly negative. The total effects (directs plus indirects) of the components were positively contributed to the Z by path analyses. The seed yield components Y1, Y2, Y4 and Y5 were significantly (P<0.001) correlated with the Z for 4 years totally, while in the individual years, Y-2 were not significant correlated with Y-3, Y-4 and Y-5 by Peason correlation analyses in the five components in the plant seed production. Therefore, selection for high seed yield through direct selection for large Y-1, Y-2 and Y-3 would be effective for breeding programs in grasses. Furthermore, it is the most important that, via ridge regression, a steady algorithm model between Z and the five yield components was founded, which can be closely estimated the seed yield via the components.

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