René Gislum

Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions

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  • e18245

    Submitted manuscript, 400 KB, PDF-document

DOI

  • Quanzhen Wang, Department of Grassland Science, College of Science and Technology, Northwest A&F University, China
  • Tiejun Zhang, Institute of Animal Science, Chinese Academy of Agricultural Sciences, China
  • Jian Cui, Department of Grassland Science, College of Science and Technology, Northwest A&F University, China
  • Xianguo Wang, Institute of Grassland Science, College of Animal Science and Technology, China Agricultural University, China
  • He Zhou, Institute of Grassland Science, College of Animal Science and Technology, China Agricultural University, China
  • Jianguo Han, Institute of Grassland Science, College of Animal Science and Technology, China Agricultural University, China
  • René Gislum
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 (Y1), spikelets per fertile tillers (Y2), florets per spikelet- (Y3), seed numbers per spikelet (Y4) and seed weight (Y5) were counted and the Z were determined in field experiments from 2003 to 2006 via big sample size. Y1 was the most important seed yield component describing the Z and Y2 was the least. The total direct effects of the Y1, Y3 and Y5 to the Z were positive while Y4 and Y2 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, Y2 were not significant correlated with Y3, Y4 and Y5 by Peason correlation analyses in the five components in the plant seed production. Therefore, selection for high seed yield through direct selection for large Y1, Y2 and Y3 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.
Original languageEnglish
JournalP L o S One
Volume6
Issue4: e18245
Number of pages10
ISSN1932-6203
DOIs
Publication statusPublished - 2011

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